Title:
SYSTEM AND METHOD FOR AUCTIONING TARGETED ADVERTISEMENT PLACEMENT FOR VIDEO AUDIENCES
Kind Code:
A1


Abstract:
A method of auctioning targeted advertisement placement for video audiences, contains the steps of: receiving one or more advertising campaign, where each advertising campaign is defined by one or more advertisement effect criteria, and where each advertising campaign is for providing one or more advertisement to one or more viewer profile; receiving at least one bid for at least one of the one or more advertisement campaign; determining an optimal placement of a requested advertisement based upon the advertisement effect criteria and bid prices provided, so as to optimize revenue; and billing an advertiser according to a level at which a request of the advertiser, as defined by an associated advertising campaign, was supplied and actual viewership of the one or more advertisement associated with the advertising campaign.



Inventors:
Knoller, Raviv (Shoham, IL)
Paker, Alex (Modiin, IL)
Litvak-hinenzon, Anna (Hod-HaSharon, IL)
Cohen, Reuven (Rehovot, IL)
Application Number:
12/195310
Publication Date:
02/26/2009
Filing Date:
08/20/2008
Assignee:
Ads-Vantage, Ltd. (Shoham, IL, US)
Primary Class:
Other Classes:
705/34, 705/26.1
International Classes:
G06Q30/00; G06Q10/00
View Patent Images:



Primary Examiner:
HAMILTON, MATTHEW L
Attorney, Agent or Firm:
Raviv Knoller (Herziliya, IT)
Claims:
We claim:

1. A method of auctioning targeted advertisement placement for video audiences, comprising the steps of: receiving one or more advertising campaign, where each advertising campaign is defined by one or more advertisement effect criteria, and where each advertising campaign is for providing one or more advertisement to one or more viewer profile; receiving at least one bid for at least one of the one or more advertisement campaign; determining an optimal placement of a requested advertisement based upon the advertisement effect criteria and bid prices provided, so as to optimize revenue; and billing an advertiser according to a level at which a request of the advertiser, as defined by an associated advertising campaign, was supplied and actual viewership of the one or more advertisement associated with the advertising campaign.

2. The method of claim 1, wherein the number of bids from a single advertiser is limited to a predefined number of bids.

3. The method of claim 1, wherein the advertisement effect criteria is provided with priorities identifying which of the advertisement effect criteria is more important.

4. The method of claim 1, wherein a time limit is provided for allowing the receiving of a bid for the at least one of the one or more advertisement campaign.

5. The method of claim 1, wherein a commercial break has multiple advertisement locations available, and wherein the step of determining an optimal placement of a requested advertisement considers the multiple advertisement locations available.

6. The method of claim 1, further comprising the step of identifying one or more viewer profile associated with one or more set top box, wherein each of the one or more viewer profile is a potential recipient of the one or more advertisement.

7. The method of claim 1, wherein the advertisement effect criteria includes at least one viewer profile.

8. The method of claim 1, wherein the advertisement effect criteria includes at least one geographical location.

9. The method of claim 1, wherein each advertising campaign is associated with one advertiser, and wherein the method further comprises the step of selecting an advertising campaign from the one or more advertising campaigns for execution, resulting in the one or more advertisement associated with the selected advertising campaign being forwarded to a targeted viewer profile defined in the selected advertising campaign.

10. The method of claim 9, wherein the step of billing further comprises the steps of: identifying actual viewer profiles currently consuming content; determining viewer profiles that consumed the forwarded one or more advertisement; and calculating a total number of viewers of the viewer profiles that consumed the forwarded one or more advertisement.

11. The method of claim 1, wherein the step of billing further comprises using a game theoretic approach of bonuses to increase incentive of advertisers to bid an actual value of the advertisement to the advertiser.

12. A system for auctioning targeted advertisement placement for video audiences, comprising: logic configured to receive one or more advertising campaign, where each advertising campaign is defined by one or more advertisement effect criteria, and where each advertising campaign is for providing one or more advertisement to one or more viewer profile; logic configured to receive at least one bid for at least one of the one or more advertisement campaign; logic configured to determine an optimal placement of a requested advertisement based upon the advertisement effect criteria and bid prices provided, so as to optimize revenue; and logic configured to bill an advertiser according to a level at which a request of the advertiser, as defined by an associated advertising campaign, was supplied and actual viewership of the one or more advertisement associated with the advertising campaign.

13. The system of claim 12, wherein the number of bids from a single advertiser is limited to a predefined number of bids.

14. The system of claim 12, wherein the advertisement effect criteria is provided with priorities identifying which of the advertisement effect criteria is more important.

15. The system of claim 12, wherein a time limit is provided for allowing the receiving of a bid for the at least one of the one or more advertisement campaign.

16. The system of claim 12, wherein a commercial break has multiple advertisement locations available, and wherein the logic configured to determine an optimal placement of a requested advertisement considers the multiple advertisement locations available.

17. The system of claim 12, further comprising logic configured to identify one or more viewer profile associated with one or more set top box, wherein each of the one or more viewer profile is a potential recipient of the one or more advertisement.

18. The system of claim 12, wherein the advertisement effect criteria includes at least one viewer profile.

19. The system of claim 12, wherein the advertisement effect criteria includes at least one geographical location.

20. The system of claim 12, wherein each advertising campaign is associated with one advertiser, and wherein the system further comprises logic configured to select an advertising campaign from the one or more advertising campaigns for execution, resulting in the one or more advertisement associated with the selected advertising campaign being forwarded to a targeted viewer profile defined in the selected advertising campaign.

21. The system of claim 20, wherein billing further comprises: identifying actual viewer profiles currently consuming content; determining viewer profiles that consumed the forwarded one or more advertisement; and calculating a total number of viewers of the viewer profiles that consumed the forwarded one or more advertisement.

22. The system of claim 12, wherein the logic configured to bill further performs the step of using a game theoretic approach of bonuses to increase incentive of advertisers to bid an actual value of the advertisement to the advertiser.

Description:

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to copending U.S. Provisional Application entitled, “SYSTEM AND METHOD FOR PROVIDING PERSONAL ADVERTISEMENTS FOR AN ACCESS NETWORK,” having Ser. No. 60/956,728, filed Aug. 20, 2007, which is entirely incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to advertising, and more particularly is related to auctioning advertisements to video services.

BACKGROUND OF THE INVENTION

Owners of products and services, also referred to herein as advertisers, spend significant funds advertising on television. In addition, advertisers seek to maximize return from their investment in advertising on television by using different techniques. Furthermore, the service suppliers, such as channel producers, cable service operators and others seek to maximize their revenue by efficiently utilizing commercial breaks to include the highest revenue advertisements in an optimal placement and for optimal viewer crowds.

The advertisers also currently have no simple unified interface, allowing them to bid prices for different combinations of date, time, geographic location, and target audience. Such a mechanism would automate the interaction of current service providers, allowing the providers to interact and appeal to larger crowds of advertisers, including small advertisers, currently unable to compete in the television advertisement market due to cost.

There is a need for an automated advertising system and method that will increase the value for both advertisers and service providers by allowing more flexibility in the possible combinations of time slots, channel selection, population profile targeted and the optimization of the population slicing as to maximize the revenue and the costumer satisfaction.

Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a system and method for providing auctioning of targeted advertisement placement for video audiences. Briefly described, in architecture, one embodiment of the system, among others, can be implemented as follows. The system contains logic configured to receive one or more advertising campaign, where each advertising campaign is defined by one or more advertisement effect criteria, and where each advertising campaign is for providing one or more advertisement to one or more viewer profile; logic configured to receive at least one bid for at least one of the one or more advertisement campaign; logic configured to determine an optimal placement of a requested advertisement based upon the advertisement effect criteria and bid prices provided, so as to optimize revenue; and logic configured to bill an advertiser according to a level at which a request of the advertiser, as defined by an associated advertising campaign, was supplied and actual viewership of the one or more advertisement associated with the advertising campaign.

The present invention can also be viewed as providing methods for auctioning targeted advertisement placement for video audiences. One example of the method contains the steps of: receiving one or more advertising campaign, where each advertising campaign is defined by one or more advertisement effect criteria, and where each advertising campaign is for providing one or more advertisement to one or more viewer profile; receiving at least one bid for at least one of the one or more advertisement campaign; determining an optimal placement of a requested advertisement based upon the advertisement effect criteria and bid prices provided, so as to optimize revenue; and billing an advertiser according to a level at which a request of the advertiser, as defined by an associated advertising campaign, was supplied and actual viewership of the one or more advertisement associated with the advertising campaign

Other systems, methods, features, and advantages of the present invention will be or become apparent to one with skill in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the present invention, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the invention can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views.

FIG. 1 is a schematic diagram illustrating an example of an IPTV network in which the present system may be provided.

FIG. 2 is a flow chart further illustrating the process of personalizing advertisements, in accordance with one exemplary embodiment of the invention.

FIG. 3 is a flow chart further illustrating the process of identifying and associating consumer profiles to set top boxes within a supervised learning scenario.

FIG. 4 is a schematic diagram illustrating an example of a cable network in which the present system may be provided.

FIG. 5 is a schematic diagram illustrating an example of a satellite network in which the present system may be provided.

FIG. 6 is a schematic diagram illustrating an example of a terrestrial network in which the present system may be provided.

FIG. 7 is a flow chart further illustrating the steps of the supervised learning process.

FIG. 8 is a flow chart further illustrating the process of identifying and associating consumer profiles to set top boxes within an unsupervised learning scenario.

FIG. 9 is a block diagram further illustrating functionality of the management application as blocks of logic.

FIG. 10 is a detailed logical flow diagram illustrating a sequence of events performed during unsupervised learning.

FIG. 11 is a flow chart further illustrating a process for determining targeted rating.

FIG. 12 is a flow chart illustrating a process for obtaining a content to viewer profile assignment.

FIG. 13 is a flowchart illustrating functions performed by the present system and method during execution of the real time targeted rating process.

FIG. 14 is a flow chart further illustrating the process of determining if a set top box is on or off.

FIG. 15 is a schematic diagram illustrating an example of a general purpose computer that can implement the advertisement auctioning system and method of the present invention.

FIG. 16 is a schematic diagram further illustrating functional blocks representing functionality defined by the software of FIG. 15, in accordance with a first exemplary embodiment of the invention.

FIG. 17 is a flow chart further illustrating functionality provided by the present advertisement auctioning system, when advertisement is to be provided to a specific desired targeted viewership.

FIG. 18 is a flow chart further illustrating examples of functionality that may be performed during billing calculation.

DETAILED DESCRIPTION

The present system is capable of learning the viewing habits of video viewers by collecting zapping events and other events performed by the viewer. Such videos may be viewed via a television, hand held device, computer, or any device capable of displaying video. The events may be collected at a set top box, computer, or other device. Alternatively, the events may be collected at a different location, such as, but not limited to, at an access multiplexer located in a head end, or in a device located separate from the head end. The system learns the viewing habits and zapping habits of different population profiles by identifying the viewing profile of a household.

The system uses supervised or unsupervised learning functionality for identifying different population profiles, and provides a representation of the probability (or another form of representation) of each population profile to watch any given program and to present a zapping pattern. The probabilities can be utilized as a tool for advertisers searching for the demographic profile of the audience of a television program, or, using inference functionality described herein, to identify the home audience at each household, and the specific viewers of a television program. Thereafter, the system is capable of supplying personalized content, such as, but not limited to, advertisements, video selections, and other content, to the viewers. It should be noted that the following description provides an example in which the content is an advertisement, however, the invention is not intended to be limited to advertisements, but instead, any content that may be personalized.

The present system collects the operations performed by viewers at service decoders, such as, but not limited to, set top boxes (the term set top box is used hereafter). The system then employs unsupervised or supervised learning functionality, as described herein, to interpret the operations at each set top box as the sum of operations of all viewers associated with this set top box. The system learns to identify different viewer profiles in the population and associates with each set top box and profile a probabilistic model of the viewing and zapping habits of viewers.

The present system and method also provides a user interface that provides an advertiser that wishes to advertise to such profiles with means of bidding for requested time slots for specified or unspecified audience profiles, dates, channels and programs. The system and method also provides an automated method of determining the winning bids to be placed in broadcasted content. The invention further includes a billing system for determining the prices charged from the advertisers based upon the viewership and the bids placed.

It should be noted that the present system and method may be provided within different infrastructures. As an example, the following description provides examples of using the present system and method in an Internet protocol television (IPTV) infrastructure, in a cable infrastructure, and in a satellite infrastructure. While these infrastructures are described herein, the present system and method is not intended to be limited to these infrastructures.

While the following describes the present system and method in detail it is beneficial to provide certain definitions.

Set top box (STB) or service decoder: A set top box or service decoder is a device responsible for converting digital (or analog) content received into viewable content that may be fed into a television set or other monitor. The set top box or service decoder may be located at a household or another location.

Platform: A network of service decoders (e.g., set top boxes) of a specific television service provider.

Passive audience identification: Identification of the viewer's profiles without any specific actions performed by the viewer.

Zapping event: A zapping event is an event where there is switching from a current service to another service, where the switching is performed by, for example, but not limited to, use of a remote control, pushing buttons on the set top box, or any action that causes switching, including, but not limited to, voice commands, or even consumer motions without pressing buttons. In addition, a zapping event may be other means for communicating with a set top box, such as, but not limited to, pressing an electronic program guide, pressing a volume button, and other actions involving the set top box.

Zapping pattern: A zapping pattern is the behavior of a viewing individual in terms of zapping, such as, but not limited to, programs watched, frequency of zapping events, and variance of zapping frequency.

Set top box (STB) zapping signature: Records of zapping events of a particular set top box.

Set top box (STB) signature: Data model providing characteristics of a set top box including: an association between a set top box and content available to the set top box, where the content is either provided or not provided via the set top box during a time period; and/or, at least one zapping pattern associated with the set top box. It should be noted that herein when referring to set top box signatures, one or more set top box signature is included. In addition, content availability refers to content that the set top box has access to and can provide.

Zapping log: Records of the set top box zapping signatures for an entire set top box network (Platform) or for part of the network.

Channel: A stream of programs broadcasted consecutively from a content source.

Program: Content that was broadcasted on a specific channel at a specific date and time, whether on demand or generally broadcasted.

Program rating: Percent of viewers that watched the program.

Targeted program rating: Percent of viewers of specific profile that watched the program.

Channel rating: Percent of viewers that watched the channel during the specified time period.

Targeted channel rating: Percent of viewers of specific Profile that watched the channel during the specified time period.

Profile: The classification of an individual into one of several population groups that is targeted. Such profiles may be, for example, but not limited to, psychographic (for example, behavioral) or demographic profiles. Examples of such groups include, but are not limited to, gender, age, income, marital status, and possibly also by interests in different fields.

Learning functionality: Functionality used to reduce a large set of observed data and its classification into groups to a set of parameters, allowing to reconstruct the classification of the majority of the original data and to classify similar, unlearned, data, or, to produce a new type of classification. Different relevant learning methods may be utilized to provide the learning functionality such as, but not limited to, artificial neural networks, decision trees, k-Nearest Neighbor, Quadratic classifier, support vector machine, direct probability estimate using Bayesian inference, Bayesian networks, Gaussian estimators, least squares optimization methods, and other optimization methods.

Supervised learning: Supervised learning is learning in which the classification of the observed data is inferred from a sample of the data supplied by an outside source. The learning functionality searches for a parameter set allowing reconstruction of the classification from the input that later can be used for classification of new unlearned data.

Unsupervised learning: Unsupervised learning is learning in which no classification of observed data is given (i.e., no sample is provided), and the functionality attempts to classify the data into different classes under some constraints. The functionality may use a method, such as, but not limited to, vector quantization, and various learning methods and various optimization methods, to find a reduction of the data into representative classes.

FIG. 1 is a schematic diagram illustrating an example of an IPTV network 10 in which the present system may be provided. Specifically, FIG. 1 is specific to video on demand or personalized advertisements for an IPTV infrastructure. As shown by FIG. 1, an IPTV head end 20 is provided, portions of which communicate with at least one customer premises 100A-100D. As is known by those having ordinary skill in the art, a head end is the physical location in an area where a video signal is received by a provider, stored, processed, and transmitted to local customers of the provider. One having ordinary skill in the art would also appreciate that more than one head end may be provided within a network. In addition, a network may have more than one type of head end, such as, but not limited to, a cable head end, a satellite head end, an IPTV head end, and a terrestrial head end.

The head end 20 contains at least a video service splicer 30, an advertisements video server 40, a management application 50, and an access network multiplexer 60. One having ordinary skill in the art would appreciate that the head end 20 may have portions in addition to those mentioned herein. In addition, while the present description refers to a management application, it should be noted that the management application is stored on a computer.

The video service splicer 30 receives video and audio services from a satellite dish 70. It should, however, be noted that video and audio services may be received by devices other than a satellite dish 70, such as, but not limited to, a cable network or any device capable of providing video to the head end 20.

The video service splicer 30 is capable of splicing personal advertisements into a video service stream, as instructed by the management application 50 and as is further described in detail hereinbelow. The video service splicer 30 also receives advertisements from the advertisements video server 40. In addition, actions of the video service splicer 30 are controlled by the management application 50. It should be noted that, for the example of an IPTV network, the video packets received by the video service splicer 30 may carry an Internet protocol (IP) address and a User Datagram Protocol (UDP) port number. It should also be noted that the video service splicer 30 may instead receive video and audio services from a cable fiber.

The access network multiplexer 60 is responsible for routing video services to transmission units 120A-120D that are video services decoders, as explained hereinbelow. The transmission units 120 are each located within a customer premises 100A-100D. The access multiplexer 60 is connected to both the management application 50 and the video service splicer 30. Specifically, the access network multiplexer 60 may perform, for example, IP and UDP port manipulation. It should be noted that the access network multiplexer 60 may be, for example, but not limited to, an optic multiplexer or a digital subscriber line access multiplexer (DSLAM). From a multicast point of view, as described hereinbelow, connection between the access network multiplexer 60 and a set top box 110 may be a shared media connection, or any other type of connection, and there may or may not be a multicast hierarchy between the access network multiplexer 60 and the set top box 110.

The management application 50 communicates with the video service splicer 30, the advertisements video server 40, and the access network multiplexer 60. In addition, the management application 50 provides the functionality required to learn unsupervised profiles in television audiences, as is described in detail hereinbelow. It should be noted that in accordance with an alternative embodiment of the invention, the management application 50 may instead be located within a set top box 110 located within the customer premises 100A-100D.

Each customer premises 100A-100D at least contains a set top box 110A-110D and a transmission unit 120A-120D. While for exemplary purposes four customer premises 100A-100D are illustrated, one having ordinary skill in the art would appreciate that additional or fewer customer premises 100A-100D may be provided. The transmission unit 120 is capable of receiving advertisement streams and video streams and forwarding the streams to an appropriate set top box 110. For exemplary purposes, the customer premises 100A-100D is illustrated as also containing a computer 130A-130D, although a computer 130 is not intricate to the invention. It should be noted that while a single set top box is shown as being located within a customer premises 100, more than one set top box 110 may be located within the customer premises 100. In addition, in accordance with an alternative embodiment of the invention, the set top box may be a computer or any device that can decode a service. For the present example of an IPTV network, the set top box 110 receives a video service with certain TCP/IP parameters, such as, but not limited to, IP address and UDP port. It should be noted, however, that in a cable network or a satellite network, the set top box 110 may or may not receive TCP/IP parameters.

The present system enables editing of online personal video so as to provide personalized television advertisements directed toward a viewer presently watching the television. As is described in detail below, the present invention is capable of categorizing a viewer into an advertising profile, an example of which is, but in not limited to, a demographic profile. Within a single customer premises, different television viewers may have different profiles. The different television viewers may view the same television during the day. Each different viewer may be associated with a different advertising profile, such as, but not limited to a demographic profile, thus preferably receiving different advertising messages. As an example, a family structure may be described as having an adult male of age 45, an adult female of age 42, a male teenager of age 17, a female teenager of age 14, and a male child of age 7. It should be noted that while the present description refers to a demographic profile, other types of profiles may be provided for.

During the time that a television viewer consumes service transmissions the management application 50 identifies the profile of the viewer. After identifying the profile, the application 50 performs personalized advertisements editing for that particular profile. When there is a different viewer with a different advertising profile that is using the same video decoder, the management application 50 identifies the profile that the viewer belongs to and performs online personalization editing for the advertisements, as described below.

In accordance with the present invention, for both supervised and unsupervised learning, the television consumers, also referred to herein as viewers, are not individually identifying themselves to the system. As a result, the system is required to identify consumer profiles and to associate the profiles with a specific set top box. This process is described in detail hereinbelow. Prior to describing this process, a general process of IPTV advertisement insertion in a broadcast environment is described in detail.

A typical advertisement projection works as follows. During content consumption the access network multiplexer 60 receives a video signal and sends the video signal to the customer premises 100A-100D using an IP protocol. During an advertisement break the video transmissions continue to be transmitted in multicast, thus there is no personalization of advertisements. To instead personalize advertisements, the following is performed.

FIG. 2 is a flow chart 200 further illustrating the process of personalizing advertisements, in accordance with one exemplary embodiment of the invention. Any process descriptions or blocks in flow charts should be understood as representing modules, segments, or portions of code that include one or more executable instructions for implementing specific logical functions or steps in the process, and alternative implementations are included within the scope of the embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.

As shown by block 202, content is transmitted from the head end 20, via the access network multiplexer 60, to the set top box 110. An example of a protocol that may be used for the transmission is the Internet group management protocol (IGMP), which is used by IP hosts to manage their dynamic multicast group membership. Of course, other protocols may be used.

In accordance with the present example, a subset, or complete set, of the customers that are connected to the access network multiplexer 60 are viewing the same video and/or audio service (i.e., content). The management application 50 also continuously identifies the consumers (block 204). It should be noted that the management application 50 can utilize either online processing or offline processing to determine a relationship between viewed content (e.g., videos) and viewer profiles. Regarding offline processing to identify consumers, associate the consumers with content, and produce reports, in accordance with a predefined schedule, or when prompted to do so, the management application 50 reviews zapping patterns, processes the patterns, and associates each program viewed from a set top box 110 with a viewer profile. Alternatively, for online processing, during an advertising break, the management application 50 reviews only recent zapping events to determine which viewer is presently viewing content. Further description of consumer identification is provided with regard to FIG. 3, FIG. 8, and FIG. 10. It should be noted that the information received by the management application 50 may be received from a source other than a set top box.

Returning to the flowchart 200 of FIG. 2, the management application 50 decides which advertisements of the advertisement set each consumer should receive (block 206). It should be noted that the process of selecting advertisements is described in detail herein.

As shown by block 208, the video splicer 30 then splices the advertisements according to the decision of block 206. Since one having ordinary skill in the art would know how a video splicer splices advertisements, further description of the splicing process is not provided herein. As shown by block 210, when the advertisement break is over, the access multiplexer 60 continues to transmit the multicast transmission as it did prior to the advertisement break.

It should be noted that if during an advertisement break the consumer changes the consumed video service, the management application 50 supplies the new service in the same manner. Specifically, if the service transmits content, the management application 50 continues to transmit the content with the multicast protocol. In addition, if there is an advertisement break, the management application 50 may splice different advertisements.

As previously mentioned, the present system provides a consumer specific advertising environment. This environment is provided in part by the providing of online multilayer multicast groups between the access network multiplexer 60 and the set top boxes 110A-110D. The access network multiplexer 60 transmits broadcast transmissions with multicast protocol to a subset A of the set that is connected to the access network multiplexer 60. In the subset A there are different subsets B of consumers watching the same channel at a given moment that are connected to the access network multiplexer 60. Within a single subset B, consumers are associated by their profile for advertising. When there is an advertisement break, the access network multiplexer 60 is transmitting an additional layer of multicast, where each different subset Bi is receiving different advertisements according to the advertisement profile associated with subset Bi. Finally, when the advertisement break is over, subset A consumers continue to watch the same service.

While the abovementioned provides an example of an IPTV network 10, a different infrastructure in which the present system and method may be provided includes a cable network 400. FIG. 4 is a schematic diagram illustrating an example of a cable network 10 in which the present system may be provided. While there are similarities between the IPTV network of FIG. 1 and the cable network 400 of FIG. 4, there are also differences, which are described herein.

Referring the FIG. 4, a cable head end 410 of the cable network 400 is very similar to the IPTV head end 20 of the IPTV network 10. It should be noted, however, that instead of an access network multiplexer 60, the cable network 400 contains an RF interface 410, which may be, for example, but not limited to, a quadrature amplitude modulation (QAM) modulator and/or a radio frequency (RF) combiner. The cable network 400 provides for individual coaxial cables to provide communication capability from the cable head end 410 to individual set top boxes 430A-430H, where each set top box is located within a customer premises (CP) 440A-440H, such as, but not limited to, a home.

Another example of a network in which the present system and method may be provided is a satellite network. FIG. 5 is a schematic diagram illustrating an example of a satellite network 500 in which the present system may be provided. The satellite network 500 contains a satellite head end 510 that is similar to the IPTV head end 20, except that the satellite head end 510 contains an RF modulation interface 520. The RF modulation interface 520 is capable of formatting and amplifying received data for transmission to a satellite 550.

The satellite 550 is capable of reflecting received data to satellite dishes 560A-560N capable of receiving data signals from the satellite 550. Each satellite dish 560A-560N is associated with a customer premises 570A-570N, such as, for example, a home. In addition, each customer premises 570A-570N has at least one set top box 580A-580N located therein.

Still a further example of a network in which the present system and method may be provided is a terrestrial network. FIG. 6 is a schematic diagram illustrating an example of a terrestrial network 600 in which the present system may be provided. The terrestrial network 600 contains a terrestrial head end 610 that is similar to the IPTV head end 20, except that the terrestrial head end 610 contains an RF modulation interface 620. The RF modulation interface 620 is capable of formatting and amplifying received data for transmission to a radio tower 650.

The radio tower 650 is capable of reflecting received data to antennas 660A-660N capable of receiving data signals from the radio tower 650. Each antenna 660A-660N is associated with a customer premises 670A-670N, such as, for example, a home. In addition, each customer premises 670A-670N has at least one set top box 680A-680N located therein.

In accordance with the present invention, the management application 50 identifies the consumer profiles that are using video/audio decoders (i.e., set top boxes) in the network 10. For exemplary purposes the example of a single household having two television sets is provided. Each television is connected to a different set top box. A first television A is located in the living room and a second television B resides in a room for children.

In accordance with the present example, there are three consumer demographic profiles in the household, namely:

1. Profile 1: Male adult of age 37

2. Profile 2: Female adult of age 34

3. Profile 3: Male child of age 8 and male child of age 10

The consumer profiles are associated with the television sets as follows:

Television A—profiles 1, 2, and 3 (all the household residents are consuming content via television A).

Television B—profile 3 (only the children are using television B)

The process of identifying and associating consumer profiles to set top boxes may be separated in accordance with whether a supervised learning process is used or an unsupervised learning process. These two scenarios are described separately hereinbelow, although it will be noted that certain steps in the processes are similar.

In accordance with the present example, for both the supervised and unsupervised scenarios, service providers have no knowledge of the profiles existing in the household, the location of the television sets in the household, and/or associations between the television sets and the profiles. Instead, the management application 50 identifies and associates the consumer profiles with the set top boxes.

Supervised Learning

Reference is now made to the flowchart 300 of FIG. 3. The flowchart 300 of FIG. 3 further illustrates the process of identifying and associating consumer profiles to set top boxes 100A-100D within a supervised learning scenario. As shown by block 302, to acquire a sample, the service provider may send a questionnaire to the consumers. Alternatively, the service provider may use any other method of obtaining data, such as, but not limited to, having a telephone conversation. The questionnaire may refer to the household demographic details, video decoders (i.e., set top boxes), and association between the usage of each person in the household and the video decoders in the household. As shown by block 304, consumers fill out the questionnaire and return the same to the service provider. With the return of the consumer questionnaire, it is known which individual profiles and set top boxes are associated with a household.

As shown by block 306, set top boxes 110 in the network 10 record all of the zapping events that the consumers are creating. In accordance with the present description, and as is known by those having ordinary skill in the art, zapping refers to the switching from the current service to another service via use of, for example, but not limited to, a remote control or pushing buttons on the video decoder. It should be noted that this use of remote controls is provided for exemplary purposes. Instead, zapping may be associated with switching initiated by voice commands, or even consumer motions without pressing buttons.

As shown by block 308, the set top boxes 110 send the zapping events to the management application 50. The management application 50 then associates behavior of consumers and their zapping pattern with the households that either did not return the questionnaire or that never received a questionnaire (block 310).

The association process is a learning process, also referred to as a business process, which is the process of passive platform audience learning and identification, and targeted platform rating calculation and analysis. The learning process is divided into multiple steps, including data collection, modeling, learning, identification, analysis, and post processing. FIG. 7 is a flow chart 700 further illustrating the steps of the supervised learning process.

Data Collection

Referring to FIG. 7 and the step of data collection, in order to perform audience learning, audience identification, and targeted rating calculation, certain external data is collected and converted into an internal format (block 702). This external data includes the zapping log, the broadcast schedule, set top box information, and sample information. The zapping log includes the actions that were performed by the set top box user using a remote control, directly using set top box control buttons, or performing a different action that caused changing from a current service to another service, or from a current state of the set top box to another state of the set top box (e.g., switching on or off). The broadcast schedule (or AsRun) includes, for example, a timetable for the platform channels/programs during the zapping gathering period. It should be noted that the broadcast schedule may also include a schedule of video on demand programs, or a schedule of any interactive service. The broadcast schedule should be reconciled with the zapping log in terms of times and channels identifications. The set top box information includes the relevant information, for every set top box for which zapping was collected, (e.g., unique set top box identifier and address). The set top box information should also be reconciled with the zapping log in terms of set top box identifications.

Modeling

Modeling is the process of converting the zapping log into different data models that could be used by different learning and identification algorithms, thereby providing a set top box signature (block 704). In accordance with the present system and method, at least the following data models are recognized. A first data model that is recognized is a set top box viewing signature. Regarding the set top box viewing signature, for each set top box, the list of “watched” programs could be created based on the zapping log and reconciled broadcast schedule. For each watched program, an aggregated watching percentage is given. As an example, STB1 watched program number 56, 30%, means that STB1 watched 30% of the program, on overall (including leaving the program and getting back to it), during the whole time of broadcast of program number 56. A second data model that is recognized is a set top box time signature. The set top box time signature is, for each set top box, the list of percentages of viewing every channel during the specific time aggregated for weekdays. As an example, set top box 1 (STB1) watched CNN on Sundays between 12:00 and 13:00, 25%, means that during the learning period, the average time that this particular set top box watched CNN between 12:00 and 13:00 on Sundays was fifteen minutes.

A third data model that is recognized is a set top box zapping frequency signature. Specifically, every profile does zapping with different frequencies. Calculating zapping frequencies of every set top box during the predefined time periods provides a Zapping Frequency Signature.

Unfortunately, the zapping log is not noise free. Most of the viewers use the remote control in the same fashion, but there is a small minority of users that would use the remote control differently. This affects the general zapping frequency, surfing periods (when the viewer changes the channels with high frequency in order to find something interesting), etc. In order to handle these irregular behaviors, a set of data filters should be applied to the zapping log prior to modeling.

Learning

For supervised learning, learning is a process in which the set top box signatures (viewing, time, and/or zapping frequency), created at the data modeling stage, are used with a list of set top boxes and profiles to provide an Association Rule (block 706). The Association Rule provides knowledge of how to associate a list of profiles within a network to a set top box within the network. The Association Rule is determined due to not having received filled out questionnaires from all parties and wanting to determine unknown relationships between profiles and set top boxes.

It should be noted that during supervised learning, it is not determined which profiles are associated with which set top boxes. Instead, as mentioned above, an Association Rule is determined to provide knowledge of how to associate a list of profiles to each set top box.

As mentioned above, during supervised learning there is an association of set top box signatures (e.g., viewing) for each set top box in the data model to a predefined list of profiles, based on a sample, for further use in the identification functionality. A sample is a partial list of set top boxes for which both the zapping log and the list of profiles associated with each set top box are provided. The sample may be provided by an operator of the set top box collection. Predefined profiles can be, for example, but not limited to, demographic profiles that define gender, age, marital status, income level, or psychographic (behavioral) profiles.

The Association Rule can be applied to any set top box in the same network, as is performed during identification. An example of a process that may be used to derive the Association Rule follows. The management application 50 contains knowledge of the current consumed service for a specific decoder, the profiles (demographic, or behavioral) associated with a specific decoder and household, and previously consumed content for a specific decoder. In accordance with the present invention, the management application 50 uses inference functionality to determine the current viewer/listener profile. The inference functionality defines the current profile(s) that is/are consuming the service.

An example of inference functionality follows, where the learning functionality uses Bayes rule. At this point, the management application 50 contains knowledge of the current consumed service for a specific decoder (set top box). In addition, the management application 50 knows the demographic profiles associated with a specific decoder and household. Further, the management application 50 knows previously consumed content for a specific decoder, specifically, the short-term history. The management application 50 may then use the inference functionality to determine the current viewer/listener profile.

An example for the inference functionality using Bayes rule is provided hereinafter. In the learning algorithm, data collection determines the distribution of the consumed content as a function of the classification of the viewers/listeners at the household. In addition, using the data in conjunction with the Bayes rule, the probability that the household contains a viewer/listener belonging to each demographic profile is estimated. Data utilized to perform this process includes probabilities of each consumed service for households containing each of the demographic profiles, as well as probabilities of each consumed service for households not containing each of the demographic profiles.

Bayes rule reads as shown by equation one below.


P(C|F1 . . . Fn)=P(F1 . . . Fn|C)*P(C)/(P(F1 . . . Fn|C)*P(C)+P(F1 . . . Fn|˜C)*PC)) (Eq. 1)

In equation one, P (F1 . . . Fn|C) is the probability that a household containing a certain profile (C) consumes the list of services F1 . . . Fn and does not consume any other service. In addition, P (F1 . . . Fn|˜C) is the probability that a household not containing a certain profile (C) consumes the list of services F1 . . . Fn and does not consume any other service. Further, P(C) is the probability that a household contains profile C, regardless of the services consumed and P(˜C) is the probability that a household does not contain profile C, regardless of the services consumed.

P(F1 . . . Fn|C) and P(F1 . . . Fn|˜C) may be approximated as the products P(F1|C)* . . . *P(Fn|C) and P(F1|C)* . . . *P(Fn|˜C) respectively, which may be calculated directly from the statistics gathered for the sample population. Better approximations may be obtained by considering correlations between services and between profiles in a household. From the above calculation, the result is the probability, P(C|F1 . . . Fn) that a household contains profile C, given the list of the household consumed services. The collection of all values P(C|F1 . . . Fn), calculated for the whole of sample set top boxes represents the Association Rule used for the identification step, applied to each set top box in the network, which was not part of the sample set top boxes. In addition, from this calculation, the result is the probability that a certain individual viewer from a specific profile used the set top box.

In accordance with an alternative embodiment of the invention, a sample may be provided, and post processing may be provided to associate content with profiles. Specifically, a sample may include at least one profile, a set top box associated with the profile, and zapping information associated with the set top box. Post processing may then be performed on the sample to determine which content (e.g., advertisement) is most appropriate for providing to the consumer associated with the profile. As a result, in accordance with this alternative embodiment of the invention, the learning process is not required.

Identification

Identification is a process of recognition of a list of profiles as being associated with a certain set top box (STB), based on the learning results. Every set top box in the network should be assigned with at least one profile (demographic, or behavioral). It is conceivable to assume that in front of a set top box, mostly there is more than one active profile and there are cases where the same profile should be associated a few times to the same set top box. Thus, for each set top box there should be assigned one or more profiles. For example, a young couple (male & female) between the ages of 20-30 that are living together would produce 2 profiles, specifically, one for the female and the other for the male. As another example, if a specific household has two boys of the ages seven and fourteen, the boys may both be assigned to an appropriate set top box as the same profile, “Male 6-18.”

To determine the list of profiles associated with a set top box, the Association Rule is mathematically applied to the list of set top box signatures (block 708).

Analysis

Analysis is the process of breaking down and studying the results of learning and identification in order to estimate possible identification errors, provide a set of different factors and amendments for post processing, association of definition of profiles by signatures to a third party definition, and any other functionality resulting from studying the learning and identification results.

The identification error analysis may be performed via mathematical modeling means and/or via simulation (empirical) means. For example, estimation of expected identification errors may be achieved via applying the learned results to a part of the sample and simulating the identification results.

Post Processing

Post Processing is the process of calculating the data required for presentation to potential customers, such as, targeted rating. Post processing also includes reporting and analyzing based on results of identification. The aforementioned list of results is obtained via post processing functionality described hereafter. Such functionality may be provided by, for example, algorithms. Post processing may be utilized to calculate the following data, although post processing calculation is not intended to be limited to calculating only this data; rather, by post processing any calculation done with the use of the results obtained from the learner and/or identifier is referred to as a post processed calculation/algorithm.

Targeted Rating

Targeted rating may include a percentage of viewers of a specific profile that consumed content, a percentage of viewers of a specific profile that consumed content from a channel during a specified time period, or a percentage of viewers of a specific profile that consumed content provided within the network during a specified time period. It should be noted that the term “consumed” is used herein instead of the term “watched” since content consumed by a viewer profile not only includes content that is watched by a viewer profile, but also content that is not watched, but that is provided to a set top box associated with a viewer profile, such as, but not limited to, audio content.

Herein, content may be, for example, but not limited to, a program. It should also be noted, that for exemplary purposes, the following provides the example of consuming content comprising watching content, however, one having ordinary skill in the art will appreciate that consuming of content need not be limited to watching content, but instead may include other functions such as, but not limited to, listening to content received from a channel.

More specifically, targeted rating functionality calculates the targeted rating of a content per profile (e.g., using optimization algorithms, see examples herein below) of the learned and identified data, or of any independent data (e.g., obtained from the sample) as long as the data contains information about the set top box signatures (e.g., viewing signatures) and the profile(s) associated to each set top box in the input. As an example, the targeted rating functionality may be used on data resulting from the supervised learning functionality, unsupervised learning functionality, or independent data. It should be noted that herein set top box signatures includes one or more set top box signature.

Targeted rating may include targeted program rating, targeted channel rating, and targeted time interval rating. Targeted program rating is a percentage of viewers of a specific profile that watched a program. In addition, targeted channel rating is a percentage of viewers of a specific profile that watched a channel during a specified time period. Further, targeted time interval rating is a percentage of viewers of a specific profile that watched content broadcasted within the network during a specified time period.

Targeted rating determination may be provided in general or regionally. Specifically, a regional targeted rating is a targeted rating for one region, where a region may be limited to, for example, a specific geographical location. Alternatively, general targeted rating is a targeted rating for an entire network, or a part of a network, which is region independent (for example, it may include one or several combined regions).

FIG. 11 is a flow chart 950 illustrating the process of determining a targeted rating. As shown by block 952, data representing relationships between viewer profiles and set top boxes is received, or obtained. Specifically, data showing which profiles are associated with which set top boxes is received. The data may either be obtained after performing learning and identification processes, as described herein, or received from an external source.

As shown by block 954, set top box signatures are also received, or obtained, for use in determining targeted rating. Such set top box signatures may be, for example, but not limited to, viewing signatures, time signatures, high-resolution time signatures, or zapping frequency signatures. It should be noted that other set top box signatures may also be provided for by the present system and method.

The type of set top box signature used in targeted rating determination dictates which kind of targeted rating will result. As an example, when viewing set top box signatures are used, targeted program rating results. In addition, when time set top box signatures are used, targeted time interval rating, or targeted channel per a time interval rating, results.

As shown by block 956, a first input set is derived showing the probability that each profile is associated with each set top box. It should be noted that the first input set is derived by performing the learning and identification processes, or is received from an external source. A second input set is derived containing data of set top box signatures (block 958). It should be noted that the second input set is derived by performing the modeling functionality on the collected/received zapping log. As an example, for a viewing signature, the zapping log may contain information showing whether a certain set top box consumed certain content (for example, a program), or not. For purposes of deriving the desired output set, namely, the set of targeted ratings, it is assumed that the data of the set top box signatures can be approximated by certain operations involving data associating profiles to set top boxes and targeted rating.

As is shown by block 960, certain operations are applied on the set of data associating profiles to set top boxes and the set of data containing set top box signatures (the input sets), resulting in a targeted rating (the output set). Different forms of data sets and different operations may be used to provide the targeted rating. As an example, matrices may be used to derive the targeted rating, where it is assumed that multiplying a matrix A (matrix A shows the probability that each profile is associated with each set top box) by a matrix B (matrix B is the targeted rating) would result in a matrix C (matrix C is the set top box signature data). Of course, other examples of operations may be used. Two examples of operations that may be used to determine targeted rating are provided below.

If the network covers more than one region and information on the regions in which the different set-top boxes in the network reside is available, a regional targeted rating (RTR) may be calculated using similar methods to those described below. In addition, regional targeted rating of high-resolution time steps, where a time step may be for example, but not limited to, per each thirty seconds, may be calculated for each specific channel and profile.

Input to the regional targeted rating functionality includes the region in which each of the set top boxes is stationed, the set top box signatures for set top boxes within that region, such as, but not limited to, viewing signatures, time signatures, zapping frequency signatures, and high-resolution time signatures, and lists of profiles associated with each of the set top boxes within the region, from any source. It should be noted that a region may have one or more set top boxes therein. In addition, a set top box may be located within more than one region.

The output of the regional targeted rating functionality is the percentage of viewers of each predefined profile, within a specific region, that watched each of the contents, for example, programs, in the case of when viewing signatures are the input, or of each channel at a certain time interval, in the case of when time signatures are the input.

Two examples of methods that may be used to calculate targeted rating are provided herein below. It should be noted that the present invention is not intended to be limited to the following examples, but instead that the following examples are merely provided for exemplary purposes and are not intended to limit the present invention.

EXAMPLE 1

An example of a method to calculate targeted rating, given a list of set top boxes with viewing signatures and profile(s) associated to each set top box, can be given via the use of a linear regression optimization algorithm. In calculating the targeted rating, it is assumed that multiplying the set of parameters representing the association of profile(s) to set top boxes (let us call it A) by the aggregation of targeted rating values of each of the profiles per each program watched by at least a portion of the set top boxes of the network for which the zapping log contains records of set top box zapping signatures (the yet unknown and desired output, let us call it B) corresponds to the parameters representing the aggregation of the set top box viewing signatures (part of the input, let us call it C).

For purposes of this example, it is assumed that the sets of parameters A, B, and C are utilized to provide matrices A, B, and C. A minimization algorithm on the squared norm of the matrix (AB−C) may then be performed (a random initial guess is provided to the algorithm for the values of B). In other words, given A and C, the output of applying this algorithm is the set of probabilities, B, representing the probability of each profile to watch each of the programs broadcasted to the collection of set top boxes. An example table for such an output is presented below after example 2 is described.

EXAMPLE 2

As a second example of a method to calculate targeted rating, the matrices A, B and C are as in example one, where A is a matrix containing list(s) of demographic, or psychographic, profiles that is (are) associated to each set top box (of the whole network, a part of the network, a specific region within the network, or statistically representing any of those), which is obtained from any source, either via local identification, via receiving an external sample, or via another means.

The matrix C is a matrix that contains, per each of the set top boxes, a list of set top box signatures per a channel, or a program. Examples of forms of set top box signatures include, but are not limited to, viewing signatures, time signatures, high-resolution time signatures or any other form of set top box signatures that associates knowledge of some viewing habits in a certain period per each set top box. The unknown set of probabilities per each of the pre-defined profiles, represented by the matrix B, may then be obtained by the use of solving equation two (Eq. 2):


B≅A+C (Eq. 2)

In equation two, A+ is the pseudo-inverse of the matrix A, which is unique in mathematical terms, thereby insuring that the targeted rating matrix B computed in equation two is well-defined. An example of a pseudo-inverse is the Moore-Penrose pseudo-inverse. Calculating A+ and multiplying it by the matrix C gives a good approximation to the matrix B, of the targeted ratings.

The algorithm of equation two is extremely accurate and allows for the performance of targeted rating calculations on very large amounts of data (more than an order of millions of entries) in an extremely short computing time. Specifically, when performing linear regression, for example, in accordance with one exemplary embodiment of the invention, there is a requirement that for each targeted rating element a separate optimization process is performed, thereby requiring a long computation period. A targeted rating element may be, for example, but not limited to, a program, a time interval, or a channel.

Alternatively, in accordance with another exemplary embodiment of the invention, if a pseudo-inverse is utilized, performing a matrix multiplication, instead of multiple optimization processes, is very fast and is performed for all the targeted rating elements at once, even if there are tens of thousands of targeted rating elements.

An Example of Data and Targeted Rating Output Follows.

  • If the pre-defined profiles are:

1. Female of age 30-55 with high income.

2. Male of age 18-40 with average income.

3. Male child of age 6-16 with low income.

4. Female child of age 6-16 with average income.

And the list of programs (as specified in the viewing signatures) is:

1. Saturday night live.

2. Lost.

3. 24.

  • Then the targeted rating (TR) output would be the following table:

Rating (in %
Program IDProfile IDof each profile)
11 0.5%
2  1%
30.01%
40.04%
21  3%
21.54%
30.01%
40
312.31%
22.11%
30
40

Content to Profile Assignment

In addition to a targeted rating of a content (for example, program) per profile, a content to viewer profile assignment (C2P) may be determined so as to provide an identification of what content is being consumed by what viewer profile. For exemplary purposes, it should be noted that content may be, for example, but not limited to, a program. Specifically, a content to profile assignment is beneficial to calculate for those set top boxes within the network to which more than one viewer profile has been associated so as to enable determination of which viewer profile of the list of viewer profiles associated with the set top box actually consumed a specific content.

The present description provides examples of how to determine content to profile assignment for illustration purposes only and is not intended to limit the invention to these examples. Specifically, as previously shown above, the learning and identification processes result in an association of at least one viewer profile to a set top box for which a set top box signature is provided. In addition, determining a targeted rating results in a percentage of viewer profiles that consumed content, wherein the content may be, for example, a program. Having the learning and identification process result and the targeted rating result, it is beneficial to determine what content is being consumed by what viewer profile. Similarly an assignment of any content in a specific time slot to a specific viewer profile in the household that consumed this content may be made.

As previously mentioned, obtaining a content to profile assignment involves determining for each content that was consumed by a certain set top box, which is the specific viewer profile, or viewer profiles, of the profiles associated to this set top box, that consumed the content. Alternatively, if more than one viewer profile has a probability of consuming the content, a list of viewer profiles associated to this set top box that consumed the content with certain probabilities may be calculated. This calculation can be done, for example, via use of algorithms applying algebraic manipulations to the sets of parameters representing the aggregation of viewing (or other) set top box signatures (denoted by C, as above), the parameters representing the association of viewer profile(s) to set top boxes (denoted by A, as above), and parameters representing targeted rating values (denoted by B, as above).

Once the association of profile(s) lists to set top boxes is obtained (the input set A), either by performing a supervised/unsupervised learning and identification process, or obtained from an external source, it is possible to utilize statistical, algebraic, or other methods on input set A, together with the set top box signatures of the set top boxes (input set C), and the set of targeted ratings B, to infer the specific viewer profile that watched each specific content via any given set top box. The targeted ratings may be obtained either by one of the methods described above, or by other methods, or received from an external source.

For exemplary purposes, FIG. 12 is a flow chart 1000 illustrating a process for obtaining a content to viewer profile assignment. As shown by block 1002, at least one set top box signature is received, wherein each received set top box signature provides an association of content consumed via an associated set top box. Such set top box signatures may be, for example, but not limited to, viewing signatures, time signatures, or high-resolution time signatures. It should be noted that other set top box signatures may also be provided for by the present system and method.

Data representing relationships between viewer profiles and set top boxes is received (block 1004), wherein the data may either be obtained after performing learning and identification processes, as described herein, or received from an external source. Such data includes an association between at least one viewer profile and at least one set top box. Preferably, the data is provided as a list of viewer profiles that are associated with a specific set top box.

Including the functionality of block 1002 and block 1004, the result is an association of content consumed via an associated specific set top box and a list of at least one viewer profile associated with the specific set top box. These results may be obtained for one or more set top boxes within the network, wherein the content to profile assignment may be determined for each such set top box.

As shown by block 1006, operations are performed on the set top box signatures and the association of viewer profiles to set top boxes to obtain content to profile assignment. It should be noted that many different examples of operations may be provided. The following provides two examples of operations that may be used to obtain content to profile assignment.

EXAMPLE 1

Using the targeted rating of viewer profiles, or other data describing the viewing habits of each viewer profile associated with the network of set top boxes, or associated with a part of the network of set top boxes; and further having the association of viewer profile lists to the set top boxes, as obtained from supervised or unsupervised learning and identification methods, or by an algorithm, or obtained from an external source; then the probabilities for any viewer profile to watch given content are deduced using statistical analysis or any algebraic, or other method. Assuming, for illustration purposes, that in example 1 content is a program, let us denote by Pj(f) the probability that a specific program, denoted by j, was consumed via a certain set top box, denoted as STBi, within the network, by a certain viewer profile, f, identified to be in the list of profiles using this specific STBi. Then, for example, Pj(f) may be calculated as:

Pj(f)=TRj(f)fTRj(f),

where TRj(f) denotes the targeted rating of the specific program j (where program j is a program for which we are determining viewer profile(s) that watched program j) for profile f, and f′ range over the profile list, of profile(s) that had been associated with this STBi, via which program j had been consumed.

The association of the list of profiles to this specific STBi may be obtained by learning and identification processes, or any other method, or received from an external source (or, alternatively, assuming all profiles are associated with this STBi with some probability if no other information is given). In the case that association of the profile f to the STBi, via which program j had been consumed, is given with a certain probability, it is possible to get the probability that the profile f watched the program j in the STBi in a more accurate way, for example by multiplying each targeted rating by the appropriate probability.

Let us note that the accuracy of Pj(f) gets higher if the watching correlations between the different profiles, f′, associated with the STBi that watched program j, is as low as possible. It should be noted that zero correlations means that only one profile, f out of the list of profiles, f′, which are associated with the STBi, would usually watch the program j.

Applying a maximization on all probabilities Pj(f), obtained for each of the viewer profiles, f, that are associated with the STBi, would then result in obtaining the content to viewer profile assignment, where the profile having the highest probability as determined, is the viewer profile that watched the program. It should be noted that if more than one profile has the same high probability as determined, then both viewer profiles watched the program.

EXAMPLE 2

While example 1 usually provides accurate results, it might take a long computation time, in case it needs to be computed for each content, for example for each program, and each set top box that consumed this content (for example, watched the program), separately. Moreover, example 1 depends upon the input set B of targeted ratings.

An alternative example, as shown by example 2, would just apply algebraic manipulations on the sets A and C, described above, where set A is either obtained from the processes of learning and identification, or is received from an external source. It should be noted that in accordance with the second example, there is no requirement for calculating or receiving the targeted rating (set B above).

Assuming for illustration purposes of this example that the sets A and C are matrices and that a content is a program, the following method of C2P may be considered. For each program j, that had been watched via STBi, Pj(f), the probability that a profile f (of a list of profiles associated with STBi) is the one who watched program j, is obtained via algebraic manipulations on the matrices A and C and statistical inference:

Pj(f) is calculated as the number of set top boxes that were associated with profile f via which program j had been watched, divided by the number of set top boxes via which program j had been watched. Then, the quantity Pij(f) is obtained as the probability that STBi contains profile f and via it program j had been consumed. Then, as in example 1, a maximization on all probabilities Pij(f) may be applied for each of the viewer profiles, f, that are associated with the STBi, thereby resulting in obtaining the content to viewer profile assignment, where the profile having the highest probability as determined, is the viewer profile that watched the program. Again, it should be noted that if more than one profile has the same high probability as determined, then both viewer profiles watched the program.

It should be noted that other methods may be used to associate content to viewer profiles and such methods are intended to be included within the present description.

Total Viewership

Further, a total viewership may be calculated (using, e.g., a program—time slot map and applying to it a calculation algorithm which utilizes data obtained in the previous steps described here), which is the calculation of total aggregated viewing activities for each of the pre-defined profiles (these may be demographic or behavioral), during a twenty-four hour period for each week day.

For example, having the association of profile(s) with each set top box, represented as a set of probabilities (either obtained as an output from the learning and identification steps or given from an outside source), and given the set top box signatures (e.g., as an output from the data modeling stage), given in addition the broadcasting time table (showing for a pre-defined period of time at which time and date and for which duration each program was broadcasted), the following calculation is performed.

The data is aggregated and modulated in such a form that for each day of the week (24 hours) it is calculated how many of each of the pre-defined profiles watched any content during each of the pre-defined time intervals. For example, if the period decided upon is three months and there were 12 Sundays during this period, the 24 hour period is divided to intervals of 15 minutes and for each such interval it is calculated (using the set top box signatures and the data mentioned above) how many times each of the pre-defined profiles watched any content during each of the 15 minute intervals aggregated for all 12 Sundays on a 24 hours span. Then this information is presented in a graph showing the viewing peaks during a 24 hour Sunday divided to 15-minute slots per each profile. This is done for each day of the week (aggregated to the number of time this weekday appeared during the three months period).

In addition to the abovementioned, a targeted rating distribution may be determined, which involves, for every channel, for every profile, calculating the rating of the channel for every brief period of time (e.g., thirty seconds), for every minimally defined region. Further, a viewership flow may be determined, which includes, for every channel, calculating the number (or percentage) of viewers of every profile that join and leave the channel during every short period of time (e.g., thirty seconds), for every minimally defined region. Still further, creative reports may be determined such as, for example, during an advertisement break, for each second, calculating the rating and viewership flow. All the aforementioned are merely examples of the post processing possibilities.

In the supervised case, with the knowledge gained by the functionality of block 310, for any households that did not fill out the questionnaire, the management application 50 uses identification functionality to associate the rest of the set top boxes 110 with the profiles that are using the set top boxes 110 (block 312). An example of the functionality, which is used as a basis for such an identification functionality, is provided herein below. It should be noted that different relevant learning methods may be used to perform the identification functionality. Examples of such learning methods may include the use of any one of the following, or other learning methods: Bayesian learning, various statistical methods, artificial neural networks; decision trees; k-nearest neighbor; quadratic classifier; support vector machine; various optimization methods, and direct calculation of probabilities. Of course, other learning methods may be used and are intended to be included within the present description.

Viewership Flow

Using the identified profiles data and high-resolution time signatures, a viewership flow may be calculated. It should be noted that a high-resolution time signature is a representation of which channel each set top box watched during each time step of a specific time interval, such as, but not limited to, thirty seconds. In addition, a viewership flow is the number of viewers of each profile that left or joined watching a specific channel during each time interval (e.g., 30 seconds), during a day or any pre-defined time interval. Viewership flow may be calculated using, for example, but not limited to, a high-resolution regional targeted rating, in addition to the data of signatures and lists of profiles associated with each set top box.

Calculation of viewership flow is performed in a few steps. It should be noted that the following is an example of steps that may be used to calculate viewership flow, however, the following example is not the only way to calculate viewership flow and this example is not intended to be limiting. As a first step, the high-resolution regional targeted rating is calculated. Calculation of the high-resolution regional targeted rating provides, per each channel and per each viewer profile, the percentage of viewers of this viewer profile that watched this channel per each time interval (for example, 30 seconds) during each day of a specified period. Such targeted rating may be calculated, for example, but not limited to, using a method similar to the method described in the targeted rating section of the present description, where the word program is replaced by channel per time interval.

To calculate viewership flow, the differences between the targeted ratings of same viewer profiles, per different time intervals, may be calculated to record the change in number of viewers of each profile between successive time intervals. Moreover, using for example, but not limited to, the method described above as content to profile assignment, the number of viewers that left or joined the viewers of each channel at each time interval may be calculated. To summarize: the viewership flow application may contain various descriptions of changes in viewers per channel per time interval. For Examples of the abovementioned include, but are not limited to, targeted rating and the changes in targeted rating per time interval, and number of viewers of each profile who left or joined the viewers of the channel at each time interval.

Unsupervised Learning

Reference is now made to the flowchart 800 of FIG. 8. The flowchart 800 of FIG. 8 further illustrates the process of identifying and associating consumer profiles to set top boxes 100A-100D within an unsupervised learning scenario. It should be noted, that unlike with supervised learning, with unsupervised learning no sample relating viewer profiles to set top boxes is provided. Moreover, the type of viewer profiles might be unknown at the stage of the learning. As a result, the viewer profiles must be determined. It should be noted that different types of viewer profiles may exist, including, but not limited to, demographic and psychographic types of viewer profiles. For example, for the psychographic type of viewer profile, the profile may contain multiple categories, such as, but not limited to, watching habits, purchasing behavior, social class, lifestyle, opinions, and values.

To determine viewer profiles one of many methods may be used, such as, but not limited to, using clustering algorithms to find common denominators within a population in association with viewing habits of the population. An example of a method that may be used for profile learning and determination is provided below.

As shown by block 802, set top boxes 110 in the network 10 record all zapping events created by the consumers. The set top boxes 110 send the zapping events to the management application 50 (block 804). It should be noted that the zapping events include an identification of the set top box from which the zapping events were derived. The management application 50 then associates behavior of consumers and their zapping patterns (block 806).

FIG. 9 is a block diagram further illustrating functionality of the management application 50 as blocks of logic. As shown by FIG. 9, the management application 50 contains modeling logic 902, learning logic 904, identification logic 906, analyzer logic 908, profiles determination logic 910, post processor logic 912, and reporting logic 914. The logic of the management application 50 is further described in detail with regard to the logical flow diagram of FIG. 10.

FIG. 10 is a detailed logical flow diagram illustrating a sequence of events performed during unsupervised learning. The zapping log and the broadcast schedule (arrows 1) are the inputs to modeling functionality of the management application 50, the output of which is a collection of set top box signatures (arrow 2), wherein the collection of set top box signatures includes a signature for each set top box in the network. The set top box signatures may be one of multiple classes of signatures, wherein the classes of signatures include viewing signatures, time signatures, and zapping frequency signatures. Each set top box in the network may have multiple signatures, wherein the signatures for a single set top box are selected from the classes of signatures. In fact, for example, a single set top box may even have one or more of each class of signature. Each such set top box also has a unique identification (ID). Viewing signatures are vectors of all the programs watched during a specified period by each of the set top boxes in the network.

The set top box signatures are the input used by learning functionality (arrow 3) of the management application 50. The learning functionality clusters profiles into groups of profiles that are yet unresolved. It should be noted that an unresolved profile is a profile for which a type is not yet known. Specifically, the learning functionally, which is further described in detail below under the section entitled “learning”, is capable of using the set top box signatures and determining relationships between profiles to derive clusters of profiles, where a type of a profile is not yet known. As an example, an optimization algorithm may be used to cluster the profiles into groups of unresolved profiles, an example of which is illustrated below. The learning step may be performed a few times, to determine the number of existing profile groups available for identification from viewing signature data. This may be done by, for example, but not limited to, throwing out, after each iteration, the profile groups that have similarity to each other, which is greater than a pre-defined threshold.

As previously mentioned, the output of the learning functionality of the management application 50 is clusters of yet unresolved profiles (arrow 4). The clusters of the yet unresolved profiles, together with a profile description (arrows 5), are the input to the profiles determination functionality of the management application 50.

The profiles description is a classification, or definition, of profiles of viewers by groups that associates between, for example, viewing habits and purchasing habits of individuals. The profiles description is provided by an external source, such as, but not limited to, a single source researcher. It should be noted that the profile description input is some external definition of profiles that is fed to the system.

The profiles determination functionality performs a match between the profiles found by the learning functionality (unresolved profiles) and the profiles description from the external source, which determines whether to match the profiles to demographic clustering or to a specific psychographic clustering, for example, by consuming habits. The profile determination with respect to a given profile description may be done, for example, by performing a standard best match procedure on each of the profiles in both groups (unresolved and pre-defined) and by finding the best possible match to each profile from the unresolved group from the defined profiles. It should be noted that sometimes one unresolved profile might fit to two described profiles and vise versa—two or more unresolved profiles can match one profile from the described profiles group.

The output of the profiles determination functionality are the resolved profiles (arrow 6), which are the input, together with the set top box signatures, to an identification functionality (arrows 7).

In accordance with an alternative embodiment of the invention, the learning and the profiles determination functionalities may be performed simultaneously by combining these two functionalities (learning and profile determination) of the management application 50 into one. In accordance with this embodiment, the profiles description and the set top box signatures are both fed as inputs to the learning and profiles determination functionalities (arrows 3 and 5). In this case, the learning and profiles determination functionalities are performed together. The output of the learning and profiles determination functionalities is resolved profiles (arrow 6). In the case of combining these two functionalities, directing the learning process toward the input profiles description may be done by, for example, but not limited to, feeding the described profiles as an initial guess to the optimization process and using the number of the defined profiles as the number of profiles to found.

The resolved profiles are sometimes used together with the set top box signatures as an input to the identification functionality of the management application 50 (arrows 7), to associate each set top box in the network with at least one profile, during which, for example, a quantization process may be performed and each set top box in the network may be associated with at least one profile.

A quantization process is a process during which, rather than having a continuous range of probabilities of having each of the profiles associated with some set top box, some profiles would be decided as not associated to that set top box (due to having a too small probability of being associated), while other profiles would be decided as being associated (with some higher probability, or 1). A quantization process may be performed by, for example, calculating a statistical constant related to the association of profiles to set top boxes (see detailed explanation below) and performing rounding steps. A quantization procedure may be performed at various steps of the learning and identification process.

The identification of lists of profiles associated with each set top box in the network may be performed by, for example, but not limited to, combining the association rule between unresolved profiles to set top boxes and the association rule between resolved and unresolved profiles to create an association rule associating lists of resolved profiles to set top boxes. For example, the association rules may be matrices of parameters and the application of the association rules may be performed, by using matrix multiplication.

The output of the identification functionality (arrow 8) is the identification of which profile(s) uses each of the set top boxes in the network. In other words, the output is an identification of at least one profile associated with each set top box in the network.

The profiles description, set top box signatures, and profiles associated with each set top box (arrows 9) are fed to analyzer functionality of the management application 50, the output of which is an estimation of identification quality and error estimation (arrow 11). Specifically, the analyzer is a self-assessment tool of the management application. The analysis in the case of unsupervised learning is performed with respect to the profiles definition input. The output of the analyzer may be, for example, the quality of the ability of the system to classify the profiles into groups according to the given profile definition, ranking the quality of the input data in view of desired output versus the actual output, and error estimation regarding the accuracy of the identification process.

The estimated errors may be, for example, the expected deviation from the actual situation, and false positive and false negative identification rates. Moreover, correlations between the different profiles groups may be calculated, thereby providing information regarding identification possibilities of certain profiles with respect to their correlations with other profiles. This may be done, for example, by performing comparison of results with known statistics, or by comparing results obtained for all of the network with results obtained from a well representing subgroup of the network.

The identified profiles associated with a set top box are fed as an input, together with the set top box signatures (either the same ones used for the learning and identification functionalities, or others, such as time signatures or high-resolution time signatures) and additional set top box data, if required, to post processor functionality of the management application 50 (arrows 12). The post processing functionality computes various data, such as: regional targeted rating (RTR), content to profile assignment (C2P), total viewership and viewership flow. A description of these functionalities was presented above. Note that the computation of the functionalities of the post processor may remain the same for data (associating lists of profiles to set top boxes) obtained via supervised learning, unsupervised learning, or an external source.

Reporting functionality of the management application 50 uses the computed data to produce business and other reports (arrow 13). As with the supervised scenario, the association process, also referred to as the learning and identification process, is divided into multiple steps. The steps in the association process include data collection, modeling, learning, profiles determination, identification, analysis, and post processing. Of the multiple steps, usually the data collection, modeling, analysis and post processing remain the same for both the supervised and unsupervised processes. The main difference in the supervised and unsupervised processes is in the learning step, which may also include a profile determination step, and which may inflict some differences in the identification steps. Note that the steps of learning, profile determination, and identification are sometimes called here for short, “unsupervised learning”. The unsupervised learning process is further defined herein below.

Learning

For unsupervised learning, each set top box signature is learned to be associated with a certain list of unresolved profiles defined solely using the set top box signatures. Examples of such set top box signatures include, but are not limited to, viewing signatures, time signatures, high-resolution time signatures, and zapping frequency signatures. It should be noted that the main difference from the supervised learning process is that no sample is provided in this case. An unsupervised learning algorithm receives the set top box signatures only as an input, resulting in a classification of profiles into, for example, a certain type of psychographic (for example, behavioral) or demographic profile groups. After the first step (unless the steps of learning and profile resolving are combined) the resulting learned profiles are usually yet unresolved, meaning that their nature is yet to be resolved.

Examples of unsupervised learning algorithms include, but are not limited to, least squares algorithms and algorithms that provide minimization via steepest decent. Other outputs from the learning algorithms include an association of profiles to set top boxes and obtaining a targeted rating of the defined profiles at the same time, thereby providing a probability that a profile is associated with a set top box.

The following is provided as an example of an unsupervised learning algorithm. An input to the unsupervised learning process is the collection of set top box signatures, which is the output of the data modeling process. Assume as an example that these are viewing signatures (although these might be time signatures, etc.), where we denote their parametrical representation by a matrix C. For example, each row of the matrix C may refer to one set top box, and each column of the matrix C may refer to, for example, but not limited to, one program, where the entries of matrix C may be, for example, the portions of the programs that each set top box watched, or, for example, the probabilities with which each of the set top boxes represented in matrix C watched each of the programs represented in matrix C. Let us denote by a matrix A the collection of probabilities, representing viewer profiles association to the set top boxes, where the entries of the matrix A are the probabilities of each of the viewer profiles to be associated with each of the set top boxes. Note that the viewer profiles might be yet unresolved viewer profiles at this stage. Let us denote by the matrix B, targeted rating probabilities. Both A and B are unknown in the case of unsupervised learning. To obtain the desired outputs A and B, we use, for example, but not limited to, the following method. We minimize the squared norm of the difference (AB−C) (see equation three), to obtain the approximation of the matrix C as the product AB. For this, we are using, for example, but not limited to, a convex optimization algorithm (or, for example, some other nonlinear minimization algorithm) under various constrains, such as, but not limited to, that each quantity in A is greater than zero and smaller than one, and each quantity in B is greater than zero and smaller than, for example, 0.5. The following description further describes this process.

Following this example, to determine a possible algorithm for achieving the minimization of the squared norm of the matrix (AB−C), (see equation three), considered above, it is assumed that the population consists of viewers that can be divided into several groups of different profiles, where each viewer may belong to one or more group of viewers profiles. Each such group of profiles is associated, for example, with a behavior pattern in terms of watching habits, where the pattern consists of, for example, but not limited to, the viewing signatures and the targeted rating per content and per each profile, where the targeted rating for the profile is the probability of a viewer of this profile watching each program, or some other definition of content.

Since usually the number of all possible profile groups is low compared to the number of programs and set top boxes in the network, one is actually looking for a low rank approximation of the matrix C, the term low rank (of matrices A and B) refers in this case to the fact that the number of different profile groups is smaller than the dimensions of C, representing for example the number of programs and the number of set top boxes in the network, where due to this low rank the matrices A and B may be obtained using this approximation. One approach to obtaining a low rank approximation of the matrix C is to search for the matrices A and B that minimize the squared norm of the matrix (AB−C). This can be done using, for example, a convex optimization method on the quantity of equation three, which reads:

n=AB-C2=i,j(kAikBkj-Cij)2=Trace((AB-C)T(AB-C))(Eq.3)

where n denotes the squared norm of (AB−C), and trace is a known operation on a matrix providing the sum of the diagonal. In order to minimize this efficiently, one may use the derivatives of equation three, described in equations four and five, each of which read as follows:

nAab=2i,j(AaiBij-Caj)BbjnA=2(AB-C)BT(Eq.4)

and correspondingly,

nB=2AT(AB-C)(Eq.5)

The second derivatives may also be calculated in order to perform this minimization and they are given by the combination of equations six, seven, and eight below:

2nAabAcd=2δac(BBT)bd(Eq.6)2nBabBcd=2δbd(ATA)ac(Eq.7)2nAabBcd=2AacBbd+2δbc(AB-C)ad(Eq.8)

Using any standard convex optimization technique and the derivatives above with the (convex) constraints 0≦Aij, Bij≦1, a solution of the optimization problem may be found, where the joint dimension of the matrices A and B is chosen as the desired, or expected, number of profiles.

The matrix A is to be understood as the set of probabilities of association of each of the profiles per each of the set top boxes and the matrix B is the targeted rating matrix. Since the matrix A is expected to contain binary quantities (either a profile exists in a household or not), and since the optimal solution is defined up to a multiplicative constant for each profile, it is desirable to find a good quantization criterion for A.

Instead of the above-described example, for the unsupervised learning algorithm, one may consider the slightly more complex example described below. Moreover, these alternative ways may be used to address specific different cases and the present invention is not limited to these examples. An example of an alternative way is, instead of minimizing the squared norm of the matrix (AB−C), minimizing the squared norm of (B−(A+)C), denoted herein by m:


m=∥B−(A+)C∥2 (Eq. 9)

In addition, it is also possible to minimize the squared norm of (A−C(B+)), denoted by v:


v=∥A−C(B+)∥2, (Eq. 10)

where A+ denotes the pseudo-inverse of the matrix A, and B+ denotes the pseudo-inverse of the matrix B. For example, the Moore-Penrose pseudo-inverse may be used. This enables a reduction of the dimensionality of the problem as the dimensions of the later matrices are usually much smaller than of the matrix (AB−C). Further, this approach creates a sharper distinction between the probabilities in A (desired to be binary) and of B (usually small probabilities representing targeted rating) in the minimization process. The pseudo-inverse of a matrix is unique in mathematical terms, hence minimizing equations nine or ten is well defined. In the case of minimizing, for example, the quantity m, one would need to use the derivatives

mAandmB,

which involves calculating derivatives of the form

A+Aab,where: Aij+Aab=(A+A+T)ibδja-Aia+Abj+-(A+A+T)ib(A+TAT)aj(Eq.11)

The result of applying the derivative in equation eleven to obtain the derivatives

mA,andmB,

so as the second derivatives, of the quantity m, results in slightly longer expressions than the derivatives presented above, in equations 4-8, but similar in nature.

Moreover, instead of using convex minimization routines, we may use various nonlinear minimizations with slightly altered constrains to minimize the squared norms of the differences above.

An initial guess, for example, but not limited to, a random guess, is given to the algorithm for any of the probabilistic quantities in A and B. Additional constrains may be given to the algorithm to increase its accuracy. Of course, other optimization (or learning) algorithms may be used. The output is a set of probabilities, A, associating groups of profiles to the set top boxes, which later may be quantized and/or resolved (using, when needed a profile resolving procedure and quantization), and a set of probabilities, B, providing the targeted rating for each (for example) program and each profile (also to be used in the profile resolving scheme when needed). It should be noted that the targeted rating may be recalculated during the post-processing to increase the accuracy. It should be noted that the abovementioned examples, equations, and functionalities are based upon the general premise that matrix C can be approximated by matrix A multiplied by matrix B. Of course, further examples for achieving such approximation may be provided and such examples are intended to be included within the present invention.

Quantization

The quantization step is typically, but not necessarily, to be used after the learning and profile determination stage, in the identification functionality, or a few times during the steps of learning, profile determination, and identification.

One approach to finding the quantizing constants (a set of constants that each of the probabilities relating each of the found profiles to set top boxes should be divided by to determine whether a certain profile should indeed be associated with a certain set top box or not) is to assume that A is approximately a binary matrix with a constant multiplicative factor per column, si (1≦i≦number of profile groups), or in other words, assume that each of the i profile groups has its own quantization constant. Since the entries are supposed to be binary quantities, one expects the following from calculating the mean and variance using the binomial distribution, as shown by equations 12 and 13.


ΣaAai=siNp (Eq. 12)


ΣaAai2/N−(ΣaAai)2/N2=si2pq (Eq. 13)

where N is the number of set top boxes in the network, p is the probability that a profile is associated to a set top box, and q=1−p. Solving equation twelve and equation thirteen for si, dividing Aai/si and rounding to a pre-defined threshold, leads to an association rule, associating each of the profiles (resolved or yet unresolved) to each of the set top boxes.

Profile Determination

Profile determination, or resolving, is a process that defines the nature of identified profiles. During profile resolving, profiles definition, for example from a single source research results, such as, but not limited to, viewing habits and behavior, may be used as inputs. In addition, the profile list and targeted rating of defined profiles may be used as inputs. The inputs are provided to a resolving algorithm resulting in profile descriptions that describe each profile in the list.

The single source research addresses a focus group that answers a questionnaire. There are two groups of questions in this questionnaire, namely, a first group and a second group. The first group refers to identity of a person, examples including behavior (i.e., purchasing behavior, rest and relaxation preferences, etc) and demographic profile of the answering person. The second group refers to media consumption, for example, about the time a person would watch television each day of the week and his preferred shows.

The single source research associates the media consumption habits with other habits, such as, but not limited to, purchasing habits and preferred vacation habits. The output of the single source research is a set of profiles and their habits, while each profile is associated with its media consumption habits. The resolving algorithm finds the best correlation between two sets of data, namely, for example, the media consumption habits of the focus group; and, for example, the targeted rating of the defined profiles (the output of the unsupervised learning algorithm). Therefore, the resolving algorithm has the capability of defining the traits of the learned profile in the unsupervised algorithm.

In accordance with the present invention, after the learning and identification are performed, the management application 50 knows online, or offline, the current psychographic or demographic profiles that are consuming content for at least a portion of the set top boxes of the network for which the zapping log contains records of set top box zapping signatures. The information regarding the current demographic/psychographic profiles that are consuming content for set top boxes within the network for which sufficient input was received, may be the basis for personalized advertisements deployment in accordance with the present invention.

Real Time Targeted Rating (RTTR)

The present system and method provides the capability of determining whether a set top box within a network is on or off. In addition, if the set top box is on, the present system and method provides the capability of identifying in real time, or near real time, which viewer profile is currently consuming content provided by the set top box, what is the targeted rating of the viewer profile, or profiles currently consuming content provided by the set top box, and a targeted rating of all viewer profiles that consumed content of the set top boxes within the network, which are part of the real time targeted rating system, for a predefined time interval. This real time process is referred to herein as the real time targeted rating. As previously mentioned, the content may be, for example, but not limited to, video, audio, data, or any combination of these. As will be described in additional detail herein, the real time targeted rating functionality uses the functionalities and methodologies mentioned above with regard to supervised learning, unsupervised learning, identification, content to profile assignment, and targeted rating. The real time targeted rating process is described in detail hereafter.

Functionality performed in real time targeted rating may be performed by a separate or the same management application of the present system and method, located in a head end or in a different location, or a management application located in a different location, as described hereinabove. In addition, the functionality may be performed by a separate computer and/or server (not shown). The embodiments are intended to be covered by the present description. It should further be noted that certain functions of the real time targeted rating process may instead be performed by the set top box itself.

In accordance with the present invention, queries can be made by users of the present system and method for execution of the real time targeted rating functionality for each set top box within the network that is covered by the present system and method. These queries may be made, for example, but not limited to, through a remote web client. For example, multiple web based clients may subscribe to the system described herein for retrieving pre-configured reports, reports which are created automatically by the system periodically every pre-defined time interval (for example, 5 minutes), or per query. Such queries may be made, for example, regarding each of the pre-defined viewer profiles to find out in real time, or near real time, whether a set top box in question is on or off, to identify what viewer profile, or viewer profiles are currently consuming (or consumed for the last predefined time interval) content via the set of box in question, to determine a targeted rating for a specific viewer profile that consumed content from this set top box, and to determine targeted ratings of all viewer profiles that consumed content of the set top box, or set top boxes, within a predefined time interval. An example of determining targeted ratings of all viewer profiles that consumed content of the set top box, or set top boxes, within a predefined time interval includes calculating targeted ratings for viewer profiles that were determined to be consuming content provided by the set top box, or several set top boxes, within the last five minutes. Such a determination uses, per each set top box, at least one set top box signature summarizing activities of the set top box for the last five minutes. Further description is provided herein.

The real time targeted rating system may include a part, or all, of the following capabilities: data collection, modeling, learning, identification, content to profile assignment, targeted rating (or regional targeted rating), and a reporting capability, which can be utilized, for example, via a web interface, or other interface, to produce, for example, business reports, system reports, or any other reports involving the produced data. These reports may be generated automatically, periodically (for example, every 5 minutes), or per a query, or both. A query may be initiated, for example, by a user of the system, by a web client, or by any other interface interacting with the system and having the capability of making a query. Such a query may be, for example, automatic, or manual, or provided by another method.

The real time targeted rating system may be beneficial for content placement, for example, at a certain time and a certain channel, or a certain time and a certain set top box; where content, may refer, for example, to an advertisement. Other examples of content may be a program, or an audio content, or any other example of content that may be consumed via a set top box.

FIG. 13 is a flowchart 1100 illustrating functions performed by the present system and method during execution of the real time targeted rating process. As shown by block 110, a determination is made regarding whether a set top box is on or off. The process of determining whether a set top box is on or off is described in detail with regard to the flowchart of FIG. 14, as provided hereafter.

Returning to FIG. 13, as shown by block 1120 if the set top box is on, the real time targeted rating functionality determines what viewer profile or profiles are currently consuming content provided by the set top box. Determining which viewer profile or profiles are currently consuming content provided by the set top box is described in detail hereafter.

After determining which viewer profiles are currently consuming content, a targeted rating or targeted ratings may be determined (block 1130). The targeted rating may either be a targeted rating for a viewer profile currently consuming content provided by the set top box or the targeted ratings may be targeted ratings of all viewer profiles that consumed content of the set top box, or of several set top boxes, within a predefined time interval. It should be noted that the real time targeted rating process may be repeated after the passing of a predefined time period, per user query, or both. By repeating this process, after the passing of the predefined time period, the real time, or near real time, determination of which viewer profiles are consuming content from the set top box, may change, and is maintained current (in the sense that after each predefined time period, a new determination of what viewer profile(s) are consuming content of the set top box is achieved, maintaining always the most current identification). The time interval is small enough to be considered ‘now’ and big enough to allow for the accumulation of enough data.

Generating reports (for example, busyness reports, and/or system reports), as shown by block 1140, either automated, periodic, per a user query, or both, may also be provided.

On/Off Set Top Box Determination with Real Time Targeted Rating

In accordance with the present invention, the present system and method provides the capability of determining whether content provided by a set top box within the network is being consumed by a viewer profile. Determining whether content is currently being consumed allows the present system and method to determine if a set top box is currently on or off.

One method that is used by the present system and method to determine if content is currently being consumed is to continuously update a set top box zapping signature with the occurrence of each new zapping event associated with a set top box. By continuously updating the set top box zapping signature of the set top box, the set top box zapping signature remains current and may be considered for determining if a set top box is currently on or off. It should be noted herein that, in accordance with the present invention, a set top box is considered to be off not only if no power is being received by the set top box, but also if content provided by the set top box is not being consumed by a viewer profile within a predefined period (for example, if no zapping event occurred during a predefined time period, with or without association to a schedule).

FIG. 14 is a flow chart 1200 further illustrating the process of determining if a set top box is on or off, in accordance with an exemplary embodiment of the invention. As shown by block 1210 a broadcast schedule for a set top box within a network is received. As previously mentioned a broadcast schedule includes, for example, a timetable for the platform channels/programs during the zapping gathering period. It should be noted that the broadcast schedule may also include a schedule of video on demand programs, a schedule of audio programs, or a schedule of any interactive services.

As shown by block 1220, a determination is made regarding when content provided by the set top box is complete. As an example, a determination may be made regarding when a video program or audio program is complete. After completion of content provided by the set top box a predefined time period is allowed to pass (block 1230). As shown by block 1240 a determination is then made regarding whether a zapping event has occurred prior to the expiration of the predefined time period after the completion of content provided by the set top box. If the predefined time period expired and no new zapping event occurred, the set top box is considered to be off. Alternatively, if a zapping event occurred within the predefined time period, the set top box is considered to be on. Alternatively, if for example a schedule is unavailable, the determination whether a set top box is on or off may be achieved by checking if a zapping event occurred during an elapsed predefined time period.

Determining whether the set top box is on is important for multiple reasons. One such reason is that content provided by or to a set top box when the set top box is considered to be off should not be considered when determining whether a viewer profile, and what viewer profile, is currently consuming content from the set top box. Such determination provides for a more accurate determination of current content consumed by a viewer profile. This determination is important when determining if and what advertisement, or other content, to send to a set top box for consumption by a viewer profile. Specifically, if no one is consuming content provided by a set top box, resulting in the set top box being considered to be off, there is no benefit in forwarding advertisements to the set top box. In fact, determining whether a set top box is on or off is important for other calculations performed by the present system and method. If a set top box is considered to be off, perhaps due to a lack of zapping events occurring, content being provided by the set top box, or that is available to the set top box, should not be considered for calculation purposes, such as in determining which viewer profile is associated with which set top box. Specifically, for example, when a set top box is off, the input set C, which has been used in many calculations described hereinabove, may have a value of zero, in a place representing content transmitted during a time when the set top box was considered by the application to be off. Alternatively, depending on data representation, the set C may contain no entries corresponding to time intervals, or contents available to the set top box, during which the set top box was determined by the system to be off.

It should be noted that the updating of the set top box zapping signature may instead be updated in accordance with a predefined schedule so as to alleviate the need for acquiring and processing a schedule, for updating the set top box zapping signature with each new zapping event. As an example, if the real time targeted rating process is being performed every five minutes, it would be beneficial to have the set top box zapping signature updated at least every five minutes. Of course, the timing in which the set top box signature is updated may have many different values.

It should also be noted that, in accordance with alternative embodiments of the invention, as mentioned above, a broadcast schedule may not be necessary for determination of whether a set top box is on or off. Specifically, a time gap between zapping events may be considered to determine if a set top box is on or off. As an example, if a predefined time period passes between zapping events, a set top box may be considered to be off. Other methods of determining whether a set top box is on or off may also be used, and such methods are intended to be included within the present description.

As previously mentioned, with determination that a set top box is on, the real time targeted rating functionality determines what viewer profile or profiles are currently consuming content provided by the set top box. The real time targeted rating functionality applied depends upon whether supervised or unsupervised learning was performed by the present system and method for determining what viewer profiles are usually associated with which set top boxes. Herein, the term, usually is used to distinguish between currently (i.e. in real time, or nearly real time), and during a ‘relatively long’ period of time during which data was collected. The data regarding the ‘usual’ (rather than current) association of viewer profiles to a certain set top box, may also be periodically updated, for example every three months (or any other time interval, which is longer than the time interval defined as current).

The following describes the real time targeted rating process used for determining what viewer profile or profiles are currently consuming content provided by the set top box, in accordance with the present system and method, when a determination has been made that a set top box is on. As mentioned above, after determining that a set top box is on, the real time targeted rating process then depends upon whether supervised or unsupervised learning was performed by the present system and method for determining what viewer profiles are associated with which set top boxes in real time, or a nearly real time.

The following first illustrates steps taken in real time targeted rating when supervised learning was performed to determine what viewer profiles were associated with which set top boxes. Thereafter, illustration is provided of the steps taken in real time targeted rating when unsupervised learning was performed to determine what viewer profiles were associated with which set top boxes. It should be noted that the following provides examples of processes that may be performed during the real time targeted rating process and the invention is not intended to be limited to the same.

Real Time Targeted Rating with Supervised Learning

Referring to the supervised learning scenario, the Association Rule derived after performing supervised learning, as previously described, is gathered. As previously mentioned, the Association Rule provides knowledge of how to associate a list of profiles within a network to a set top box within the network. A list of one or more of the viewer profiles that are determined to be associated with the set top box, as determined after performing the identification process, are gathered. The identification process is not repeated here since it has been described in detail hereinabove.

To determine which of the list of one or more viewer profiles that were determined to be associated with the set top box are currently consuming content provided by the set top box, the previously obtained association rule, together with the list of one or more of the viewer profiles that were determined to be associated with the set top box, through the identification functionality previously described, are applied to a newly obtained set top box signature for the set top box in question. Specifically, the set top box signature used is one that is current, or one that has been updated at least within a predefined period. Alternatively, instead of using the association rule, but still using the list of one or more viewer profiles determined to be associated with the set top box, the present system and method may provide real time, or near real time, determination of the one or more viewer profiles that are currently associated with a set top box by applying the content to profile procedure, as previously described, to currently consumed content, as identified by a set top box signature, and to the list of the one or more viewer profiles determined to be associated with the set top box.

To apply the content to profile procedure the process used by the content to profile functionality is performed. Specifically, for example, a set A and a set C are provided, where the set A is a list of one or more viewer profiles associated with a set top box within a network, and set C is a summary of which set top boxes within the network consumed content. To determine which profiles consumed content within the last predefined time period, via use of the content to profile functionality, we start with a summary of which set top boxes within the network provided content within the predefined period. This summary of which set top boxes within the network provided content within the predefined period can be obtained by reviewing the set top box signatures of each set top box in the network.

Having the list of set top boxes that provided content within the last predefined time period, a determination is made as to which profiles are associated with the set top boxes that provided content within the predefined period. As an example, profiles f1 and f3 may be associated with set top box 1 (STB1), and profiles f1 and f2 may be associated with the set top box 2 (STB2). This example may be represented as STB1 has (f1, f3), and STB2 has (f1, f2).

A determination is then made as to the probability that, within the predefined time period, a specific profile consumed content provided by a set top box that provided content within the predefined time period. An example of a method that may be used to determine the probability follows. If there are ten set top boxes in a network that provided content within the predefined period, and five of these set top boxes are associated with profile f1, while four of these set top boxes are associated with the profile f2, the probability that a profile f1 consumed content within the predefined period, wherein the content was provided by a set top box that provided content within the predefined period, can be represented as P(f1)=5/10. In addition, the probability that a profile f2 consumed content within the predefined period, wherein the content was provided by a set top box that provided content within the predefined period, can be represented as P(f2)=4/10.

The probability that one or more viewer profiles associated with a specific set top box consumed content within the predefined period, from the specific set top box, is then considered by selecting probabilities having values closest to one (for example, P(f3)=0.93), where the probability is for a profile known to be associated with the specific set top box, and the specific set top box provided content within the predefined time period. Profiles associated with the probability having a value closest to one are selected as the profiles that consumed content from the set top box within the predefined period. It should be noted that this example may be made more accurate if, to the calculation, the probabilities of association of each of the profiles to a specific set top box, and/or the probabilities of the presence of each of the viewer profiles within the network, are added.

It should be noted that the above is merely an example, and any other method of calculating content to profile assignment, as described herein above, or in any other form, may be used.

Real Time Targeted Rating with Unsupervised Learning

For the unsupervised learning scenario, completion of the unsupervised learning process and the identification process results in a list of one or more viewer profiles associated with a set top box in question. The list of one or more viewer profiles that are determined to be associated with the set top box, are gathered.

To determine which of the list of one or more viewer profiles that were determined to be associated with the set top box are currently consuming content provided by the set top box, the list of one or more of the viewer profiles determined to be associated with the set top box is applied to a newly obtained set top box signature for the set top box in question. Specifically, the set top box signature used is one that is current, or one that has been updated at least within a predefined period. For example, such application may include performing all steps described in the unsupervised learning process, but with the input of only the at most few resolved viewer profiles that were previously determined to be usually associated with the specific set top box.

Alternatively, instead of applying the list of one or more of the viewer profiles determined to be associated with the set top box in question to a newly obtained set top box signature for the set top box in question, the present system and method may provide real time, or near real time, determination of the one or more viewer profiles that are currently associated with a set top box by applying the content to profile procedure, as described above with regarding to the supervised process.

With the supervised and unsupervised scenarios described above, it should be noted that the present system and method is also capable of determining what viewer profiles that are associated with a set top box are currently consuming content provided by the set top box, even if there is no previous data or knowledge regarding viewer profiles associated with set top boxes. In such a situation, the set A is missing, where the set A is a list of one or more viewer profiles associated with a set top box within a network. The set C can then be obtained for a predefined period, such as, but not limited to, the last five minutes, where the set C is a summary of which set top boxes within the network consumed content. With there being a sample, the supervised process mentioned above may be performed, resulting in a viewer profile or profiles that are currently consuming content provided by the set top box. Alternatively, if there is no sample, the unsupervised learning process described above may be performed, resulting in a viewer profile or profiles that are currently consuming content provided by the set top box. As has been previously mentioned, herein, the term currently consuming is intended to be the same as consuming within a predefined period.

It should be noted that, with regard to set top box signatures, the method of real time targeted rating includes the steps of data collection (for example, zapping log and schedule), and modeling, periodically, every pre-defined time interval (for example, every five minutes), to obtain the set top box signatures. Alternatively, to obtain a set top box signature, the collection and the modeling may be performed per each event occurring at the set top box, for example, any interaction of a viewer profile with the set of box, such as pressing the info button.

By the present system and method performing the real time targeted rating functionality, the system and method contains the following data, or is ready to obtain the same upon request: either an association of at least one viewer profile to at least a one set top box within the network, that had been consuming content using this set top box during the last short pre-defined time interval (for example five minutes), or report that the set top box at question being shut off during this time interval; then, the targeted rating of each of the pre-defined in the system viewer profiles for the last time interval (for example, five minutes) may be provided.

It should be noted that while examples of time intervals for updating set top box signatures and other content are exemplified as being five minutes, the time interval is not limited to five minutes, but instead may be any other time interval.

The following provides an example of a way to calculate and operate the real time targeted rating functionality.

As one example, the real time targeted rating application may receive as an input the results of learning (supervised or unsupervised) from the management application, performed for any period of data collection, in the form of ‘learned matrices’, which are sets of parameters providing an association rule between set top boxes within the network and at least one viewer profile (for example demographic or psychographic). In addition, the real time targeted rating application may receive as an input any set top box information, such as the identification of viewer profiles that had been associated to this set top box, via learning and identification procedures, performed, for example, at the management application, or at the server, or received from an another external source. Other set top box information, may include, for example, the region in which the set top box is located, and/or other status information regarding the set top box.

Any additional inputs, obtained for the set top boxes within the network, or for viewer profiles, at an earlier time, such as the description of viewing habits of profiles or set top boxes within the network, or any other relevant information may be used as well.

The output of the real time targeted rating functionality is the identity of the viewer profile (out of a pre-defined list, for example, a list containing a few demographic profile types or a list containing a few psychographic types or any mixture of those, that had been associated to a specific set top box) that is currently watching each of the set top boxes within the network, that are part of the real time targeted rating system, that data was received for, and that are known not be switched off; and, a targeted rating, or a regional targeted rating for each of the identified profiles, per content provided at the pre-defined time interval (for example, 5 minutes), per which the identification of current viewer profiles was performed. The later identification is referred to as online identification and it may take place online, or nearly online, with a small time delay needed to receive and process the data, or gather sufficient amount of data, in pre-defined time steps. One way to obtain these outputs may be, for example, using the identification functionality, described above. The use of the identification functionality in this example, may be performed via applying the currently obtained (for example, for the last 5 minutes interval) set top box signatures (for example, viewing signatures) to the learned matrices; this can be done, for example, by using mathematical, or other operations, such as multiplication (for example, multiplication of a vector and a matrix). The later may be done either using the whole learned matrix obtained for a ‘relatively long’ pre-defined previous time period (for example, a month), or using just the part of the learned matrix, which is narrowed, for each specific set top box, only to the list containing at least one viewer profile, which is associated to each specific set top box, for which at least one set top box signature was obtained. Due to the fact that the identification is done on the basis of viewing behavior that occurred in a very short time period (for example, 5 minutes), the identified viewer profile, or profiles, would usually be those consuming content at the specific set top box, in real time, or nearly real time.

The learned matrices, together with the list(s) of profile(s) associated with each set top box within the network, may be stored within the real time targeted rating server, or downloaded to the set top boxes themselves (where each set top box would contain only the part of the learned data associated with it). In addition, the set top box signatures may be inferred at the set top box level, or, if the learned matrices are stored at the server level, set top box zapping signature(s) may be uploaded (per a time interval, or per a zapping event) to the real time targeted rating server, and the set top box signature(s) may be updated if during the pre-defined time interval (for example, 5 minutes) a new zapping event occurred, which was not yet included in the set top box zapping signature. In such a case, the identification may be applied once, or again and again after each zapping event within the predefined short time period (for example, 5 minutes); where the newly obtained signature (the set top box signature obtained, for example for the last 5 minutes period) will usually contain the information regarding the latest occurring zapping event(s).

In the case that during the short predefined time interval (for example, 5 minutes) the set top box signature is updated with each occurring zapping event and the identification process is applied repeatedly with each such zapping event, the identification of the current viewer profile consuming content via a specific set top box within the network is expected to be of high accuracy, as the identification accuracy would increase with each such iteration.

To summarize, the real time targeted rating system is capable of receiving previously processed data (such as previous results of learning and identification); continuous real time, or nearly real time, data collection (set top box zapping signatures, and possibly a schedule) for any pre-defined time interval prior to the desired identification, for example, 5 minutes, and in some cases per each zapping event (such as turning the set top box on/off), and processing/modeling capabilities of the continuously collected data. After each such short predefined time interval (for example, 5 minutes), the real time targeted rating system outputs a snapshot of the set top boxes within the network, where for each such set top box, the viewer profiles, currently consuming content via the set top box are identified and the targeted rating of these viewer profiles may be calculated. Reports (busyness and/or system) may be automatically periodically generated, or may be generated per user query.

All collected and calculated data may be stored within the real time targeted rating server and may be made available for use for future identification(s)/calculation(s), for any required time period.

The real time targeted rating server operates so that at each given moment a query might be posed to it regarding who is the current viewer(s) using each of the set top boxes, which are part of the network and part of the real time targeted rating system, and which the real time targeted rating system inferred to be currently on. As a result to such a query, an output regarding the identification of a certain viewer profile using these set top boxes by the last identification, or of a few viewer profiles with the probabilities of each of them using these set top boxes, with respect to the last identification performed, is prepared. In addition, a targeted rating, or a regional targeted rating, per each of these viewer profiles may be calculated.

The online (or nearly online) identification and the targeted rating calculation may be performed, for example, at the real time targeted rating server, located, for example at the head end, where the real time targeted rating server receives continuously inputs both from the management application and from the set top boxes, for example those connected to the head end, and automatically performing per each pre-defined time step, and/or per each zapping event occurring at any of the set top boxes, the steps of labeling each of the set top boxes within the real time targeted rating system as being switched on or off, and for those on, who are the viewer profile(s) using it in the current time interval, with or without assigned probabilities, and the (regional) targeted rating associated with each of the identified profiles, and the last time interval for which identification took place. Alternatively, for example, the ‘learned matrices’ may be sent by the real time targeted rating server to each of the set top boxes and stored there, the collection of the last occurring zapping events may be performed at the set top box level and the identification of each viewer profile using each of the set top boxes may be performed at the level of each set top box, where the result is sent back to the real time targeted rating server and the (regional) targeted rating is calculated at the real time targeted rating server. In this example, in addition, the list of profiles associated with each set top box within the network may be sent to be stored at the set top boxes. Then, the identification of which viewer profile is currently consuming content via the set top box at question, for the set top boxes within the network, may be performed out of the short profile list, only out of those fewer viewer profiles, associated to the set top box at question, or from the whole list of profiles, if such a short list is not provided.

The identification of the profiles may be performed in a more accurate way, where the time interval, referring to ‘current identification’, maybe narrowed, so that as few profiles as possible are identified as current viewer profiles associated with each set top box within the network, that are part of the real time targeted rating system.

Any of the described above methods, or combination of them, or other methods, may be used to address different specific situations.

Bidding

Advertisers require a method for easily purchasing advertisement time in, for example, but not limited to, broadcast networks. It should be noted that the advertisement time may be purchased in many different environments and is not intended to be limited to within broadcast networks. The following provides the example of advertising within a broadcast network, however, this is only provided for exemplary purposes. Moreover, advertisers require being able to suggest their maximum price for a placement of an advertisement, in an efficient way. It should be noted that such advertisements may be in many different forms, including, but not limited to, video advertisements that might run a predefined period of time between programs, banners run during programs, audio advertisements, text run during or between programs, and many other types of advertisements.

The present advertisement auctioning system and method allows advertisers to suggest their maximum price for placement of an advertisement through a bidding process. For example, a small business might not being able to compete on expensive advertisement placing, however, the business would like to reach its advertising goals in a convenient way, and for a price that is worthwhile for the business to pay. In addition, for the limited budget that a small business typical contains it would be beneficial for the small business to be able to provide specific advertising, for example, but not limited to, for an exact audience and at a specific time, at a limited cost. Such a process is provided by the advertisement auctioning system and method as described herein.

In addition to the abovementioned, network operators require an automated system for decision-making and advertisement placement in the most profitable and optimal arrangement possible. The present system and method provides such an automated system, which benefits both the advertiser and the network operator.

As previously mentioned, the present system and method provides a user interface that provides an advertiser that wishes to advertise to viewer profiles with a means for bidding on, for example, but not limited to, requested time slots for specified or unspecified audience profiles, dates, channels, and programs. The system and method also provides an automated method of determining winning bids, to allow associated advertisements to be placed in provided content. A billing system for determining the prices charged from the advertisers based upon the viewership and the bids placed is also provided. The portion of the present system and method capable of performing the bidding and associated functionalities is referred to herein as an automated advertising system and method.

The bidding capability of the automated advertising system and method may be provided within a computer 1500 that is located separate from a head end, such as the head end 20 of FIG. 1. Such a computer 1500 is capable of communicating with the head end 20 and other portions of an associated network. Alternatively, the automated advertising system may be provided as software within the head end 20, such as, for example but not limited to, within the management application 50 of FIG. 1. The automated advertising system of the invention can be implemented in software (e.g., firmware), hardware, or a combination thereof. In the currently contemplated best mode, the automated advertising system is implemented in software, as an executable program, and is executed by a special or general purpose digital computer, such as a personal computer (PC; IBM-compatible, Apple-compatible, or otherwise), workstation, minicomputer, or mainframe computer. Specifically, the automated advertising system, as provided by the computer, may be accessible via a Web site, through which parties using the automated advertising system may interact, via a graphical user interface. Further description of the automated advertising system, and interaction therewith is provided below.

An example of a general purpose computer that can implement the automated advertising system of the present invention is shown in FIG. 15. In FIG. 15, the automated advertising system is denoted by reference numeral 1500, which is the computer. It should be noted that communication with the automated advertising system may be provided by multiple means such as, but not limited to, the Internet. Further description with regard to use of the automated advertising system via use of the Internet is provided below.

Generally, in terms of hardware architecture, as shown in FIG. 15, the computer 1500 includes a processor 1510, memory 1520, storage device 1530, and one or more input and/or output (I/O) devices 1540 (or peripherals) that are communicatively coupled via a local interface 1550. The local interface 1550 can be, for example but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface 1550 may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface 1550 may include address, control, and/or data connections to enable appropriate communications among the aforementioned components.

The processor 1510 is a hardware device for executing software 1600, particularly that stored in the memory 1520. The processor 1510 can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the computer 1500, a semiconductor based microprocessor (in the form of a microchip or chip set), a macroprocessor, or generally any device for executing software instructions.

The memory 1520 can include any one or combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.). Moreover, the memory 1520 may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory 1520 can have a distributed architecture, where various components are situated remote from one another, but can be accessed by the processor 1510.

The software 1600 in memory 1520 may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions of the automated advertising system, as described below. In the example of FIG. 15, the software 1600 in the memory 1520 defines the automated advertising system functionality in accordance with the present invention. In addition, the memory 1520 may contain an operating system (O/S) 1560. The operating system 1560 essentially controls the execution of computer programs and provides scheduling, input-output control, file and data management, memory management, and communication control and related services.

Functionality of the automated advertising system 1500 may be provided by a source program, executable program (object code), script, or any other entity comprising a set of instructions to be performed. When a source program, then the program needs to be translated via a compiler, assembler, interpreter, or the like, which may or may not be included within the memory 1520, so as to operate properly in connection with the O/S 1560. Furthermore, the automated advertising system 1500 can be written as (a) an object oriented programming language, which has classes of data and methods, or (b) a procedure programming language, which has routines, subroutines, and/or functions.

The I/O devices 1540 may include input devices, for example but not limited to, a keyboard, mouse, scanner, microphone, or other device. Furthermore, the I/O devices 1540 may also include output devices, for example but not limited to, a printer, display, or other device. Finally, the I/O devices 1540 may further include devices that communicate via both inputs and outputs, for instance but not limited to, a modulator/demodulator (modem; for accessing another device, system, or network), a radio frequency (RF) or other transceiver, a telephonic interface, a bridge, a router, or other device.

When the automated advertising system 1500 is in operation, the processor 1510 is configured to execute the software 1600 stored within the memory 1520, to communicate data to and from the memory 1520, and to generally control operations of the computer 1500 pursuant to the software 1600.

When the automated advertising system 1500 is implemented in software, as is shown in FIG. 15, it should be noted that the automated advertising system 1500 can be stored on any computer readable medium for use by or in connection with any computer related system or method. In the context of this document, a computer readable medium is an electronic, magnetic, optical, or other physical device or means that can contain or store a computer program for use by or in connection with a computer related system or method. The automated advertising system 1500 can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this document, a “computer-readable medium” can be any means that can store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

The computer readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples (a nonexhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM) (electronic), a read-only memory (ROM) (electronic), an erasable programmable read-only memory (EPROM, EEPROM, or Flash memory) (electronic), an optical fiber (optical), and a portable compact disc read-only memory (CDROM) (optical). Note that the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

In an alternative embodiment, where the automated advertising system 1500 is implemented in hardware, the automated advertising system 1500 can be implemented with any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

In accordance with the present invention, the automated advertising system 1500 provides a user interface for allowing a user to interact with the automated advertising system 1500. One or more of many different communication capabilities may be provided for user interface implementation. As an example, the user interface may be provided as a computer program, a Web form, or electronic mail, which allows advertisers to choose their requested requirements for desired advertisement content. Interaction with the user interface may be provided via the I/O devices 1540.

FIG. 16 is a schematic diagram further illustrating functional blocks representing functionality defined by the software 1600 of FIG. 15, in accordance with a first exemplary embodiment of the invention.

User Interface Manager

As is shown by FIG. 16, the software 1600 defines a user interface manager 1610. The user interface manager 1610 allows for providing of advertisement effect criteria to a user of the automated advertising system 1500. Examples of such criteria include, but are not limited to the following.

    • a time slot for the advertisement placement (e.g., 8 pm-10 pm)
    • a geographic region for the advertisement
    • duration of the advertisement airing in terms of calendar dates
    • desired channel or channels for the advertisement broadcasting
    • possible desires for specific programs/content in which the advertisements will be included
    • number of airings desired in the time slot or during a day or any other time unit
    • number of individual viewings or individual viewers desired by the advertiser
    • demographic viewer profile or profiles of the desired audience (e.g., males 25-50 high income, females 25-40 with children, etc.)
    • psychographic profile of viewers
    • individual advertisement slot for the airing of the advertisement
    • location of the advertisement in the slot (e.g., first, last, second, etc.)
    • suggested price for the airing or for each individual exposure.
      Of course, many other criteria may be provided for selection by the user/advertiser. In fact, the advertiser may select one or more combinations of the criteria to obtain a desired effect for their advertisement. The advertiser may also arrange the suggestions by priority or assign each possibility a value or utility function.

The user interface manager 1610 provides user selection capability through one or more options. As an example, automated forms may be provided by the user interface manager 1610. Such automated forms may be, for example, Web forms containing different fields for selection and/or completion by the advertiser, or others. Examples of such fields may include, but are not limited to, the following fields.

    • 1. the advertiser identifier (e.g., email address or any other identification)
    • 2. the campaign details (e.g., campaign name, client, etc.)
    • 3. the geographic regions for the campaign, chosen using a map interface, zip codes, or other means
    • 4. the advertisements in the campaign chosen using some identification, or directly uploaded (as video files) using the web interface
    • 5. the maximum bid price for the campaign and/or for each exposure to the public
    • 6. the desired number of airings and/or exposures
    • 7. the target audience using any combination of demographic and/or psychographic profiles
    • 8. the campaign dates
    • 9. the desired channel or channels for the campaign
    • 10. the desired programs in which the advertisements will be places
    • 11. the desired time slots for the airing of the advertisements
    • 12. the desired location of the advertisement on the advertisement slot (e.g., first, last, middle)
      It should be noted that certain of the fields may possibly be duplicated with different bid values. As an example, fields 9-12 may be repeated several times with different bids assigned to different channel/program/slot combinations. Similarly, different values may be assigned to different target audience profiles. The combination of different fields selected and defined provides an advertisement campaign, for which a bid price may be provided.

Advertisement Auctioning Manager

As is shown by FIG. 16, the software 1600 also defines an advertisement auctioning manager 1620. The advertisement auctioning manager 1620 allows for providing of an optimal placement of requested advertisements based upon advertisement effect criteria provided and bid price, so as to optimize revenue for a network operator.

Specifically, after several requests for campaigns and appropriate bids have been made, the placement of advertisements should be determined. A mechanism for such placement combines an optimization algorithm, attempting to find an optimal placement of the requested advertisements, based upon the preferences of the advertisers combined with the bid values suggested by the advertisers. The optimization algorithm finds an exact or approximate placement of part or all of the suggested advertisements such that the revenue is maximized, by searching the combination of advertisement placement for each set top box and airing time as to maximize the revenue for the network operator.

In accordance with an alternative embodiment of the invention, the bidding process may also be limited in duration or to any number (one or more) of bids, after which the winners will be notified, and another bidding process may begin with the remaining, unsold time slots.

In the case of one request (i.e., campaign) per advertiser the maximum revenue may be achieved by direct competition. For each combination of some or all of date, population profile, geographic region and time slot, there may exist one or more possible placements, depending on the number of single advertisements that may be aired during the time dedicated to advertisements in the desired channel/date/time of day combination. If there are M advertisements that may be placed under these constraints, the highest M bids will be aired and all the rest rejected. It is also possible to auction some of the slots by this mechanism and some by any other mechanism or criteria, for example, the slots may be divided into a group being sold to bidders using the herein described auctioning mechanism and a group sold for a constant price.

If the advertisements are of different lengths and the advertisement time slot is of a constant, known length, the problem of optimal placement becomes algorithmically difficult, and should be approximated by any approximation algorithm, unless the number of bidders is small and exhaustive searching may be conducted. A possible approximation method uses the known approximation scheme of adding the advertisements one by one sorted by decreasing revenue per second. That is, the advertisement with highest bid price per second is placed, the second highest is placed next, and so on, until no extra time for another advertisement is left during the time interval. This approximation scheme is beneficial since, for example, the scheme may guarantee at least half the maximum revenue obtained by any optimal placement.

The order of advertisement airing in the commercial break (if more than one advertisement is aired) may be based on the advertisers' preferences or may be based on their bids with highest bid winning the better location (such as closer to the beginning of the commercial break).

In case each advertiser may include more than one option for the advertisement airing, such as several different alternatives for advertisement placement suggested by the advertisers, the problem may become algorithmically complex again. The optimal placement may still be found, for example, using a brute force approach of checking all possible placements when the number of conflicting suggestions is not too high, or using any standard approximation algorithm for constraint satisfaction when the number of placements is large. Approximation of the constraint satisfaction problem may be obtained, e.g., by assigning a weight for each constraint broken, and executing a local search algorithm, such as A* or simulated annealing to find an approximate solution. One having ordinary skill in the art would know of other approximation techniques and such techniques are intended to be included within the present invention.

A possible alternative to revenue maximization is using game theoretic considerations to design a truthful auctioning mechanism that will lead the advertisers to a Nash equilibrium in which each will prefer to bid the real value of the advertisement. A possible mechanism will be discussed under “the bonus mechanism”. One example of such an auctioning mechanism is the Vickrey-Clarke-Groves, which is discussed hereafter.

The automated advertising system may directly interact with a broadcasting, multicasting, or unicasting system, such as a head end, to allow for automatic placement of winning advertisements in the broadcasted content.

Billing Manager

As is shown by FIG. 16, the software 1600 also defines a billing manager 1630. For any aired advertisement the advertiser should be billed according to the bid of the bidder, and possibly other bidders, as well as according to the level at which the request of the advertiser was supplied and the actual viewership of the advertisement. The billing manager 1630 automatically collects information on the bids and requests of the advertisers for determining billing.

As an example consider the case that an advertiser has requested a certain campaign for a period of two weeks requesting that each of two advertisements is aired on a certain channel, in prime time, and at least three times. If, using the advertising auctioning manager 1620, or by any other decision method, the bid was accepted, and the advertisement was actually aired (or, possibly, is scheduled to be aired) the bidder is charged with the amount bid. It is also possible that a lower amount can be charged based upon some game theoretic mechanism, such as the ones discussed with regard to a bonus mechanism mentioned below, or any other possible bonus mechanism.

As a more advanced and effective billing policy, the billing manager 1630 may provide billing capability based on actual total viewership or targeted viewership. In this case, the billing is based upon a system capable of identifying the set top box in which the advertisement or the program within which it was aired was provided. As previously mentioned within the present description, watching of a program is the same as a viewer profile consuming content. Determining in which set top boxes an advertisement was aired can be performed by reviewing the set top box signature for set top boxes to which the advertisement has been provided. In this case, billing is performed after the airing of the advertisement or the whole advertising campaign, with the possible exception of a down payment based on the anticipated audience.

Billing by targeted viewership is based upon the present system and method being capable of identifying not only the set top boxes to which the advertisement was provided, but also the profiles of the viewers associated with these set top boxes and possibly the profiles of the specific viewers that watched the advertisement when it was aired. Billing by targeted viewership may be achieved either by rating data by calculating the targeted rating at the time of airing (both methods discussed above herein), or by a survey conducted after the advertisement airing.

Billing by targeted viewership may be performed automatically by the billing manager 1630 based on inference of the viewer profile based on supervised or unsupervised learning. Another example of billing by targeted viewership by rating data includes using the content to profile assignment method. Specifically, profiles of specific viewers that consumed content of a set top box may be determined by performing the previously described content to profile assignment, which provides an identification of what content is being consumed by what profile. This information may be used in providing a more accurate billing than the current state (where the billing is based upon predicted rating for a program based on past rating information), where the advertising bill is directly associated with which advertisements had viewers of a certain profile view the advertisement.

In the case of billing by targeted or general viewership, the billing is based on price suggested per viewing, or per individual viewer, multiplied by the number of viewers. This price may be different for different viewer profiles and also may be limited by a minimum and/or a maximum value. The following provides an example of billing by targeted or general viewership. Consider that a toy store bids $0.05 per each viewing of its advertisement by a female with high income with young children living in Cambridge, Mass. The store also bids $0.02 per each viewing of its advertisement by a medium income female of the same characteristic up to a maximum of $1000. The billing for the store is then based on the bid value multiplied by the number of viewings, and is limited by the maximum.

It should be noted that the billing manager 1630 may also provide the capability of receiving payment for bills incurred by the advertiser. As an example, the advertiser may make payment for advertisements via the Internet.

Bonus Manager

As is shown by FIG. 16, the software 1600 also defines a bonus manager 1640. To increase the incentive of the advertisers to declare the actual value of the advertisement to them, a game theoretic approach of bonuses may be used. Different known mechanisms used to increase actual value declaration, known as truthful mechanisms, include the Vickery auction and its generalization, the VCG (Vickrey-Clarke-Groves) mechanism, as well as generalized second-price auctions, or any other game theoretic approach.

The following provides a brief example of the VCG mechanism. As an example consider several advertisers bidding for the same spot. The highest bidder will win. However, this bidder will only be charged the price of the second highest bid. This mechanism ensures that the optimal strategy per bidder is to bid the actual value of the advertisement, i.e., the utility of the airing of the advertisement.

The VCG mechanism allows the generalization of this method to more generalized settings. For example, if m spots are available, the m highest bidders will win, and they will all pay the bid of the m+1 highest bidder. Similar considerations apply in cases of different values per bidder for each spot or in the case of different bids for long and short advertisements or different bids for different audience profile sections. In either case a mechanism may be built of a bonus in the billing of the advertisers such that the optimal strategy of the advertisers is to bid the true value of the advertisement to them.

Combining a bonus mechanism in the automatic billing system can increase the revenue by encouraging the bidders to bid the true value of the advertisement to them, knowing that this is the rational decision in terms of guaranteeing optimal revenue to each bidder.

EXAMPLE

Consider the following scenario, which illustrates a possible application of the combination of all the above:

A single advertisement spot (e.g., the Superbowl advertisement) is being auctioned by a cable company. Several advertisers use the Web interface to make the following bids:

Advertiser A bids 5 cents per viewing by males of all ages in Manhattan.

Advertiser B bids 4 cents per viewing by males and females of all ages in Manhattan and Queens.

Advertiser C bids 3 cents per viewing by males of all ages in Queens and 5 cents per viewing by females of all ages in Queens.

Advertiser D bids 2 cents per viewing by males and females of all ages in Manhattan and Queens.

The following will be aired: For identified males in Manhattan advertisement A will be aired. For identified females in Manhattan and males in Queens and identified males in Queens, advertisement B will be aired. For identified females in Queens, advertisement C will be aired. In a case in which no positive identification can be made, the system may choose, for instance, the suggestion, X, that maximizes the value: PmaleRmaleX+PfemaleRfemaleX, where Pmale is the probability, as estimated by the system that a male is watching via a certain set top box at the moment of advertisement airing and RmaleX is the bid made by an advertiser of X for a male viewing, and respectively for females.

Billing is based on the number of viewers identified by the system, multiplied by the bid per viewer. If, as a bonus mechanism, for example, a Vickery auction is used, the following billing will occur. For purposes of this example, we assume the following rating: 100,000 identified male viewers and 150,000 identified female viewers in Manhattan; and 120,000 identified male viewers and 130,000 identified female viewers in Queens. As a result, advertiser A will be charged 4 cents per male viewer in Manhattan (the value of the second highest bid for that audience—by advertiser B), and thus will pay 100,000*0.04=$4,000. In addition, advertiser B will pay 2 cents for every female viewer in Manhattan and 3 cents for every male in Queens for a total of 150,000*0.02+120,000*0.03=$6,600. Further, advertiser C will pay 4 cents for every female in Queens for a total of 130,000*0.04=$5,200.

When the present advertisement auctioning system makes no positive identification, or no identification at all to compensate for cases of mistaken identification, the system may use the number of estimated actual viewings, (i.e, the sum on the probabilities of identification of the target audience), as estimated by the system, rather than the number of identified individuals.

FIG. 17 is a flow chart 1700 further illustrating functionality provided by the present advertisement auctioning system 1500, when advertisement is to be provided to a specific desired targeted viewership. As shown by block 1710, by using the system described with regard to supervised and unsupervised learning mentioned above, viewer profiles associated with each set top box are identified.

As shown by block 1720, advertisement effect criteria is provided to a user of the automated advertising system 1500. It should be noted that in accordance with this embodiment of the invention, viewer profiles are included among the advertisement effect criteria, or criteria defining a viewer profile may be included. The user may then define an advertisement campaign by selecting from advertisement fields and defining a bid (block 1730). As an example, an advertiser may select some or all of: desired target audience profiles and geographic location; dates of the campaign; airing times; and desired channels. It should be noted that functionality associated with blocks 1720 and 1730 has been defined within the section of the detailed description entitled user interface manager. It should also be noted that, in accordance with an alternative embodiment of the invention, an advertisement may be uploaded to the advertisement auctioning system for future use.

As shown by block 1740 the system selects the advertisement winners according to the highest bidders for the available time interval for each combination of, for example, but not limited to, geographic location and profile. It should be noted that functionality associated with blocks 1740 has been defined within the section of the detailed description entitled advertisement auctioning manager. Advertisement winners may then be notified, as shown by block 1750. Such notification may be provided by many different methods such as, but not limited to, using electronic mail or short message service.

As shown by block 1760, the system then provides the advertiser provided advertisement to the targeted viewers. As an example, the present system provides concurrent advertising to defined targeted viewers in accordance with the advertising campaigns of the winning advertisers.

The advertisement auctioning system then calculates billing for the winner, as shown by block 1770. The process of calculating billing has been described above, and is further illustrated with regard to FIG. 18, which is described hereafter. As shown by block 1800, the advertisement auctioning system then bills the advertiser in accordance with the calculated billing. As an example, the advertiser may be billed electronically and the advertiser may pay electronically.

FIG. 18 is a flow chart 1772 further illustrating examples of functionality that may be performed during billing calculation. As shown by block 1774, the real time targeted rating functionality described herein identifies actual current viewer profiles currently consuming content. The video splicer may then be used by the advertisement auctioning system to forward advertisements intended for specific viewer profiles, to set top boxes associated with the specific viewer profiles (block 1776).

As shown by block 1778, profiles of specific viewers that consumed advertisements provided to the set top box by the advertisement auctioning system may be determined by performing the previously described content to profile assignment, which provides an identification of what content is being consumed, for example, advertisement, by what profile. This data may be provided as updates to advertisers so that the advertisers can keep track of the progress of their advertising campaign.

As shown by block 1780, a total number of viewers of the desired profile that consumed (e.g., watched) the advertisement is calculated. This determination may be made by adding the viewer profiles determined to be currently consuming the advertisement.

It should be noted that, in accordance with an alternative embodiment of the invention, the bonus mechanism previously described may be used in calculation of billing. As an example, the Vickery auction method previously described may be used.

In accordance with an alternative embodiment of the invention, the process of defining an advertising campaign, as provided by an advertiser, may instead be performed without a computer. As an example, the advertising campaign may be described to an individual, who may, in turn, enter the campaign criteria within the present advertisement auctioning system.

It should be emphasized that the above-described embodiments of the present invention are merely possible examples of implementations, merely set forth for a clear understanding of the principles of the invention. Many variations and modifications may be made to the above-described embodiments of the invention without departing substantially from the spirit and principles of the invention. All such modifications and variations are intended to be included herein within the scope of this disclosure and the present invention and protected by the following claims.