Targeting Ads to Subscribers based on Privacy Protected Subscriber Profiles

• US 20070233571A1
• Filed: 06/06/2007
• Published: 10/04/2007
• Est. Priority Date: 01/11/2001
• Status: Abandoned Application

• Current Assignee: Prime Research Alliance E, LLC
• Original Assignee: Prime Research Alliance E., Inc.
• Inventors: Schlack, John, Eldering, Charles, Lustig, Herbert
• Application Number:
  US11/758,845
• Publication Number:
  US 20070233571A1
• Time in Patent Office: Days
• US Class Current: 705/14.530
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0258 Registration
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: H04H 20/10 Arrangements for replacing or switching information during the broadcast or the distribution
• CPC Class Codes: H04H 60/64 for providing detail information
• CPC Class Codes: H04N 21/23424 involving splicing one content stream with another content stream, e.g. for inserting or substituting an advertisement
• CPC Class Codes: H04N 21/25883 being end-user demographical data, e.g. age, family status or address arrangements for identifying locations of users in broadcast systems H04H60/52
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/44016 involving splicing one content stream with another content stream, e.g. for substituting a video clip
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4662 characterized by learning algorithms
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 7/162 Authorising the user terminal, e.g. by paying; Registering the use of a subscription channel, e.g. billing
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

Monitoring subscriber viewing interactions, such as television viewing interactions, and generating viewing characteristics therefrom. Generating at least one type of subscriber profile from at least some subset of subscriber characteristics including viewing, purchasing, transactions, statistical, deterministic, and demographic. The subscriber characteristics may be generated, gathered from at least one source, or a combination thereof. Forming groups of subscribers by correlating at least one type of subscriber profile. The subscriber groups may correlate to elements of a content delivery system (such as head-ends, nodes, branches, or set top boxes (STBs) within a cable TV system). Correlating ad profiles to subscriber/subscriber group profiles and selecting targeted advertisements for the subscribers/subscriber groups based on the correlation. Inserting the targeted ads in place of default ads in program streams somewhere within the content delivery system (head-end, node, or STB). Presenting the targeted ads to the subscriber/subscriber group via a television.

Claims

1. A method for matching advertisements to subscribers, the method comprising:

(a) receiving advertisement profiles that include traits associated with an intended target market for an associated advertisement;

(b) gathering subscriber data from at least one source, wherein the subscriber data is selected from at least a subset of transactional data, public data, private data, and demographic data;

(c) generating subscriber profiles based on at least a subset of gathered subscriber data, wherein the subscriber profiles predict traits about the subscribers without revealing any private data or raw transaction data associated with the subscribers;

(d) correlating the advertisement profiles with the subscriber profiles;

(e) selecting at least one associated advertisement for each of the subscriber profiles; and

(f) presenting the targeted advertisements in avails within program streams delivered to the subscribers.

2. The method of claim 1 wherein, the selecting of (e) is realized by grouping subscribers according to the advertisement profiles and returning groups of subscribers matched to each associated advertisement.

3. The method of claim 1, wherein the traits are expected characteristics of the intended target market.

4. The method of claim 1, wherein said presenting the targeted advertisements includes delivering a plurality of the targeted advertisements to each subscriber and inserting the targeted advertisements within advertisement opportunities in the delivered program streams.

5. The method of claim 1, wherein the targeted advertisements are stored in a queue at a PVR, and the targeted advertisements are inserted, at the PVR, in the program streams based on the queue.

6. The method of claim 1, wherein said presenting the targeted advertisements includes delivering a plurality of the advertisements to each subscriber;

delivering an advertisement profile for each of the plurality of advertisements;

determining if each of the advertisements is applicable by correlating the associated advertisement profile with the subscriber profile;

storing the applicable advertisements; and

inserting the applicable advertisements within advertisement opportunities in delivered program streams.

7. The method of claim 1, wherein said generating includes applying the appropriate weighting factor to the subscriber data.

8. The method of claim 1, wherein said weighting factor is assigned according to the source of the subscriber data.

9. The method of claim 1, wherein said weighting factor is assigned according to heuristic rules.

10. The method of claim 1, wherein the subscriber profiles do not contain any private data or raw transaction data.

11. The method of claim 1, wherein the subscriber profiles contain characteristics about the private data or raw transaction data, but do not contain private data or raw transaction data.

12. The method of claim 11, wherein any private data or raw transaction data is periodically purged from memory.

13. A computer-implemented method of matching advertisements to subscribers, the method comprising:

(a) receiving intended target market characteristics for an associated advertisement, the intended target market characteristics including at least one discretionary consumer characteristic;

(b) retrieving computer-stored consumer characteristic data corresponding to a plurality of consumers;

(c) generating consumer characteristics based on at least a subset of the computer-stored consumer characteristic data, wherein the consumer characteristics predict characteristics about the consumers without revealing any private data;

(d) correlating the intended target market characteristics with the consumer characteristics; and

(e) identifying a subset of the consumers that have a sufficient level of correlation between the at least one consumer characteristics and the intended market characterstics, the subset of consumers corresponding to the intended target market.

14. The method of claim 13, wherein said generating includes applying the appropriate weighting factor to the consumer characteristic data.

15. The method of claim 14, wherein said weighting factor is assigned according to the source of the consumer characteristic data.

16. The method of claim 14, wherein said weighting factor is assigned according to heuristic rules.

17. The method of claim 13, wherein the consumer characteristics do not contain any consumer characteristic data.

18. The method of claim 13, wherein the consumer characteristics store generalized characteristics about the consumer characteristic data but not the consumer characteristics data itself.

19. The method of claim 13, wherein said consumer characteristics are probabilistic.

20. The method of claim 13, wherein the consumer characterization data includes consumer purchase data.

21. The method of claim 13, wherein the consumer characterization data includes consumer television interaction data.

22. The method of claim 13, wherein the consumer characterization data includes consumer Internet usage data.

23. The method of claim 13, wherein said retrieving of (b) occurs at a secure profiling server.

24. The method of claim 13, wherein said generating of (c) occurs at a secure profiling server.

25. The method of claim 13, wherein said correlating of (d) occurs at a secure correlation server.

26. The method of claim 13, further comprising:

(f) delivering the associated advertisement to the subset of consumers.