Privacy-protected targeting system

• US 20010049620A1
• Filed: 02/28/2001
• Published: 12/06/2001
• Est. Priority Date: 02/29/2000
• Status: Abandoned Application

• Current Assignee: Prime Research Alliance E, LLC
• Original Assignee: Prime Research Alliance E, LLC
• Inventors: Blasko, John P.
• Application Number:
  US09/796,339
• Publication Number:
  US 20010049620A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

A system and method for transaction profiling in a privacy-protected manner, wherein the transaction generally refers to an intentional action by a user. For example, in the context of television programming, the transaction data may relate to programming and advertisements watched by the user over a predetermined period of time. A transaction profile vector based on the evaluation of the recorded transaction data is then computed, wherein the transaction profile vector may include demographic attributes such as probable age, household size, income level of the user, or preference attributes indicating probable products and services preferred by the user. To protect privacy, the generation of the transaction profile vector (also known as profile vector) preferably takes place local to the transaction.

Claims

1. A computer-implemented method for presenting one or more targeted advertisements to a user, the method comprising:

monitoring user behavior for one or more intentional actions to collect transaction related data; and

processing the transaction related data to generate one or more profile vectors.

2. The method of

claim 1

, wherein each transaction is identified by unique transaction identifications.

3. The method of

claim 2

, wherein the transaction identification is based on an arbitrary number selected randomly to preserve user privacy.

4. The method of

claim 1

, wherein the user is identified by a unique profile identification.

5. The method of

claim 4

, wherein the profile identification is based on an arbitrary number selected randomly to preserve user privacy.

6. The method of

claim 1

, wherein the profile vector includes one or more demographic attributes about the user.

7. The method of

claim 6

, wherein the demographic attributes represent a probability that a user falls within a certain demographic category, such as an age group, gender, household size, or income range.

8. The method of

claim 6

, wherein the demographic attributes further include one or more interest categories organized according to broad areas.

9. The method of

claim 1

, wherein the profile vector represents one or more product preference categories of the user.

10. The method of

claim 9

, wherein the product preference categories are organized according to broad areas, such as music, travel and restaurants.

11. The method of

claim 1

, wherein the profile vector contains non-deterministic information about the user.

12. The method of

claim 1

, wherein the profile vector is generated locally to a user interface.

13. The method of

claim 1

, wherein the transaction refers to a television viewing session.

14. The method of

claim 13

, wherein the profile vector is locally generated in a set-top box.

15. The method of

claim 14

, wherein the profile vector refers back a MAC_ID of the set-top box.

16. The method of

claim 14

, wherein the set-top box comprises a memory for storing one or more profile vectors.

17. The method of

claim 13

, wherein a head-end receives and processes a plurality of the locally generated profile vectors.

18. The method of

claim 1

, further comprising aggregating a plurality of profile vectors to compute an aggregated profile vector.

19. The method of

claim 18

, wherein the aggregated profile vector is updated each time a new transaction corresponding to a particular user occurs.

20. The method of

claim 18

, wherein the aggregated profile vector is computed within a set-top box.

21. The method of

claim 18

, wherein a head-end receives and processes a plurality of aggregated profile vectors.

22. The method of

claim 1

, further includes utilizing the profile vector to find a target advertisement to be presented to the user.

23. The method of

claim 1

, further comprising forwarding the profile vector to a secure correlation server.

24. The method of

claim 23

, further includes matching one or more targeted advertisements to be presented to the user based on the contents of the profile vector.

25. The method of

claim 24

, wherein the matching is performed by the secure correlation server.

26. The method of

claim 1

, wherein the transaction related data includes Internet surfing data.

27. The method of

claim 1

, wherein the transaction related data includes purchase transaction data.

28. The method of

claim 1

, wherein the profile vectors are generated based on one or more heuristic rules.

29. The method of

claim 28

, wherein the heuristic rules are expressed as conditional probabilities.

30. A computer system for presenting one or more targeted advertisements to one or more users in a privacy protected manner, the system comprising:

a plurality of remote databases storing transactional information relating to one or more user transactions;

a plurality of local profilers coupled to the remote databases for processing the transactional information and generating one or more profile vectors; and

a secure profiling server coupled to the local profilers wherein the secure profiling server receives and processes one or more locally generated profile vectors.

31. The system of

claim 30

, wherein the secure profiling server computes an aggregated profile vector based on the locally generated profile vectors.

32. The system of

claim 30

, wherein the remote database stores Internet-related transactional data.

33. The system of

claim 32

, wherein the remote database stores point-of-sale data.

34. The system of

claim 32

, wherein the remote database stores Internet surfing data.

35. The system of

claim 32

, wherein the secure profiling server communicates to a secure correlation server.

36. The system of

claim 35

, wherein the secure correlation server based on the information from the secure profiling server selects one or more targeted advertisements to be presented to the user.