Behavioral Targeting System That Generates User Profiles for Target Objectives

• US 20150324430A1
• Filed: 07/17/2015
• Published: 11/12/2015
• Est. Priority Date: 03/29/2006
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Koran, Joshua M., Chung, Christina Yip, Yin, Hongfeng, Lin, Long-ji
• Granted Patent: US 9,817,868 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/212 with details for data modelling support
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/28 Databases characterised by their database models, e.g. relational or object models
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0251 Targeted advertisements

Abstract

A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, is model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Claims

1-20. -20. (canceled)

21. A method comprising:

receiving, by a processor from a user, a search query;

parsing, by the processor, the search query to obtain one or more query terms;

retrieving, by the processor from a database, a plurality of tokens matching the query terms;

identifying, by the processor, a plurality of categories, each identified category in the plurality associated with a retrieved token;

assigning, by the processor, an ambiguity parameter value to each retrieved token;

selecting, by the processor, a dominant token from the retrieved tokens based on one or more associated categories in the identified plurality of categories and based on its assigned ambiguity parameter value; and

categorizing, by the processor, the search query based on the one or more categories associated with the dominant token.

22. The method of claim 21, further comprising selecting, by the processor, tokens in the plurality that have a highest probability to appear within the search query.

23. The method of claim 22, wherein the selecting is based on a unit frequency parameter associated with each token, which specifies how many times each token is contained within the search query.

24. The method of claim 21, further comprising selecting, by the processor, tokens in the plurality that are longest possible tokens.

25. The method of claim 24, wherein the longest possible tokens comprise tokens having a greatest number of words.

26. The method of claim 24, wherein the longest possible tokens comprise tokens having a greatest length.

27. The method of claim 21, wherein the selecting of the dominant token further comprises selecting the dominant token based on rules specifying elimination of tokens that contain stop words.

28. The method of claim 21, wherein the selecting of the dominant token further comprises selecting the dominant token based on rules specifying a minimum frequency of token appearance within stored search queries.

29. The method of claim 21, wherein the selecting of the dominant token further comprises selecting the dominant token based on rules designed to rank the retrieved tokens.

30. A computing device comprising:

a processor;

a storage medium for tangibly storing thereon program logic for execution by the processor, the stored program logic comprising;

receiving logic executed by the processor for receiving, from a user, a search query;

parsing logic executed by the processor for parsing the search query to obtain one or more query terms;

retrieving logic executed by the processor for retrieving, from a database, a plurality of tokens matching the query terms;

identifying logic executed by the processor for identifying a plurality of categories, each identified category in the plurality associated with a retrieved token;

assigning logic executed by the processor for assigning an ambiguity parameter value to each retrieved token;

selecting logic executed by the processor for selecting a dominant token from the retrieved tokens based on one or more associated categories in the identified plurality of categories and based on its assigned ambiguity parameter value; and

categorizing logic executed by the processor for categorizing the search query based on the one or more categories associated with the dominant token.

31. The computing device of claim 30, further comprising second selecting logic executed by the processor for selecting tokens in the plurality that have a highest probability to appear within the search query.

32. The computing device of claim 31, wherein the second selecting logic comprising selecting based on a unit frequency parameter associated with each token, which specifies how many times each token is contained within the search query.

33. The computing device of claim 30, further comprising second selecting logic executed by the processor for selecting tokens in the plurality that are longest possible tokens.

34. The computing device of claim 33, wherein the longest possible tokens comprise tokens having a greatest number of words.

35. The computing device of claim 33, wherein the longest possible tokens comprise tokens having a greatest length.

36. The computing device of claim 30, wherein the selecting logic for selecting the dominant token further comprises selecting the dominant token based on rules specifying elimination of tokens that contain stop words.

37. The computing device of claim 30, wherein the selecting logic for selecting the dominant token further comprises selecting the dominant token based on rules specifying a minimum frequency of token appearance within stored search queries.

38. The computing device of claim 30, wherein the selecting logic for selecting the dominant token further comprises selecting the dominant token based on rules designed to rank the retrieved tokens.

39. A non-transitory computer readable storage medium comprising program code that when executed by a processor causes the processor to execute instructions, the instructions comprising:

receiving, by the processor from a user, a search query;

parsing, by the processor, the search query to obtain one or more query terms;

retrieving, by the processor from a database, a plurality of tokens matching the query terms;

identifying, by the processor, a plurality of categories, each identified category in the plurality associated with a retrieved token;

assigning, by the processor, an ambiguity parameter value to each retrieved token;

selecting, by the processor, a dominant token from the retrieved tokens based on one or more associated categories in the identified plurality of categories and based on its assigned ambiguity parameter value; and

categorizing, by the processor, the search query based on the one or more categories associated with the dominant token.

40. The non-transitory computer readable storage medium of claim 39, further comprising instructions comprising:

selecting, by the processor, tokens in the plurality that have a highest probability to appear within the search query.