SYSTEM AND METHOD FOR PROFILING CLIENTS WITHIN A SYSTEM FOR HARVESTING COMMUNITY KNOWLEDGE

• US 20130041906A1
• Filed: 08/07/2012
• Published: 02/14/2013
• Est. Priority Date: 03/25/2002
• Status: Active Grant

• Current Assignee: Valtrus Innovations Limited (f/k/a Dolya Holdco 9 Limited) (Key Patent Innovations Limited)
• Original Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Inventors: Adar, Eytan, Lukose, Rajan Mathew, Tyler, Joshua Rogers, Sengupta, Caesar
• Granted Patent: US 9,053,458 B2
• Time in Patent Office: Days
• US Class Current: 707/750
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]
• CPC Class Codes: H04L 9/40 Network security protocols

Abstract

A privacy-preserving system and method is disclosed for profiling clients within a system for knowledge management. The method of the present invention discloses steps for generating a client profile in support of receiving and processing messages using scoring techniques and/or filtering techniques. The method of the present invention further includes steps for generating a client profile in support of a method for generating and obtaining responses to messages using scoring techniques and/or filtering techniques. The system of the present invention, includes all means for implementing the method.

Claims

1-20. -20. (canceled)

21. A method comprising:

accessing a set of data targets;

collecting data target information from the data targets, wherein the data target information includes user behavioral data with respect to the data targets, the user behavioral data indicating which data targets a user reads and stores and which data targets the user ignores and deletes;

generating a client profile from the data target information, the client profile including keywords and associated frequencies of occurrence of the respective keywords in the data targets;

storing the client profile on a client computer;

receiving a data target at the client computer; and

scoring, by the client computer, the received data target using the client profile that includes the keywords and the associated frequencies.

22. The method of claim 21, wherein the data targets include messages, wherein the data target information includes the user behavioral data indicating which messages the user reads and stores and which messages the user ignores and deletes, and wherein the keywords are keywords in the messages.

23. The method of claim 22, wherein the data targets further include files and the data target information further includes user behavioral data indicating which files the user views, modifies, and deletes, and wherein the keywords in the client profile further include keywords in the files.

24. The method of claim 22, wherein the data targets further include web pages and the data target information further includes user behavioral data indicating which web pages the user visits, and wherein the keywords in the client profile further includes keywords in the web pages.

25. The method of claim 22, wherein the data targets further include hardware and software on the client computer, and wherein the data target information further includes user behavioral data relating to interaction between the user and the hardware or software, and wherein the scoring is further based on the user behavioral data relating to the interaction.

26. The method of claim 21, wherein generating the client profile comprises generating the client profile that further includes key phrases and associated frequencies of occurrence of the key phrases in the data targets.

27. The method of claim 21, wherein the keywords are part of unstructured data in the data targets, and wherein collecting data target information further comprises collecting structured data including at least one from among email addresses and uniform resource locators (URLs), wherein the client profile further includes the at least one from among the email addresses and URLs, and wherein the scoring is further based on the at least one from among the email addresses and URLs.

28. The method of claim 21, wherein the client profile includes a positive profile and a negative profile, wherein the positive profile includes user behavioral data with respect to the data targets the user reads or stores, and the negative profile includes user behavioral data with respect to the data targets the user ignores or deletes, and

wherein the scoring includes providing a higher score for the received data target if the received data target includes content matching content of the positive profile, and providing a lower score for the received data target if the received data target includes content matching content of the negative profile.

29. The method of claim 21, wherein the generating includes:

identifying unstructured data within the data target information; and

performing a statistical analysis on the unstructured data to identify the keywords and calculate the associated frequencies of occurrence of the keywords.

30. The method of claim 29, wherein performing the statistical analysis further identifies key phrases and calculates associated frequencies of occurrence of the key phrases within the unstructured data.

31. The method of claim 21, wherein the storing includes storing the client profile only on the client computer.

32. The method of claim 21, wherein scoring the received data target comprises scoring a received message.

33. The method of claim 32, further comprising:

displaying the received message if a score produced by the scoring exceeds a threshold; and

not displaying the received message if the score is less than the threshold.

34. A system comprising:

a computer including a network module to communicate over a network, and at least one processor to;

access data targets;

collect data target information from the data targets, wherein the data target information includes user behavioral data with respect to the data targets, the user behavioral data indicating which data targets a user reads and stores and which data targets the user ignores and deletes;

generate a client profile from the data target information, the client profile including keywords and associated frequencies of occurrence of the respective keywords in the data targets;

receive a data target; and

score the received data target using the client profile, wherein the scoring is based on the keywords and the associated frequencies.

35. The system of claim 34, wherein the data targets include messages, wherein the data target information includes the user behavioral data indicating which messages the user reads and stores and which messages the user ignores and deletes, and wherein the keywords are keywords in the messages.

36. The system of claim 35, wherein the data targets further include files and the data target information further includes user behavioral data indicating which files the user views, modifies, and deletes, wherein the keywords in the client profile further includes keywords in the files, and wherein the scoring is further based on the keywords in the files and the associated frequencies of the keywords in the files.

37. The system of claim 35, wherein scoring the received data target comprises scoring a received message, the scoring based on determining whether content of the received message is present in the client profile.

38. The system of claim 34, wherein the generated client profile further comprises key phrases and associated frequencies of occurrence of the key phrases in the data targets.

39. The system of claim 34, wherein the keywords are part of unstructured data in the data targets, and wherein collecting the data target information further comprises collecting structured data including at least one from among email addresses and uniform resource locators (URLs), and wherein the client profile further includes the at least one from among the email addresses and URLs, and wherein the scoring is further based on the at least one from among the email addresses and URLs.

40. The system of claim 34, wherein the client profile includes a positive profile and a negative profile, wherein the positive profile includes user behavioral data with respect to the data targets the user reads or stores, and the negative profile includes user behavioral data with respect to the data targets the user ignores or deletes, and

wherein the scoring includes providing a higher score for the received data target if the received data target includes content matching content of the positive profile, and providing a lower score for the received data target if the received data target includes content matching content of the negative profile.