System and method for providing feeds based on activity in a network environment

• US 9,465,795 B2
• Filed: 12/17/2010
• Issued: 10/11/2016
• Est. Priority Date: 12/17/2010
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Malegaonkar, Ashutosh A., Gannu, Satish K., Patil, Deepti
• Primary Examiner(s): Hutton, Nan
• Application Number:
  US12/971,946
• Publication Number:
  US 20120158719A1
• Time in Patent Office: 2,125 Days
• Field of Search: 707/827
• US Class Current: 1/1
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 40/30 Semantic analysis
• 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 50/01 Social networking
• CPC Class Codes: H04L 67/306 User profiles
• 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 67/564 Enhancement of application control based on intercepted application data

Abstract

A method is provided in one example and includes receiving network traffic associated with a particular user; developing a personal vocabulary for the particular user based on the network traffic; determining areas of interest for the particular user based on the personal vocabulary; determining associations for the particular user in relation to additional users; and generating a feed based on a portion of the network traffic. The feed is delivered to a subset of the additional users.

Claims

1. A method, comprising:

receiving enterprise network traffic associated with a particular user;

identifying irrelevant documents in the received network traffic using a document filter;

developing a personal vocabulary for the particular user based on the enterprise network traffic, wherein the irrelevant documents are not evaluated to develop the personal vocabulary, wherein the personal vocabulary is developed independent of additional users;

determining an expertise associated with the particular user based, at least in part, on the personal vocabulary and activity of the additional users;

determining a category associated with the particular user, wherein the category is at least partially based on applications used by the particular user;

determining areas of interest for the particular user based on the personal vocabulary, the category, and inter-category terms, wherein the inter-category terms are used to link similar categories;

determining associations for the particular user in relation to the additional users; and

generating a feed based on a portion of the enterprise network traffic and areas of interest for the particular user, wherein the feed is automatically delivered to a subset of the additional users.

2. The method of claim 1, wherein the feed is delivered to the subset of the additional users as a function of their respective personal vocabularies, expertise, and tagging.

3. The method of claim 1, wherein a profile is developed for the particular user, and wherein the profile can be manually changed by the particular user adding tags to be included in the personal vocabulary.

4. The method of claim 1, wherein the expertise is based on a number of occurrences of a specific term identified in the network traffic.

5. The method of claim 1, wherein developing the personal vocabulary for the particular user includes filtering keyword clusters.

6. The method of claim 1, wherein weights are used to filter the network traffic in order to develop the feed for the subset of the additional users.

7. The method of claim 1, wherein the personal vocabulary is updated in order to develop an additional feed to be delivered to at least some of the additional users.

20. The method of claim 1, wherein at least one of the irrelevant documents includes a JPEG picture.

8. Logic encoded in one or more tangible non-transitory media that includes code for execution and when executed by a processor is operable to perform operations comprising:

receiving enterprise network traffic associated with a particular user;

identifying irrelevant documents in the received network traffic using a document filter;

developing a personal vocabulary for the particular user based on the enterprise network traffic, wherein the irrelevant documents are not evaluated to develop the personal vocabulary, wherein the personal vocabulary is developed independent of additional users;

determining an expertise associated with the particular user based, at least in part, on the personal vocabulary and activity of the additional users;

determining a category associated with the particular user, wherein the category is at least partially based on applications used by the particular user;

determining areas of interest for the particular user based on the personal vocabulary, the category, and inter-category terms, wherein the inter-category terms are used to link similar categories;

determining associations for the particular user in relation to the additional users; and

generating a feed based on a portion of the enterprise network traffic and areas of interest for the particular user, wherein the feed is automatically delivered to a subset of the additional users.

9. The logic of claim 8, wherein the feed is delivered to the subset of the additional users as a function of their respective personal vocabularies, expertise, and tagging.

10. The logic of claim 8, wherein a profile is developed for the particular user, and wherein the profile can be manually changed by adding tags to be included in the personal vocabulary.

11. The logic of claim 8, wherein the expertise is based on a number of occurrences of a specific term identified in the network traffic.

12. The logic of claim 8, wherein weights are used to filter the network traffic in order to develop the feed for the subset of the additional users.

13. The logic of claim 8, wherein the personal vocabulary is updated in order to develop an additional feed to be delivered to at least some of the additional users.

14. The logic of claim 8, wherein developing the personal vocabulary for the particular user includes filtering keyword clusters.

15. An apparatus, comprising:

a memory element configured to store data;

a processor operable to execute instructions associated with the data;

a central engine configured to interface with the memory element and the processor, wherein the apparatus is configured for;

receiving enterprise network traffic associated with a particular user;

identifying irrelevant documents in the received network traffic using a document filter;

developing a personal vocabulary for the particular user based on the enterprise network traffic, wherein the irrelevant documents are not evaluated to develop the personal vocabulary, wherein the personal vocabulary is developed independent of additional users;

determining an expertise associated with the particular user based, at least in part, on the personal vocabulary and activity of the additional users;

determining a category associated with the particular user, wherein the category is at least partially based on applications used by the particular user;

determining areas of interest for the particular user based on the personal vocabulary, the category, and inter-category terms, wherein the inter-category terms are used to link similar categories;

determining associations for the particular user in relation to the additional users; and

generating a feed based on a portion of the enterprise network traffic and areas of interest for the particular user, wherein the feed is automatically delivered to a subset of the additional users.

16. The apparatus of claim 15, wherein the feed is delivered to the subset of the additional users as a function of their respective personal vocabularies, expertise, and tagging.

17. The apparatus of claim 15, wherein a profile is developed for the particular user, and wherein the profile can be manually changed by adding tags to be included in the personal vocabulary.

18. The apparatus of claim 15, wherein the expertise is based on a number of occurrences of a specific term identified in the network traffic.

19. The apparatus of claim 15, wherein weights are used to filter the network traffic in order to develop the feed for the subset of the additional users.