Activity Stream Tuning Using Multichannel Communication Analysis

• US 20130297689A1
• Filed: 05/03/2012
• Published: 11/07/2013
• Est. Priority Date: 05/03/2012
• Status: Abandoned Application

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Bhat, Raghurama, Griffin, Keith, Gandhi, Biren
• Application Number:
  US13/463,227
• Publication Number:
  US 20130297689A1
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

A social graph is constructed to be representative of a social network by including nodes and edges representing activities in the social network. Activities in communication transactions of a communication network are identified and activity stream tuning parameters are determined for a user of the social network from the identified relationships. Activity stream data is presented to the user in accordance with the tuning parameters.

Claims

1. A method comprising:

constructing a social graph representative of a social network, the social graph including nodes and edges representing activities in the social network;

extracting content from communication transactions of a communication network;

modifying the social graph in accordance with the extracted content;

determining activity stream tuning parameters for a user of the social network from the modified social graph; and

presenting activity stream data to the user in accordance with the tuning parameters.

2. The method of claim 1, wherein modifying the social graph includes:

identifying relationships between the activities conducted through a social network service and other activities determined from the extracted content of the communication transactions outside the social network service;

representing the other activities in the social graph; and

storing data representing the social graph.

3. The method of claim 2, wherein representing the activities includes:

identifying semantic elements in the extracted content;

assigning the identified semantic elements to additional nodes and edges in the social graph; and

adding the additional nodes and edges to the social graph.

4. The method of claim 3, wherein the semantic elements are of noun-verb-noun form identifying at least participants in the respective communication transactions as a noun.

5. The method of claim 3, wherein determining activity stream tuning parameters includes:

identifying nodes connected to a node in the social graph representing the user;

establishing weights on respective edges connecting the nodes to the user node; and

assigning values to the weights based on a quality of the respective activities represented by the edges.

6. The method of claim 5, further comprising:

monitoring the activities to determine a frequency, duration or timing of an activity represented by a corresponding one of the weighted edges connected to the user node; and

updating the values of the weights in accordance with a change in the frequency, the duration or the timing.

7. The method of claim 6, further comprising:

performing background analysis on the clustered nodes, the background analysis being performed independently of the monitoring; and

modifying the values assigned to the weights in accordance with the background analysis.

8. The method of claim 7, wherein the background analysis includes at least one of collaborative filtering and regression analysis.

9. The method of claim 8, wherein modifying the tuning parameters includes:

determining a relevance score from the weights; and

assigning a presentation priority to the tuning parameters in accordance with the relevance score.

10. The method of claim 9, further comprising:

decreasing the relevance score upon a determination of a reduction in one or more values of the weights.

11. An apparatus comprising:

a network interface through which communication transactions occur over a communication network;

a memory in which to store a social graph representative of a social network, the social graph including nodes and edges representing activities in the social network;

a processor configured to;

extract content from the communication transactions;

modify the social graph in accordance with the extracted content; and

determine activity stream tuning parameters for a user of the social network from the modified social graph; and

a user interface to present activity stream data to the user in accordance with the tuning parameters.

12. The apparatus of claim 11, wherein the processor is configured to:

identify relationships between the activities conducted through a social network service and other activities determined from the extracted content of the communication transactions outside the social network service;

represent the other activities in the social graph; and

store data representing the social graph.

13. The apparatus of claim 12, wherein the processor is configured to:

identify semantic elements in the extracted content;

assign the identified semantic elements to additional nodes and edges in the social graph; and

add the additional nodes and edges to the social graph.

14. The apparatus of claim 13, wherein the processor is configured to:

identify nodes connected to a node in the social graph representing the user;

establish weights on respective edges connecting the nodes to the user node; and

assign values to the weights based on a quality of the respective activities represented by the edges.

15. The apparatus of claim 14, wherein the processor is configured to:

monitor the activities to determine a frequency, duration or timing of an activity represented by a corresponding one of the weighted edges connected to the user node; and

update the values of the weights in accordance with a change in the frequency, the duration or the timing.

16. The apparatus of claim 15, wherein the processor is configured to:

determine a relevance score from the weights; and

assign a presentation priority to the tuning parameters in accordance with the relevance score.

17. The apparatus of claim 16, wherein the processor is further configured to:

decrease the relevance score upon a determination of a reduction in one or more values of the weights.

18. A non-transitory tangible computer-readable medium having encoded thereon instructions that, when executed by a processor, are operable to:

construct a social graph representative of a social network, the social graph including nodes and edges representing activities in the social network;

extract content from communication transactions of a communication network;

modify the social graph in accordance with the extracted content;

determine activity stream tuning parameters for a user of the social network from the modified social graph; and

present activity stream data to the user in accordance with the tuning parameters.

19. The computer-readable medium of claim 18, including processor instructions that, when executed by the processor, are operable to:

identify relationships between the activities conducted through a social network service and other activities determined from the extracted content of the communication transactions outside the social network service;

represent the other activities in the social graph; and

store data representing the social graph.

20. The computer-readable medium of claim 19, including processor instructions that, when executed by the processor, are operable to:

identify semantic elements in the extracted content;

assign the identified semantic elements to additional nodes and edges in the social graph; and

add the additional nodes and edges to the social graph.

21. The computer-readable medium of claim 20, including processor instructions that, when executed by the processor, are operable to:

identify nodes connected to a node in the social graph representing the user;

establish weights on respective edges connecting the nodes to the user node; and

assign values to the weights based on a quality of the respective activities represented by the edges.

22. The computer-readable medium of claim 21, including processor instructions that, when executed by the processor, are operable to:

monitor the activities determine a frequency, duration or timing of an activity represented by a corresponding one of the weighted edges connected to the user node;

update the values of the weights in accordance with a change in the frequency, the duration or the timing.

23. The computer-readable medium of claim 22, including processor instructions that, when executed by the processor, are operable to:

perform background analysis on the clustered nodes, the background analysis being performed independently of the monitoring; and

modify the values assigned to the weights in accordance with the background analysis.

24. The computer-readable medium of claim 23, including processor instructions that, when executed by the processor, are operable to:

determine a relevance score from the weights; and

assign a presentation priority to the tuning parameters in accordance with the relevance score.

25. The computer-readable medium of claim 24, including processor instructions that, when executed by the processor, are operable to:

decrease the relevance score upon a determination of a reduction in one or more values of the weights.