Knowledge management system automatically allocating expert resources

• US 7,865,457 B2
• Filed: 08/25/2004
• Issued: 01/04/2011
• Est. Priority Date: 08/25/2004
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: International Business Machines Corporation
• Inventors: Ravin, Yael, Sharpe, James J., Stern, Edith H.
• Primary Examiner(s): STARKS, WILBERT L
• Application Number:
  US10/926,154
• Publication Number:
  US 20060047615A1
• Time in Patent Office: 2,323 Days
• Field of Search: 706/46, 706/45, 715/716, 600/300, 714/4, 705/50
• US Class Current: 706/46
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

A knowledge management system allocating expert resources, method of allocating expert resources and program product therefor. Information requests are provided over networked devices, e.g., over voice and data networks. Data on experts may be stored in an expert database and data on requesters stored in a requestor database. A pairing unit identifies an appropriate expert matched to each request and requestor. The pairing unit includes a request/requestor characterization unit collecting request/requestor attributes, a matching unit matching request/requestor attributes with an expert, and a routing unit routing each incoming request to a selected matching expert.

Claims

1. A computer implemented knowledge management system allocating expert resources, said knowledge management system comprising:

a plurality of devices communicating information requests from associated requestors;

expert data storage storing expert data in an expert database;

requestor data storage storing data on said associated requestors in a requestor database; and

a pairing unit pairing information requests with appropriate experts, said pairing unit determining an associated requestor for each incoming request, matching both requestor data for said associated requestor and said incoming request with expert data for available experts, and selecting an appropriate expert from said available experts;

said pairing unit comprising;

a request/requestor characterization unit determining request/requestor attributes characteristic of said each incoming request;

said request/requestor characterization unit comprising;

 

means for determining request attributes for said each incoming request;

 

means for determining requestor attributes for said determined requestor; and

 

means for gathering request and requestor data, gathered said request data being provided to said means for determining request attributes and gathered said requestor data being provided to said means for determining requestor attributes;

a matching unit comparing said determined request/requestor attributes with said expert data for said available experts, said appropriate expert being selected responsive to the comparison results; and

a routing unit routing said each incoming request to a selected expert.

2. A knowledge management system as in claim 1, wherein said means for gathering request and requestor data gathers said data from said requestor database and from remotely stored data.

3. A knowledge management system as in claim 1, wherein said means for gathering request and requestor data further comprises:

means for querying said requestor for said request and requestor data.

4. A knowledge management system as in claim 1, wherein said matching unit comprises:

means for ranking compared said expert profiles; and

means for comparing ranked said expert profiles against a minimum acceptance threshold, any said ranked expert profiles above said minimum acceptance threshold being an acceptable match to said incoming request and said determined requestor.

5. A knowledge management system as in claim 4, wherein said means for ranking ranks experts based on expert operational attributes.

6. A knowledge management system as in claim 5, wherein said matching unit dynamically determines expert availability, said expert operational attributes including dynamically determined expert availability.

7. A knowledge management system as in claim 1, wherein said matching unit comprises:

a plurality of expert profiles, each of said plurality of expert profiles indicating at least one field of expertise and a level of said expertise in said field;

means for gathering expert data, gathered said expert data being gathered from and included in said plurality of expert profiles; and

means for comparing said expert profiles with request/requestor attributes for said each incoming request.

8. A knowledge management system as in claim 7, wherein said means for gathering expert data gathers said expert data from said expert database and from remotely stored data.

9. A knowledge management system as in claim 7, wherein said means for gathering expert data further comprises:

means for querying a participating expert for said expert data.

10. A knowledge management system as in claim 1, wherein said routing unit provides said associated requestor with a list of experts and contact information for each listed expert, whereby said associated requestor may select an expert from said list and contact said selected expert.

11. A knowledge management system as in claim 1, further comprising:

a call termination point for voice over Internet protocol (VoIP) communications with said devices; and

a call center providing data and voice communications with local communications devices, experts staffing said local communications devices.

12. A knowledge management system as in claim 1, wherein said plurality of devices communicate over a network comprising:

a voice network; and

a data network.

13. A computer implemented method of allocating expert resources in a knowledge management system, said method comprising the steps of:

a) receiving an incoming request for information;

b) determining request/requestor attributes for said incoming request;

further comprising the steps of;

i) determining request attributes;

ii) determining requestor attributes; and

iii) determining request/requestor attributes characterizing said incoming request and said requestor;

c) searching available experts responsive to said request/requestor attributes; and

d) routing said request to an identified one of said available experts.

14. A method of allocating expert resources as in claim 13, wherein the step (b) of determining said request/requestor attributes further comprises gathering request and requestor data, gathered said request and requestor data being used in the step (i) of determining request attributes and the step (ii) of determining requestor attributes.

15. A method of allocating expert resources as in claim 14, wherein gathered said request and requestor data gathered in the step (b) of determining request/requestor attributes further are gathered from locally stored request/requestor data, from remotely stored request/requestor data, from data mining and from responses to request/requestor attribute related questions.

16. A method of allocating expert resources as in claim 13, wherein said request/requestor attributes comprise knowledge attributes, operational attributes and communications attributes.

17. A method of allocating expert resources as in claim 16, wherein said knowledge attributes indicate requestor knowledge in and out of the request domain, operational attributes include requestor operating constraints for the request and communications attributes indicate communications preferences for communications between said requestor and an identified said expert.

18. A method of allocating expert resources as in claim 13, wherein the step (c) of searching available experts comprises the steps of:

i) gathering data about said available experts;

ii) combining gathered said data with expert profiles;

iii) comparing said expert profiles against said request/requestor attributes; and

iv) selecting a matching expert as said identified one.

19. A method of allocating expert resources as in claim 18, wherein gathered said data are gathered from locally stored expert data, from remotely stored expert data and from data mining.

20. A method of allocating expert resources as in claim 18, wherein said expert attributes comprise knowledge attributes, operational attributes and communications attributes.

21. A method of allocating expert resources as in claim 20, wherein said knowledge attributes indicate expert knowledge in and out of the request domain, operational attributes indicate expert operating conditions and communications attributes indicate expert communications capabilities for communications with said expert.

22. A method of allocating expert resources as in claim 20, wherein said expert operational attributes are dynamically determined responsive to expert availability.

23. A method of allocating expert resources as in claim 20, wherein said expert operational attributes comprise schedule workload, time zone location and cost.

24. A method of allocating expert resources as in claim 18, wherein the step (iv) of selecting said matching expert comprises the steps of:

A) ranking comparison results from comparing step (iii); and

B) comparing ranked said comparison results against a threshold, a highest ranked expert above said threshold being said identified one.

25. A method of allocating expert resources as in claim 24, wherein whenever in comparison step (B) all of said ranking comparison results are below said threshold, the comparing step further comprises providing an exception indicating that a matching expert has not been identified.

26. A method of allocating expert resources as in claim 18, wherein the step (d) of routing said request comprises:

(i) providing a list of experts and contact information for each listed expert;

(ii) selecting an expert from said list; and

(iii) contacting said selected expert.

27. A method of allocating expert resources as in claim 18, wherein whenever a matching expert is not found in searching step (c), an exception is routed in routing step (d) to an originating requestor indicating that a matching expert has not been identified.

28. A computer program product for allocating expert resources in a knowledge management system, said computer program product comprising a computer usable medium having computer readable program code thereon, said computer readable program code comprising:

computer readable program code means for receiving an incoming request for information;

computer readable program code means for determining request/requestor attributes for each received said incoming request; and

said computer readable program code means for determining request/requestor attributes, further comprising;

computer readable program code means for gathering attributes about incoming requests, requestors associated with said incoming requests and participating experts;

computer readable program code means for storing and maintaining requestor attributes responsive to gathered said attributes; and

computer readable program code means for storing and maintaining expert attributes responsive to gathered said attributes;

computer readable program code means for determining attributes unique to both said incoming request and said requestor.

computer readable program code means for selecting a most appropriate expert from a plurality of available experts responsive to determined said request/requestor attributes.

29. A computer program product for allocating expert resources in a knowledge management system as in claim 28, wherein said computer readable program code means for gathering said attributes gathers said attributes from locally stored attributes, from remotely stored data, from data mining and from responses to attribute related questions.

30. A computer program product for allocating expert resources in a knowledge management system as in claim 28, wherein said attributes include:

knowledge attributes indicating knowledge in and out of a domain associated with a corresponding said incoming request;

operational attributes indicate operating constraints for the request; and

communications attributes indicate communications preferences for communications between with an identified said expert.

31. A computer program product for allocating expert resources in a knowledge management system as in claim 30, further comprising computer readable program code means for dynamically determining said operational attributes about said participating experts.

32. A computer program product for allocating expert resources in a knowledge management system as in claim 30, wherein said dynamically determined said operational attributes are determined responsive to expert schedule workload, expert time zone location and cost of using each said expert.

33. A computer program product for allocating expert resources in a knowledge management system as in claim 28, said computer readable program code further comprising:

computer readable program code means for determining attributes unique to each said incoming request and an originating requestor.

34. A computer program product for allocating expert resources in a knowledge management system as in claim 28, wherein said computer readable program code for selecting a most appropriate expert comprises:

computer readable program code means for comparing said expert profiles with said request/requestor attributes;

computer readable program code means for ranking compared said expert profiles; and

computer readable program code means for comparing ranked compared said expert profiles with a threshold.

35. A computer program product for allocating expert resources in a knowledge management system as in claim 28, said computer readable program code further comprising:

computer readable program code means for providing a list of experts and contact information for each listed expert, whereby an associated requestor may select an expert from said list and contact said selected expert.

36. A computer program product for allocating expert resources in a knowledge management system as in claim 28, said computer readable program code further comprising:

computer readable program code means for routing said request to an identified one of said available experts.