PROACTIVE KNOWLEDGE OFFERS

• US 20160246885A1
• Filed: 02/23/2015
• Published: 08/25/2016
• Est. Priority Date: 02/23/2015
• Status: Active Grant

• Current Assignee: Genesys Cloud Services Incorporated
• Original Assignee: Genesys Telecommunications Laboratories Incorporated (Genesys Cloud Services Incorporated)
• Inventors: Aravamudhan, Bharath, Krishnanand, Nitin, Klepar, Bohdan, Bell, Gordon, Gutierrez, Frank, Lypchanskyy, Stanislav, McGann, Conor, Ryabchun, Andrey, Eisner, Josef Eric, Korolev, Nikolay, Ristock, Herbert Willi Artur
• Granted Patent: US 10,127,321 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

A system and method for proactively making knowledge offers. A processor is configured to gather information on interactions by a user with resources provided by an enterprise having a customer contact center. The processor anticipates need of the user based on the gathered information, and generates a query based on the anticipated need. Prior to the user expressly requesting knowledge relating to a particular topic, the processor proactively identifies and suggests the knowledge to the user based on the generated query. The processor receives feedback relating to the suggested knowledge and outputs based on the feedback, a relevance score for the suggested knowledge.

Claims

1. A method for proactively making a knowledge offer comprising:

gathering, by a processor, information on interactions by a user with resources provided by an enterprise having a customer contact center;

anticipating, by the processor, need of the user based on the gathered information;

generating a query based on the anticipated need;

prior to the user expressly requesting knowledge relating to a particular topic, proactively identifying and suggesting the knowledge to the user, by the processor, based on the generated query;

receiving, by the processor, feedback relating to the suggested knowledge; and

outputting, based on the feedback, a relevance Score for the suggested knowledge.

2. The method of claim 1, wherein the resources include a website provided by the enterprise.

3. The method of claim 2, wherein the interaction is a query typed by the user.

4. The method of claim 1, wherein the resources include agents of the contact center.

5. The method of claim 1, wherein the interactions include past interactions.

6. The method of claim 1, wherein the anticipating the need of the user includes anticipating, by the processor, an express inquiry from the user.

7. The method of claim 1, wherein the anticipating the need of the user includes anticipating user intent for a current interaction with one of the resources provided, by the enterprise.

8. The method of claim 1 wherein the query includes text retrieved from the gathered information.

9. The method of claim 6, wherein the information includes context of the interactions.

10. The method of claim 1, wherein the generating of the query includes:

identifying, b the processor, terms for describing the anticipated need.

11. The method of claim 1 further comprising:

conducting, by the processor, a first level search for a candidate set of knowledge documents based on the knowledge set, and outputting a first score for each of the documents in the candidate set;

conducting, by the processor, a second level search of the candidate set of knowledge documents for relevant documents, and outputting a second score for each of the documents in the candidate set;

generating, by the processor, a third score as a function of the first and second scores; and

outputting, by the processor, one or more of the documents in the candidate set based on the third score.

12. The method of claim 9 further comprising:

retrieving, by the processor, a classifier model for a particular document in the candidate set, wherein the classifier model includes query samples for which the particular document was tagged as being relevant or not relevant;

calculating, by the processor, the second score based on the generated query and the retrieved model, wherein the second score indicates a chance that the generated query is relevant to the particular document.

13. The method of claim 10, wherein the feedback is for the particular document, the method further comprising:

training, by the processor, the classifier model for the particular document based on the feedback.

14. The method of claim 1, wherein the feedback is an express indication by the user as to whether the suggested knowledge was helpful or not.

15. The method of claim 1, Wherein the feedback is implied from actions by the user.

16. A system for proactively making a knowledge offer comprising:

processor; and

memory, wherein the memory includes instructions that, when executed by the processor, cause the processor to;

gather information on interactions by a user with resources provided by an enterprise having a customer contact center;

anticipate need of the user based on the gathered information;

generate a query based on the anticipated need;

prior to the user expressly requesting knowledge relating to a particular topic, proactively identify and suggest the knowledge to the user based on the generated query;

receive feedback relating to the suggested knowledge; and

output based on the feedback, a relevance score for the suggested knowledge.