METHOD AND SYSTEM FOR IMPROVING UTILIZATION OF HUMAN SEARCHERS

• US 20100138402A1
• Filed: 12/02/2009
• Published: 06/03/2010
• Est. Priority Date: 12/02/2008
• Status: Abandoned Application

• Current Assignee: ChaCha Search, Inc.
• Original Assignee: ChaCha Search, Inc.
• Inventors: Ellis, John T., Cooper, Thomas E., Burroughs, Michael
• Application Number:
  US12/629,500
• Publication Number:
  US 20100138402A1
• Time in Patent Office: Days
• US Class Current: 707/706
• CPC Class Codes: G06F 16/3344 using natural language analysis

Abstract

A method and system for improving utilization of human searcher(s) in a search system including utilizing a processing such as Natural Language Processing to improve the ability of the search system to recognize an intent of a user request, provide information to an expeditor to respond to queries, and automatically route a query to a relevant and qualified searcher. Using NLP, a request is processed to allow improved use of automated responses, more effective use of the expeditor, and more effective use of search specialists.

Claims

1. A computer-implemented method, comprising:

modifying an original query received based on a processing applied to the query prior to performing a search; and

performing the search using the modified query and providing a response to the query as a result of the search.

2. The computer-implemented method of claim 1, wherein the processing is a natural language processing.

3. The computer-implemented method of claim 1, comprising:

determining a process to be carried out for responding to the original query based on said modifying.

4. The computer-implemented method of claim 1, wherein said modifying includes mapping the original query to a vetted query, and

using the vetted query to produce a response to the original query.

5. The computer-implemented method of claim 1, wherein said modifying includes augmenting the original query based on review by a human ambassador.

6. A computer-implemented method, comprising:

receiving a request for information;

comparing the request with a predefined query pattern;

correcting spelling;

substituting words;

removing signatures using information of stored requests;

creating automatically an annotation graph for the request;

selecting a category;

obtaining queries determined to match the annotation graph using an index of queries;

calculating a matching function comparing the queries to the annotation graph;

calculating a rating of a search result associated with the queries;

ranking the queries based on the matching function;

ranking the search results based on the rating; and

providing a highest ranked search result responsive to the request.

7. The computer-implemented method of claim 6, wherein the correcting, the substituting, the query pattern, the removing and the creating are modified based on a source of the request.

8. The computer-implemented method of claim 6, wherein the queries conform to a predetermined grammar construct.

9. The computer-implemented method of claim 6, comprising:

providing the request and an output of the comparing, correcting, substituting, removing, and creating to a human searcher;

providing to the human searcher a toolset selected based on the output; and

selecting by the human searcher a method for responding to the request.

10. The computer-implemented method of claim 9, comprising:

constructing by the human searcher a sentence; and

calculating the matching function and the rating using the sentence.

11. The computer-implemented method of claim 10, comprising:

providing a list of search results in order of the ranking to the human searcher;

the human searcher selecting a search result; and

ranking the result selected by the human searcher as the highest ranked result.

12. The computer-implemented method of claim 6, wherein the matching function comprises:

comparing a noun phrase of the stored query to a noun phrase of the annotation graph;

comparing a noun of the stored query to a noun of the annotation graph;

comparing a named entity of the stored query to a named entity of the annotation graph;

comparing a location of the stored query to a location associated with the request; and

comparing a category of the stored query to a category of the request.

13. The computer-implemented method of claim 6, wherein the rating comprises:

comparing a time stamp of an answer to a time stamp of other answers;

comparing a usage frequency of an answer to a usage frequency of other answers;

comparing a logic flag of an answer to a logic flag of other answers;

comparing a category of an answer to a category of other answers; and

combining the comparisons arithmetically.

14. A system, comprising:

a search service device receiving a search query, performing a natural language processing of the search query, comparing the search query to a corpus of requests, and determining a process for responding to the search query;

a searcher device receiving information of the search query, and providing a response to the search query; and

a database including information of search queries and search results.

15. The system of claim 14, wherein the determining includes selecting an automated process, a human assisted process, and a combined human assisted and automated process;

wherein the comparing is based on word frequency; and

wherein the natural language processing is adapted to information of a device submitting the request.

16. A computer readable storage medium storing therein a program for causing a computer to execute an operation including responding to an inquiry, comprising:

receiving the inquiry;

processing the inquiry based on semantic rules;

ranking interrogatives using a result of processing of the inquiry;

determining a procedure; and

initiating the procedure.

17. The computer readable storage medium of claim 16 wherein the operation comprises:

comparing the inquiry to a template associated with an automated process;

substituting a word indicated in the inquiry with a synonym of the word;

replacing a word indicated in the inquiry with a highest ranked word from a lexicon;

selecting a lexicon for the processing;

removing leading and trailing text from the inquiry which matches a previous inquiry; and

associating a category with the inquiry using word frequency associated with an index.

18. The computer readable storage medium of claim 16 wherein the operation comprises:

receiving a first message from a first source;

receiving a second message from a second source; and

determining the inquiry based on the first message and the second message.

19. The computer readable storage medium of claim 16 wherein the operation comprises:

ranking interrogatives using the inquiry.

20. The computer readable storage medium of claim 16 wherein the operation comprises:

determining a probability that the procedure is an automated procedure;

presenting the inquiry and the result to a first human guide;

providing a suggested response to the first human guide; and

performing a procedure indicated by the first human guide.

21. The computer readable storage medium of claim 20 wherein the operation comprises:

transferring by the first human guide the inquiry to a second human guide; and

adjusting a parameter used to determine the probability based on the procedure indicated by the first human guide.

22. The computer readable storage medium of claim 20 wherein the operation comprises:

modifying the inquiry by the first human guide;

adjusting a parameter of the ranking; and

ranking the interrogatives based on the modified inquiry.

23. The computer readable storage medium of claim 16, wherein the operation comprises:

determining the procedure automatically; and

initiating the procedure by a human guide.

24. The computer readable storage medium of claim 16, wherein the operation comprises:

determining a probability that an automated response may be provided; and

selecting a human guide to respond to the inquiry based on the probability.

25. The computer readable storage medium of claim 16, wherein the operation comprises:

receiving a first message from a first source;

receiving a second message from a second source;

determining the inquiry based on the first message and the second message;

comparing the inquiry to a template associated with an automated process;

substituting a word indicated in the inquiry with a synonym of the word;

replacing a word indicated in the inquiry with a highest ranked word from a lexicon;

selecting a lexicon for the processing;

removing leading and trailing text from the inquiry which matches a previous inquiry; and

associating a category with the inquiry using word frequency associated with an index;

ranking interrogatives using the inquiry;

determining a probability that an automated response may be provided;

selecting a human guide to respond to the inquiry based on the probability;

presenting the inquiry and the result to a first human guide;

providing a suggested response to the first human guide;

performing a procedure indicated by the first human guide;

transferring by the first human guide the inquiry to a second human guide;

adjusting a parameter used to determine the probability based on the procedure indicated by the first human guide

modifying the inquiry by the first human guide;

ranking the interrogatives based on the modified inquiry;

ranking a plurality of responses associated with the interrogatives based on a selection by a third human guide, temporal information, and location information;

receiving a selection by the first human guide; and

modifying a ranking of the plurality of responses based on the selection.