SYSTEMS AND METHODS FOR IMPROVING INFORMATION DISCOVERY

• US 20090089271A1
• Filed: 12/08/2008
• Published: 04/02/2009
• Est. Priority Date: 12/05/2003
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Hirst, Roy
• Granted Patent: US 7,987,170 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 8/35 model driven
• CPC Class Codes: G06F 9/453 Help systems
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

The present invention relates to a system and methodology to facilitate automated retrieval and classification of information. A system and associated methods are provided that facilitate generation of code and/or documents. The system includes a component that receives data relating to at least one of a user'"'"'s request for desired code functionality and one or more desired documents. A mapping component correlates parsed subsets of the data to specific functional objects respectively located remote from the user, wherein a generator employs the functional objects to form at least one of the desired code and the documents.

Claims

1. A system that facilitates automated retrieval and classification of information, comprising:

a query component that receives user input in the form of a request for information, the request for information is in the form of a natural language or syntax that is familiar to the user including terms and expressions that have been employed over time by the user;

a parser that processes the user input into parsed strings to facilitate desired information retrieval; and

a mapping component that analyzes the parsed strings in view of one or more functional objects.

2. The system of claim 1, the one or more functional objects reside in a data store and include vocabulary and terminology that is familiar to the user and that has been modeled or learned from past user patterns when searching for information.

3. The system of claim 2, the one or more functional objects are employed by the mapping component to build search terms that are applied to a remote or local database.

4. The system of claim 2, wherein the one or more functional objects are associated with a decision-theoretic analysis that includes analyzing extrinsic evidence or data of a user'"'"'s present context state and directing information in accordance with the data.

5. The system of claim 1, the mapping component further comprises at least one of a rule, a learning algorithm, an automated classification method, an inference model, a probability model, a statistical model, a neural network, a Support Vector Machine (SVM), a Naive Bayes model, a Bayes network, a decision tree, a similarity-based model, a vector-based model, and a Hidden Markov Model.

6. The system of claim 1, the mapping component further comprises at least one of a technical vocabulary object, a development vocabulary object, a synonym object, an index object, and a prioritization object to facilitate retrieval of information.

7. The system of claim 1, further comprising a discovery component that retrieves the request for information based upon terminology that is semantically equivalent to the user input.

8. The system of claim 7, the discovery component includes at least one of an automated search engine, an indexing engine, and a structured query language engine for retrieving information from a database.

9. The system of claim 7, wherein the discovery component comprises an instrumentation component that determines an importance value for a retrieved request for information, the instrumentation component monitors at least one of visible technical documentation, search engine activity, and network traffic activity.

10. The system of claim 9, the instrumentation component tracks and maps successful and unsuccessful attempts to discover and interpret technology-specific and programming-language-specific functionality employing a natural or professional language.

11. The system of claim 1, further comprising a graphical user interface to determine a value for a technical document which was retrieved based on the user'"'"'s request for information.

12. The system of claim 11, the graphical user interface includes at least one of a ratings scale, a feedback component to allow users to determine what others thought of the technical document, and an input box to enable users to submit feedback as to why they ranked a particular technical document in the manner that was selected.

13. A method to facilitate the automated retrieval and classification of information, comprising:

receiving user input in the form of a request for information, the request for information is in the form of a natural language or syntax that is familiar to the user including terms and expressions that have been employed over time by the user;

processing the user input into parsed strings to facilitate desired information retrieval;

mapping the parsed strings in view of one or more functional objects;

association the one or more functional objects with a decision-theoretic analysis that includes analyzing extrinsic evidence or data of a user'"'"'s present context state and directing information in accordance with the data; and

retrieving the request for information based upon terminology that is semantically equivalent to the user input, and providing the request for information to the user.

14. The method of claim 13, wherein the one or more functional objects reside in a data store and include vocabulary and terminology that is familiar to the user and that has been modeled or learned from past user patterns when searching for information.

15. The method of claim 13, wherein the request for information includes at least one of text input, voice encoded input, video camera input and audio input.

16. The method of claim 13, further comprising determining an importance value for a retrieved request for information and monitoring at least one of visible technical documentation, search engine activity, and network traffic activity.

17. The method of claim 13, further comprising allowing users to interactively input relevancies of search results and utilizing the relevancies to perform further searches.

18. A system that facilitates automated document retrieval, comprising:

means for receiving user input in the form of a request for information, the request for information comprising terminology of a first vocabulary corresponding to a first programming language, and wherein the request for information is in the form of a natural language or syntax that is familiar to the user including terms and expressions that have been employed over time by the user;

means for processing the user input into parsed strings to facilitate desired information retrieval;

means for mapping the parsed strings in view of one or more functional objects, wherein the one or more functional objects reside in a data store and include vocabulary and terminology that is familiar to the user and that has been modeled or learned from past use patterns when searching for information;

means for associating the one or more functional objects with a decision-theoretic analysis that includes analyzing extrinsic evidence or data of a user'"'"'s present context state and directing information in accordance with the data; and

means for retrieving the request for information based upon terminology that is semantically equivalent to the user input, and providing the request for information to the user.

19. The system of claim 18, wherein the means for mapping includes mapping the first vocabulary to a second vocabulary that corresponds to a second programming language.

20. The system of claim 18, further comprising means for determining an importance value for a retrieved technical document via monitoring at least one of visible technical documentation, search engine activity, and network traffic activity.