Systems and methods for improving information discovery

• US 7,472,118 B2
• Filed: 12/05/2003
• Issued: 12/30/2008
• Est. Priority Date: 12/05/2003
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Hirst, Roy
• Primary Examiner(s): Chace; Christian P.
• Assistant Examiner(s): RADTKE, MARK A
• Application Number:
  US10/729,520
• Publication Number:
  US 20050125440A1
• Time in Patent Office: 1,852 Days
• Field of Search: 707/3, 707/4, 707/5, 707/10, 707/104.1, 715/500, 725/116
• 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 finding documentation, comprising:

a computer processor;

memory containing instructions for executing the following components;

a query component that receives a request for technical articles, the request comprising terminology of a first vocabulary corresponding to a first programming language, the query component receives user input as a request for information, the request including at least one of text input, voice encoded input, video camera input, and audio input;

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; and

wherein the user input is processed by a parser into functional objects relating to information components that are processed to facilitate desired information retrieval, the 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;

a mapping component that correlates terminology of the first vocabulary to semantically equivalent terminology of a second vocabulary related to a second programming language; and

a discovery component that retrieves technical articles based upon the terminology of the second vocabulary that is semantically equivalent to the terminology of the first vocabulary employed in the request, wherein the discovery component comprises an instrumentation component to determine an importance value for a retrieved technical document, the instrumentation component monitors at least one of visible technical documentation, search engine activity, and network traffic activity, and further monitors at least one of a counter, a type of word or phrase employed in a search, an implied or inferred measurement of data activity and an explicit request from users regarding a data source'"'"'s technical value, ranking or merit.

2. The system of claim 1, the mapping component includes rules or models that map or analogize a first set of terms with a second set of terms.

3. The system of claim 1, the functional objects are employed by the mapping component to build or create search terms or queries that are applied to a remote or local database.

4. 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 (SYM), a Naive Bayes model, a Bayes network, a decision tree, a similarity-based model, a vector-based model, and a Hidden Markov Model.

5. The system of claim 1, the evidence includes at least one of keyboard activities, mouse movements, microphone inputs, camera inputs, time information, and electronic calendar information.

6. The system of claim 1, 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.

7. The system of claim 1, the mapping component further comprising 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.

8. The system of claim 1, 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.

9. The system of claim 1, further comprising a database for search attempts that indicates a technical value for selected technical information within a documentation set or other data structure residing in the database.

10. The system of claim 1, further comprising a graphical user interface to determine a value for a technical document.

11. The system of claim 10, the 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.

12. The system of claim 1, further comprising a graphical user interface to depict a cross reference of terminology of the first vocabulary with terminology of the second vocabulary.

13. The system of claim 12, the cross reference includes at least one of a table of terminology of the first vocabulary displayed with a table of terminology of the second vocabulary, a single table showing cross-functional relationships including arrows or other indicators depicting relationships between terminology, a modular or graphical output including a block diagram in terminology of the first vocabulary that highlights or points to a corresponding block diagram of terminology of the second vocabulary, a system drawing to show one component'"'"'s relationship to the system in terminology of the first vocabulary while also illustrating, fading, superimposing, or highlighting a related term on the system drawing to detail a relationship between terminology of the second vocabulary, and contrast blocks or diagrams that are displayed detailing differences with conventional terminology or design practices.

14. A computer readable medium having computer readable instructions stored thereon for implementing the query component, the mapping component, and the discovery component of claim 1.

15. A method to facilitate automated information retrieval, comprising:

automatically generating a first object set as a function of monitored programmer behavior, the first object set associated with a primary development environment;

receiving a developer'"'"'s request for functionally equivalent information via a query component, the request comprising terminology of a first vocabulary corresponding to a first programming language, and the request including at least one of text input, voice encoded input, video camera input, and audio input, 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;

processing the request by a parser into a functional second object set relating to information components that are processed to facilitate desired information retrieval, the second object set is 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;

automatically comparing the first object set to the second object set associated with a different development environment;

correlating the first and second object sets with alternative terms or phrases;

automatically searching for functionally equivalent technical articles based at least in part on determined differences between the first object set and the second object set; and

automatically 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, and further monitoring at least one of a counter, a type of word or phrase employed in a search, an implied or inferred measurement of data activity and an explicit request from users regarding a data source'"'"'s technical value, ranking or merit.

16. The method of claim 15, further comprising automatically deriving the functionally equivalent information from the second object set.

17. The method of claim 15, further comprising at least one of:

receiving feedback from a developer; and

generating a cross-index of familiar terms and unfamiliar terms to the developer.

18. The method of claim 15, the first object set further comprising at least one of a technical vocabulary object, a development vocabulary object, a synonym object, an index object, and a prioritization object.

19. The method of claim 15, further comprising automatically ranking the functionally equivalent information.

20. A computer implemented method for finding technical documentation, the method comprising:

receiving a first query string from a user via a query component, the query string comprising terminology of a first vocabulary corresponding to a first programming language, the query component receives the first query string as a request for information, the request including at least one of text input, voice encoded input, video camera input, and audio input, 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;

processing the first query string by a parser into functional objects relating to information components that are processed to facilitate desired information retrieval, the 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;

mapping the first vocabulary to a second vocabulary that corresponds to a second programming language;

generating a second query string in terminology of the second vocabulary by employing the second vocabulary and the first query string, the second query string semantically corresponds to the first query string;

searching a document set for articles relevant to the second query;

providing articles found by the search to the user based upon the terminology of the second vocabulary that is semantically equivalent to the terminology of the first vocabulary employed in the request;

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; and

further monitoring at least one of a counter, a type of word or phrase employed in a search, an implied or inferred measurement of data activity and an explicit request from users regarding a data source'"'"'s technical value, ranking or merit.

21. The method of claim 20, further comprising allowing users to interactively input relevancies of search results and utilizing the relevancies to perform further searches.