Extraction and Publication of Reusable Organizational Knowledge

• US 20110179061A1
• Filed: 06/18/2010
• Published: 07/21/2011
• Est. Priority Date: 01/19/2010
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Sharma, Ashish, Wang, Ye-Yi, Phan, Tu Huy, Chitrapu, Arun, Chilakamarri, Venkat Pradeep, Powell, Kevin, Dimmick, Steven, Kuo, Shiun-Zu, ter Horst, Paulus Willem, Gamon, Michael, Manis, Kimberly, Azzam, Saliha, Caldwell, Nicholas, Cai, Yizheng, Shah, Jignesh, Walvekar, Mukta Pramod, Childs, Benjamin Edward, Perez Del Carpio, Diego, O'Keefe, Courtney Anne, Ludwig, Jonathan C., Kohlmeier, Bernhard SJ
• Application Number:
  US12/818,718
• Publication Number:
  US 20110179061A1
• Time in Patent Office: Days
• US Class Current: 707/769
• CPC Class Codes: G06Q 10/00 Administration; Management

Abstract

An analysis module, when triggered by a synchronization framework when a new data item is added to a project data store, runs a series of analysis feature extractors on the new content. An analysis may be conducted, and features of interest may be extracted from the data item. The analysis utilizes natural language processing, as well as other technologies, to provide an automatic or semi-automatic extraction of information. The extracted features of interest are saved as metadata within the project data store, and are associated with the data item from which it was extracted. The analysis module may be utilized to discover additional information that may be gleaned from content that is already in the project data store.

Claims

1. A method for providing extraction of a feature of interest from a data item, and population of the feature of interest into a data store, the method comprising:

receiving an indication of a new data item added to a data store;

analyzing the new data item for one or more features of interest;

extracting one or more features of interest from the new data item; and

storing the extracted features of interest as metadata associated with the new data item in the data store.

2. The method of claim 1, wherein the one or more features of interest includes a keyword, a question, an answer, a term, a link, an image, an author, a sender, a receiver, a name, a portion of text, or a date.

3. The method of claim 1, wherein analyzing the new data item for one or more features of interest includes analyzing the new data item for one or more features of interest via a natural language interpretation of the new data item.

4. The method of claim 1, wherein receiving an indication of a new data item added to a data store includes receiving the indication of a new data item added to a data store via a synchronization framework.

5. The method of claim 1, wherein a data item is one of electronic documents, electronic mail, calendar items, contacts items, tasks items, notes, text messages, conversations, and social networking communications.

6. The method of claim 1, wherein the new data item is analyzed for one or more features of interest regardless of its data type.

7. The method of claim 1, wherein the data store is a shared and searchable data repository.

8. The method of claim 1, further comprising associating the metadata associated with the new data item with one or more other data items, wherein the stored metadata is discoverable through a search of the one or more other data items.

9. A computer-readable medium which stores a set of instructions which when executed performs a method for providing extraction of a feature of interest from an unstructured data item, and population of the feature of interest into a structured data store, the method executed by the set of instructions comprising:

receiving an indication of a new data item added to a data store via a synchronization framework;

analyzing the new data item for one or more features of interest;

analyzing previously stored data items for one or more features of interest;

extracting one or more features of interest from the new data item;

suggesting the one or more extracted features of interest;

in response to an acceptance of the suggested one or more extracted features of interest, storing the extracted features of interest as metadata associated with the new data item in the data store; and

utilizing data associated with an acceptance or declination of one or more suggested extracted features of interest for learning functionalities for future analyses.

10. The computer-readable medium of claim 9, wherein analyzing the new data item for one or more features of interest includes analyzing the new data item for one or more features of interest via a natural language interpretation of the new data item.

11. The computer-readable medium of claim 10, wherein a data item is one of electronic documents, electronic mail, calendar items, contacts items, tasks items, notes, text messages, conversations, and social networking communications.

12. The computer-readable medium of claim 10, wherein one or more features of interest includes a keyword, a question, an answer, a term, a link, an image, an author, a sender, a receiver, a name, a portion of text, or a date.

13. The computer-readable medium of claim 9, wherein receiving an indication of a new data item added to a data store via a synchronization framework includes receiving the indication of the new data item added to the data store via a data collector included in the synchronization framework.

14. The computer-readable medium of claim 9, wherein the new data item is analyzed for one or more features of interest regardless of its data type.

15. The computer-readable medium of claim 9, wherein the data store is a shared and searchable data repository.

16. The computer-readable medium of claim 9, further comprising associating the metadata associated with the new data item with one or more other data items, wherein the stored metadata is discoverable through a search of the one or more other data items.

17. A system for providing extraction of a feature of interest from an unstructured data item, and population of the feature of interest into a structured data store, the system comprising:

a memory storage;

a processing unit coupled to the memory storage;

an analysis module operative to;

receive an indication of a new data item added to a data store;

analyze the new data item for one or more features of interest;

extract one or more features of interest from the new data item; and

store the extracted features of interest as metadata associated with the new data item in the data store.

18. The system of claim 17, further comprising a synchronization framework operative to receive the indication of the new data item added to a data store.

19. The system of claim 17, wherein the analysis module is further operative to utilize natural language interpretation to analyze various types of data items for one or more features of interest.

20. The system of claim 17, wherein the data store is a shared and searchable repository.