Relational linking among resoures

• US 8,099,429 B2
• Filed: 12/11/2006
• Issued: 01/17/2012
• Est. Priority Date: 12/11/2006
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Deyo, Roderic C., Sahasrabudhe, Sandeep, Swami, Sunil, Price, Brian D.
• Primary Examiner(s): Jalil, Neveen Abel
• Assistant Examiner(s): PEACH, POLINA G
• Application Number:
  US11/608,878
• Publication Number:
  US 20080140679A1
• Time in Patent Office: 1,863 Days
• Field of Search: 707/1, 707/2, 707/726, 707776-778
• US Class Current: 707/776
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9558 Details of hyperlinks; Management of linked annotations
• CPC Class Codes: G06F 2216/03 Data mining

Abstract

Systems and methods that integrate user assigned association among a plurality of resources or entities. The subject innovation employs an association component that relates such resources or entities, based on aggregate of user notions that are assigned for relationships; and/or based on how users perceive existence of relationships among such resources. Accordingly, resources can be related (e.g., linked, matched, tagged and the like) based on relevance of collective user behavior during tagging.

Claims

1. A computer implemented system comprising the following computer executable components:

an association component that relates resources based on an aggregate of user notions that are assigned to relationships, the association component incorporating an artificial intelligence component in conjunction with a training model to determine tagging trends via an automatic classifier system, a classifier in the automatic classifier system comprising a function that maps an input attribute vector x=(x1, x2, x3, x4, xn) to a confidence that an input belongs to a class comprising the function f(x)=confidence(class);

a storage medium that stores the relationships;

unique references that tie the resources together, the unique references comprising a plurality of links, the plurality of links appearing to users of the resources to be directly added to the resources, wherein at least one of the plurality of links is utilized to link two existing web pages based on user preferences, wherein the user preferences are independent of association preferences set by creators of the web pages;

an inference component that infers relationships between the resources upon the resources being tagged as being relevant for a particular purpose, wherein the relationships are inferred by scoring at least one potential tagging trend from a list of potential tagging trends for auto suggestion to the users of the resources,

wherein the scoring comprises assigning a point for each time one of the resources has been employed with a tagging trend, wherein the list of potential tagging trends is selected by employing a statistical analysis, the statistical analysis comprising a number of standard deviations away from a statistical mean,

wherein resources more than two standard deviations away are employed for auto suggesting a tagging trend based on a collective user behavior;

a client component that performs tagging via metadata derived from the relationships, wherein the client component further;

adds the metadata to the resources;

connects into an external search system for performing a search with the metadata, wherein the metadata is exposed as fake web pages, the fake web pages comprising at least one list of tagged user Uniform Resource Locators (URLs), wherein the at least one list of tagged URLs is employed directly by the external search system; and

enhances an inverted look up table via additional rows based on the metadata, the metadata implementing user notions regarding resource relationships; and

a middle tier that implements logic involved to relate the resources and infer states of the computer implemented system, an environment and a user from a set of observations captured via events and data,

wherein an inference is employed to generate a probability distribution over the states to update previously inferred schema and tighten criteria on an inferring algorithm based upon a kind of data being processed.

2. The computer implemented system of claim 1, the aggregate of user notions further based on at least one of:

machine learning;

assigned notions; and

inferred notions.

3. The computer implemented system of claim 2 further comprising a security component that restricts access to the relationships.

4. The computer implemented system of claim 1 further comprising a client tier that manages user experience for the relationships.

5. The computer implemented system of claim 4 further comprising a backend tier that supplies database pivots and joins in support of the resource relationships.

6. The computer implemented system of claim 4 further comprising a metadata extraction component that pulls text forms to be added to index.

7. The computer implemented system of claim 6 further comprising an indexer that stores relationships as part of Structured Query Language.

8. The computer implemented system of claim 7 further comprising a puller component that performs off-line pulling of resources for metadata extraction.

9. A computer implemented system comprising the following computer executable components:

an association component that relates resources or entities based on an aggregate of user notions that are assigned to relationships, the association component incorporating an artificial intelligence component in conjunction with a training model to determine tagging trends via an automatic classifier system, a classifier in the automatic classifier system comprising a function that maps an input attribute vector x=(x1, x2, x3, x4, xn) to a confidence that an input belongs to a class comprising the function f(x)=confidence(class), wherein the entities comprise at least one of people, paper documents, static/dynamic web pages, files, emails, and multimedia files;

a storage medium that stores the relationships;

unique references that tie at least one of the resources or entities together, wherein the unique references comprise a plurality of links, the plurality of links appearing to users of the at least one of the resources or entities to be directly added to the at least one of the resources or entities, the plurality of links comprising at least one of data types, metadata, resource locations, and hash signatures, wherein at least one of the plurality of links is utilized to link at least two existing resources or entities based on user preferences, wherein the user preferences are independent of association preferences set by creators of the resources or entities;

an inference component that infers relationships between the resources upon the resources being tagged as being relevant for a particular purpose, wherein the relationships are inferred by scoring at least one potential tagging trend from a list of potential tagging trends for auto suggestion to the users of the resources,

wherein the scoring comprises assigning a point for each time one of the resources has been employed with a tagging trend, wherein the list of potential tagging trends is selected by employing a statistical analysis, the statistical analysis comprising a number of standard deviations away from a statistical mean,

wherein resources more than two standard deviations away are employed for auto suggesting a tagging trend based on a collective user behavior, wherein a pseudo-hierarchy is created, based, at least in part, upon the tagged resources and user behavior relationships between the resources;

a client component that performs tagging via metadata derived from the relationships, wherein the client component further;

adds the metadata to the resources;

connects into an external search system for performing a search with the metadata, wherein the metadata is exposed as fake web pages, the fake web pages comprising at least one list of tagged user Uniform Resource Locators (URLs), wherein the at least one list of tagged URLs is employed directly by the external search system; and

enhances an inverted look up table via additional rows based on the metadata, the metadata implementing user notions regarding resource relationships; and

a middle tier that implements logic involved to relate the resources or entities and infer states of the computer implemented system, an environment and a user from a set of observations captured via events and data,

wherein an inference is employed to generate a probability distribution over the states to update previously inferred schema and tighten criteria on an inferring algorithm based upon a kind of data being processed.

10. The computer implemented system of claim 9, the aggregate of user notions further based on at least one of:

machine learning;

assigned notions; and

inferred notions.

11. The computer implemented system of claim 10 further comprising a security component that restricts access to the relationships.

12. The computer implemented system of claim 9 further comprising a client tier that manages user experience for the relationships.

13. The computer implemented system of claim 12 further comprising a backend tier that supplies database pivots and joins in support of the resource relationships.

14. The computer implemented system of claim 12 further comprising a metadata extraction component that pulls text forms to be added to index.

15. The computer implemented system of claim 14 further comprising an indexer that stores relationships as part of Structured Query Language.

16. The computer implemented system of claim 15 further comprising a puller component that performs off-line pulling of resources for metadata extraction.