Method and apparatus to link to a related document

• US 8,005,858 B1
• Filed: 09/13/2007
• Issued: 08/23/2011
• Est. Priority Date: 05/31/2001
• Status: Expired due to Term

• Current Assignee: Longsand Limited (Open Text Corporation)
• Original Assignee: Autonomy Corporation PLC (HP Inc.)
• Inventors: Gover, Timothy J., Hughes, Robin J., Lynch, Michael R.
• Primary Examiner(s): Wong; Leslie
• Application Number:
  US11/855,127
• Time in Patent Office: 1,440 Days
• Field of Search: 707/999.003, 707/999.104, 707/E17.082, 707/E17.133, 707/723, 707/758, 707/771, 715/205, 715/234, 715/778
• US Class Current: 707/771
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/838 Presentation of query results
• CPC Class Codes: G06F 40/143 Markup, e.g. Standard Generalized Markup Language [SGML] or Document Type Definition [DTD]
• CPC Class Codes: G06F 40/194 Calculation of difference between files
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A method, apparatus, and system exist to generate a ranked list of one or more related documents and a link to each of the one or more related documents. The one or more related documents are ranked based upon relevance to the first representation of content associated with a specified field of the reference extensible markup language document. The one or more related documents may also be ranked based upon relevance to content in an active desktop window.

Claims

1. A computer-readable media that store instructions, which when executed by a machine, cause the machine to perform operations, comprising:

analyzing content in an active desktop window to generate a set of most relevant terms and mathematical values associated with those relevant terms from the content in the active desktop window;

generating a query on the content from the active desktop window;

executing a search on content in one or more documents based on the content from the active desktop window in the query;

generating a ranked list of documents related to the content in one or more documents based on relevance to at least the set of most relevant terms and mathematical values associated with those relevant terms from the content in the active desktop window;

generating links to the ranked list of documents, wherein each link points to one or more relevant fields within the ranked list of documents, wherein the analyzing of content in the active desktop window and the generating of the query and the executing of the search and the generating of the ranked list and the generating of the links are all executed without a user having to request any of these operations; and

displaying the generated ranked list of documents on a display of a computing device.

2. The computer-readable media of claim 1, wherein the set of most relevant terms is determined based upon use of a Bayesian algorithm.

3. The computer readable medium of claim 2, storing instructions, which when executed by the machine, cause the machine to perform further operations comprising:

creating a first representation of the content from the active desktop window for the query by performing at least the following,

eliminating redundant terms in a corpus of terms contained within the content by using a first algorithm to generate a set of most relevant terms;

determining one or more weighted values with each term in the set of most relevant terms; and

creating a structure for the first representation of the content from the active desktop window to contain the set of most relevant terms from the content and the one or more weighted values associated with each term in the set of most relevant terms.

4. The computer readable medium of claim 3, storing instructions, which when executed by the machine, cause the machine to perform further operations comprising:

comparing the most relevant terms from a first instance of a first similar representation to the most relevant terms in a historical instance of the first similar representation;

refining the one or more weighted values associated with each most relevant term in the first instance based upon the comparison; and

correlating a similarity between the first representation of the content from the active desktop window and representations of the one or more documents based upon the set of most relevant terms and weighted values associated with each term.

5. The computer readable medium of claim 3, storing instructions, which when executed by the machine, cause the machine to perform further operations comprising:

creating two or more separate representations of each of the one or more documents to be searched; and

creating links between each separate representation of the one or more documents to be searched.

6. The computer readable medium of claim 5, wherein a first extensible markup language document from the one or more documents to be searched has a first extensible markup language schema, and a second extensible markup language document from the one or more documents to be searched has a second extensible markup language schema that is different from the first extensible markup language schema, and the extensible markup language schemas define a tag set and names associated with each tag and the differing extensible markup language schemas are reconciled by inserting an additional tag-value pair into each representation to establish a common extensible markup language schema.

7. The computer readable medium of claim 1, storing instructions, which when executed by the machine, cause the machine to perform further operations comprising:

importing the content in the active desktop window as a first reference extensible markup language document having a first extensible markup language schema;

analyzing a first extensible markup language document from the one or more documents to be searched that has a second extensible markup language schema, which is different than the first extensible markup language schema;

creating a first representation of content associated with the first reference extensible markup language document, the first representation including a first set of terms and one or more weighted values associated with each term in the first set of terms;

identifying a second representation of content associated with the first extensible markup language document, the second representation including a second set of terms and a second weighted value associated with each term in the second set of terms;

comparing an internal extensible markup language tag-value pair that was inserted into the first and second representations while allowing the first representation and second representation to keep their original tag-value pair nomenclature from their original extensible markup language schemas, wherein the internal extensible markup language tag-value pair creates a common extensible markup language tag-value pair regarding a particular topic; and

comparing the similarity between the first representation of the content from the active desktop window for the query to the second representation of the first extensible markup language document from the one or more documents to be searched by using the set of most relevant terms and weighted values associated with each term and the internal extensible markup language tag-value pair inserted into each representation.

8. A computer-readable media that stores instructions, which when executed by a machine, cause the machine to perform operations comprising:

executing a search on content in one or more documents based on a content of a query, wherein the content of the query comes from one or more specified fields but less than all of the fields in a reference extensible markup language document;

creating a first representation of content associated with the one or more specified fields in the reference extensible markup language document;

generating a list of one or more related documents to the query ranked based upon relevance to the first representation of content associated with the one or more specified fields in the reference extensible markup language document;

generating a link for each of the one or more related documents in the list of one or more related documents, wherein each link points to a relevant field within each of the one or more related documents; and

creating the first representation of content associated with the one or more specified fields of the reference extensible markup language document by performing at least the following;

eliminating redundant terms in a corpus of terms contained within the content by using a first algorithm to generate a set of key terms; and

determining one or more weighted values with each term in the set of key terms based on at least adaptive modeling techniques and a Bayesian algorithm.

9. The computer readable medium of claim 8, wherein a first field from the one or more specified fields in the reference extensible markup language document is specified at the time a query is generated.

10. The computer readable medium of claim 8, wherein the executed search on the query is generated without a user having to request the query.

11. The computer readable medium of claim 8, storing instructions, which when executed by the machine, cause the machine to perform further operations comprising:

storing the first representation of the content, the first representation containing the set of key terms from the content and one or more weighted values associated with each term in the set of key terms;

comparing the key terms from a first instance of the first representation to the key terms in a historical instance of the representation;

refining the one or more weighted values associated with each key term in the first instance based upon the comparison; and

correlating similarity between similar but literally different representations of the one or more documents to be searched based upon the set of key terms and the weighted values associated with each term.

12. The computer readable medium of claim 8, storing instructions, which when executed by the machine, cause the machine to perform further operations comprising:

reconciling differing extensible markup language schemas when the first representation of content derived from the reference extensible markup language document has a first extensible markup language schema, and a second representation of content derived from first document of the one or more related documents has a second extensible markup language schema by inserting an internal extensible markup language tag-value pair into the first and second representations while allowing the first representation and second representation to keep their original tag-value pair nomenclature from their original extensible markup language schemas, wherein the internal extensible markup language tag-value pair creates a common extensible markup language tag-value pair regarding a particular topic.

13. The computer readable medium of claim 8, storing instructions, which when executed by the machine, cause the machine to perform further operations comprising:

creating a plurality of representations for each of the one or more documents to be searched, wherein each one of the plurality of representations includes a plurality of key terms and relational probabilities of the corresponding descriptive content;

storing the plurality of representations in a common database; and

creating links between the plurality of representations.

14. The computer readable medium of claim 8, wherein one or more fields to be searched but less than all of the fields, in a first document from the one or more related documents to be search, are specified fields at the time a query is generated.

15. A system, comprising:

a software engine stored on a computer-readable media which has executable instructions, which when executed by a machine, cause the machine to perform operations, wherein the engine further includes

an intelligence layer having code to analyze content in an active desktop window and to generate a set of most relevant terms and mathematical values associated with those relevant terms from the content in the active desktop window;

a query handler having code to generate a query on the content from an active desktop window and to initiate a search on content in one or more documents based on at least the set of most relevant terms and mathematical values associated with those relevant terms from the content in the active desktop window;

wherein the intelligence layer has code to generate a ranked list of documents related to the content in one or more documents based on relevance to at least the set of most relevant terms and mathematical values associated with those relevant terms from the content in the active desktop window, and has code to generate links to the ranked list of documents, wherein the links point to one or more relevant fields within the ranked list of documents,

wherein the intelligence layer has code to analyze content in the active desktop window and generate the ranked list and generate the links without a user having to request any of these operations,

wherein the query handler has code to the generate the query and initiate the search without a user having to request any of these operations; and

computing device having a display to display the generated ranked list of documents on the display.

16. The system of claim 15, wherein the software engine further comprises:

a reconciler module and a hierarchical module to cooperate with the intelligence layer to create a first representation of the content from the active desktop window for the query by performing at least the following,

eliminating redundant terms in a corpus of terms contained within the content by using a first algorithm to generate an initial set of most relevant terms;

refining the initial set of most relevant terms by determining a statistical relevance and relational probability between the terms for each term in the initial set of most relevant terms to create the set of most relevant terms;

determining one or more weighted values with each term in the set of most relevant terms;

generating a structure for the first representation content in the active desktop window to contain the set of most relevant terms from the content and the one or more weighted values associated with each term in the set of most relevant terms; and

inserting an additional extensible markup language tag-value pair into the first representation while allowing the first representation to keep its original tag value pair nomenclature from its original extensible markup language schema to establish a common extensible markup language schema with representations

derived from content in documents having a different extensible markup language schema than the original extensible markup language schema for the first representation.

17. The system of claim 16, wherein the software engine further comprises:

a database to store representations including the first representation, wherein the intelligence layer correlates similarity between similar but literally different representations based upon at least the set of most relevant terms and weighted values associated with each term and the internal extensible markup language tag-value pair.

18. The system of claim 16, wherein the software engine further comprises:

a content converter to extensible markup language module and an extensible markup language parser module to convert and pass the content in an active desktop window to the intelligence layer for the analysis.