IP Library Granted Patent US 11,475,469
Granted Patent B2
US 11,475,469 · App. 16/837,457 · Granted Oct 18, 2022

Business lines

Inventors: Amit Gupta (Gurgaon, IN); Michael Prospero (Foster City, CA); Binay Mohanty (New Delhi, IN); Aparna Gupta (Gurgaon, IN); David Cooke (Los Altos, CA)
Assignee: Aurea Software, Inc.
G06Q30/0204
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Quick Facts
Patent No.
US 11,475,469
App. No.
16/837,457
Granted
Oct 18, 2022
Kind
B2
Abstract

Some embodiments provide a method for evaluating a content segment for relevancy to several of categories. The method retrieves the content segment. For each of the several categories, the method determines the relevancy of the content segment to the category by using a scoring model for the category. The scoring model accounts for (i) the presence of key word sets in the content segment and (ii) the context of the key word sets in the content segment. For each of the several categories, the method tags the content segment when the content segment is determined as relevant to the category.

Claims (27)

1. A method for operating a search engine to identify documents on a network based on evaluating content relevancy, the method comprising:

executing code stored in a memory to cause an electronic device to perform operations comprising:

executing a web crawler to search for and retrieve documents on the network;

retrieving the documents;

storing in an electronic memory a plurality of company data structures for a plurality of companies, each company data structure storing patterns of document elements in documents retrieved including anchor word sets and other word sets with a context of each anchor word in an anchor word set;

parsing a document to identify words in the document;

identifying anchor words in the document;

if a predetermined number of anchor words are present in the document perform an evaluation process a. through h.:

a. comparing, by the electronic device, a set of documents from a plurality of resources with a first set of content relevance models that define relevance of the documents to different companies and a second set of content relevance models that define relevance of the documents to different criteria, wherein each content relevance model includes (i) data that is used to identify documents related to a criteria that the model represents, (ii) the patterns of document elements associated with scores, and (iii) parameters used in the analysis of documents by the model;

b. accessing the first and second patterns and based on the patterns and calculating a content relevance score as an arithmetic function of the patterns and parameters of the content relevance models, wherein the content relevant score represents at least a number of anchor words in each document related to one or more of the criteria;

c. when a particular document in the set of documents satisfies a particular content relevance score of a particular content relevance model, in the first set of content relevance models, associated with a particular company, associating the particular company with the particular document by storing an identifier of the particular company in a data structure for the document;

d. when a particular document in the set of documents satisfies a particular content relevance score of a particular content relevance model, in the second set of content relevance models, associated with particular criteria, associating the particular criteria with the particular document by storing an identifier of the particular criteria in a data structure for the document;

e. determining a first threshold number and a second threshold number, wherein (i) the first threshold number is dependent on first criteria, (ii) the second threshold number is dependent on second criteria, and (iii) the first threshold number is different from the second threshold number;

f. when more than the first threshold number of documents are associated with the first criteria, specifying the first by storing an identifier of the first criteria line in a data structure for the first criteria;

g. when more than the second threshold number of documents are associated with second criteria, specifying the second criteria by storing an identifier of the second criteria in a data structure; and

h. upon receiving a request for the first criteria, accessing, searching the data structure for the identifiers, and displaying a set of data associated with the first criteria based on the stored identifiers in the data structure for the first criteria;

if a predetermined number of anchor words are not present in the document, do not perform the evaluation process a. through h.

2. The method of claim 1 , wherein the documents are retrieved from the world wide web.

3. The method of claim 1 , wherein a particular threshold number is higher for criteria in a first set of industries than for criteria in a second set of industries.

4. The method of claim 1 , wherein a particular threshold number is lower when the overall number of documents classified as relevant to an associated criteria is lower.

5. The method of claim 1 , wherein the first threshold number is further dependent on the first criteria.

6. The method of claim 5 , wherein the second threshold number is further dependent on the second criteria.

7. The method of claim 1 , further comprising:

specifying the first criteria as a business line of a first company;

storing a reference to the first company in a data structure for the first business line;

specifying the second criteria as a business line of a second company;

storing a reference to the second company in a data structure for the second business line.

Assignments (1)
SECURITY INTEREST Recorded May 31, 2022
From: AUREA SOFTWARE, INC.; NEXTDOCS CORPORATION,; MESSAGEONE, LLC
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 060220/0673 →
Continuity (5)
Continuation 12831237 · Jul 6, 2010
Continuation In Part 12772166 · Apr 30, 2010
Provisional Application 61361510 · Jul 5, 2010
Provisional Application 61316824 · Mar 23, 2010
Related Publication 20200226627A1 · Jul 16, 2020