IP Library Granted Patent US 10,324,598
Granted Patent B2
US 10,324,598 · App. 14/832,106 · Granted Jun 18, 2019

System and method for a search engine content filter

Inventors: John W. Kelly (New York, NY); Vladimir D. Barash (Somerville, MA); Adam Fields (New York, NY)
G06F3/04842G06F16/212G06F16/278G06F16/285G06F16/9535G06Q10/10G06Q30/02
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Quick Facts
Patent No.
US 10,324,598
App. No.
14/832,106
Granted
Jun 18, 2019
Kind
B2
Abstract

Computerized search methods and systems generally include presenting, to a user, a computer interface for specifying one or more search terms for a search query and presenting at least one selectable item corresponding to at least one of art M score and a cluster focus index (CFI) score filter for the search query. The methods and systems include generating an amended search query based on a selected item; and performing a search using the amended search query. The M score is calculated using the formula M score=count (alpha)+CFI (1-alpha), where the count is the overall number of members on a cluster focus map that has engaged with a target.

Claims (32)

1. A computerized search method, the method comprising:

presenting, to a user, a computer interface for specifying one or more search terms for a search query;

presenting at least one selectable item through the computer interface corresponding to at least one of an M score and a cluster focus index score (CFI) for the search query, wherein the CFI represents a degree to which a particular target is disproportionately cited in a particular cluster;

generating an amended search query based on a selected item; and

performing a search through the computer interface using the amended search query, wherein the M score is calculated using a formula of M score=count(alpha)+CFI(1-alpha), where the count is an overall number of members of the particular cluster that has engaged with the particular target, and values of alpha being between 0 and 1.

2. The method of claim 1 , wherein the search is of the Internet.

3. The method of claim 1 , wherein the search is of a CFI-filtered set of clusters within an online network.

4. The method of claim 1 , wherein the search is of a set of nodes having the M score greater than a threshold.

5. The method of claim 1 , wherein the search is of a document-corpus.

6. The method of claim 1 , wherein the CFI further represents a degree to which an event, characteristic or behavior disproportionately occurs in the particular cluster, or a degree to which the particular cluster, relative to a network, preferentially manifests the event, characteristic or behavior.

7. The method of claim 1 , wherein the computer interface is configured for the user to select content to search with the one or more search terms.

8. The method of claim 7 , wherein the content is taken from an online creator network partitioned into at least one set of source nodes.

9. The method of claim 8 , wherein the at least one set of source nodes is configured with a similar linking history to form an attentive cluster and at least one set of outlink targets with a similar citation profile to form an outlink bundle.

10. The method of claim 8 , further comprising performing a search of the selected content using the search query.

11. The method of claim 1 , further comprising:

generating a list of targets in a network, cluster, or segment; and

filtering the list by criteria to limit whom to a message in the network, cluster, or segment in order to maximize the impact of the message on the cluster or segment.

12. The method of claim 11 , wherein the filtering the list by the criteria includes filtering by at least one of the CFI score, the M score, a number of followers, a following status, a follower status, a number of mentions or re-tweets, a number of distinct mentions, a status of exposure to content, and a frequency of tweets or publication.

13. The method of claim 12 , wherein the generating of the list of targets in the network, cluster, or segment includes using at least one of the CFI score, the M score, the number of followers, the number of mentions or re-tweets, a number of distinct mentions, and a number of tweets.

14. The method of claim 13 , wherein the M score is based on the CFI that further details a degree to which the particular target disproportionately occurs in the particular cluster, or a degree to which the particular cluster, relative to a network, preferentially engages with the particular target, wherein an overall number of members of the cluster or network that have engaged with the particular target is determined.

15. A method for automatically analyzing and determining whether an overall number of members have engaged with a target, the method comprising:

presenting, to a user, a computer interface hosted by a computing device, wherein the computer interface is configured to specify one or more search terms for a search query;

presenting on the computer interface at least one selectable item corresponding to at least one of an M score and a cluster focus index score (CFI) for the search query, wherein the CFI represents a degree to which a particular target is disproportionately cited in a particular cluster;

generating an amended search query with the computing device based the at least one selectable item; and

performing a search on the computer interface with the computing device using the amended search query, wherein the M score is calculated using a formula of M score=count(alpha)+CFI(1-alpha), where the count is an overall number of members of the particular cluster that has engaged with the particular target, and values of alpha being between 0 and 1.

16. The method of claim 15 , wherein the search is of the Internet.

17. The method of claim 15 , wherein the search is of a CFI-filtered set of clusters within an online network.

18. The method of claim 15 , wherein the search is of a set of nodes having the M score greater than a threshold.

19. The method of claim 15 , wherein the search is of a document-corpus.

20. The method of claim 15 , wherein the CFI further represents a degree to which an event, characteristic or behavior disproportionately occurs in the particular cluster, or a degree to which the particular cluster, relative to a network, preferentially manifests the event, characteristic or behavior.

21. The method of claim 15 , wherein the computer interface is configured for the user to select content to search with the one or more search terms, and wherein the content is taken from an online creator network partitioned into at least one set of source nodes.

22. The method of claim 21 , wherein the at least one set of source nodes is configured with a similar linking history to form an attentive cluster and at least one set of outlink targets with a similar citation profile to form an outlink bundle.

Assignments (5)
SECURITY INTEREST Recorded May 5, 2025
From: GRAPHIKA TECHNOLOGIES, INC.
To: WESTERN ALLIANCE BANK
Reel/Frame 071020/0536 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 27, 2024
From: OCTANT DATA, LLC
To: GRAPHIKA TECHNOLOGIES, INC.
Reel/Frame 066920/0855 →
CHANGE OF NAME Recorded Mar 27, 2024
From: GRAPHIKA, INC.
To: OCTANT DATA, LLC
Reel/Frame 066925/0048 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 18, 2017
From: KELLY, JOHN W.; BARASH, VLADIMIR D.; FIELDS, ADAM
To: GRAPHIKA, INC.
Reel/Frame 043037/0788 →
CONFIRMATORY LICENSE Recorded Apr 6, 2016
From: ALLIANCE FOR SUSTAINABLE ENERGY, LLC
To: ENERGY, UNITED STATES DEPARTMENT OF
Reel/Frame 038369/0209 →
Continuity (7)
Continuation In Part 13859396 · Apr 9, 2013
Continuation In Part 12973296 · Dec 20, 2010
Provisional Application 62040075 · Aug 21, 2014
Provisional Application 61760652 · Feb 5, 2013
Provisional Application 61621845 · Apr 9, 2012
Provisional Application 61287766 · Dec 18, 2009
Related Publication 20160048556A1 · Feb 18, 2016
Cited By (1)
US 12,499,160