IP Library Granted Patent US 12,468,769
Granted Patent B2
US 12,468,769 · App. 18/244,158 · Granted Nov 11, 2025

Search result filters from resource content

Inventors: Ian MacGillivray (Brooklyn, NY); Kaylin Spitz (Brooklyn, NY); Selena Sunling Yang (New York, NY); Varun Jasjit Singh (Brooklyn, NY); Emma S. Persky (New York, NY); Yonatan Erez (Yehud, IL)
Assignee: GOOGLE LLC
G06F16/9535G06F16/3322
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,468,769
App. No.
18/244,158
Granted
Nov 11, 2025
Kind
B2
Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing filters from resource content. In one aspect, a system receives data identifying a set of resources that are determined to be responsive to a search query and extracts a set of keywords from the contents of the resources and related queries. The keywords are processed according to candidate selection criteria, and a set of candidate query filters are determined. The candidate filters may be used to filter the resources that are responsive to the query.

Claims (41)

1 . A method implemented by one or more processors, the method comprising:

determining a set of queries that are related to a first query;

determining, based on the set of queries that are related to the first query, a set of candidate filters, the set of candidate filters comprising informational terms in the set of queries that are related to the first query;

determining, for each candidate filter in the set of candidate filters, a quality score for the candidate filter, the quality score for the candidate filter being based on one or more attributes of the candidate filter in a set of resources that are determined to be responsive to the first query; and

selecting, from the set of candidate filters, based on the respective quality score for each candidate filter in the set of candidate filters, a set of query filters for the first query.

2 . The method according to claim 1 , further comprising providing, in response to the first query, for display on a user device and with content results that identify content in the set of resources, the set of query filters for the first query.

3 . The method according to claim 2 , further comprising:

receiving a selection of a particular query filter of the set of query filters for the first query; and

in response to receiving the selection of the particular query filter of the set of query filters, providing, for display on the user device, a filtered set of content that identifies a set of content results for the particular query filter that is different from an unfiltered set of content results, and that is a proper subset of the unfiltered set of content results.

4 . The method according to claim 1 , wherein determining the set of queries that are related to the first query is based on the set of resources that are determined to be responsive to the first query.

5 . The method according to claim 1 , wherein the set of candidate filters excludes stop terms in the set of queries that are related to the first query.

6 . The method according to claim 1 , wherein the one or more attributes of the candidate filter in the set of resources that are determined to be responsive to the first query, used in determining the quality score for the candidate filter, comprise locations of the candidate filter in the set of resources that are determined to be responsive to the first query.

7 . The method according to claim 6 , wherein, in determining the quality score, a first candidate filter that appears in a more prominent location in one or more resources in the set of resources is assigned a higher quality score than a second candidate filter that appears in a less prominent location in the one or more resources in the set of resources.

8 . The method according to claim 1 , wherein the one or more attributes of the candidate filter in the set of resources that are determined to be responsive to the first query, used in determining the quality score for the candidate filter, comprise a frequency of occurrence of the candidate filter in the set of resources that are determined to be responsive to the first query.

9 . The method according to claim 1 , wherein the set of query filters for the first query is a proper subset of the set of candidate filters.

10 . The method according to claim 1 , wherein selecting the set of query filters for the first query is further based on diversity of respective filtered sets of content resulting from applying respective candidate filters to the set of resources.

11 . The method according to claim 1 , further comprising:

for each candidate filter in the set of candidate filters, applying the candidate filter to the set of resources to obtain a respective filtered set of resources; and

grouping, into a single candidate filter, a pair of candidate filters in the set of candidate filters for which the respective filtered sets of resources satisfy a similarity threshold,

wherein the single candidate filter is included in the set of query filters for the first query.

12 . A computer program product comprising one or more computer-readable storage media having program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable to:

determine a set of queries that are related to a first query;

determine, based on the set of queries that are related to the first query, a set of candidate filters, the set of candidate filters comprising informational terms in the set of queries that are related to the first query;

determine, for each candidate filter in the set of candidate filters, a quality score for the candidate filter, the quality score for the candidate filter being based on one or more attributes of the candidate filter in a set of resources that are determined to be responsive to the first query; and

select, from the set of candidate filters, based on the respective quality score for each candidate filter in the set of candidate filters, a set of query filters for the first query.

13 . The computer program product according to claim 12 , wherein the program instructions are further executable to provide, in response to the first query, for display on a user device and with content results that identify content in the set of resources, the set of query filters for the first query.

14 . The computer program product according to claim 13 , wherein the program instructions are further executable to:

receive a selection of a particular query filter of the set of query filters for the first query; and

in response to receiving the selection of the particular query filter of the set of query filters, provide, for display on the user device, a filtered set of content that identifies a set of content results for the particular query filter that is different from an unfiltered set of content results, and that is a proper subset of the unfiltered set of content results.

15 . The computer program product according to claim 12 , wherein determining the set of queries that are related to the first query is based on the set of resources that are determined to be responsive to the first query.

16 . The computer program product according to claim 12 , wherein the set of candidate filters excludes stop terms in the set of queries that are related to the first query.

17 . The computer program product according to claim 12 , wherein the one or more attributes of the candidate filter in the set of resources that are determined to be responsive to the first query, used in determining the quality score for the candidate filter, comprise locations of the candidate filter in the set of resources that are determined to be responsive to the first query.

18 . The computer program product according to claim 17 , wherein, in determining the quality score, a first candidate filter that appears in a more prominent location in one or more resources in the set of resources is assigned a higher quality score than a second candidate filter that appears in a less prominent location in the one or more resources in the set of resources.

19 . The computer program product according to claim 12 , wherein the one or more attributes of the candidate filter in the set of resources that are determined to be responsive to the first query, used in determining the quality score for the candidate filter, comprise a frequency of occurrence of the candidate filter in the set of resources that are determined to be responsive to the first query.

20 . The computer program product according to claim 12 , wherein the set of query filters for the first query is a proper subset of the set of candidate filters.

21 . A system comprising:

a processor, a computer-readable memory, one or more computer-readable storage media, and program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable to:

determine a set of queries that are related to a first query;

determine, based on the set of queries that are related to the first query, a set of candidate filters, the set of candidate filters comprising informational terms in the set of queries that are related to the first query;

determine, for each candidate filter in the set of candidate filters, a quality score for the candidate filter, the quality score for the candidate filter being based on one or more attributes of the candidate filter in a set of resources that are determined to be responsive to the first query; and

select, from the set of candidate filters, based on the respective quality score for each candidate filter in the set of candidate filters, a set of query filters for the first query.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 11, 2023
From: MACGILLIVRAY, IAN; SPITZ, KAYLIN; YANG, SELENA SUNLING; SINGH, VARUN JASJIT; PERSKY, EMMA S.; EREZ, YONATHAN
To: GOOGLE INC.
Reel/Frame 064863/0438 →
CHANGE OF NAME Recorded Sep 11, 2023
From: GOOGLE INC.
To: GOOGLE LLC
Reel/Frame 064867/0840 →
Continuity (5)
Continuation 17850655 · Jun 27, 2022
Continuation 16265714 · Feb 1, 2019
Continuation 15183455 · Jun 15, 2016
Provisional Application 62192713 · Jul 15, 2015
Related Publication 20240143679A1 · May 2, 2024
References Cited (39)
US 8280900B2 · Pickens et al. · 2012 [cited by applicant]
US 8589399B1 · Lee · 2013 [cited by applicant]
US 8930356B2 · Kawale et al. · 2015 [cited by applicant]
US 10242112B2 · MacGillivray et al. · 2019 [cited by applicant]
US 11372941B2 · Macgillivray et al. · 2022 [cited by applicant]
US 20020194166A1 · Fowler · 2002 [cited by applicant]
US 20090024467A1 · Fontoura et al. · 2009 [cited by applicant]
US 20090259646A1 · Fujita et al. · 2009 [cited by applicant]
US 20100114928A1 · Bonchi et al. · 2010 [cited by applicant]
US 20100306249A1 · Hill et al. · 2010 [cited by applicant]
US 20110179021A1 · Wen et al. · 2011 [cited by applicant]
US 20120317141A1 · Qiao · 2012 [cited by applicant]
US 20130159348A1 · Mills · 2013 [cited by examiner]
US 20130238587A1 · Annau et al. · 2013 [cited by applicant]
US 20130268517A1 · Madhavan et al. · 2013 [cited by applicant]
US 20140330813A1 · Lee et al. · 2014 [cited by applicant]
US 20150026155A1 · Martin · 2015 [cited by applicant]
US 20150081656A1 · Wang et al. · 2015 [cited by applicant]
US 20160012052A1 · Zoryn · 2016 [cited by examiner]
US 20190163713A1 · MacGillivray et al. · 2019 [cited by applicant]
US 20220327175A1 · Macgillivray et al. · 2022 [cited by applicant]
CN 103150409 · 2013 [cited by applicant]
CN 103294815 · 2013 [cited by applicant]
CN 103544190 · 2014 [cited by applicant]
CN 103577595 · 2014 [cited by applicant]
CN 104090963 · 2014 [cited by applicant]
RU 2487404 · 2013 [cited by applicant]
RU 2542936 · 2015 [cited by applicant]
China National Intellectual Property Administration; Decision of Rejection issued for Application No. 201680029668.4, 5 pages, dated Jun. 2, 2022. [cited by applicant]
China National Intellectual Property Administration; Notification of First Office Action issued in Application No. 201680028668.4; 19 pages; dated Apr. 22, 2021. [cited by applicant]
China National Intellectual Property Administration; Notification of Second Office Action issued in Application No. 201680028668.4; 13 pages; dated Dec. 29, 2021. [cited by applicant]
IN Office Action in Indian Application No. 201847004382; 6 pages; dated Sep. 25, 2020. [cited by applicant]
EP Office Action in European Application No. 16825180.9; 9 pages; dated Aug. 7, 2020. [cited by applicant]
EP Office Action in European Application No. 16825180; 8 pages; dated Feb. 4, 2020. [cited by applicant]
RU Office Action issued in Russian Application No. 2017137752/08(065914); 10 pages; mailed on Dec. 6, 2018. [cited by applicant]
International Search Report and Written Opinion in Application No. PCT/US2016/042289, 12 pages; mailed on Oct. 3, 2016. [cited by applicant]
EP Extended European Search Report issued in European Application No. 16825180.9, 8 pages; mailed on Sep. 19, 2018. [cited by applicant]
Intellectual Property India; Hearing Notice issued in IN Application No. 201847004382; 3 pages; dated Aug. 10, 2023. [cited by applicant]
China National Intellectual Property Administration; Reexamination Notice issued in Application No. 201680028668.4; 14 pages; dated May 26, 2025. [cited by applicant]