US 11,922,470 B2
Impact-based strength and weakness determination
Bradley William Null, Millbrae, CA (US); Pranav Desai, San Mateo, CA (US); Hsin-wei Tsao, Menlo Park, CA (US); and Chun Fai Chau, Millbrae, CA (US)
Assigned to Reputation.com, Inc., San Ramon, CA (US)
Filed by Reputation.com, Inc., San Ramon, CA (US)
Filed on Dec. 22, 2022, as Appl. No. 18/087,228.
Application 18/087,228 is a continuation of application No. 16/817,236, filed on Mar. 12, 2020, granted, now 11,570,131.
Claims priority of provisional application 62/952,683, filed on Dec. 23, 2019.
Prior Publication US 2023/0281678 A1, Sep. 7, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/02 (2023.01); G06F 16/2457 (2019.01); G06F 16/901 (2019.01); G06F 16/9535 (2019.01); G06F 16/9536 (2019.01); G06N 5/02 (2023.01); G06N 20/00 (2019.01); G06Q 10/107 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0203 (2023.01); G06Q 30/0282 (2023.01); H04L 9/40 (2022.01); H04L 51/212 (2022.01); G06Q 50/00 (2012.01)
CPC G06Q 30/0282 (2013.01) [G06F 16/24578 (2019.01); G06F 16/901 (2019.01); G06F 16/9535 (2019.01); G06F 16/9536 (2019.01); G06N 5/02 (2013.01); G06N 20/00 (2019.01); G06Q 10/107 (2013.01); G06Q 30/02 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0203 (2013.01); H04L 51/212 (2022.05); H04L 63/1408 (2013.01); G06Q 50/01 (2013.01)] 33 Claims
OG exemplary drawing
 
1. A system, comprising:
a processor configured to:
receive a plurality of industry-wide feedback items, the plurality of industry-wide feedback items pertaining to a plurality of entities associated with an industry;
based at least in part on an evaluation of the plurality of industry-wide feedback items, train an industry-wide reputation scoring machine learning model usable to determine an expected reputation score for a typical entity in the industry based at least in part on a combination of one or more reputation score components, and wherein training the industry-wide reputation scoring machine learning model comprises determining (1) a baseline reputation score, and (2) an expected impact of a reputation score component on reputation scoring for the typical entity in the industry;
based at least in part on the expected impact of the reputation score component on reputation scoring for the typical entity in the industry, determine, for a target entity, an impact of the reputation score component on reputation scoring for the target entity; and
based at least in part on the determined impact of the reputation score component on the target entity, automatically generate a tagging rule usable to tag ingested feedback items pertaining to the target entity that are determined to be associated with the reputation score component; and
a memory coupled to the processor and configured to provide the processor with instructions.