US 11,941,691 B2
Dynamic business governance based on events
Jean Belanger, Austin, TX (US); Alain Briancon, Germantown, MD (US); James Stojanov, Burlington, CA (US); and Gabriel M. Silberman, Austin, TX (US)
Assigned to CEREBRI AI INC., Austin, TX (US)
Filed by Cerebri AI Inc., Austin, TX (US)
Filed on Aug. 12, 2022, as Appl. No. 17/887,344.
Application 17/887,344 is a continuation of application No. 16/937,475, filed on Jul. 23, 2020, granted, now 11,449,931.
Application 16/937,475 is a continuation of application No. 16/510,840, filed on Jul. 12, 2019, granted, now 10,762,563, issued on Sep. 1, 2020.
Application 16/510,840 is a continuation in part of application No. 16/127,933, filed on Sep. 11, 2018, granted, now 10,402,723, issued on Sep. 3, 2019.
Application 16/127,933 is a continuation in part of application No. 15/456,059, filed on Mar. 10, 2017, granted, now 10,783,535, issued on Sep. 22, 2020.
Claims priority of provisional application 62/698,769, filed on Jul. 16, 2018.
Prior Publication US 2023/0085451 A1, Mar. 16, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/03 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)
CPC G06Q 40/03 (2023.01) [G06N 7/01 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:
obtaining, with a computer system, one or more out of plurality of datasets having a plurality of interaction-event records, wherein:
the interaction-event records describe respective interaction events,
the interaction-events are interactions in which a first entity has experiences or obtains other information pertaining to second entity, and
at least some of the interaction-event records are associated with respective risks by which sequences of at least some of the interaction events relative to one another are ascertainable;
determining, with the computer system, using a trained machine learning model with a transformer architecture, based on at least some of the interaction-event records, sets of event-risk scores, the sets corresponding to at least some of the interaction events, wherein:
at least some of the event-risk scores are determined at least in part with a machine learning classifier;
at least some respective event-risk scores are indicative of an effective of a respective risk ascribed by the first entity to a respective aspect of the second entity; and
at least some respective event-risk scores are based on both:
respective contributions of respective corresponding events to a subsequent event in the one or more out of the plurality of datasets, and
a risk ascribed to a subsequent event in the one or more out of the plurality of datasets, the subsequent event occurring after the respective corresponding events; and
storing, with the computer system, the sets of event-risk scores in memory.