US 12,282,954 B2
Information and interaction management in a central database system
Peter Sugimura, Diamond Bar, CA (US); Karlotcha Hoa, San Francisco, CA (US); Benjamin Paik, San Francisco, CA (US); Sarah Lippitt, Studio City, CA (US); and Mariam Issa, Antioch, CA (US)
Assigned to ZENPAYROLL, INC., San Francisco, CA (US)
Filed by ZenPayroll, Inc., San Francisco, CA (US)
Filed on Jun. 24, 2024, as Appl. No. 18/752,126.
Application 18/752,126 is a continuation of application No. 18/464,865, filed on Sep. 11, 2023, granted, now 12,045,881.
Application 18/464,865 is a continuation of application No. 18/080,102, filed on Dec. 13, 2022, granted, now 11,790,441, issued on Oct. 17, 2023.
Application 18/080,102 is a continuation of application No. 17/012,050, filed on Sep. 4, 2020, granted, now 11,556,982, issued on Jan. 17, 2023.
Prior Publication US 2024/0346583 A1, Oct. 17, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/03 (2023.01); G06N 20/00 (2019.01); G06Q 40/12 (2023.01)
CPC G06Q 40/03 (2023.01) [G06N 20/00 (2019.01); G06Q 40/125 (2013.12)] 20 Claims
OG exemplary drawing
 
1. A method to streamline database interactions comprising:
accessing, by the central database system, a machine-learned model trained using training data comprising historical interactions by historical entities with the central database system and characteristics of the historical entities, the machine-learned model configured to determine a rate of requiring a submission of evidence to the central database system from entities associated with interactions with the central database system;
determining, by the central database system, a rate of requiring the submission of evidence to the central database system from target entities by applying the machine-learned model to characteristics of the target entities; and
in response to determining that the rate of requiring the submission of evidence to the central database system is too low, retraining, by the central database system, the machine-learned model by adjusting weights and parameters of the machine-learned model such that an updated rate of requiring the submission of evidence to the central database system associated with the retrained machine-learned model is higher than the rate of requiring the submission of evidence to the central database system.