US 12,216,692 B1
Systems and methods for automatic generation of electronic activity content for record objects using machine learning architectures
Oleg Rogynskyy, Menlo Park, CA (US); Sergey Surkov, Las Vegas, NV (US); Andrii Kvachov, Toronto (CA); and Dmitry Starshov, San Francisco, CA (US)
Assigned to People.ai, Inc.
Filed by People.ai, Inc., San Francisco, CA (US)
Filed on Jul. 31, 2024, as Appl. No. 18/790,474.
Claims priority of provisional application 63/672,201, filed on Jul. 16, 2024.
Claims priority of provisional application 63/530,251, filed on Aug. 1, 2023.
Int. Cl. G06F 16/332 (2019.01); G06F 16/33 (2019.01)
CPC G06F 16/3329 (2019.01) [G06F 16/3334 (2019.01)] 21 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors configured by machine-readable instructions to:
identify a plurality of electronic activities matched to a record object of a customer relationship management (CRM) system;
generate, by inputting a first set of text strings of one or more text strings obtained from the plurality of electronic activities into one or more large language models, (i) a first set of topics, (ii) a first set of references indicating one or more subsets of the first set of text strings, each subset of text strings corresponding to a different topic of the first set of topics, and (iii) an attribute for each of the first set of topics, each attribute for a topic indicating a level of relevance of the topic to the record object;
generate, by inputting a first subset of text strings of the first set of text strings corresponding to a first topic selected using the first set of references and based on the level of relevance of the first topic and a second set of text strings of the one or more text strings into the one or more large language models, a second set of topics; and
transmit one or more topics of the second set of topics to a computing device for presentation.