Invoca, Inc.

Michael Lawrence

Michael McCourt

Victor Jara Borda

Sean Storlie

Anoop Praturu

Michael Mccourt

Victor Borda

Systems and methods for improving machine learning systems used to model topics on a plurality of calls are described herein. In an embodiment, a server computer receives plurality of digitally stored call transcripts that have been prepared from digitally recorded voice calls. The server computer uses a topic model of an artificial intelligence machine learning system, the topic model modeling words of a call as a function of one or more word distributions for each topic of a plurality of topics, to generate an output of the topic model which identifies the plurality of topics represented in the plurality of call transcripts. The server computer computes, for a particular topic of the plurality of topics a first value representing a vocabulary of the particular topic and a second value representing a consistency of the particular topic in two more call transcripts of the plurality of call transcripts which include the particular topic. Based, at least in part, on one or more of the first value or the second value, the server computer determines that the particular topic meets a particular criterion and, in response, updates the output of the topic model to remove the particular topic or distinguish the particular topic from other topics of the plurality of topics which do not meet the particular criterion.

Detecting extraneous topic information using artificial intelligence models

Systems and methods for improving call topic models are described herein. In an embodiment a server computer receives call transcript data comprising an electronic digital representation of a verbal transcription of a call between a first person of a first person type and a second person of a second person type. The server computer splits the call transcript data into first person type data comprising words spoken by the first person in the call and second person type data comprising words spoken by the second person type in the call. The server computer uses a stored topic model to determine a topic of the call, the topic model simultaneously modeling the first person type data as a function of a first probability distribution of words used by the first person type for one or more topics and the second person type data as a function of a second probability distribution of words used by the second person type for the one or more topics, both the first probability distribution of words and the second probability distribution of words being modeled as a function of a third probability distribution of words for the one or more topics. The server computer then stores a data record identifying the topic of the call and/or stores data identifying the topic of the call with the call transcripts.

Signal discovery using artificial intelligence models

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Santa Barbara, CA, United States of America

Michael Lawrence