US 12,230,253 B2
Automatic classification of phone calls using representation learning based on the hierarchical pitman-yor process
Michael McCourt, Santa Barbara, CA (US)
Assigned to Invoca, Inc., West Hollywood, CA (US)
Filed by Invoca, Inc., Santa Barbara, CA (US)
Filed on Aug. 9, 2021, as Appl. No. 17/397,709.
Prior Publication US 2023/0055948 A1, Feb. 23, 2023
Int. Cl. G10L 15/14 (2006.01); G06N 20/00 (2019.01); G10L 15/06 (2013.01); G10L 15/08 (2006.01)
CPC G10L 15/14 (2013.01) [G06N 20/00 (2019.01); G10L 15/063 (2013.01); G10L 2015/0631 (2013.01); G10L 2015/088 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
digitally storing first call transcript data that is associated with an observed label of a set of observed labels, the first 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 first call transcript data having been created based on speech-to-text recognition of an audio recording of the call, the first call transcript data being divided 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 in the call;
digitally generating and storing a machine learning statistical topic model in computer memory, the topic model comprising a word branch, a topic branch, and a classifier that defines a joint probability distribution over topic vectors and observed labels, the classifier being conjoined to the topic branch, 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;
wherein the classifier is a linear classifier that has been trained to use a determined probability distribution of topics and a determined topic importance to determine classifications of target calls;
programmatically training, using a set of call transcript data that includes the first call transcript data, the topic model using the classifier, the set of call transcript data comprising at least one call transcript data not associated with an observed label;
receiving target call transcript data comprising an electronic digital representation of a verbal transcription of a target call;
programmatically determining, using the topic model and the linear classifier, at least one of one or more topics of the target call and a classification of the target call;
digitally storing the target call transcript data with additional data indicating at least one of the one or more topics of the target call or the classification of the target call;
wherein the word branch and the topic branch are conditionally independent;
wherein the topic model uses probability distributions to infer any data missing from the first call transcript data.