Topic-based semantic search of electronic documents based on machine learning models from Bayesian Belief networks
US12322382B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 17, 2023 |
| Grant date | Jun 3, 2025 |
| Priority date | — |
| Expiry date | Dec 8, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method executed using a computing device comprises digitally generating and storing a machine learning statistical topic model in computer memory, the topic model being programmed to model call transcript data representing words spoken on a call as a function of one or more topics of a set of topics, the set of topics being modeled to comprise a set of pre-seeded topics and a set of non-pre-seeded topics, and the one or more topics being modeled as a function of a probability distribution of topics; programmatically pre-seeding the topic model with a set of keyword groups, each keyword group associating a respective set of keywords with a topic of the set of pre-seeded topics; programmatically training the topic model using unlabeled training data; conjoining a classifier to the topic model to create a classifier model, the classifier defining a joint probability distribution over topic vectors and one or more observed labels; programmatically training the classifier model using labeled training data; receiving target call transcript data comprising an electronic digital representation of a verbal transcription of a target call; programmatically determining, …
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.