US 2026/0228436 A1 · COMPUTING SYSTEMS AND METHODS FOR MACHINE LEARNING TO AUTOMATICALLY GENERATE TOPICS LINKED TO DIGITAL TEXT

Abstract

Computing systems and methods for discovering new topics from digital documents are provided, including using a natural language neural network. Digital documents are processed using an encoder to obtain vectors, and the vectors are processed to form clusters. Topics are identified in association with each of the clusters. Based on the clusters, a word-topic matrix of probabilities of words detected in each topic is computed. A word-document matrix of probabilities of words detected in each one of the digital documents is also computed. A topic-document matrix of probabilities of each one of the plurality of topics in each one of the digital documents is then computed by factorizing the previous two matrices. This third matrix is then used to compute and output a topic distribution for each of the digital documents, which shows the topics generated by the computing system.

Claims20

Dependent claims

A server system for automatically generating a plurality of topics from a plurality of digital documents, the server system comprising:

a memory, a network interface, and a processor, the processor operably coupled to the memory and the network interface, the processor configured to:
ingest the plurality of digital documents;
process the plurality of digital documents using an encoder to respectively obtain a plurality of vectors;
cluster the plurality of vectors to identify a plurality of clusters from amongst the plurality of vectors;
identify the plurality of topics that are respectively associated with the plurality of clusters;
compute a word-document matrix of probabilities of words detected in each one of the plurality of digital documents;
compute a word-topic matrix of probabilities of words detected in each topic;
compute a topic-document matrix of probabilities of each one of the plurality of topics in each one of the plurality of digital documents based on the word-document matrix of probabilities and the word-topic matrix of probabilities;
and
compute and output a topic distribution for each of the plurality of digital documents, wherein a given topic distribution comprises one or more probabilities of one or more topics, from amongst the plurality of topics, that are associated with a given digital document.

7 more elements

A method for automatically generating a plurality of topics from a plurality of digital documents, the method executed in a computing environment comprising one or more processors and memory, the method comprising:

ingesting the plurality of digital documents;
processing the plurality of digital documents using an encoder to respectively obtain a plurality of vectors;
clustering the plurality of vectors to identify a plurality of clusters from amongst the plurality of vectors;
identifying the plurality of topics that are respectively associated with the plurality of clusters;
computing a word-document matrix of probabilities of words detected in each one of the plurality of digital documents;
computing a word-topic matrix of probabilities of words detected in each topic;
computing a topic-document matrix of probabilities of each one of the plurality of topics in each one of the plurality of digital documents based on the word-document matrix of probabilities and the word-topic matrix of probabilities;
and
computing and outputting a topic distribution for each of the plurality of digital documents, wherein a given topic distribution comprises one or more probabilities of one or more topics, from amongst the plurality of topics, that are associated with a given digital document.

6 more elements

Dependencies are inferred from the claim text.

Computing systems and methods for machine learning to automatically generate topics linked to digital text

Life of this patent

Computing systems and methods for machine learning to automatically generate topics linked to digital text

Life of this patent

Abstract

Claims20