Raymond A Lucas

US Patent:

20220318681, Oct 6, 2022

Filed:

Jun 16, 2022

Appl. No.:

17/842662

Inventors:

- McLean VA, US
R. M. Keelan DOWNTON - Arlington VA, US
Matthew DER - Richmond VA, US
Raymond LUCAS - Richmond VA, US

Assignee:

Capital One Services, LLC - McLean VA

International Classification:

G06N 20/00
G06F 16/904

Abstract:

A topic tracking platform is disclosed that includes a machine-learning model that may be trained to expose topics in a corpus in response to a training table. Because topics are exposed, rather than searched for using existing taxonomies, the sensitivity of a topic tracking platform may be increased, and emerging topic trends may be more quickly flagged. Exposed topics may be automatically labelled, increasing the specificity of the topic tracking platform by overcoming the potential for topic labelling inconsistencies currently experienced in the art. Documents may be scored for each topic using information provided at a token granularity, and the contribution that each token of each document contributes to the topic may be visually represented. In some aspects, mechanisms are provided for reviewing topics of the corpus at varying granularities, including at a topic level, document level or token level granularity.

US Patent:

20200364610, Nov 19, 2020

Filed:

May 15, 2019

Appl. No.:

16/412783

Inventors:

- McLean VA, US
R. M. Keelan DOWNTON - Arlington VA, US
Matthew DER - Richmond VA, US
Raymond LUCAS - Richmond VA, US

Assignee:

Capital One Services, LLC - McLean VA

International Classification:

G06N 20/00
G06F 16/904

Abstract:

A topic tracking platform is disclosed that includes a machine-learning model that may be trained to expose topics in a corpus in response to a training table. Because topics are exposed, rather than searched for using existing taxonomies, the sensitivity of a topic tracking platform may be increased, and emerging topic trends may be more quickly flagged. Exposed topics may be automatically labelled, increasing the specificity of the topic tracking platform by overcoming the potential for topic labelling inconsistencies currently experienced in the art. Documents may be scored for each topic using information provided at a token granularity, and the contribution that each token of each document contributes to the topic may be visually represented. In some aspects, mechanisms are provided for reviewing topics of the corpus at varying granularities, including at a topic level, document level or token level granularity.