US 12,135,737 B1
Graphical user interface and pipeline for text analytics
Murali Krishna Pagolu, Cary, NC (US); and Corey Kyle Kozak, Durham, NC (US)
Assigned to SAS INSTITUTE INC., Cary, NC (US)
Filed by SAS Institute Inc., Cary, NC (US)
Filed on Mar. 25, 2024, as Appl. No. 18/615,319.
Claims priority of provisional application 63/528,606, filed on Jul. 24, 2023.
Claims priority of provisional application 63/522,373, filed on Jun. 21, 2023.
Int. Cl. G06F 16/34 (2019.01)
CPC G06F 16/34 (2019.01) 29 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors; and
one or more memories including program code that is executable by the one or more processors for causing the one or more processors to perform operations including:
receiving unstructured text documents from one or more sources, wherein the unstructured text documents describe issues with entities;
determining entity-issue descriptions corresponding to the unstructured text documents, wherein each of the entity-issue descriptions is a text snippet extracted from a corresponding unstructured text document and wherein the text snippet includes a combination of at least two terms, and wherein the combination of at least two terms includes an entity term, an issue term, and a connector term that links the entity term to the issue term; and
generating a graphical user interface indicating the entity-issue descriptions corresponding to the unstructured text documents, the graphical user interface indicating assignments of the unstructured text documents to one or more categories of a predefined schema by a user or a rule-based model, the one or more categories being different from the entity-issue descriptions, the graphical user interface being configured to allow the user to adjust the assignments of the unstructured text documents to the one or more categories based on the entity-issue descriptions in the unstructured text documents;
wherein the graphical user interface includes a table of rows, each row in the table corresponding to one of the unstructured text documents and indicating a respective entity-issue description in the unstructured text document, each row further indicating the one or more categories of the predefined schema assigned to the unstructured text document, and each row of the graphical user interface further including a graphical button that is selectable to allow the user to selectively view the unstructured text document corresponding to the row; and
wherein the assignments of the unstructured text documents to the one or more categories are usable to tune the rule-based model or to generate training data for training a machine-learning model that is different than the rule-based model.