Patent ID: 11893505
Assignee: AON RISK SERVICES, INC. OF MARYLAND
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A method, comprising:
generating first data representing documents received from one or more databases, the first data including components of the documents;
generating a user interface configured to display:
the components of individual ones of the documents; and
an element configured to accept user input indicating whether the individual ones of the documents are in class or out of class;

generating a positive training dataset indicating positive vectors from a first portion of documents marked as in class in response to the user input;
generating a negative training dataset indicating negative vectors from a second portion of documents marked as out of class in response to the user input;
generating a machine learning model based at least in part on user input data corresponding to the user input, the machine learning model configured to determine whether a sample vector representing a document is closer to the positive vectors than the negative vectors in a coordinate system;
determining, based at least in part on the machine learning model, a third portion of the documents that are in class and a fourth portion of the documents that are out of class; and
causing the user interface to display:
an indication of the third portion and the fourth portion of the documents;
a first section indicating first keywords determined to be statistically relevant by the machine learning model for identifying the third portion of the documents, wherein the first keywords are displayed in a manner that indicates a first ranking of statistical importance of the first keywords, wherein the first keywords are selectable via user input to be removed from the first section; and
a second section indicating second keywords determined to be statistically relevant by the machine learning model for identifying the fourth portion of the documents, wherein the second keywords are displayed in a manner that indicates second ranking of the statistical relevance of the second keywords, wherein the second keywords are selectable via user input to be removed from the second section; and

based at least in part on receiving the user input indicating that at least one of the first keywords or the second keywords should be removed, retraining the machine learning model to account for removal of the at least one of the first keywords or the second keywords.