Patent ID: 11861733
Assignee: ORACLE INTERNATIONAL CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 20:
21. A method comprising:
training a machine learning model using a set of labeled expense data to estimate a label for an expense, wherein the machine learning model comprises a set of feature vectors of attributes extracted from the labeled expense data, wherein each feature vector is associated with a respective label and wherein the respective label corresponds to a classification of the labeled expense data;
iteratively applying the trained machine learning model to additional expense data; updating the trained machine learning model based on results generated by iteratively applying the trained machine learning model to additional sets of expense data;
receiving, by an intelligent agent, a natural language query inquiring whether a potential expense is reimbursable;
responsive to receiving the natural language query, determining, by the intelligent agent, one or more attributes associated with the potential expense, wherein the intelligent agent applies natural language processing to the natural language query to determine the one or more attributes;
generating, by the intelligent agent, at least one feature vector based at least in part on the one or more attributes determined by the intelligent agent by applying natural language processing to the natural language query;
inputting the at least one feature vector into the trained machine learning model to generate, by the trained machine learning model, an estimated label for the potential expense; and
generating, by the intelligent agent, a natural language response to the natural language query based at least in part on the estimated label generated by the trained machine learning model for the potential expense, wherein the natural language response indicates whether the potential expense is reimbursable.