Oracle International Corporation

International Business Machines Corporation

Kelly Bailey

Parker Ralph Kuncl

Winston Wang

Matthew Brigante

Elizabeth A Jensen

Raphael L Bouchard

Emma L Verrecchia

Techniques for real-time expense auditing and machine learning are disclosed. An expense auditing system trains a machine learning model to compute audit risk scores as a function of expense descriptions. The auditing system receives an expense description associated with an employee. The expense auditing system computes, using the trained machine learning model, an audit risk score associated with the expense description. The expense auditing system compares the audit risk score with an audit trigger. The audit trigger includes one or more conditions that, when satisfied, identifies expense descriptions that are at risk of being audited. The expense auditing system determines that the audit risk score satisfies the audit trigger. Responsive to determining that the audit risk score satisfies the audit trigger, the expense auditing system alerts the employee that the expense description is at risk of being audited.

Real-time expense auditing and machine learning system

Techniques for expense report submission are disclosed. An expense report submission system receives, via a graphical user interface, a user query that corresponds to requesting whether a particular expense is allowed. The expense report submission system applies the user query to a machine learning model configured to evaluate data associated with expenses against one or more expense policy rules. The expense report submission system generates a response to the user query based at least on a result of applying the user query to the machine learning model. The response to the user query indicates whether the expense is allowed based at least on the expense reporting rule(s). The expense report submission system presents, in the graphical user interface, the response to the user query.

Expense report submission interface

An approach is described for representing search results via a three-dimensional matrix. An associated method may include receiving a search query, querying at least one database based upon the search query, and receiving results to the search query from the at least one database. The method further may include representing the results via a three-dimensional matrix graphically depicting a plurality of categories corresponding to the results. Each of the plurality of categories may be associated with the results via a respective tag in the at least one database. Upon detecting activity from an input device, the method further may include filtering the plurality of categories of the three-dimensional matrix, and prioritizing the results based upon the filtering. Representing the results via the three-dimensional matrix may include presenting the results in a list correlated to the three-dimensional matrix, and prioritizing the results based upon the filtering may include prioritizing the list.

Representing search results via a three-dimensional matrix

Growing community of inventors

San Francisco, CA, United States of America

Kelly Bailey