Coupa Software Inc.

Mikin Faldu

Maggie Mae Joy

Arjun Ramaratnam

Kiran Ratnapu

Prasanna Kumar

Fang Chang

Amit Vijayant

Ankit Narang

Jeffrey Hellman

Shoan Jain

Angela Welchel

Ethan Laub

Anshuman Nene

Daniel Benson

Fang-Kuey Chang

Raghunandan Somaraju

Dipeshkumar Vasantbhai Prajapati

Ketan Vasantkumar Darji

Rucha Apte

Computer-implemented techniques for repeatable and interpretable divisive analysis. In one embodiment, for example, a method comprises: identifying top-level cohorts of data items based on one or more characteristics of the data items in common; recursively or iteratively dividing a selected top-level cohort in a top-down manner resulting in a plurality of sub-level cohorts arranged in a hierarchy; detecting a particular data item that is a statistical outlier among data items of a leaf cohort in the hierarchy; and causing display of an indication in a computer user interface that the particular data item is an outlier.

System and method for repeatable and interpretable divisive analysis

Systems and methods for improving a computing system comparing past post-approved transaction records to past pre-approved transaction records are described herein. In an embodiment, a server stores a first plurality of digital electronic records identifying a plurality of past post-approved transactions by a first entity and a second plurality of digital electronic records for the first entity identifying a plurality of past pre-approved transactions by the first entity. The server uses a first machine learning system to determine that a subset of the first plurality of digital electronic records which identify a subset of the plurality of past post-approved transactions correspond to one or more pre-identified categories and a second machine learning system to match one or more particular digital electronic records of the subset of the first plurality of digital electronic records with one or more records of the second plurality of digital electronic records.

Matching past post-approved transactions with past pre-approved transactions using machine learning systems

Techniques for using a graphical user interface to automatically generate expense lines from images are described herein. According to an embodiment, a server computer causes storing in a data repository, data identifying a plurality of users, the data comprising feature data extracted from one or more images of the users. The server computer receives a digital photographic image captured using a camera in a mobile computing device. The server computer identifies one or more faces in the digital photographic image and determines that the one or more faces in the digital photographic image match a particular user. The server computer generates and stores an expense line in an expense data table in the data repository, the expense line comprising an identification of the particular user.

Automatic generation of expense data using facial recognition in digitally captured photographic images

Growing community of inventors

San Ramon, CA, United States of America

Mikin Faldu