Coupa Software Inc.

Prasanna Kumar

Amit Vijayant

Kiran Ratnapu

Maggie Mae Joy

Mikin Faldu

Scott Harris

Vincent Toesca

Arjun Ramaratnam

Fang Chang

Ankit Narang

Shoan Jain

Angela Welchel

Ketan Vasantkumar Darji

Fang-Kuey Chang

Rucha Apte

Raghunandan Somaraju

Daniel Benson

Dipeshkumar Vasantbhai Prajapati

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

A method and apparatus for generating recommendation data for cataloging items in an e-procurement system is provided. In various embodiments, a database of records is created and maintained corresponding to a plurality of transactions in an e-procurement system. In various embodiments, database records are weighted and sorted according a transaction method associated with the records. In various embodiments, recommendation data is generated for items associated with the records to suggest more efficient methods for offering items for procurement in an e-marketplace based on the weights and sort order of the records.

Catalog enablement data for supplier systems based on community activities

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

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Saratoga, CA, United States of America

Prasanna Kumar