Bottomline Technologies Limited

Anne Baron

Michael Marcinelli

Jonathan Hewitt

John Canneto

William Cashman

Flora Kidani

Cobie Chin

A legal spend management solution is described herein, a solution that utilizes improved machine learning algorithms to match lines of a legal invoice to lines in a receipt from a set of receipts. The matching uses cosine similarity algorithms and Levenshtein distances to determine whether there is a match between the receipt and the invoice lines. The machine learning results are displayed using a novel set of icons that present the confidence score with a set of three squares below a document icon.

Machine learning comparison of receipts and invoices

A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Calibration of a machine learning confidence score

Visualization of a machine learning confidence score

Visualization of a machine learning confidence score and rationale

Machine learning visualization

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Ansonia, CT, United States of America

Anne Baron