Coupa Software Inc.

Ankit Narang

Kiran Ratnapu

Shoan Jain

Yutaka Hosoai

Maggie Mae Joy

Prasanna Kumar

Mikin Faldu

Angela Welchel

Brent Sisson

Jyotirmaya Mahanta

Raghunandan Somaraju

Arindam Bhattacharya

Daniel Benson

Dipeshkumar Vasantbhai Prajapati

Fang-Kuey Chang

Ketan Vasantkumar Darji

Rucha Apte

Hari Teja Murakonda

Commodity category values can be determined automatically for suppliers in an e-procurement system using a computer-implemented process that is supplier-focused and uses successive heuristics, supplemented with machine learning models that predict category and subcategory values based on supplier names and invoice descriptions. Embodiments can support community intelligence applications to enable buyer computers to query and obtain lists of suppliers corresponding to categories and to generate graphs or charts that aggregate historic invoice data based on canonical category values that have been determined for suppliers.

Category classification of records of e-procurement transactions

A computer-implemented method is disclosed. The method includes obtaining, by a de-duplication server, a candidate pair of a plurality of digitally stored documents from a document database. Text elements are identified from each digitally stored document in the candidate pair in response, and the text elements are stored as document extraction attributes. The method then automatically computes and stores relative positional differences of the text elements between each digitally stored document of the candidate pair and a document similarity score based on the relative positional differences. The relative positional differences are compared with a similarity function to form a difference similarity vector for the candidate pair. The difference similarity vector comprises components corresponding to each relative positional difference. The components of the difference similarity vector are aggregated to determine a final score for the candidate pair. A document-level similarity metric is determined from the final score. The method includes determining whether the final score is above a cutoff value, and in response to determining that the final score for the candidate pair is above the cutoff value, comparing the document extraction attribute with the final score. The method also determines whether the document-level similarity metric is above a threshold value by the de-duplication server. The candidate pair is classified based on determining that the document-level similarity metric is above the threshold value to de-duplicate the plurality of digitally stored documents in the candidate pair. Based on the classifying, duplicate transaction documents are removed from the document database by any of deleting records, marking records, updating column attributes, or writing records to a different table.

De-duplicating transaction records using targeted fuzzy matching

A computer-implemented method for detecting and managing duplicate invoices is provided. In one embodiment, the method includes accessing, in a digital storage device, invoices associated with a buyer computer. Candidate invoices having similar attributes are identified from the invoices and nodes representing the candidate invoices are created and stored in computer memory. At least two of the candidate invoices are determined likely to be duplicate invoices and an edge is generated between nodes representing the at least two candidate invoices, indicating that the at least two candidate invoices are likely to be duplicate invoices. A set of filters is programmatically applied using the at least two candidate invoices as inputs. The at least two candidate invoices are determined to be duplicate invoices based on an output of the set of filters and a notification is sent to the buyer computer indicating that the at least two candidate invoices are duplicate invoices.

Duplicate invoice detection and management

In one embodiment, a method includes assessing unlabeled transaction data and unlabeled candidate transaction data. The method includes determining a first plurality of features for the unlabeled transaction data and a second plurality of features for the unlabeled candidate transaction data using a Bag of Words model. The method includes determining matching pairs using the unlabeled transaction data and the unlabeled candidate transaction data. The method includes calculating similarity scores for the matching pairs using the first plurality of features and the second plurality of features. The method includes generating a machine learning model to predict a subcategory label and a ranking value for the unlabeled candidate transaction data using the similarity scores of the matching pairs. The method includes determining, using the machine learning model, the subcategory label and the ranking value associated with the unlabeled candidate transaction data.

Method and system using value-based identification for unlabeled transaction

A computer-implemented method for detecting and managing duplicate invoices is provided. The method includes accessing, in a digital storage device, invoices associated with a buyer computer. Candidate invoices having similar attributes are identified from the invoices and nodes representing the candidate invoices are created and stored in computer memory. At least two of the candidate invoices are determined likely to be duplicate invoices and an edge is generated between nodes representing the at least two candidate invoices, indicating that the at least two candidate invoices are likely to be duplicate invoices. A set of filters is programmatically applied using the at least two candidate invoices as inputs. The at least two candidate invoices are determined to be duplicate invoices based on an output of the set of filters and a notification is sent to the buyer computer indicating that the at least two candidate invoices are duplicate invoices.

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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Maharashtra, India

Ankit Narang