Coupa Software Inc.

Sandip Mandlecha

Hanieh Borhanazad

Hongyang Yu

Neha Arora

Shashank Dass

Rebecca Mengell

Keeyoung Kim

Tim Durkin

A computer implemented method comprising receiving invoice data comprising at least one of invoice descriptions and invoice characters from user computers, each of the invoice descriptions and invoice characters defines contexts and patterns, wherein each of the invoice data comprising a supplier-customer information that corresponds to a supplier-customer transaction; analyzing the at least one of the invoice descriptions and the invoice characters with corresponding contexts and patterns; determining that amount of the invoice characters is more than a threshold number of characters, for performing: matching invoice data, invoice characters with predefined historical invoice data that corresponds to the same supplier-customer information; computing a similarity score for each of the invoice data; and displaying recommendations including first account codes to map the one or more first account codes to each of the one or more invoice data based on the similarity score; determining that amount of the invoice characters is not more than the threshold number of characters, for performing: filtering the invoice descriptions of the invoice data based on predetermined constraints to extract filtered invoice data comprising filtered description lines; identifying categories associated with each of the filtered description lines of the filtered invoice data; matching each of the identified categories, including corresponding contexts and patterns that is associated with a supplier-customer information with one or more predefined historical categories associated with predefined invoice description of the one or more predefined historical invoice data that corresponds to the same supplier-customer information, wherein each of the contexts and the patterns are matched with predefined contexts and patterns of the predefined historical invoice data that corresponds to the same supplier-customer information; computing a categorical similarity score for each of the categories associated with the supplier-customer information; and displaying on the graphical user interface, second recommendations including second account codes based on the computed categorical similarity score of each of the categories to map the second account codes to the invoice data.

Systems and methods for automatically recommending account codes

Embodiments of the disclosed technologies provide solutions for automatically reading digital electronic documents that contain tables and correctly extracting table data, rows and columns from the documents with high accuracy and high throughput. Embodiments are capable of converting a table portion of a read-only document to a searchable, editable data record using text rectangle (TR)-level numerical data that indicates probabilities of TRs belonging to canonicals and at least one convolutional neural network (CNN) that processes the TR-level numerical data to produce table-level numerical data.

Text-based machine learning extraction of table data from a read-only document

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Pune, India

Sandip Mandlecha