Coupa Software Incorporated

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.

Embodiments of the present disclosure provide a method that may include defining an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end. The method may include accessing a machine learning classifier comprising a plurality of rule sets. The method may include applying the plurality of rule sets to one or more words of each corresponding contract document. The method may include linking identified one or more core attributes and one or more words of each corresponding contract document to an applicable object of the object model, determining prevailing terms of each corresponding contract document, and evaluating contract data variables and assigning a contract data risk value to one or more of contract data values. The method may include communicating an alert via email or text message when a contract risk exceeds a threshold value.

In one embodiment, the disclosure provides a machine-implemented technique that improves the traditional methods of expense reimbursement processes by allowing an authorized delegate, e.g., administrative assistant, that is acting on behalf of another, e.g., account owner, to add expense items through a device on which the delegate is logged-in to, as opposed to a device on which the account owner is logged-in to, all without requiring the delegate to manage multiple user accounts and in an automated fashion.

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.

Input data comprises an order to provide a quantity of one or more finished goods to a site. Software is programmed for: accessing information that defines an architecture of a supply chain network comprising a plurality of sites connected by a plurality of path fragments; generating one or more path solutions to fulfill the order; removing one or more loops from the one or more path solutions; generating based on the one or more path solutions and the one or more removed loops, one or more revised path solutions; outputting the one or more revised path solutions for display on a client device; and transmitting, only to computers of the sites of a revised subset of a plurality of path fragments, a plurality of signals to initiate movement of one or more of raw materials or finished goods.

Embodiments of the present disclosure provide a method that may include defining an object model containing a structural representation of events and artefacts through which contracts are created, changed, and brought to an end. The method may include accessing a machine learning classifier comprising a plurality of rule sets. The method may include applying the plurality of rule sets to one or more words of each corresponding contract document. The method may include linking identified one or more core attributes and one or more words of each corresponding contract document to an applicable object of the object model, determining prevailing terms of each corresponding contract document, and evaluating contract data variables and assigning a contract data risk value to one or more of contract data values. The method may include alerting a user when a contract risk exceeds a threshold value.

In an embodiment, a method for dynamically generating and using peer groups in an e-procurement system includes creating an entity database with a plurality of attributes associated with entities and an ordered table of matching rules. Each matching rule having a priority value and two or more matching attributes. The method includes receiving a user input specifying a particular entity for generating a current peer group of other entities. The method includes accessing the entity database to retrieve particular attributes of the particular entity and querying the entity database to receive a result set of matching entities as the current peer group of other entities for the particular entity based on the particular attributes of the particular entity and the matching rules. The method further includes appending data for the matching entities in the result set to a peer group data structure associated with the particular entity.

Embodiments disclosed herein may provide capabilities for multi-source data gathering, adaptive item cross-referencing, data preparation, and data extraction. These capabilities may allow the creation of item master records, which can provide richer information than available from any one discrete source. Additional functionality that may be provided in some embodiments may include providing commodity-based predictive pricing and/or a visual spend map.

In an embodiment, a computer-implemented method comprises storing invoice data comprising a plurality of invoice objects, each invoice object comprising a digital programmatic object that is maintained in computer memory, each invoice object comprising one or more data fields that are programmatically linked to and storing data for an invoice between a supplier account and a buyer account, the one or more data fields including a remittance address that specifies an address to which a payment is to be sent; determining, based on the plurality of invoice objects, a set of unique remittance addresses, each unique remittance address of the set of unique remittance addresses associated with a distinct supplier account; generating a first set of payment objects based on the set of unique remittance addresses, each payment object of the first set of payment objects associated with a unique remittance address of the set of unique remittance addresses; determining, based on the plurality of invoice objects, that a particular unique remittance address of the set of unique remittance addresses is associated with multiple buyer entities of a particular buyer account; in response to determining that the particular unique remittance address of the set of unique remittance addresses is associated with the multiple buyer entities of the particular buyer account, generating a second set of payment objects, each payment object of the second set of payment objects associated with the particular unique remittance address and a distinct buyer entity of the multiple buyer entities; causing multiple payment transactions based on the first set of payment objects and the second set of payment objects.

In an embodiment, a method for dynamically generating and using peer groups in an e-procurement system includes creating an entity database with a plurality of attributes associated with entities and an ordered table of matching rules. Each matching rule having a priority value and two or more matching attributes. The method includes receiving a user input specifying a particular entity for generating a current peer group of other entities. The method includes accessing the entity database to retrieve particular attributes of the particular entity and querying the entity database to receive a result set of matching entities as the current peer group of other entities for the particular entity based on the particular attributes of the particular entity and the matching rules. The method further includes appending data for the matching entities in the result set to a peer group data structure associated with the particular entity.