Coupa Software Incorporated

In an embodiment, a computer-implemented method includes parsing a communication record, determining one or more intents of the communication record using a trained machine learning model, linking the communication record to one or more record IDs, identifying one or more fields within the communication record, presenting the communication record in a standardized form, including identification of intent, the standardized form resulting from the identified one or more fields, and determining a corresponding intent related action on the determined one or more intents.

The computer-implemented method utilizes machine learning models to classify input data records. The method includes selecting machine learning models from a plurality of machine learning models using a router model and generating classification inferences for each selected model. The classification inferences include factors indicating the probability of the input data record belonging to various candidate classes. Any one of the plurality of machine learning models is configured to use a weight matrix calculated as the sum of a base weight matrix and a model-associated difference matrix obtained as a product of two low-rank matrices. The classification inferences from selected models are then combined to create an overall classification inference, providing factors related to the probability of the input data belonging to each candidate class. This method enables effective classification by leveraging multiple machine learning models.

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.

Systems and methods for presenting configurable machine learning systems through graphical user interfaces are disclosed. In an embodiment, a machine learning server computer stores one or more machine learning configuration files. A particular machine learning configuration file of the one or more machine learning configuration files comprises instructions for configuring a machine learning system of a particular machine learning type with one or more first machine learning parameters. The machine learning server computer displays through a graphical user interface, a plurality of selectable parameter options, each of which defining a value for a machine learning parameter. The machine learning server computer receives a particular input dataset. The machine learning server computer additionally receives, through the graphical user interface, a selection of one or more selectable parameter options corresponding to one or more second machine learning parameters different from the one or more first machine learning parameters. The machine learning server computer replaces in the particular machine learning configuration file, the one or more first machine learning parameters with the one or more second machine learning parameters. Using the particular machine learning configuration file, the machine learning server computer configures a particular machine learning system. Using the particular machine learning system and the particular input dataset, the machine learning server computer computes a particular output dataset.

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.

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.

Systems and methods for presenting configurable machine learning systems through graphical user interfaces are disclosed. In an embodiment, a machine learning server computer stores one or more machine learning configuration files. A particular machine learning configuration file of the one or more machine learning configuration files comprises instructions for configuring a machine learning system of a particular machine learning type with one or more first machine learning parameters. The machine learning server computer displays through a graphical user interface, a plurality of selectable parameter options, each of which defining a value for a machine learning parameter. The machine learning server computer receives a particular input dataset. The machine learning server computer additionally receives, through the graphical user interface, a selection of one or more selectable parameter options corresponding to one or more second machine learning parameters different from the one or more first machine learning parameters. The machine learning server computer replaces in the particular machine learning configuration file, the one or more first machine learning parameters with the one or more second machine learning parameters. Using the particular machine learning configuration file, the machine learning server computer configures a particular machine learning system. Using the particular machine learning system and the particular input dataset, the machine learning server computer computes a particular output dataset.

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 one embodiment, a method can access an expense line for a travel expense for an enterprise. The expense line is the subject of a procurement action under a relevant contract. The method can use a first machine learning model, determining a category associated with the expense line. The method can evaluate the expense line using the category associated with the expense line and a table of categories that are procurable with references to related travel contracts. The method can determine a match between a description field of the expense line and description fields of historic invoice records associated with travel contracts. In response to determining the match, the method can execute a responsive action for the expense line, wherein the responsive action includes generating a notification to a user, marking the expense line, and initiating a workflow to apply the contract to the expense line.

Systems and methods for presenting configurable machine learning systems through graphical user interfaces are disclosed. In an embodiment, a machine learning server computer stores one or more machine learning configuration files. A particular machine learning configuration file of the one or more machine learning configuration files comprises instructions for configuring a machine learning system of a particular machine learning type with one or more first machine learning parameters. The machine learning server computer displays through a graphical user interface, a plurality of selectable parameter options, each of which defining a value for a machine learning parameter. The machine learning server computer receives a particular input dataset. The machine learning server computer additionally receives, through the graphical user interface, a selection of one or more selectable parameter options corresponding to one or more second machine learning parameters different from the one or more first machine learning parameters. The machine learning server computer replaces in the particular machine learning configuration file, the one or more first machine learning parameters with the one or more second machine learning parameters. Using the particular machine learning configuration file, the machine learning server computer configures a particular machine learning system. Using the particular machine learning system and the particular input dataset, the machine learning server computer computes a particular output dataset.