Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Technologies for computing a relativized entity score include generating first actionable output that identifies a particular supplier and an intrinsic score for the particular supplier that is calculated using intrinsic factor data for the particular supplier, where the intrinsic factor data is obtained from a plurality of instances of procurement software; determining a set of weights using peer spend data of a set of peer suppliers, where the set of peer suppliers is identified based on a spend label that is associated with the peer spend data by an artificial intelligence-based process; applying the set of weights to a set of intrinsic scores for the set of peer suppliers to produce a set of peer scores, where a weight in the set of weights is calculated using the peer spend data; aggregating the set of peer scores to produce an aggregate peer score; generating second actionable output, where the second actionable output modifies or supplements the first actionable output based on a comparison of the intr

Abstract: Technologies for computing a relativized entity score include generating first actionable output that identifies a particular supplier and an intrinsic score for the particular supplier that is calculated using intrinsic factor data for the particular supplier, where the intrinsic factor data is obtained from a plurality of instances of procurement software; determining a set of weights using peer spend data of a set of peer suppliers, where the set of peer suppliers is identified based on a spend label that is associated with the peer spend data by an artificial intelligence-based process; applying the set of weights to a set of intrinsic scores for the set of peer suppliers to produce a set of peer scores, where a weight in the set of weights is calculated using the peer spend data; aggregating the set of peer scores to produce an aggregate peer score; generating second actionable output, where the second actionable output modifies or supplements the first actionable output based on a comparison of the intr

Abstract: Systems and methods for generating machine learning systems using slave server computers are disclosed. In an embodiment, a first server computer stores one or more machine learning training datasets, each of the datasets comprising input data and verified output data. The first server computer receives a particular input dataset and a request to run a machine learning system with the particular input dataset. The first server computer sends the particular input dataset, a particular machine learning training dataset of the one or more machine learning training datasets, and one or more configuration files for building a machine learning system to a second server computer.

Abstract: Systems and methods for generating and training machine learning systems using stored training datasets are disclosed. In an embodiment, a machine learning server computer stores a plurality of machine learning training datasets, each machine learning training dataset of the plurality of machine learning training datasets comprising input data and output data. The machine learning server computer displays, through a graphical user interface, a plurality of selectable options, each selectable option of the plurality of selectable options identifying a machine learning training dataset of the plurality of machine learning training datasets. The machine learning server computer receives a particular input dataset and a selection of a particular selectable option identifying a particular machine learning training dataset. The machine learning server computer trains a particular machine learning system using the particular machine learning training dataset.

Abstract: 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.