Abstract: A method and apparatus for generating recommendation data for cataloging items in an e-procurement system is provided. In various embodiments, a database of records is created and maintained corresponding to a plurality of transactions in an e-procurement system. In various embodiments, database records are weighted and sorted according a transaction method associated with the records. In various embodiments, recommendation data is generated for items associated with the records to suggest more efficient methods for offering items for procurement in an e-marketplace based on the weights and sort order of the records.

Abstract: A computer-implemented method for improving efficiency in an electronic procurement system for sourcing resources, comprising, during digital electronic interactions of a buyer computer with one or more software platforms and without receiving explicit request for recommendations from the buyer computer: automatically generating, at a coding computer, implicit observation data of the buyer computer; automatically determining, at the coding computer, one or more active sourcing events from a plurality of sourcing events, based on at least the implicit observation data of the buyer computer; using the coding computer, causing to display at least one of the one or more active sourcing events in a graphical user interface.

Abstract: In one embodiment, a method includes using a first computing device to access digital data representing a contract set of contract documents and digital contract metadata, weight differences between data field values, use the weighted differences to calculate distance scores, use the distance scores and a temporal ordering of the contract documents to generate and cause displaying a time curve graph comprising a geometric shape that comprises time curves and a spatial proximity at a second computing device, a measure of each time curve calculated to indicate an amount of time between a creation of two contract documents, the spatial proximity calculated to indicate a metric of similarity between the contract documents, in response to determining that the geometric shape indicates a lack of convergence over a threshold amount of time, generate and cause displaying a notification indicating that the contract set is at risk at the second computing device.

Abstract: A method for improving performance of a computer procurement application includes using the procurement control system computer, determining a peer group associated with a first entity; using a procurement control system computer, obtaining, from client computers respectively associated with the entities, configurations that instruct a procurement application how to perform procurement tasks for the respective entities of the entities in the peer group; using a procurement control system computer, obtaining, from a first client computer associated with the first entity, a first configuration that instruct the procurement application how to perform procurement tasks for the first entity; obtaining a desired end result from the first entity; using the procurement control system computer, using a machine learning algorithm, determining configurations of the peer groups that have a causal relationship to the desired end result; providing a description of the configuration value to the client computer associated w

Abstract: In an embodiment, a computer-implemented method comprises receiving supplier invoice data comprising one or more supplier payment objects corresponding to a supplier to a customer of a supplier-customer transaction. A match determination is made between the supplier payment object and a verified payment object. In response to a no-match determination, a customer transaction system is queried to validate the supplier, and in response to the supplier validation, a validation payment object is created, validating the supplier. A match determination is made between the supplier payment object and the verified payment object, and the supplier invoice is automatically generated. In response to receiving subsequent supplier invoice data corresponding to the supplier of subsequent supplier-client transactions, for each subsequent supplier-client transaction, the subsequent supplier invoice is automatically generated with the verified payment object.

Abstract: 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.

Abstract: Techniques for communicating with a third-party management database are described herein. According to an embodiment, a computer receives a search-style query, searches the third-party management database, retrieves multiple computer-readable database search strings, displays the computer-readable database search strings, and receives a selected computer-readable database search string. The computer may receive data associated with one or more third party surveys, and questions and answer options may be displayed according to various metadata attributes associated with previously-selected answer options.

Abstract: A computer-implemented method comprising receiving a set of rules that define assigning tax codes for a first entity based on a plurality of parameters, and storing the set of rules in a first data repository in association with an entity record of the first entity; receiving invoice data that defines an invoice directed to the first entity; automatically determining a commodity type applicable to the invoice data by digitally cross-referencing line items in the invoice data representing goods or services to community transaction data stored in a second data repository, the community transaction data comprising a plurality of different line item data for different invoices of other entities different from and unrelated to the first entity; automatically assigning tax codes to the invoice data based on the commodity type and the set of rules; and causing to display the commodity type and the tax codes in a graphical user interface.

Abstract: Computer-implemented techniques for repeatable and interpretable divisive analysis. In one embodiment, for example, a method comprises: identifying top-level cohorts of data items based on one or more characteristics of the data items in common; recursively or iteratively dividing a selected top-level cohort in a top-down manner resulting in a plurality of sub-level cohorts arranged in a hierarchy; detecting a particular data item that is a statistical outlier among data items of a leaf cohort in the hierarchy; and causing display of an indication in a computer user interface that the particular data item is an outlier.

Abstract: 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.

Abstract: This application discloses systems, methods, and computer products related to generation, analysis, and transmission of multiple types of data among multiple computers within a networked computer environment. The disclosed approaches further enable integration of different types of data, notification of availability of certain types of data, and coordination of communication of specific types of data.

Abstract: Described herein is a computer-implemented method for automatic extraction of canonical data from an electronic document. The method comprises classifying a first text rectangle in an electronic document as a label and a second text rectangle as a value using a first machine learning algorithm. A first probability score of a likelihood of the first text rectangle corresponding to a first canonical category is determined using a second machine learning algorithm. A second probability score of a likelihood of the second text rectangle corresponding to a first canonical category is determined using a third machine learning algorithm. A relative spatial position of the second text rectangle relative to the first text rectangle is calculated. Based on the relative spatial position, the first probability score, and the second probability score, the first text rectangle, and the second text rectangle are classified into the first canonical category.

Abstract: Input data comprises a particular quantity of one or more finished goods to be produced over a time period. Software is programmed for: accessing infrastructure data that defines an infrastructure of a particular production facility; generating one or more sequence-dependent production plans comprising a particular sequence of one or more production process steps; calculating one or more optimized production plans for the time period by: determining a sub-period optimization plan for a first sub-period by adjusting one or more of a plurality of binary variables; generating a neighborhood optimization plan by adjusting one or more of the plurality of binary variables for a predefined neighborhood; and generating a particular optimized production plan for the time period by adjusting one or more of the plurality of binary variables; filtering the one or more optimized production plans based on one or more filtering criteria; and outputting the optimized production plans for display.

Abstract: A method and apparatus are herein disclosed for allowing suppliers to generate an invoice from a purchase order without requiring them to log in or pre-register with a networked application. Implementation is provided by having a first user provide a first document to a networked application. An electronic notification is dispatched to a receiver for the first document with a unique link to a data entry page within the networked application. The data entry page is sandboxed with limited functionality within the networked application, but accepts data from the receiver to be sent directly into the first user's system.

Abstract: The present disclosure provides for a method of machine representation and tracking of contract terms over the lifetime of a contract including a step of defining an object model having object model components. Object model components are associated with other object model components where the object model components have object model component types. Further, words of object model components are evaluated to identify whether the words contain one or more core attributes pertaining to details of the contract terms. From the object model components, and the terms they contain, prevailing terms of the contract are evaluated, stored and updated as changes are made to the object model components.

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: In an embodiment, an automated computer-based method for improving a computer system to be able to predict rejections of requisitions submitted to the computer system, the method comprising receiving a requisition from a client device; determining, at a scoring unit of a computer system, a probability value indicating a likelihood that the requisition would be rejected if the requisition is submitted to a requisition approval chain; transmitting the probability value from the computer system to the client device to be displayed on a display of the client device.

Abstract: An improved procurement system and method are provided.

Abstract: Techniques are disclosed for improving spend approval cycle time in a software-as-service (SasS) electronic spend management system based on community spend approval information. The techniques include the spend management system collecting information about spend approval cycle times across a community of users that use the system. A graphical user interface is then provided by the spend management system to individual customer users of the spend management system where the graphical user interface includes a graphical comparison of the user's individual spend approval cycle time performance against the community's spend approval cycle time performance. By doing so, the individual user can be motivated to improve or maintain his or her spend approval cycle time performance.

Abstract: In one or more embodiments, a system and/or a method may implement: receiving data representing different performance behavior metrics that are associated with software instances that are respectively associated with consumer computers and that specify values of performance factors of the software instances as the consumer computers interoperate with the software instances; determining data throughput values that represent processing throughput of the software instances; determining relative capacity values of the software instances; adapting each of prediction models to the relative capacity values; determining correlation coefficients from the prediction models and the multiple data throughput values; executing a prediction model of the prediction models associated with a correlation coefficient closest to a value of “1” to result in outputting threshold values associated with the performance factors; and transmitting the threshold values to a particular consumer computer among the consumer computers as par