Coupa Software Incorporated

A computer-implemented method of detecting account codes and displaying the detected account codes on a graphical user interface comprising receiving, by a recommendation engine of a recommendation system, invoice data comprising supplier-customer information that corresponds to a supplier-customer transaction, wherein the invoice data comprises invoice descriptions and invoice characters, wherein the invoice descriptions and the invoice characters define contexts and patterns; determining, by the recommendation engine, that an amount of the invoice characters is not more than a preset threshold number of characters; in response to determining that the amount of the invoice characters is not more than the preset threshold number of characters, filtering, by the recommendation engine, the invoice descriptions of the invoice data based on predetermined constraints to extract filtered invoice data comprising filtered description lines and to generate a training corpus for a pre-trained Natural Language Processing (NLP) model; identifying, by classifying the filtered description lines with the pre-trained NLP model, one or more categories associated with each of the filtered description lines of the filtered invoice data; matching, by the recommendation engine, each of the identified one or more categories with one or more predefined historical categories, wherein the contexts and patterns associated with the filtered description lines are matched with predefined contexts and patterns of predefined historical invoice data that corresponds to the same supplier-customer information; generating, by the recommendation engine, a feature vector for the invoice data based on the matching; computing, by the recommendation engine, a categorical similarity score for each of the identified one or more categories based on the feature vector and an additional feature vector, wherein the additional feature vector is based on the predefined historical invoice data; and displaying, by the recommendation engine on the graphical user interface, a recommendation including an account code based on the computed categorical similarity score of each of the one or more categories to map the account code to the invoice data.

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.

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.

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.

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 disclose a computer-implemented method that is implemented using a travel and expense application. The method includes receiving trip input data comprising the origin of travel, the destination of the travel, and the period of an event from a web-based application implemented in a computing device of a candidate. The trip input data associated with an event record having an event identifier of the event. The method populates a plurality of trip fields of a travel database having a plurality of trip database records with the event identifier associated with the trip input data to automatically create and store a trip record for a new trip of the candidate. The travel database is communicatively coupled to the travel and expense application. The method then automatically creates, for the new trip, a database search query for a plurality of data servers based on a plurality of search criteria associated with the candidate. The plurality of search criteria comprises a plurality of candidate preference data, candidate historical booking patterns, booking intents associated with the event, and the trip input data. The method automatically executes the database search query for the new trip to provide a plurality of recommendations including commute options and accommodation options based on the plurality of search criteria. The method receives a selection of one of a commute option and an accommodation option among the plurality of recommendations for the new trip, and in response to receiving the selection, automatically creating booking information corresponding to booking a commute option among the commute options and an accommodation option among the accommodation options. The method then updates an itinerary of the new trip of the candidate with the booking information and presenting the booking information and the plurality of recommendations at a graphical user interface (GUI) of the computing device of the candidate.

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.

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.

A computer-implemented method for managing a graphical user interface for a computer procurement application is disclosed. The graphical user interface is configured to show data related to key performance indicators (KPIs) for an entity and those for a peer group, show recommendations for improving values of the KPIs by adjusting values of configurations associated with the KPIs for the entity based on values of the KPIs and the associated configurations for the peer group in response to a first request, and implement the adjustment in response to a second request.

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.