Coupa Software Incorporated

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.

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.

An improved electronic procurement system is disclosed. The electronic procurement system (“system”) implements features such as real-time adaptive extraction of online data. The system is configured to manage a plurality of page types for webpages and for each page type a plurality of field types for fields in webpages. The system is configured to further generate a computer application that can be integrated into a web browser. For each page type, the computer application is programmed to initially generate a signature for each field type based on minimal user interaction with webpages of the page type. The system is configured to also generate an agent using the signature. The agent is programmed to automatically extract data corresponding to the field types from additional webpages of the page type using the signatures. The agent or an associated background process is programmed to automatically update the signatures when data extraction fails.

A method and apparatus for facilitating the utilization of virtual payment card data is provided. In various embodiments, virtual payment card data is received by a centralized e-procurement service directly from buyer or issuer entities participating in the service. In various embodiments, secure processes protect the security of virtual payment card data on the centralized service by actively monitoring the service for abnormal view and access behaviors of the virtual payment card data. In various embodiments, viewing interfaces for the e-procurement service are actively and responsively altered to ensure secure utilization of the virtual payment card data. In various embodiments, preventive actions are taken to actively alter virtual payment card data or access methods for entities utilizing the e-procurement service to protect the integrity and security of virtual payment card data.

One computer-implemented method comprises receiving requests to create subforms associated with a digitally stored expense trip preapproval form; in a particular subform, receiving input specifying input values for attributes of the particular subform, the plurality of input values collectively specifying the travel expense item; programmatically transmitting, to a price modeling server, a query comprising the input values and requesting a cost value for a travel item corresponding to the input values, and receiving a predicted cost of the travel expense item; automatically updating the particular subform to display the predicted cost and to populate the predicted cost into a total cost attribute, the total cost attribute being separate from the predicted cost; receiving an updated value of the total cost attribute and updating it; in response to determining that the updated total cost attribute differs from the predicted cost, presenting a notification specifying an amount of the difference.

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.

An improved electronic procurement system is disclosed. The electronic procurement system (“system”) implements features such as real-time adaptive extraction of online data. The system is configured to manage a plurality of page types for webpages and for each page type a plurality of field types for fields in webpages. The system is configured to further generate a computer application that can be integrated into a web browser. For each page type, the computer application is programmed to initially generate a signature for each field type based on minimal user interaction with webpages of the page type. The system is configured to also generate an agent using the signature. The agent is programmed to automatically extract data corresponding to the field types from additional webpages of the page type using the signatures. The agent or an associated background process is programmed to automatically update the signatures when data extraction fails.

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.

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.