Premier has filed a patent for a system that uses machine learning to categorize and select suggested source entities. The system receives a resource file from a user, normalizes supplier entity names, matches them with authenticated names in a database, applies predetermined variables and categories to a suggestion model, and generates a suggested source entity interface component for the user’s device. GlobalData’s report on Premier gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Premier,It's grant share as of June 2023 was 1%. Grant share is based on the ratio of number of grants to total number of patents.
The patent filed is for a system using machine learning to categorize and select suggested source entities
A recently filed patent (Publication Number: US20230153740A1) describes a system that utilizes machine learning to categorize and select suggested source entities. The system includes a memory device with computer-readable program code and at least one processing device. The system is designed to receive a resource file from a user, which contains supplier entity names and resource line items. It then sorts the resource line items based on the supplier entity names and normalizes the supplier entity names to generate normalized versions. The system compares the normalized supplier entity names with an authenticated supplier entity name in a master record and updates a source entity management database accordingly.
Additionally, the system allows users to provide a predetermined variable standard and a selected category through a user indication. It applies these inputs to a source entity suggestion model, which outputs suggested source entity names that match the predetermined variable standard and selected category. Based on these suggested source entity names, the system generates a suggested source entity interface component to configure the graphical user interface of a user device.
The system also includes features such as categorizing supplier entity names in identified categories associated with authenticated supplier entity names, applying an agreement engine to resource agreements to generate key performance indicators and update the source entity management database, and utilizing a supplier entity name machine learning model when supplier entity names do not match authenticated names.
The patent further describes the use of a bayes theorem in the source entity suggestion model and the collection of previously tagged variables to train the model. It also mentions the categorization of normalized supplier entity names as medical device manufacturers or medical equipment manufacturers.
The system can handle resource line items that contain various data, including supplier names, service types, amounts owed, due dates, payment terms, and balances. It utilizes an entity name master record with authenticated supplier entity names and associated categories, as well as a predictive plan database with electronic records and transaction amounts.
Overall, this patent presents a system that leverages machine learning to categorize and select suggested source entities based on user inputs and resource file data. It aims to streamline the process of managing supplier entities and improve efficiency in resource management.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies
