Coupa Software. has been granted a patent for a method that enables configurable machine learning systems via graphical user interfaces. The system allows users to select neural network types and parameters, process input datasets, and compute output datasets, including confidence score evaluations for data items. GlobalData’s report on Coupa Software 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 Coupa Software, was a key innovation area identified from patents. Coupa Software's grant share as of June 2024 was 63%. Grant share is based on the ratio of number of grants to total number of patents.
Configurable machine learning systems via graphical user interfaces
The granted patent US12039177B2 outlines a computer-implemented method for configuring and training machine learning systems using a graphical user interface (GUI). The method involves storing multiple machine learning configuration files, each containing instructions for different machine learning types and parameters. Users can select from various neural network types, such as Naive Bayes, Encoder Decoder, and Long Short Term Memory, and specify machine learning parameters through the GUI. The system processes an input dataset, configures the machine learning system based on user selections, and computes an output dataset. Additionally, it incorporates a confidence score threshold to evaluate the output, allowing for the replacement of low-confidence data items with those from a secondary output dataset.
Further claims in the patent detail the ability to manage multiple training datasets, categorize them, and display relevant training options based on user input. The method also allows for the configuration of additional machine learning systems using different parameters and datasets. It includes mechanisms for training these systems and updating training datasets based on computed outputs. The patent emphasizes flexibility in machine learning configurations and the ability to adaptively improve model performance by leveraging various training datasets and parameters, ultimately enhancing the accuracy and reliability of machine learning outputs.
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