首页|Researchers Submit Patent Application, 'Leveraging Machine Learning Models to Identify Missing or Incorrect Labels in Training or Testing Data', for Approval (USPTO 20240054390)
Researchers Submit Patent Application, 'Leveraging Machine Learning Models to Identify Missing or Incorrect Labels in Training or Testing Data', for Approval (USPTO 20240054390)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – From Washington, D.C., NewsRx journalists report that a patent application by the inventors Beltrao Colaco Costa, Lauro Ivo (Kirkland, WA, US); Koukoumidis, Emmanouil (Kirkland, WA, US); Tata, Sandeep (San Francisco, CA, US); Wendt, James Bradley (San Francisco, CA, US), filed on August 19, 2022, was made available online on February 15, 2024. No assignee for this patent application has been made. News editors obtained the following quote from the background information supplied by the inventors: “Machine learning is a field of computer science in which models are learned or trained using training data and tested using testing data. In many instances, training or testing data can be generated by humans. For example, a training example can be provided to a human labeler and the human can chose to apply one or more labels to the training example. The labeled data can then be used to train and/or test a machine learning model. “However, humans often make mistakes or miss labels within the training data. These mistakes lead to inaccuracies in machine-learning model training and/or testing, which in turn leads to reduced performance of the model outside of the training data (e.g., when deployed to production) and/or incorrect assessments of model performance.” As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments.
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