Thales had four patents in big data during Q4 2023. Thales SA filed patents in Q4 2023 for methods of recognizing individuals using biometric fusion, building data classifiers based on transfer learning, securing avionics computing infrastructure with a gateway, and improving human-machine dialog through bidirectional translations and displaying intermediate reasoning. GlobalData’s report on Thales gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Thales grant share with big data as a theme is 0% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Multimodal biometric fusion based authentication (Patent ID: US20230401302A1)
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The patent filed by Thales SA describes a method for recognizing individuals by combining match results from different biometric recognition systems into a fused match result. The method involves training a fusion model using machine learning to optimize parameters for achieving a target performance metric. These optimized parameters are then updated in a database and applied to match results to recognize individuals. The system can handle multiple biometric modalities and uses various techniques such as tanh-estimators, quality values, and probability-ratio models to calculate the fused match result accurately. Additionally, the system includes a training engine, fusion engine, and fusion controller to manage the recognition process efficiently.
Furthermore, the patent details the process of training the fusion model, receiving fusion requests, and applying the fusion model to calculate the fused match result. Different scenarios are considered, such as using matching scores or rank lists from single-modal biometric recognition systems, and incorporating quality values for improved accuracy. The system also allows for the evaluation of performance metrics, comparison with target metrics, and adaptation to new biometric modalities or vendors. Overall, the method and system described in the patent offer a comprehensive approach to multimodal biometric fusion for accurate and reliable individual recognition across various biometric modalities.
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