Wistron had 35 patents in artificial intelligence during Q4 2023. The patents filed by Wistron Corp in Q4 2023 include a machine learning method for continual learning, a processing method for medical images, an evaluation method for sleep quality using radar echo data, a data predicting method using machine learning models, and a method for updating a neural network model based on quantized weights and activation functions. These inventions aim to improve prediction accuracy, evaluate sleep quality through non-touch sensing, and enhance the efficiency of machine learning models. GlobalData’s report on Wistron gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Wistron grant share with artificial intelligence as a theme is 25% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Machine learning method for continual learning and electronic device (Patent ID: US20230410481A1)
The patent filed by Wistron Corp. describes a machine learning method for continual learning that involves capturing an input image, performing feature extraction using sub-models corresponding to different tasks determined by a neural network model and channel-wise masks, converting feature maps into energy scores, selecting a target sub-model based on energy scores, and outputting a prediction result for the target task. The method also includes steps for training the system using training data, determining loss functions, backward propagation gradients, and updating the neural network model and channel-wise masks based on the training process.
The machine learning method outlined in the patent focuses on continual learning by utilizing sub-models, neural network models, and channel-wise masks to extract features, convert them into energy scores, and select the appropriate sub-model for a given task. The method also involves training the system using training data, determining loss functions, and updating the neural network model and channel-wise masks based on the training process. This approach allows for efficient and effective continual learning in machine learning systems, ensuring adaptability and accuracy in handling various tasks over time.
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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 across the world’s largest industries.
