Acer had 16 patents in artificial intelligence during Q1 2024. Acer Inc filed a patent for an electronic device and method for diagnosing heart state using ECG data, converting files to obtain integrated ECG data for generating a diagnostic result based on a deep learning model. Another patent involves a system and method for configuring virtual buttons by displaying and capturing images of touch screens to generate virtual button configurations. GlobalData’s report on Acer gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Acer grant share with artificial intelligence as a theme is 62% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Electronic device and method for diagnosing heart state based on electrocardiogram (Patent ID: US20240062899A1)
The patent filed by Acer Inc. describes an electronic device and method for diagnosing heart conditions based on electrocardiogram (ECG) data. The method involves obtaining an ECG file in a first format, converting it to a second format to extract ECG data from multiple leads, integrating this data through zero-padding and stacking operations, and generating a diagnostic result using a deep learning model. The device includes a processor and storage device to perform these operations, with the ECG file formats specified as PDF and SVG.
Furthermore, the method and device allow for the extraction of trace description coordinate data from the ECG file, conversion of this data into ECG data for each lead, and inputting patient data along with the integrated ECG data into the deep learning model for accurate diagnosis. The leads involved in the diagnosis can include various standard leads like I, II, III, aVR, aVL, aVF, V1-V6, with the method accommodating the generation of integrated ECG data for multiple leads through zero-padding and stacking operations. Overall, the patent outlines a comprehensive approach to diagnosing heart conditions using ECG data and deep learning models, enhancing the accuracy and efficiency of the diagnostic process.
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