Konica Minolta had four patents in internet of things during Q1 2024. Konica Minolta Inc filed a patent for a biological condition diagnosis system that uses a reflective photoelectric plethysmography sensor to obtain biological data. The system includes components for preprocessing the data, extracting feature points, and diagnosing biological conditions using machine learning algorithms. GlobalData’s report on Konica Minolta gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Konica Minolta grant share with internet of things as a theme is 75% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Biological condition diagnosis system (Patent ID: US20240000324A1)
The patent filed by Konica Minolta Inc. describes a biological condition diagnosis system that includes components such as an obtainer, preprocessor, processor, and diagnoser for biological data. The system utilizes a reflective photoelectric plethysmography sensor to obtain biological data, preprocesses the data optimally, extracts feature points, and diagnoses biological conditions using machine learning based on the extracted data. The system can continuously measure and obtain biological data, simultaneously measure multiple types of data, and combine the obtained data to generate integrated data items. Additionally, the diagnoser uses machine learning models, including deep learning, to analyze the biological data and diagnose conditions accurately.
The reflective photoelectric plethysmography sensor used in the system includes planar light emitting and light receiving elements, with the light emitting element serving as a planar light source for illuminating biological tissue. The system ensures that the distance between the light emitting and receiving elements falls within a specific range based on the thickness of the skin to optimize data collection. Furthermore, the system can include multiple light emitting elements arranged in a concentric circle around the light receiving element to enhance data acquisition. Overall, the patent outlines a comprehensive biological condition diagnosis system that leverages advanced technology and machine learning to provide accurate and efficient diagnoses based on biological data obtained through reflective photoelectric plethysmography sensors.
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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.
