Koninklijke Philips had 21 patents in big data during Q4 2023. The patents filed by Koninklijke Philips NV in Q4 2023 focus on developing adaptable predictive analytics for medical facilities, generating predictive models for surgery duration, monitoring maintenance prediction for medical imaging apparatus, implementing a federated learning system, and storing genomic data efficiently within a data structure. These innovations aim to improve patient care, resource planning, equipment maintenance, and data management in healthcare settings. GlobalData’s report on Koninklijke Philips gives a 360-degreee view of the company including its patenting strategy. Buy the report here.

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Koninklijke Philips grant share with big data as a theme is 19% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Developing adaptable predictive analytics for subjects in medical facilities (Patent ID: US20230420138A1)

The patent filed by Koninklijke Philips NV describes a method for developing adaptable predictive analytics for subjects in a medical facility. The method involves training a predictive algorithm to predict adverse medical events before they occur, determining real risk scores over time, creating required risk scores based on the real risk score trend, mapping annotations to minimize differences between required and real risk scores, fine-tuning the algorithm, and monitoring subjects using the fine-tuned predictive algorithm. The system includes an interface, processor, and memory to execute the method, with additional features like weighting risk scores, adjusting patient management, and preventing early alerts of medical events.

The method and system outlined in the patent aim to improve patient care by predicting adverse medical events in advance and adjusting patient management accordingly. By training a predictive algorithm and continuously monitoring real risk scores, the system can provide modified probabilities of medical events occurring at predetermined times. The method involves fine-tuning the algorithm based on new annotations and mapping to minimize differences between required and real risk scores. Additionally, the system includes features like adjusting staff and resources based on notification times, preventing early alerts, and optimizing cut-offs for risk scores. The use of a recurrent neural network (RNN)-based model enhances the accuracy of predictions for various medical events like hemodynamics instability, atrial fibrillation, and acute kidney injury, among others.

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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.