Koninklijke Philips had two patents in ecommerce during Q1 2024. The radiology workstation by Koninklijke Philips NV includes display and user input devices, with a processor predicting billing codes for a radiology examination based on a radiology report. It also identifies missing content for supporting the billing codes and either displays an indication of missing content or stores the report in a database if no content is missing. GlobalData’s report on Koninklijke Philips gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Koninklijke Philips grant share with ecommerce as a theme is 50% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Real-time radiology report completeness check and feedback generation for billing purposes based on multi-modality deep learning (Patent ID: US20240020740A1)
The patent filed by Koninklijke Philips NV describes a radiology workstation that includes display devices, user input devices, and a processor. The workstation is designed to provide a reading environment for radiology examinations, analyze radiology reports to predict billing codes, identify missing content for supporting the predicted billing codes, and display indications of missing content or store reports accordingly. The system utilizes artificial intelligence to predict missing billing codes, rank them based on probability, and suggest additions to the radiology report for completeness.
Furthermore, the patent details methods for displaying billing codes, receiving authorization to add suggested content, predicting candidate billing codes, and transmitting data between hospitals and teleradiology services. The AI component, based on historical reports, uses a Bidirectional Encoder Representations from Transformers (BERT) language model to score the completeness of reports. The patent also outlines a training method for the AI component, involving datasets of labeled radiology reports with billing codes, converting data into feature vectors, updating the AI component, and outputting information on missing content in reports. Overall, the patent focuses on enhancing radiology examination reading support through AI-driven prediction and completion of billing codes in radiology reports.
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