Johnson & Johnson has filed a patent for a computer-implemented method to detect energy tool activations during surgical procedures. The process involves analyzing surgical videos using deep-learning models to identify activation events with associated confidence levels. GlobalData’s report on Johnson & Johnson gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Johnson & Johnson, Surgical robots was a key innovation area identified from patents. Johnson & Johnson's grant share as of January 2024 was 52%. Grant share is based on the ratio of number of grants to total number of patents.
Detection of energy tool activations during surgical procedures
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A computer-implemented method for automatically detecting energy tool activations in surgical videos has been outlined in a filed patent (Publication Number: US20240037385A1). The method involves receiving a surgical video, applying sampling windows to generate windowed samples, and utilizing a deep-learning model to infer activation/non-activation events with associated confidence levels. By identifying sequences of activation events based on these inferences, the method aims to accurately count and determine the duration of activation events during surgical procedures. The training of the deep-learning model involves annotated surgical videos with identified activation events, where labeled training data is generated by sampling the videos and assigning ground truth labels based on temporal locations relative to the activation events.
Furthermore, the patent also describes a system for automatically detecting energy tool activations, comprising processors and memory storing instructions for applying sampling windows, utilizing a deep-learning model, and identifying activation events in surgical videos. The system is designed to increment an activation count based on consecutive activation inferences and output the total activation count for the video. Additionally, a method for constructing a high-quality training dataset for training an energy tool activation detection model is detailed, involving temporal clustering of annotated activation events, computing statistical consensuses for clusters, and outputting these consensuses as ground truth for model building. Anomalies within clusters are identified and updated to ensure accuracy in the training dataset, ultimately improving the performance of the energy tool activation detection model.
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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
