NeuroPace has developed a records classification processor using deep learning and dimensionality reduction to label physiological information. The processor extracts feature vectors, forms clusters, enables user labeling, and automatically assigns labels to related records. This innovation streamlines data analysis and enhances efficiency in healthcare settings. GlobalData’s report on NeuroPace 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 NeuroPace, Electrical stimulation wearables was a key innovation area identified from patents. NeuroPace's grant share as of January 2024 was 80%. Grant share is based on the ratio of number of grants to total number of patents.

Physiological information records classification processor

Source: United States Patent and Trademark Office (USPTO). Credit: NeuroPace Inc

A recently granted patent (Publication Number: US11842255B2) discloses a records classification processor designed to label records of physiological information efficiently. The processor comprises various modules, including a feature extraction module to derive multi-dimensional feature vectors, a feature reduction module to reduce these vectors, a similarities module to form clusters based on the reduced vectors, and a labeling module to assign labels to records within clusters. The processor also includes an interface for user interaction, enabling the display of clusters, selection of records, assignment of labels, and automatic labeling of associated records. The feature extraction module utilizes a deep learning model, while the feature reduction module applies dimensionality reduction processes to the feature vectors.

Furthermore, the processor employs clustering algorithms like k-means clustering and spectral clustering to form clusters, ensuring efficient classification of records. The labeling module validates assigned labels and provides mechanisms for user confirmation or correction of labels. Additionally, the processor can automatically associate records with adjacent clusters based on similarity measures if label accuracy is in question. The method outlined in the patent involves deriving multi-dimensional feature vectors, reducing them, forming clusters, assigning labels, and validating them. The method also includes selecting records based on common parameters from a database, ensuring a systematic approach to labeling physiological information records. Overall, the patent presents a comprehensive system and method for effectively classifying and labeling records of physiological information, enhancing data organization and accessibility in the field.

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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.