iRhythm Technologies‘s patent involves a monitor with sensors to detect physiological signals, a processor to extract features, and an encoder to transmit data wirelessly to a server. The server uses machine learning to infer physiological events. GlobalData’s report on iRhythm Technologies 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 iRhythm Technologies, Treatment progress monitoring was a key innovation area identified from patents. iRhythm Technologies's grant share as of May 2024 was 26%. Grant share is based on the ratio of number of grants to total number of patents.
Physiological data monitoring and event detection system
A recently granted patent (Publication Number: US11998342B2) discloses a monitor designed to detect continuous physiological signals of a user through sensors and process these signals to extract user features. The monitor includes a hardware processor that processes the signals to generate data output, which is then used by an external computing system to determine physiological events of the user, such as hypertension, depression, congestive heart failure, or sleep apnea. The monitor can be in the form of a wearable patch, chest patch, or a wearable watch, with the data output being encrypted and processed through a decoder or machine learning model by the external computing system.
Furthermore, the patent describes a monitor with sensors that detect signals from a patient's body, which are then processed to extract derived signals. A second set of signals is detected based on these derived signals, and a signal processor within the monitor processes these signals through a machine learning network to generate an output. This output is then processed by an external computing system through another machine learning network. The monitor may include a transmitter to send the output to the computing system, with the dimensionality of the output being smaller than the signals detected by the second sensor. The system aims to determine physiological characteristics of the user, such as the likelihood of arrhythmia occurrence or heart abnormalities, through the processing of physiological signals and derived data using neural networks.
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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.
