Alarmcom. has been granted a patent for a health and wellness management technology that utilizes sensor data to classify activities and detect behaviors within a monitored property. The system generates predictive models to evaluate behaviors and trigger automated actions to enhance individual health and safety. GlobalData’s report on Alarmcom gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Alarmcom, Property assessment drones was a key innovation area identified from patents. Alarmcom's grant share as of June 2024 was 65%. Grant share is based on the ratio of number of grants to total number of patents.

Health and wellness management using sensor data analysis

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

The patent US12046366B1 outlines a computer-implemented method and system for monitoring individual behaviors within a property using data from multiple sensors. The method begins by determining an activity classification based on sensor data, which indicates a specific behavior performed by an individual. A synthesized data stream is generated from various sensor data streams, utilizing stream logic that allows for arithmetic and logical operations. This synthesized data stream is then analyzed using a predictive model, which is created from behavioral event data that includes user-defined annotations. The system detects any behavioral deviations from expected activity trends, triggering automated actions if these deviations suggest potential health or safety risks to the individual.

Additionally, the patent details various techniques for enhancing the accuracy of behavior detection. This includes translating statistical measurements of the synthesized data stream into approximations of different activities, employing heuristic classifications based on multiple variables such as sensor state and time of day. The system can also generate a confidence score to assess the reliability of the activity classification, further refining the predictive model's effectiveness. Notably, the detection of behavioral deviations can occur without direct visual observation of the individual, emphasizing the system's reliance on sensor data and predictive analytics to ensure safety and health monitoring within the property.

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