Alarmcom had 14 patents in artificial intelligence during Q1 2024. Holdings Inc filed patents for methods and systems related to real-motion prediction, object embedded learning, and detecting/handling unauthenticated commands in a property monitoring system. These patents involve using machine learning models to analyze image frames for motion detection, determining object detection results and embeddings, and analyzing input commands for authentication before performing actions in a property monitoring system. GlobalData’s report on Alarmcom gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Alarmcom grant share with artificial intelligence as a theme is 57% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Real-motion prediction (Patent ID: US20240046485A1)

The patent filed by Holdings Inc. describes methods, systems, and apparatus for real-motion prediction using machine learning models and optical flow signatures. The process involves providing image frames of a scene, obtaining a temporally aggregated optical flow signature, detecting real-motion events by comparing motion vectors, and taking actions in response to these events. The system can detect spatiotemporally coherent motion of objects, generate event notifications, and train the machine learning model using training inputs with ground truth data.

The method outlined in the patent involves utilizing machine learning models to predict real-motion events based on optical flow signatures derived from image frames captured over time. By comparing motion vectors and setting thresholds, the system can detect and respond to real-motion events, including those involving inanimate or undefined objects. Additionally, the system can train the machine learning model using training inputs with ground truth data, generate event notifications, and compute traversal scores based on object locations over time. The patent also details the process of generating ground truth optical flow signatures using semantic scene segmentation algorithms and temporal smoothing filters to improve the accuracy of the predictions.

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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.