Apollo Global Management. has been granted a patent for a diagnostics system designed for premises monitoring. The system utilizes processing circuitry to analyze premises system data through supervised learning, generating classified resolutions linked to specific premises events related to the monitoring system. GlobalData’s report on Apollo Global Management 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 Apollo Global Management, was a key innovation area identified from patents. Apollo Global Management's grant share as of July 2024 was 64%. Grant share is based on the ratio of number of grants to total number of patents.
Diagnostics system for monitoring premises using deep learning
The granted patent US12073707B2 outlines a diagnostics system designed for monitoring premises through advanced data processing techniques. The system utilizes processing circuitry to receive and analyze premises system data, which includes customer support information and resolution code data. By employing supervised learning within a deep learning framework, the system classifies a set of resolutions linked to various premises events, such as security alarms, sensor activities, and HVAC performance. The data processed can include time series data, system-level data, and user analytics, with each resolution assigned a confidence score to indicate its reliability.
Additionally, the diagnostics system is capable of autonomously initiating corrective actions based on the classified resolutions, particularly when the confidence score surpasses a predetermined threshold. This feature allows the system to modify configurations of the premises monitoring system without user intervention. Furthermore, the system can assess customer experience by determining a customer experience score, which is displayed on a configurable user interface when it exceeds a specific threshold. The deep learning process employed in the system incorporates convolution and pooling layers to enhance the analysis of premises system data, ultimately improving the overall monitoring and management of premises events.
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