CACI International. has been granted a patent for a carrier system featuring multiple ruggedized, rack-mountable drawers. Each drawer includes a main body, fans, a power supply, and a card connector, allowing for the installation of plugin cards and communication with external parties via a processor. GlobalData’s report on CACI International 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 CACI International, was a key innovation area identified from patents. CACI International'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.
Ruggedized drawer system for modular electronic components
The patent US11978978B2 describes a sophisticated carrier system designed to enhance the management and functionality of multiple drawers, each equipped with a main body, fans, power supply, and a card connector. The card connector features a backplane that allows for the removable installation of plugin cards. A key component of this system is a processor within the card connector that facilitates communication with external third parties, enabling the representation of the drawers and prioritization of information from different card connectors. The system also includes capabilities for controlling the functionality of the plugin cards, orchestrating operations among drawers, and determining the operational state of each drawer based on internal monitoring and external reconciliatory information.
Additionally, the patent outlines a method for load balancing that integrates multiple drawers into a rack, with each drawer housing a set of plugin cards. The card connectors are designed with multiple printed circuit boards (PCBs) to ensure robust connections and management capabilities. The processor within the dedicated management card connector assesses the state of the drawers using various sensor outputs and input signals, categorizing them into operational states such as needing maintenance or being tampered with. Furthermore, the system incorporates machine learning to predict orchestration among the drawers, enhancing operational efficiency by utilizing data from both internal and external sensors. This innovative approach aims to streamline the management of complex systems, ensuring optimal performance and reliability.
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