# US 12,111,745 B1 · Integrated production automation for real-time multi-frequency data processing and visualization in internet of things (IoT) systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,111,745 B1 - Document ID: US12111745B1 - Status: Granted - Kind: B1 - Application number: 18/613,907 - Filed: 2024-03-22 - Granted: 2024-10-08 - Earliest claimed: 2024-03-22 - Assignees: Enovate AI Corporation - Applicants: Enovate AI Corporation - Inventors: Camilo Mejia; Daniel Martinez; Rebecca Nye - Web page: https://patentgrep.com/patent/US12111745B1 ## CPC - G06F 11/328: {Computer systems status display (G06F11/327 takes precedence)} - G06F 11/3006: {where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50)} - G06F 11/3013: {where the computing system is an embedded system, i.e. a combination of hardware and software dedicated to perform a certain function in mobile devices, printers, automotive or aircraft systems (testing or monitoring of control systems or parts thereof G05B23/02)} - G06F 11/323: {Visualisation of programs or trace data} - G06F 11/3409: {for performance assessment} - G16Y 30/00: IoT infrastructure - H04L 41/16: using machine learning or artificial intelligence - G06F 2201/86: Event-based monitoring - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G16Y: INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT] - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A system comprising an interoperable digital architecture configured for real-time multi-frequency data collection, processing, and visualization of data from various IoT devices in real-time, an automated event detection module configured to detect events from the IoT devices in real-time and manage multi-frequency data, a modular AI module in communication with the automated event detection module, and configured to process the multi-frequency data in real-time through separate aggregated containers for increased computational efficiency, where the modular AI module is configured to parallelize AI processing, receive the multi-frequency data from the event detection module and processes it in parallel using a plurality of AI algorithms. A data sorting module configured to sort the processed multi-frequency data and send it to destinations across multiple cloud environments, and a relational database for segregation of raw multi-frequency data and processed multi-frequency data. ## Claims ### Claim 1 A system for processing and managing data in Internet of Things (IoT) systems, the system comprising: an interoperable digital architecture configured for real-time multi-frequency data collection, processing, and visualization, the interoperable digital architecture is designed to process data from various IoT devices and sensors in real-time; an automated event detection module integrated within the interoperable digital architecture, configured to detect events from the IoT devices and sensors in real-time and manage multi-frequency data; a modular AI module integrated within the interoperable digital architecture, in communication with the automated event detection module, and configured to process the multi-frequency data in real-time through separate aggregated containers for increased computational efficiency, the modular AI module further configured to parallelize AI processing for simultaneous logic layers in real-time, wherein the AI module receives the multi-frequency data from the event detection module, and processes it in parallel using a plurality of AI algorithms; a data sorting module integrated within the interoperable digital architecture, in communication with the modular AI module, configured to sort the processed multi-frequency data and send it to predefined destinations across multiple cloud environments; and a relational database integrated within the interoperable digital architecture, in communication with the data sorting module, for segregation of raw multi-frequency data and processed multi-frequency data. ### Claim 2 (depends on 1) The system of claim 1, wherein the automated event detection module is configured to process complex structures of the multi-frequency data from the IoT systems in real-time. ### Claim 3 (depends on 1) The system of claim 1, wherein the data sorting module is further configured to send the processed multi-frequency data to the predefined destinations across the multiple cloud environments, regardless of a design of the cloud environments or data structure requirements. ### Claim 4 (depends on 1) The system of claim 1, wherein the modular AI module is further configured to process the multi-frequency data in the separate aggregated containers, thereby increasing computational efficiency. ### Claim 5 (depends on 4) The system of claim 4, wherein the modular AI module is further configured to parallelize the AI processing for the simultaneous logic layers in real-time. ### Claim 6 (depends on 1) The system of claim 1, further comprising an automated alarm system configured for event recognition and data processing. ### Claim 7 (depends on 1) The system of claim 1, wherein the relational database segregates raw multi-frequency data and processed multi-frequency data, facilitating efficient data management and retrieval. ### Claim 8 (depends on 1) The system of claim 1, further comprising raw data hashed to assure the integrity of the data for tokenization and or blockchain applications. ### Claim 9 (depends on 1) The system of claim 1, further comprising automated signed URLs with digital tokens for signature identification, ensuring secure and efficient data transfer. ### Claim 10 (depends on 1) The system of claim 1, wherein the modular AI module further comprises multiple AI models operating in parallel, each AI model configured to process a subset of the multi-frequency data simultaneously, thereby increasing computational efficiency and enabling real-time processing of the multi-frequency data. ### Claim 11 A method for processing and managing data in Internet of Things (IoT) systems, the method comprising: collecting data in real-time from various IoT devices and sensors using an interoperable digital architecture configured for real-time multi-frequency data collection, processing, and visualization; detecting events from the IoT devices and sensors in real-time and managing multi-frequency data using an automated event detection module integrated within the interoperable digital architecture; processing the multi-frequency data in real-time through separate aggregated containers for increased computational efficiency using a modular AI module integrated within the interoperable digital architecture, in communication with the automated event detection module, the modular AI module further configured to parallelize AI processing for simultaneous logic layers in real-time, wherein the AI module receives data from the event detection module, and processes it in parallel using a plurality of AI algorithms; sorting the processed multi-frequency data and sending it to predefined destinations across multiple cloud environments using a data sorting module integrated within the interoperable digital architecture, in communication with the modular AI module; and segregating raw multi-frequency data and processed multi-frequency data using a relational database integrated within the interoperable digital architecture, in communication with the data sorting module. ### Claim 12 (depends on 11) The method of claim 11, wherein the detecting events includes processing complex structures of data from the IT systems in real-time. ### Claim 13 (depends on 11) The method of claim 11, wherein sorting the processed multi-frequency data includes sending the processed multi-frequency data to the predefined destinations across the multiple cloud environments, regardless of a design of the cloud environments or data structure requirements. ### Claim 14 (depends on 11) The method of claim 11, wherein processing the multi-frequency data includes processing the multi-frequency data in the separate aggregated containers, thereby increasing computational efficiency. ### Claim 15 (depends on 14) The method of claim 14, wherein processing the multi-frequency data further includes the parallelizing AI processing for the simultaneous logic layers in real-time. ### Claim 16 (depends on 11) The method of claim 11, further comprising recognizing the events and processing data using an automated alarm system. ### Claim 17 (depends on 11) The method of claim 11, wherein segregating raw multi-frequency data and processed multi-frequency data facilitates efficient data management and retrieval. ### Claim 18 (depends on 11) The method of claim 11, further comprising hashing raw data to assure the integrity of the data for tokenization and or blockchain applications. ### Claim 19 (depends on 11) The method of claim 11, further comprising generating automated signed URLs with digital tokens for signature identification, ensuring secure and efficient data transfer. ### Claim 20 (depends on 11) The method of claim 11, wherein processing the multi-frequency data further comprises operating multiple AI models in parallel, each AI model configured to process a subset of the multi-frequency data simultaneously, thereby increasing computational efficiency and enabling real-time processing of the multi-frequency data.