Local AI Robot Control With Risk-Verified Language Models
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Solution Overview
Problem
Conventional AI language model-based control methods for intelligent devices and robots require high-performance computers and separate operation, leading to increased risks, errors, and resource consumption due to network communication and cryptographic procedures, which can cause significant damage.
Innovation Solution
A method and device using small language models integrated within standard computing units, continuously providing device data, and evaluating control risks to ensure safe and reliable operation without separate AI language model operation, minimizing errors and reducing system requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate large language models and high-performance hardware are used, then safety and reliability of control are improved, but system resource requirements and complexity increase
Solution Approach 1:
The patent combines the AI language model processing and device control functions into a single integrated system. The control unit directly executes the language model without separate intermediary systems, merging what were previously distinct components (separate LLM operation, network communication infrastructure, cryptographic processing systems) into one unified control architecture that reduces overall system complexity while maintaining safety and reliability
Solution Approach 2:
The patent extracts and eliminates unnecessary intermediary components from the control architecture. Specifically, it removes the requirement for separate enterprise-like high-performance hardware systems and complex network infrastructure by enabling direct execution of language models on standard computing devices, thereby reducing system resource requirements while preserving control reliability
2Adaptability or versatility
If separate AI language model operation with network communication is used, then control capabilities are improved, but communication delays and cryptographic processing time increase
Solution Approach 1:
The patent merges the AI language model execution environment with the device control unit into a single integrated system. This eliminates network communication between separate AI model servers and device controllers, removing cryptographic processing overhead and network latency entirely. The control unit directly runs the language model locally, combining what were previously separate operations into one immediate process that maintains full control capabilities without time loss
Solution Approach 2:
The patent eliminates the need for intermediary network communication and cryptographic processing layers. By enabling direct local execution of language models on the control unit, it removes the intermediary infrastructure (network protocols, encryption/decryption processes, remote server communication) that previously caused delays, while preserving complete control functionality
3Extent of automation
If conventional AI language model-based control methods are used, then control functionality is achieved, but risks from faulty control and errors increase
Solution Approach 1:
The patent implements continuous feedback loops where the integrated control unit constantly monitors device data and control outcomes. The system continuously provides device data back to the locally-executed language model for real-time assessment and adjustment, creating a closed-loop feedback mechanism that detects and corrects potential errors immediately, thereby reducing risks from faulty control while maintaining full automation functionality
Solution Approach 2:
The patent performs preliminary risk assessment and continuous evaluation of control commands before execution. The integrated system proactively analyzes potential errors and safety concerns in advance using the locally-executed language model, allowing preventive measures to be taken before faulty control can cause harm, thereby reducing risks while preserving automation capabilities
Data Source
AI summary
The present invention relates to a device and a method for controlling intelligent devices, intelligent technical systems, robots or the like as well as a intelligent device, intelligent technical system, robot or the like, which has such a device and/or can be controlled by such a method comprising a computer-implemented method and a device enabled by a AI small as well as large language model. The computer-implemented method generates control commands and includes verification steps to reduce negative effects and the control risk by modifying the control command, due to a possibly incorrect control, using deep neural networks comprising an AI language model executed at least on an artificial intelligence computing unit.