Natural Language Command Routing for Low-Power Autonomous Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for controlling remote autonomous objects with natural language commands require high-performance computing and dedicated hardware, which are bulky and power-intensive, or smaller versions are inadequate and inefficient due to the need for multiple verbal commands.
Innovation Solution
A system that selectively routes natural language commands through a simple or complex processing path based on confidence factors, using a basic processor to extract parameters, and optionally engages neural networks for context and situational awareness, reducing computational overhead and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If high-performance computing systems are used to control remote autonomous objects with natural language commands, then command and control capability is improved, but system size and power consumption increase
Solution Approach 1:
The system segments natural language processing into multiple specialized modules including speech-to-text conversion, intent recognition, parameter extraction, and command generation. Each module handles specific aspects of language processing independently, allowing the system to achieve comprehensive NLP capability without requiring a single large-scale computing system, thereby reducing overall power consumption while maintaining operational effectiveness.
2Use of energy by moving object
If smaller scale computing systems are used for remote autonomous object control, then power consumption is reduced, but processing efficiency and understanding capability deteriorate
Solution Approach 1:
The system dynamically adapts its processing depth based on command complexity and confidence levels. For straightforward commands, the system uses lightweight processing paths that consume minimal power. For complex or ambiguous commands, it selectively engages more computationally intensive modules only when necessary, optimizing the balance between processing efficiency and power consumption in real-time operations.
Solution Approach 2:
The system performs preliminary processing steps such as speech-to-text conversion and basic intent recognition using lightweight algorithms before determining whether full neural network processing is required. This preliminary action filters out simple commands that don't require intensive computation, thereby improving overall processing efficiency while reducing unnecessary power consumption.
3Measurement precision
If multiple verbal commands are required for simple tasks, then processing accuracy is improved, but operational time and complexity increase
Solution Approach 1:
The system implements confidence level assessment and feedback mechanisms that monitor the clarity and completeness of received commands. When a command is received with high confidence, the system executes it immediately without requiring additional clarification. When confidence is low, the system selectively requests clarification only for ambiguous portions, thereby maintaining high accuracy while minimizing the time loss from additional verbal exchanges.
Data Source
AI summary
Exemplary systems and methods are directed to reducing processing complexity for command and control of a remote autonomous object in response to natural language commands. A processor receives a user command in a natural language format and extracts designated parameters for matching with a control prompt stored in a library of control prompts. The processor determines a confidence from a result of the operation. The confidence factor is used to selectively route the user command to a simple command processing path to generate a simple command message or a complex command processing path having one or more neural networks to generate a complex command message. The processor compares the simple command objective message, or the complex command objective message generated from the selective routing operation with known capabilities of the remote autonomous object to identify a remote autonomous object command, which is formatted and transmitted to the remote autonomous object.