AI Boundary Control Using Environmental Context Filters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI systems face challenges in dynamically adjusting their control boundaries based on environmental contexts, leading to potential execution of incorrect actions due to unnecessary data input.
Innovation Solution
A method and system for controlling the boundary of an AI system by receiving data input from various sources, identifying contextual situations, and dynamically adjusting the input data collection boundaries and span of control to connect with appropriate data generation modules and execute actions within the defined control boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the AI system processes all available data from the surrounding environment, then the completeness of information is improved, but the accuracy of action execution deteriorates due to unnecessary data input
Solution Approach 1:
The patent segments the data collection process by defining specific boundaries (input data collection boundaries and span of control boundaries) that divide the environment into relevant and irrelevant zones. The AI-enabled device only collects data within these defined boundaries, separating necessary information from unnecessary data, thereby maintaining information completeness for relevant contexts while filtering out distracting irrelevant data that would reduce action execution accuracy.
Solution Approach 2:
The patent applies local quality by making the data collection scope context-dependent. Different contextual situations trigger different boundary configurations, allowing the system to adapt the quality and scope of data collection to local environmental conditions. This ensures that only data with appropriate relevance quality for the current context is processed, preventing both information loss and accuracy degradation.
2Adaptability or versatility
If the AI system dynamically adjusts its control boundaries, then the adaptability to environmental context is improved, but the system complexity increases
Solution Approach 1:
The patent implements dynamics by making the input data collection boundaries and span of control boundaries adjustable and context-dependent rather than fixed. The boundaries dynamically expand or contract based on the identified contextual situation, allowing the system to adapt its data collection scope to match environmental requirements. This dynamic adjustment provides high adaptability while the automated boundary management based on contextual rules keeps the control complexity manageable.
Solution Approach 2:
The AI-enabled device performs self-service by automatically identifying contextual situations and adjusting its own boundaries without external intervention. The system autonomously determines when to expand or contract its data collection scope based on environmental context, reducing the need for complex external control mechanisms and simplifying the overall system architecture while maintaining high adaptability.
3Quantity of substance
If the AI system connects to multiple data generation modules, then the quantity of available data is improved, but the processing efficiency deteriorates
Solution Approach 1:
The patent applies partial action by having the AI-enabled device connect to only those data generation modules that fall within its dynamically determined input data collection boundaries. Rather than connecting to all available modules excessively, the system selectively connects to the partial set of modules relevant to the current contextual situation. This ensures sufficient data quantity for accurate decision-making while avoiding the processing overhead of unnecessary connections, thereby maintaining processing efficiency.
Data Source
AI summary
An embodiment for controlling a boundary of an AI system based on an environmental context is provided. The embodiment may include receiving data input from one or more sources. The embodiment may also include identifying one or more contextual situations in a surrounding environment. The embodiment may further include identifying one or more input data collection boundaries and a span of control boundary for an AI-enabled device. The embodiment may also include connecting the AI-enabled device to one or more data generation modules within the one or more input data collection boundaries. The embodiment may further include in response to determining none of the one or more data generation modules are unable to connect, identifying an action to be performed by the AI-enabled device. The embodiment may also include executing the action in a first set of one or more machines within the span of control boundary.