AI Unit for Autonomous Device Operation via Object Representation Learning
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Solution Overview
Problem
Existing computing devices rely on user-directed operations and lack the capability to learn and perform autonomous operations based on their surroundings, limiting their ability to adapt and function independently.
Innovation Solution
A system comprising a processor circuit, memory unit, and artificial intelligence unit that detects objects using sensors, learns object representations correlated with instruction sets, and anticipates and executes instructions for autonomous operation based on partial matches between new and learned object representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If devices operate based on user-directed instructions, then operational control is maintained, but autonomous operation capability is limited
Solution Approach 1:
The system is divided into distinct functional modules: sensor unit for environmental detection, AI unit for learning and anticipation, processor circuit for instruction execution, and memory unit for data storage. This segmentation enables autonomous operation while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The AI unit performs preliminary learning of object representations and their correlations with instruction sets before actual operation. This pre-learning phase enables the system to anticipate required instructions in advance, achieving autonomous operation without real-time complexity.
2Adaptability or versatility
If devices rely on user input for operation, then operational accuracy is maintained, but adaptability to new circumstances is reduced
Solution Approach 1:
The sensor unit continuously detects environmental objects and feeds this information to the AI unit, which compares current object representations with learned representations. This feedback mechanism enables the system to adapt to new circumstances while maintaining operational accuracy through correlation-based instruction selection.
Solution Approach 2:
The system changes its operational parameters dynamically by selecting different instruction sets based on the detected object representations. The AI unit adjusts the correlation thresholds and matching parameters to balance adaptability to new objects with accuracy in instruction execution.
3Reliability
If comprehensive object detection is implemented, then environmental awareness is improved, but processing time increases
Solution Approach 1:
The AI unit performs partial matching of object representations rather than complete analysis. By identifying key correlating features between detected objects and learned representations, the system achieves sufficient environmental awareness without exhaustive processing, reducing time loss while maintaining reliability.
Data Source
AI summary
Aspects of the disclosure generally relate to computing enabled devices and/or systems, and may be generally directed to devices, systems, methods, and/or applications for learning a device's operation in various circumstances, storing this knowledge in a knowledgebase (i.e. neural network, graph, sequences, etc.), and enabling autonomous operation of the device.