Ambient Device Orchestration for Capability Gap Filling
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Solution Overview
Problem
Existing systems struggle to efficiently assemble and utilize available machines and components in ambient computing environments to achieve specific objectives, especially when existing capabilities are insufficient or lacking.
Innovation Solution
A device management engine that communicates over a network fabric to identify and orchestrate available agent devices, determining their capabilities and instructing them to perform necessary subtasks, or assembling new configurations using 3D printing and other resources to fill capability gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing machines and components are assembled to achieve objectives, then system versatility is improved, but device complexity increases
Solution Approach 1:
The device management engine enables machines and components to autonomously discover themselves, register their capabilities, and be automatically orchestrated to achieve objectives without human intervention. The system self-organizes by having devices publish their capabilities to the network fabric and the engine automatically matching them to task requirements.
Solution Approach 2:
The device management engine creates a universal orchestration system that can handle diverse machine types and components through a common interface. The capability-based approach allows the same engine to manage different device types by evaluating their published capabilities rather than requiring device-specific control logic.
2Adaptability or versatility
If 3D printing and component assembly are used to fill capability gaps, then adaptability is improved, but manufacturing complexity increases
Solution Approach 1:
The system performs preliminary capability assessment and gap identification before physical assembly. The device management engine analyzes required capabilities versus available capabilities, plans the necessary modifications or additions, and only then triggers 3D printing or component assembly operations, avoiding unnecessary manufacturing complexity.
Solution Approach 2:
The system dynamically adjusts manufacturing parameters based on capability gaps. When components are needed to fill capability gaps, the engine determines optimal 3D printing parameters, material selection, and assembly sequences based on the specific capability requirements, transforming a complex manufacturing problem into a parameter optimization problem.
3Adaptability or versatility
If capabilities are dynamically determined and devices are reconfigured, then system adaptability is improved, but information processing requirements increase
Solution Approach 1:
The device management engine segments capability information into discrete, publishable units that devices can independently provide. Each device publishes its capabilities as separate capability descriptors to the network fabric, allowing the engine to process only relevant capability information rather than entire device state descriptions, reducing information processing load.
Data Source
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AI summary
A device management engine comprising a transceiver element operable to communicate with machines over a network fabric of an ambient computing environment; and an intelligent machine learning element operable to: responsive to a scenario prompt, define an overall objective comprising one or more physical subtasks; determine available agent devices in the ambient computing environment; determine capabilities of the available agent devices; identify available capable agent devices configurable to perform at least one of the one or more physical subtasks; and send first subtask instructions to a primary available capable agent device to perform at least one physical subtask of the one or more physical subtasks. In some cases, agent devices may be modified or augmented to enable them to contribute to achieving the objective.