Photonic Quantum AI for Automated Crowdsensing Device Selection
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Solution Overview
Problem
Existing technologies face challenges in identifying and engaging the appropriate mobile/IoT devices for crowdsensing activities, particularly in participatory and opportunistic scenarios, without user intervention.
Innovation Solution
A computing platform leveraging photonic quantum generative AI to generate crowdsensing device configurations by analyzing user prompts, identifying device clusters, and orchestrating data extraction and acquisition rules through smart contracts, while monitoring and remediating issues in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If photonic quantum generative AI is used to generate crowdsensing device configurations, then device selection efficiency and accuracy are improved, but system complexity increases
Solution Approach 1:
The patent introduces a photonic quantum generative AI model as an intermediary component that automatically generates device configurations based on task requirements. This AI mediator processes inputs (task descriptions, constraints) and outputs optimized device selections, eliminating the need for manual configuration and reducing operational complexity despite increasing computational system complexity
Solution Approach 2:
The patent replaces traditional mechanical selection processes (manual device selection, conventional algorithms) with photonic quantum computing mechanisms. This substitution enables exponentially faster configuration generation and optimization compared to classical computing approaches, improving productivity while managing complexity through quantum mechanical processes
2Ease of operation
If crowdsensing device configurations are automatically generated and executed, then operational convenience is improved, but control and monitoring difficulty increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors device performance, task completion status, and configuration effectiveness. This feedback loop enables automatic adjustment and optimization of device selections, maintaining ease of operation while improving control through real-time data-driven monitoring and adaptive reconfiguration
Solution Approach 2:
The system performs self-service through automated configuration generation, selection, and optimization. The photonic quantum AI model autonomously processes task requirements, selects appropriate devices, and adjusts parameters without human intervention, thereby improving operational convenience while managing control complexity through intelligent self-regulation
Data Source
AI summary
A computing platform may receive, from a user device, a prompt configured for input into a generative AI model. The computing platform may input the prompt into the generative AI model to identify a schemas for use in providing a response to the prompt, where each schema may be a configuration of device clusters, each device may be an IoT enabled device configured to provide crowdsensed information, and the generative AI model may score the schemas, and select, based on identifying that a first schema has a highest score, the first schema. The computing platform may collect, from devices comprising the first schema, crowdsensed information. The computing platform may generate, based on the crowdsensed information, a response to the prompt. The computing platform may send, to the user device, the response to the prompt and may cause the user device to display the response to the prompt.