Autonomous Device Rating System Using Performance Data
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Solution Overview
Problem
Existing vendor-based rating systems for devices in distributed systems are static, subjective, and provider-centric, failing to accurately assess the capabilities and trustworthiness of devices for autonomous transactions, as they rely on human input and fixed rating criteria that do not adapt to changing device conditions.
Innovation Solution
A dynamic and customer-centric device rating system that enables devices to autonomously determine real-time ratings based on contextual and historical performance data, using device-specific criteria and blockchain technology for trustworthy data storage, allowing for selection of suitable devices for transactions without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If vendor-based rating systems use manual human input and fixed rating criteria, then the system is simple to implement and operate, but the rating accuracy and adaptability to changing device conditions deteriorate
Solution Approach 1:
The system enables devices to autonomously generate and update their own performance ratings by automatically collecting and analyzing their operational data, transaction history, and performance metrics. This eliminates the need for manual human input while maintaining simple system operation through automated self-assessment mechanisms.
Solution Approach 2:
The rating criteria transition from fixed static definitions to dynamic parameters that automatically adapt to changing device conditions, transaction types, and performance metrics. The system continuously updates ratings based on real-time performance data, making the evaluation criteria flexible and context-aware without requiring manual reconfiguration.
2Device complexity
If vendor-based rating systems use fixed predefined rating types, then the system complexity is reduced, but the adaptability to different transaction contexts and device capabilities deteriorates
Solution Approach 1:
The system employs multiple dynamic rating parameters (reliability, responsiveness, accuracy, capability matching) that can be weighted and adjusted based on transaction context and device type. These parameters automatically adapt to different transaction scenarios without increasing system complexity, as the adaptation is driven by algorithmic parameter weighting rather than complex rule-based systems.
Solution Approach 2:
The rating system is designed to universally evaluate diverse device types and transaction contexts through a unified framework that automatically adjusts its evaluation criteria. The same core system handles different device capabilities and transaction types by dynamically selecting and weighting appropriate parameters, eliminating the need for multiple specialized rating systems.
3Productivity
If autonomous device-to-device transactions are enabled without human intervention, then transaction speed and efficiency are improved, but the trustworthiness assessment and security verification become more challenging
Solution Approach 1:
The system implements continuous feedback loops where devices receive real-time ratings and performance evaluations from their transaction history and operational data. This feedback mechanism enables autonomous devices to make informed trust decisions by automatically analyzing rating data, performance metrics, and transaction outcomes without human intervention, maintaining both efficiency and reliability.
Solution Approach 2:
The system performs preliminary trustworthiness assessments and rating evaluations before transactions occur by analyzing historical performance data, device capabilities, and previous transaction outcomes. This advance evaluation enables autonomous devices to pre-verify trustworthiness criteria, ensuring secure transactions while maintaining high efficiency through automated pre-screening.
Data Source
AI summary
An example method includes sending a request for performance information associated with one or more candidate devices with which a device may perform a new transaction, responsive to sending the request, receiving the performance information that includes data associated with at least one historical transaction previously performed by the one or more candidate devices, analyzing the performance information using one or more device-specific performance criteria defined by the device, wherein the one or more device-specific performance criteria are associated with the new transaction and/or the candidate devices, determining, based on the analyzing, rating information associated with the candidate devices, wherein the rating information is customized for the device using the device-specific performance criteria that are defined by the device, selecting, based on the rating information, a particular candidate device from the one or more candidate devices, and initiating, with the particular candidate device, performance of the new transaction.