Far-Edge IoT Intelligence Feedback Loops for Operator Assistance
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Solution Overview
Problem
Existing systems fail to effectively bridge the gap between the digital and real worlds by lacking the ability to cross data and expertise domains, construct actionable cross-domain knowledge, and leverage multiple human cognitive capabilities, leading to potential dangers and inefficiencies in decision-making.
Innovation Solution
A decentralized intelligent assistance system that aggregates and shares data, information, learning, and actions among far edge sites, utilizing machine learning and AI to support human operators, enabling collaboration between humans and machines to identify situations and take timely actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data is processed and transformed into actionable information using traditional centralized systems, then decision-making support is provided, but the system complexity and response time increase due to data transmission distances and processing bottlenecks
Solution Approach 1:
The patent divides the centralized data processing system into distributed edge computing nodes deployed across multiple locations. Each edge node independently processes local data and provides decision support, eliminating the need for all data to travel to a central processing facility. This segmentation reduces transmission time and enables parallel processing, thereby decreasing overall system response time while distributing computational complexity across multiple simpler nodes rather than concentrating it in one complex central system.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by deploying edge computing nodes across geographically distributed locations. Instead of a single centralized processing point, the system creates a three-dimensional processing architecture where data can be handled at multiple spatial levels (local edge nodes, regional aggregates, and central coordination). This dimensional expansion allows simultaneous local decision-making at edge nodes while maintaining overall system coherence, reducing response time without proportionally increasing perceived system complexity.
2Adaptability or versatility
If traditional data transformation methodologies are used, then data processing is achieved, but the ability to construct actionable cross-domain knowledge is limited due to rigid transformation rules and lack of feedback loops
Solution Approach 1:
The patent implements feedback loops where actionability scores and human operator interactions are continuously fed back into the reinforcement learning models. Edge computing nodes monitor the effectiveness of generated insights and adjust their data processing and knowledge construction approaches accordingly. This feedback mechanism enables the system to learn from actual decision-making outcomes, improving its ability to construct actionable cross-domain knowledge over time while adapting to specific domain requirements without requiring complex manual reconfiguration.
Solution Approach 2:
The patent dynamically adjusts transformation parameters and data processing approaches based on the specific domain context and the measured actionability of generated insights. Rather than using fixed rigid transformation rules, the system modifies processing parameters, feature selection, and knowledge construction strategies in response to feedback from real-world decision-making effectiveness. This parameter adaptability enables versatile cross-domain knowledge construction while the automated reinforcement learning approach manages the complexity of parameter adjustment.
3Reliability
If human operators make decisions based on raw data without intelligent assistance, then decision-making autonomy is maintained, but the quality and timeliness of decisions deteriorate due to information overload and cognitive limitations
Solution Approach 1:
The patent introduces an intermediary layer of edge computing nodes and AI models that act as intelligent mediators between raw data and human decision-makers. These intermediaries process, filter, and transform raw data into actionable insights with associated actionability scores, presenting only the most relevant information to human operators. This intermediary layer improves decision quality by reducing information overload and highlighting critical insights, while the modular architecture of distributed edge nodes keeps the complexity of the intelligent assistance system manageable and transparent.
4Reliability
If centralized data processing is implemented, then comprehensive data analysis is achieved, but the system becomes vulnerable to single points of failure and cannot provide timely local responses
Solution Approach 1:
The patent segments the centralized processing architecture into multiple independent edge computing nodes distributed across different locations. Each node can independently process local data and provide decision support, eliminating single points of failure. If one node fails, others continue to operate autonomously. The segmentation creates a resilient distributed system where failure is localized rather than system-wide, improving reliability while the standardized edge node architecture keeps individual component complexity manageable.
Solution Approach 2:
The patent merges the benefits of centralized coordination with distributed processing by having edge computing nodes operate autonomously at the local level while simultaneously participating in a coordinated network. Nodes share insights and learn from each other, combining local autonomy with global intelligence. This merging approach achieves both timely local responses from distributed nodes and comprehensive data analysis through networked collaboration, improving reliability without requiring excessive complexity in individual nodes.
Data Source
AI summary
One example method is performed at a far edge device and includes collecting data with one or more IoT (Internet of Things) devices, feeding the data to a feedback loop that includes multiple stages, running the feedback loop, providing learning information, comprising output from one or more of the stages of the feedback loop, to a central manager by way of a learn interface, accessing learning information generated by one or more other far edge devices, and updating the feedback loop using the learning information generated by the one or more other far edge devices.