Adaptive Data Analytics Service Closed-Loop System
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Solution Overview
Problem
Existing data analytics systems are typically disconnected from the physical systems they analyze, lacking the ability to automatically apply their findings, especially when dealing with multiple data streams in the physical world, and are not employed as administrative tools for real-time system adjustments.
Innovation Solution
The Adaptive Data Analytics Service (ADAS) implements a closed-loop system that characterizes system performance by analyzing user interactions and external influences, building models to optimize and predict changes, enabling ongoing monitoring and self-regulation of systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If analytics systems are disconnected from physical systems they analyze, then system complexity is reduced and ease of operation is improved, but the ability to automatically apply findings and perform real-time adjustments is lost
Solution Approach 1:
The patent implements a closed-loop feedback system where analytics results are automatically fed back to the physical system being analyzed. The analytics service receives data from sensors, processes it through machine learning models, and automatically adjusts system parameters based on the analysis, creating a continuous feedback loop that enables real-time automation while maintaining system connectivity.
Solution Approach 2:
The system enables self-service automation where the analytics service autonomously performs data collection, processing, and system adjustment without requiring constant human intervention. The machine learning models automatically learn from incoming data streams and make real-time decisions to optimize system performance, allowing the system to serve itself.
2Measurement precision
If analytics systems aggregate multiple data streams from physical systems, then measurement precision and predictive capability are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent introduces an intermediary analytics service layer that sits between the physical systems and the data processing infrastructure. This intermediary service aggregates and standardizes multiple data streams, performs preliminary processing and validation, and presents unified data to downstream analytical models, thereby managing complexity while maintaining measurement precision.
Solution Approach 2:
The analytics service is designed as a universal platform that can handle multiple types of data streams from various physical systems through standardized interfaces. The system performs multiple functions including data collection, validation, aggregation, processing, and system control through a single multi-functional service, reducing overall system complexity.
3Productivity
If analytics systems are used as administrative tools for real-time adjustments, then productivity and system efficiency are improved, but device complexity and extent of automation increase
Solution Approach 1:
The patent merges the analytics processing functions with the system control functions into an integrated administrative tool. The analytics service combines data processing, model execution, and system adjustment capabilities in a single unified system, enabling real-time productivity improvements without requiring separate complex subsystems for each function.
Data Source
AI summary
A closed-loop service, referred to as an Adaptive Data Analytics Service (“ADAS”), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined. This recursive process of observation, analysis, and adjustment provides a closed-loop system that affords adaptability to changing operating conditions and facilitates self-regulation and self-adjustment of systems.