Predictive Performance Analysis Widgets for Real-Time Insights
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Solution Overview
Problem
Performance analysis in real-time and at scale is hindered by deficiencies in existing systems, particularly for analytical units, which lack efficient methods to generate predictive performance data sets and provide actionable insights.
Innovation Solution
A system and method that utilizes trained performance analysis models, including machine learning algorithms and generative artificial intelligence, to generate predictive performance data sets for analytical units, which are then displayed via renderable virtual widgets on user devices, including augmented reality interfaces, to provide performance metrics, optimization insights, and improvement recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional performance analysis methods are used, then implementation is simple, but real-time analysis capability and scalability are insufficient
Solution Approach 1:
The system pre-generates predictive performance data sets using machine learning models before actual performance analysis is needed. This allows the system to have analysis results ready in advance, enabling real-time query and display without performing complex calculations at the moment of analysis, thus achieving real-time capability without proportionally increasing system complexity during operation.
Solution Approach 2:
The system creates virtual copies of performance data and analysis results through renderable virtual widgets that replicate the appearance and information of actual performance metrics. These virtual representations can be displayed and interacted with without requiring the underlying complex analysis infrastructure to be actively computing, enabling simple access to complex analysis results.
2Loss of information
If comprehensive performance analysis is performed, then analysis depth and insight quality improve, but processing time and computational resources increase
Solution Approach 1:
The system performs comprehensive performance analysis and generates detailed predictive data sets in advance using machine learning models. By completing the computationally intensive analysis beforehand, the system preserves full insight quality while reducing processing time during actual use, as subsequent operations only require retrieving and displaying pre-computed results.
Solution Approach 2:
The performance analysis process is divided into distinct segments: data collection, model training, predictive data generation, and result display. Each segment can be executed independently and cached, allowing the system to provide comprehensive analysis depth without requiring all processing to occur simultaneously, thus reducing perceived processing time for users.
3Adaptability or versatility
If multiple platforms are used for performance analysis, then functional coverage is comprehensive, but network traffic and system integration complexity increase
Solution Approach 1:
The system combines multiple performance analysis functionalities into a single integrated platform that provides data collection, machine learning model training, predictive analysis, and visualization capabilities. By merging these previously separate functions into one system, the patent reduces network traffic and integration complexity while maintaining comprehensive functional coverage through the unified architecture.
Solution Approach 2:
The performance analysis system is designed as a universal platform capable of handling multiple types of analytical units and performance metrics through a common architecture. The system can analyze different data types and provide various insights using the same core infrastructure, eliminating the need for multiple specialized platforms and reducing overall system complexity and network overhead.
Data Source
AI summary
Various embodiments are directed to apparatuses, methods, computer-readable media, computer program products, and systems related to predictive performance analysis. In some embodiments, the method may comprise receiving, by one or more processors and from one or more data sources, unit performance data for an analytical unit; applying, by the one or more processors, the unit performance data to one or more trained performance analysis models to generate a predictive performance data set for the analytical unit by analyzing the unit performance data using the one or more trained performance analysis models; generating, by the one or more processors, one or more renderable virtual widgets comprising one or more representations of at least a portion of the predictive performance data set for the analytical unit; and displaying, by the one or more processors, the one or more renderable virtual widgets on a screen of a user device.