Dynamic Predictive Modeling for Sparse Operational Data
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Solution Overview
Problem
Current predictive models for operating systems, hardware devices, and machines are limited by the vast amount of data they need to analyze, leading to subjective selection techniques and reduced accuracy in predicting operational outcomes, especially due to sparse and non-uniformly sampled data.
Innovation Solution
The system dynamically updates predictive models by collecting and analyzing source data over time, applying statistical models to generate probability models, validating them, and selecting the most accurate models for deployment, while also removing outdated models and re-incorporating relevant data as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vast amounts of operational data are collected for predictive analysis, then prediction accuracy can be improved, but data analysis complexity and computational burden increase significantly
Solution Approach 1:
The patent segments the vast operational data into multiple data contexts (e.g., operational contexts, environmental contexts, maintenance contexts). Each data context is analyzed separately using appropriate statistical models, which reduces the complexity of analyzing all data simultaneously while maintaining comprehensive predictive accuracy.
Solution Approach 2:
The patent dynamically changes parameters such as the type of statistical model applied, the time window for data analysis, and the specific data contexts included based on the current operational state. This allows the system to adapt the analysis complexity to the specific prediction task, improving accuracy without consistently maintaining high computational burden.
2Device complexity
If subjective selection techniques are used to choose data subsets for analysis, then data analysis becomes more manageable, but prediction accuracy deteriorates due to exclusion of relevant data
Solution Approach 1:
The system automatically identifies and selects relevant data contexts and statistical models without human intervention. The autonomous feature evaluation and model selection processes eliminate subjective bias while managing data complexity through systematic, rule-based approaches that consider all available data objectively.
Solution Approach 2:
The patent dynamically adjusts which data contexts are included in the analysis based on their relevance to the current prediction task. Statistical models automatically evaluate and weight different data sources, ensuring that relevant data is incorporated while managing analysis complexity through adaptive parameter selection.
3Ease of manufacture
If static predictive models are deployed, then implementation is simpler and faster, but models become outdated and less accurate over time as operational conditions change
Solution Approach 1:
The patent implements dynamic predictive models that automatically update as new operational data becomes available. The system continuously evaluates new data contexts and recalibrates statistical models to reflect changing operational conditions, maintaining accuracy without requiring complex manual re-deployment processes.
Solution Approach 2:
The system incorporates feedback loops where prediction outcomes and new operational data are continuously fed back into the model updating process. This automatic feedback mechanism ensures models remain accurate over time by adapting to actual system behavior while maintaining relatively simple deployment through automated updates.
4Reliability
If frequent model updates are performed to maintain accuracy, then prediction reliability improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements periodic model updates triggered by specific conditions rather than continuous updates. Updates occur periodically based on factors such as accumulated data thresholds, significant operational condition changes, or scheduled intervals, maintaining prediction reliability while reducing unnecessary computational resource consumption during stable operational periods.
Data Source
AI summary
Various examples are provided for dynamically updating or adapting predictive modeling for prediction of outcomes of interest for operating systems and processes. Embodiments of the disclosure may provide systems, apparatus, processes, and methods for generating and deploying dynamically updated predictive models. In some embodiments, the predictive model may be deployed for the purpose of predicting operational outcomes of interests in operating systems, hardware devices, machines and/or processes associated therewith prior to the operational outcomes of interest occurring. The predictions can, for example, provide sufficient time for maintenance or repairs to be scheduled and carried out to avoid the predicted operational outcome. Autonomous evaluation of features allows the predictive models to be dynamically updated in response to changes in the environment or monitored data.