Inference Model Consensus for Resource Hedging Decisions
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Solution Overview
Problem
Inference models used for predicting future events can be unreliable due to incomplete, biased, or limited training datasets, leading to unpredictable resource availability and reduced quality or interruption of computer-implemented services.
Innovation Solution
Analyze a plurality of predictions using statistical methods to obtain a statistical characterization, compare it to criteria, and generate an action set based on a template tailored to the predicted event, updating the operating state to hedge against undesired outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inference models are used to generate predictions based on training datasets, then predictions about future events can be obtained, but the predictions may be unreliable due to incomplete, biased, or limited training data
Solution Approach 1:
The patent combines multiple predictions from different inference models into a single aggregated prediction. By merging the outputs of multiple models that may have been trained on different or complementary datasets, the system achieves more reliable predictions that compensate for the limitations of individual training datasets.
Solution Approach 2:
The system implements feedback mechanisms where predictions are continuously monitored and used to refine future predictions. Statistical characterization of prediction outcomes feeds back into the system, allowing the models to improve their reliability over time by learning from past performance and adjusting accordingly.
2Reliability
If multiple predictions are aggregated to improve reliability, then prediction accuracy increases, but the complexity of the system increases
Solution Approach 1:
The patent introduces statistical characterization as an intermediary layer between multiple inference models and the final decision-making process. This mediator aggregates and analyzes predictions from multiple models using statistical methods, simplifying the complexity by providing a unified statistical view rather than requiring direct integration of multiple complex models.
Solution Approach 2:
The system changes parameters by focusing on statistical characteristics (mean, variance, confidence intervals) rather than individual model outputs. This parameter transformation simplifies the aggregation process by converting multiple complex predictions into manageable statistical metrics that can be easily combined and analyzed.
3Reliability
If statistical characterization is used to analyze multiple predictions, then the reliability of predictions improves, but the computational resources and time required increase
Solution Approach 1:
The patent applies partial statistical analysis by focusing on key statistical characteristics (such as mean and variance) rather than performing complete exhaustive analysis of all prediction aspects. This selective approach provides sufficient reliability for decision-making while significantly reducing the computational time and resources required.
Solution Approach 2:
The statistical characterization process is segmented into distinct, manageable steps: collecting predictions, calculating basic statistical metrics, assessing confidence levels, and making decisions. This segmentation allows the system to perform statistical analysis incrementally and efficiently, reducing overall computational burden while maintaining reliability.
Data Source
AI summary
Methods and systems for managing inference models are disclosed. To manage inference models, a plurality of predictions that each indicate whether a state will occur may be obtained, the plurality of predictions being generated by respective inference models of at least one inference model. The plurality of predictions may be analyzed to obtain a statistical characterization regarding agreement in the plurality of predictions. A determination may be made regarding whether the statistical characterization meets criteria. In a first instance of the determination in which the statistical characterization meets the criteria, an action set may be obtained, the action set being based on an occurrence of the state predicted by the plurality of predictions to occur and usable to update an operating state of the data processing system to hedge against a risk of an undesired outcome from the occurrence of the state.