Inference Policy Generation from Multi-Model State Predictions
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Solution Overview
Problem
Inference models used for predicting future states in data processing systems often generate unreliable or biased predictions due to incomplete training datasets and stochastic elements, leading to inappropriate resource distribution and potential interruptions or reduced quality of computer-implemented services.
Innovation Solution
A method to manage inference models by generating a plurality of predictions, analyzing them statistically, and initiating a new policy when the predictions do not correspond to existing policies, involving interaction with a subject matter expert or automated methods to update the operation of data processing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If inference models are used to predict future states, then resource distribution can be optimized, but prediction reliability deteriorates due to incomplete training datasets and stochastic elements
Solution Approach 1:
The patent segments the prediction process into multiple independent inference models that each generate predictions. By dividing the prediction task across multiple models rather than relying on a single model, the system can analyze prediction consistency and identify reliable patterns while filtering out stochastic variations, thus maintaining resource distribution efficiency while improving prediction reliability.
Solution Approach 2:
The patent implements a feedback mechanism where predictions from multiple inference models are analyzed to determine consistency. When predictions are consistent across models, the system confidently executes resource distribution actions. When predictions vary, the system can adjust or seek additional analysis, creating a feedback loop that improves reliability without sacrificing productivity.
2Stability of the object's composition
If existing policies are used to manage data processing operations, then system operation is stable, but adaptability to new states deteriorates when predictions do not correspond to existing policies
Solution Approach 1:
The patent creates a dynamic policy management system where policies can be generated and updated based on predicted future states. When the system predicts a state that doesn't match existing policies, it can dynamically create new policies through interaction with subject matter experts or automated methods. This maintains stability by using existing policies when applicable while enabling adaptability when new conditions arise.
Solution Approach 2:
The patent performs preliminary analysis of predicted states against existing policies before executing operations. By pre-evaluating whether predicted states correspond to existing policies, the system can prepare appropriate responses in advance - either selecting from existing policies or initiating policy generation processes - ensuring both stability and adaptability are maintained proactively rather than reactively.
3Reliability
If multiple predictions are generated and analyzed, then prediction reliability is improved, but system complexity increases
Solution Approach 1:
The patent implements a self-service evaluation mechanism where the system automatically analyzes predictions from multiple inference models, determines their consistency, and makes decisions about policy selection or generation without requiring constant human intervention. This self-service capability handles the complexity of multi-model analysis internally while presenting simplified outcomes, thus improving reliability without proportionally increasing operational complexity.
Solution Approach 2:
The patent changes the parameter of prediction analysis from examining individual model outputs to examining the distribution and consistency of multiple predictions. By shifting the analytical parameter from single-point predictions to multi-point prediction patterns, the system can reliably assess prediction quality through statistical consistency measures, improving reliability while managing complexity through parameter transformation rather than complex processing.
Data Source
AI summary
Methods and systems for managing inference models are disclosed. To manage inference models, input data may be obtained, the input data being usable by at least one inference model to generate a plurality of predictions. Each of the plurality of predictions may indicate whether a future state will occur. A state analysis process may be performed to determine whether a new policy is to be generated for the state. If a new policy is to be generated for the state, the new policy may include an action set to be performed under conditions indicated by the input data and/or the plurality of predictions. The action set may be performed to update operation of one or more data processing systems that may be impacted by the state and computer-implemented services may be provided based on the updated operation.