Probabilistic Filtering of Candidate Intervention Representations
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Solution Overview
Problem
The manual filtering of potential interventions is time-consuming and prone to errors, leading to inefficiencies and waste in various industries due to its complexity and potential for misidentification.
Innovation Solution
A method and system for probabilistically filtering candidate intervention representations using a processor to specify parameters, identify analytical constraints, generate probabilistic outputs based on training data, and filter interventions automatically, leveraging machine-learning and artificial intelligence techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual filtering of potential interventions is performed, then filtering can be done with human judgment and oversight, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on historical intervention data before actual filtering operations. This preliminary training enables the system to quickly evaluate new interventions without manual review, resolving the contradiction by preparing the filtering mechanism in advance so that actual filtering can proceed rapidly while maintaining accuracy through the pre-learned patterns from historical data.
Solution Approach 2:
The patent replaces the mechanical human judgment process with an automated machine learning system. The ML model substitutes human reviewers by learning from historical intervention outcomes and automatically evaluating candidate interventions against learned patterns. This substitution eliminates human errors and time constraints while maintaining or improving filtering reliability through consistent application of learned criteria.
2Reliability
If manual filtering processes are used to evaluate interventions, then human expertise can be applied, but the complexity and potential for misidentification increase
Solution Approach 1:
The filtering system is segmented into distinct functional modules: data preprocessing module, feature extraction module, machine learning model evaluation module, and decision output module. Each module handles a specific aspect of the filtering process, making the overall complex system manageable and maintainable. The segmentation allows human expertise to be embedded in specific modules rather than requiring entire manual review processes, reducing operational complexity while maintaining evaluation accuracy.
Solution Approach 2:
The machine learning model acts as an intermediary between raw intervention data and final filtering decisions. Instead of direct human evaluation of complex intervention datasets, the ML model processes the data through learned patterns and presents simplified evaluation results to users. This intermediary layer reduces the complexity of direct human-intervention interaction while maintaining reliable evaluation through the model's learned expertise from historical data.
3Productivity
If automated filtering systems are implemented, then speed and consistency improve, but the system requires training data and model development time
Solution Approach 1:
The system performs preliminary actions by collecting and preparing training data from historical interventions before deploying the automated filtering system. This advance preparation includes gathering past intervention records, outcomes, and contextual data, then using this training set to train the ML model. By completing this preliminary training phase beforehand, the system achieves high filtering speed during operation without requiring complex real-time computations, thus resolving the contradiction between productivity and setup complexity.
Data Source
AI summary
Embodiments relate to systems and methods for probabilistically filtering candidate intervention representations. Systems and methods are described that receive a candidate intervention representation; specify a plurality of parameters as a function of the candidate intervention representation; identify a plurality of analytical constraints, where each analytical constraint corresponds to an analytical parameter of the plurality of parameters; generate a probabilistic output as a function of the candidate intervention representation, the plurality of analytic constraints, and training data correlating past intervention representations to a deterministic outcome; and, filter the at least a candidate intervention representation using the probabilistic output.