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, as it involves complex processes and misidentification.
Innovation Solution
A method and apparatus using machine-learning techniques to process candidate intervention representations by specifying parameters, identifying analytical constraints, and selecting relevant data for probabilistic filtering, enabling fast and accurate automatic filtering of interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual filtering of potential interventions is performed, then complex processes can be reviewed, but deployment time increases and errors occur
Solution Approach 1:
The patent replaces the manual mechanical filtering process with an automated machine-learning-based system. The processor executes algorithms that automatically evaluate candidate interventions against stored criteria, eliminating human manual review while maintaining or improving filtering accuracy through consistent application of predefined rules and probabilistic models.
Solution Approach 2:
The system enables self-service filtering where the machine-learning model autonomously evaluates and ranks candidate interventions without human intervention. The processor automatically compares interventions against stored criteria, generates probability scores, and produces filtered results, allowing the system to serve itself rather than requiring external manual processing.
2Adaptability or versatility
If manual filtering processes are used, then complex interventions can be evaluated, but misidentification and errors increase
Solution Approach 1:
The patent replaces manual evaluation with automated machine-learning algorithms that consistently apply predefined criteria without human error. The system processes complex interventions by comparing them against stored criteria in a database, using probabilistic models to evaluate suitability, thereby eliminating misidentification while maintaining the ability to handle complex cases.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine-learning model learns from evaluation results and adjusts its probabilistic assessments. By continuously comparing candidate interventions against stored criteria and learning from outcomes, the system improves its identification accuracy over time while maintaining adaptability to complex intervention types.
3Productivity
If automated machine-learning filtering is implemented, then deployment time decreases, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-storing evaluation criteria, rules, and parameters in a database before the filtering process begins. The machine-learning model is pre-trained with relevant data and criteria, allowing it to quickly evaluate candidate interventions without requiring complex real-time computations, thus achieving fast deployment while managing system complexity through advance preparation.
Solution Approach 2:
The system segments the filtering process into distinct components: storing criteria in a database, loading them into memory, executing machine-learning algorithms in the processor, and generating separate probability scores for different criteria. This segmentation allows each component to be optimized independently, improving overall filtering speed while making the complex system more manageable and maintainable.
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.