Multi-Factor Neural Scoring for Contradictory Action Recommendations
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Solution Overview
Problem
Existing systems face challenges in analyzing complex data sets to determine optimal actions and entities for those actions due to unclear relationships and interactions between factors, leading to contradictory recommendations.
Innovation Solution
A deep learning system, such as neural networks, is employed to analyze data against multiple factors, generating weighted scores and recommendations for possible outcomes, allowing for informed decision-making on actions and entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple factors are considered for evaluating actions on entities, then the comprehensiveness of analysis is improved, but the complexity of modeling relationships and interactions between factors increases
Solution Approach 1:
The patent divides the complex multi-factor evaluation problem into separate neural network modules, each dedicated to processing specific factors (e.g., customer demographics, purchase history, product characteristics). This segmentation allows each module to handle one aspect independently, reducing the overall modeling complexity while maintaining comprehensive factor consideration through the modular architecture.
Solution Approach 2:
The patent introduces an intermediary layer of neural networks that act as mediators between individual factor evaluations and the final action recommendation. These intermediary networks process and integrate factor scores, transforming complex multi-factor relationships into simplified decision outputs. The intermediary layer bridges the gap between comprehensive factor analysis and actionable recommendations without requiring direct complex modeling of all factor interactions.
2Productivity
If factors are aggregated to evaluate action value, then the ability to make recommendations is improved, but the difficulty of combining contradictory factor recommendations increases
Solution Approach 1:
The patent transforms qualitative factor evaluations into quantitative parameter scores through neural network processing. Each factor is converted into a numerical score that can be mathematically aggregated. This parameter transformation enables systematic combination of factors using standard mathematical operations (weighting, summing, normalization), turning the difficult task of aggregating contradictory recommendations into a manageable computational process.
Solution Approach 2:
The patent creates an equipotential evaluation framework where all factors are processed through the same neural network architecture and scoring mechanism. This standardized processing ensures that each factor contributes equally to the overall evaluation in terms of methodological treatment, allowing for fair aggregation even when factors have contradictory recommendations. The uniform processing approach eliminates bias in factor aggregation.
3Measurement precision
If deep learning systems are used to analyze complex data, then the accuracy of action recommendations is improved, but the computational resources and training requirements increase
Solution Approach 1:
The patent segments the deep learning system into specialized neural network modules, each handling specific factor analysis. This segmentation allows the system to process data in smaller, manageable chunks through dedicated networks rather than using a single large complex network. The modular approach reduces computational burden on individual components while maintaining high accuracy through specialized processing of each factor type.
Solution Approach 2:
The patent applies partial action by training neural networks on specific subsets of factors rather than attempting to process all possible factors simultaneously. Each neural network module is trained on a targeted subset of data related to its specific function, reducing the overall training requirements and computational resources needed compared to training a single comprehensive system on all data.
Data Source
AI summary
Systems and methods for integrated multi-factor multi-label analysis include using one or more deep learning systems, such as neural networks, to analyze how well one or more entities are likely to benefit from a targeted action. Data associated with each of the entities is analyzed to determine a score for each of the proposed targeted actions using multiple analysis factors. The scores for each analysis factor are determined using a different multi-layer analysis network for each analysis factor. The scores for each analysis factor are then combined to determine an overall score for each of the proposed targeted actions. The entities and the proposed targeted actions with the highest scores are then identified and then used to determine which entities are to be the subject of which targeted actions.