Supervised Machine Learning Engine for Relationship Matching
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Solution Overview
Problem
Current methods for recommending successful relationships rely heavily on heuristics and rule-based matching, failing to leverage machine learning from empirical data to predict relationship quality effectively.
Innovation Solution
A supervised machine learning engine is trained on data from existing relationships, allowing it to evaluate candidate relationships and predict their quality by comparing attributes such as survey answers, facial images, and social networking data, thereby recommending likely matches based on calculated quality scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heuristics and rule-based matching are used to recommend relationships, then the system is simple to implement, but the prediction accuracy of relationship quality is poor
Solution Approach 1:
The patent replaces rule-based matching mechanisms with a machine learning model that learns relationship quality predictions from empirical data. The system substitutes deterministic rules with probabilistic machine learning algorithms that can capture complex patterns in relationship data, thereby improving prediction accuracy while managing system complexity through automated model training.
Solution Approach 2:
The patent transforms the matching approach by changing from fixed rule parameters to learned parameters from training data. The machine learning model adjusts its internal parameters based on empirical relationship data, enabling dynamic adaptation to different relationship patterns and improving prediction accuracy across diverse scenarios.
2Measurement precision
If machine learning is used to predict relationship quality, then the prediction accuracy improves, but the data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by training the machine learning model in advance on empirical relationship data before deployment. The model learns from historical relationship outcomes and stored data, preparing predictive capabilities beforehand so that when actual matching occurs, the system can efficiently process new data without requiring extensive real-time computation.
3Adaptability or versatility
If empirical data from existing relationships is used for training, then the model learns from real-world patterns, but the need for data storage and processing increases
Solution Approach 1:
The patent uses copying by storing and utilizing empirical relationship data as training examples. The machine learning model learns from copies of historical relationship data, survey responses, and outcome information, extracting patterns without requiring the original data to be continuously accessible. This allows the model to adapt to real relationship patterns while managing storage requirements through efficient data representation.
Data Source
AI summary
A system and method for recommending matches between persons are provided. Data processing is performed using artificial intelligence technology. A supervised machine learning engine is trained from empirical data about existing relationships which have been evaluated as to quality of the relationships. Quality of candidate relationships is calculated as an output of the supervised machine learning engine when provided input data of attributes of two candidate persons. Likelihood of a successful relationship between two candidate persons is predicted by comparing the calculated quality of the candidate relationship against a threshold. The prediction in this learning task may be made by a neural network. A user is notified of a candidate match that is likely to become a successful relationship.