ML Framework for Dynamic Entity Scoring and Bias Reduction
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Solution Overview
Problem
Conventional approaches for identifying high-quality entities in CRM databases rely on static qualification rules, which are manual, non-quantitative, and lack accuracy evaluation, failing to capture nonlinear correlations and introducing bias.
Innovation Solution
A machine learning framework that trains a prediction model on entity and sub-entity data, allowing for dynamic adaptation and probabilistic scoring, capturing nonlinear correlations and providing intuitive probability scores, while automatically retraining based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static qualification rules are used to identify high-quality entities, then the methodology is simple and easy to implement, but the accuracy and adaptability deteriorate due to inability to capture nonlinear correlations and manual tuning requirements
Solution Approach 1:
The patent replaces the mechanical system of static qualification rules with a machine learning-based prediction model. This model automatically learns nonlinear correlations from historical data and dynamically scores entities, eliminating the need for manual rule tuning while significantly improving identification accuracy through probabilistic scoring and continuous adaptation to changing patterns.
2Device complexity
If static qualification rules are used, then the system structure is simple, but the adaptability to real-world changes deteriorates due to manual tuning requirements
Solution Approach 1:
The patent transforms the static qualification rule system into a dynamic machine learning prediction model that continuously adapts to real-world changes. The model automatically updates its parameters and learned relationships based on new data, enabling it to capture evolving nonlinear correlations without requiring manual intervention, thus achieving high adaptability while managing complexity through automation.
Solution Approach 2:
The prediction model performs self-service by automatically training and retraining itself on historical and new data without human intervention. It autonomously identifies patterns, adjusts its internal parameters, and improves its performance over time, eliminating the need for manual rule tuning and enabling continuous adaptation to changing conditions.
3Adaptability or versatility
If manual tuning of qualification rules is performed, then the system can be adjusted, but the time consumption and productivity deteriorate due to lack of automated evaluation
Solution Approach 1:
The patent implements a feedback mechanism where the prediction model is automatically evaluated using performance metrics such as area under the curve (AUC) and mean squared error (MSE). This automated feedback loop enables rapid assessment of model effectiveness, allowing for quick adjustments and retraining based on performance feedback, thereby dramatically reducing the time required for rule adjustment while maintaining high adaptability.
Solution Approach 2:
The patent enables efficient adjustment of system parameters through automated machine learning techniques. Instead of manually tuning each qualification rule parameter, the system automatically optimizes parameters during the model training process using algorithms that search for optimal parameter configurations, significantly reducing the time and effort required for adjustment while maintaining adaptability.
4Ease of manufacture
If static qualification rules are used, then the methodology is straightforward, but the reliability deteriorates due to introduction of bias and lack of quantitative evaluation
Solution Approach 1:
The patent replaces the mechanical system of static qualification rules with a data-driven machine learning prediction model that automatically learns from historical data without human bias. This substitution eliminates the subjectivity and bias inherent in manual rule creation while providing quantitative evaluation through performance metrics, significantly improving the reliability of entity identification while maintaining methodological simplicity through automation.
Solution Approach 2:
The prediction model performs self-service by automatically learning from data and evaluating its own performance using metrics like AUC and MSE. This self-evaluation capability provides objective, quantitative assessment of reliability without manual intervention, eliminating bias while maintaining simplicity through automated processes that continuously monitor and report model effectiveness.
Data Source
AI summary
Techniques for building a machine learning framework with tracking, model building and maintenance, and feedback loop are provided. In one technique, a prediction model is generated based on features of multiple entities. For each entity indicated in a first database, multiple feature values are identified, which include feature values stored in the first database and feature values based on sub-entity data regarding individuals associated with the entity. The feature values are input into the prediction model to generate a score for the entity. Based on the score, a determination is made whether to add, to a second database, a record for that entity. The second database is analyzed to identify other entities. For each such entity, a determination is made whether to generate a training instance; if so, a training instance is generated and added to training data, which is used to generate another prediction model.