Entity Classification and Rating Models for Automatic Discovery
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Solution Overview
Problem
Entity discovery, rating, and recommendation systems face inefficiencies due to the lack of explicit entity labeling, making many entities undetectable and unrecommendable without manual tagging.
Innovation Solution
A computer-based method utilizing entity classification and rating models to predict entity types and ratings from activity records, enabling automatic discovery and recommendation by extracting entity-related activity characteristics and updating ratings based on user modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If explicit entity labeling (tags, metadata, labels) is used, then entity discoverability is improved, but system complexity and manual effort increase
Solution Approach 1:
The system enables entities to be automatically discovered and labeled through machine learning models that analyze activity data patterns. The entity classification model engine automatically predicts entity types without requiring manual tagging, and the entity rating model engine automatically generates ratings based on extracted activity characteristics, making the system self-servicing rather than relying on manual human effort for labeling
Solution Approach 2:
The patent replaces the mechanical manual labeling process with automated machine learning-based entity classification and rating systems. The entity classification model engine uses trained classification parameters to automatically determine entity types, substituting human manual tagging operations with automated computational processes that analyze activity data patterns
2Measurement precision
If manual entity labeling is performed, then entity detection accuracy is improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The system automatically performs entity classification and rating through machine learning models that process activity data. The entity classification model engine automatically predicts entity types and the entity rating model engine automatically generates ratings without requiring manual human intervention, thereby maintaining detection accuracy while dramatically increasing processing throughput and productivity
Solution Approach 2:
The system performs preliminary entity classification and rating by analyzing activity data patterns before entities need to be discovered or recommended. The machine learning models are pre-trained on annotated training activity records, enabling them to automatically and accurately classify entities and generate ratings without requiring subsequent manual processing, thus improving both accuracy and productivity
3Productivity
If automatic entity classification and rating models are implemented, then productivity and automation are improved, but model training complexity and data requirements increase
Solution Approach 1:
The system uses a unified machine learning framework where the entity classification model engine and entity rating model engine share common infrastructure and processing pipelines. Both models operate on activity data through standardized interfaces, allowing the system to handle multiple entity-related tasks (classification, rating, discovery, recommendation) through a single automated platform, thereby managing complexity while maintaining high productivity
4Measurement precision
If user feedback and modifications are incorporated, then entity rating accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system incorporates user feedback through the entity rating interface where users can modify entity rating predictions. The entity rating model engine uses these modifications as feedback to retrain and improve its rating parameters, creating a continuous learning loop that enhances rating accuracy over time while maintaining efficient processing through automated model updates
Data Source
AI summary
Systems and methods of the present disclosure include computer systems for improving data discovery and recommendation. To do so, activity records associated with multiple entities are received, including activity data for electronic activities with the entities. An entity classification model engine including an entity classification model is utilized to predict at least one entity-type classification classifying at least one first entity of the entities as a first entity type. A first plurality of entity-related activity characteristics representing an activity pattern is associated with the at least one first entity and is extracted from the activity data. An entity rating model engine comprising an entity rating model is utilized to predict at least one entity rating prediction for the at least one entity based at least in part on the activity pattern, and an entity rating interface is generated comprising the at least one entity rating prediction interface element.