Role Classification Using Text Feature Extraction
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Solution Overview
Problem
Conventional digital analytics systems face limitations in accurately classifying user IDs associated with client devices due to reliance on limited data, such as the number of interactions, failing to capture the substance of interactions and leading to operational inefficiencies.
Innovation Solution
A role classification system that employs a first machine learning model to generate feature values from a corpus of text representing interactions, and a second machine learning model, such as a multinomial logistic regression model, to classify roles, indicating relationships between user IDs and products or services, thereby improving accuracy and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques use only interaction numbers for classification, then the system is simple to operate, but classification accuracy deteriorates
Solution Approach 1:
The patent transitions from one-dimensional numerical interaction counts to multi-dimensional text-based interaction analysis. By extracting features from text corpora (e.g., interaction quality, context, sentiment), the system adds dimensional depth to the classification input, enabling more accurate role classification while maintaining manageable complexity through automated feature extraction.
Solution Approach 2:
The system changes the fundamental parameter from simple interaction counts to rich text-based features. By transforming raw text interactions into structured feature vectors using natural language processing, the system captures nuanced interaction characteristics, thereby improving classification precision without proportionally increasing operational complexity.
2Measurement precision
If conventional techniques rely on limited data, then the processing speed is fast, but classification accuracy deteriorates
Solution Approach 1:
The system extracts meaningful features from large volumes of text data using NLP techniques. By selectively extracting relevant features (e.g., interaction intent, user sentiment, contextual information) from the text corpus, the system transforms unstructured data into concentrated, high-value feature sets that improve classification accuracy without requiring proportional increases in processing resources.
Solution Approach 2:
The patent combines multiple data sources and feature types into a composite feature representation. By integrating various text-based features (semantic content, interaction patterns, contextual information) into a unified feature vector, the system creates a rich, multi-faceted classification input that significantly improves accuracy while managing data quantity through intelligent feature synthesis.
3Productivity
If conventional techniques fail to capture interaction substance, then the system is simple to implement, but operational efficiency deteriorates
Solution Approach 1:
The patent replaces manual analysis of interaction substance with automated natural language processing systems. By using machine learning models to automatically extract and analyze text features, the system captures nuanced interaction characteristics without requiring manual intervention, thereby improving operational efficiency while keeping the system relatively simple to deploy through automated processing pipelines.
Data Source
AI summary
In implementations of systems for role classification, a computing device implements a role system to receive data describing a corpus of text that is associated with a user ID. Feature values of features are generated by a first machine learning model by processing the corpus of text, the features representing questions with respect to the corpus of text and the feature values representing answers to the questions included in the corpus of text. A classification of a role is generated by a second machine learning model by processing the feature values, the classification of the role indicating a relationship of the user ID with respect to a product or service. The role system outputs an indication of the classification of the role for display in a user interface of a display device.