Neural Network Customer Data Annotation
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Solution Overview
Problem
Current systems face challenges in tagging and identifying customer data, particularly in determining useful tags for customer data sets and identifying friction points, due to the explosion of data from multiple channels and the presence of noisy data, which leads to inefficient processing and inaccurate pattern learning.
Innovation Solution
A method that summarizes touchpoints into k-hot encoding feature vectors, maps these vectors onto an embedding layer, predicts a hierarchical data sequence, extracts the most influential feature vectors, and outputs the associated touchpoints, using a neural network to classify and predict friction points and tags in customer data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If customer data from multiple channels is collected to improve comprehensive understanding, then data completeness is improved, but data complexity and noise increase
Solution Approach 1:
The patent segments customer data into distinct touchpoints and interactions across multiple channels. By dividing the complex data into manageable segments (e.g., web interactions, mobile app usage, in-store visits), the system can process and analyze each segment individually while maintaining overall completeness, thus reducing perceived complexity.
Solution Approach 2:
The patent extracts meaningful patterns and friction points from the noisy multi-channel data through automated analysis. By taking out only the relevant information (such as identifying when customers encounter friction points), the system reduces data complexity while preserving essential insights from the complete data set.
2Measurement precision
If manual tagging of customer data is performed to improve accuracy, then tagging precision is improved, but processing time increases
Solution Approach 1:
The patent implements self-service through automated machine learning models that autonomously perform tagging and friction point identification. The system trains on historical data and automatically applies tags to new customer interactions without human intervention, achieving both high precision and fast processing times simultaneously.
Solution Approach 2:
The patent replaces manual mechanical tagging processes with automated computational algorithms. Using neural networks and pattern recognition algorithms, the system substitutes human analysts with automated systems that can process data at machine speed while maintaining or improving tagging precision through learned patterns.
3Measurement precision
If friction points are identified through complex analysis to improve accuracy, then measurement precision is improved, but computational resources required increase
Solution Approach 1:
The patent applies partial action by focusing computational resources on analyzing only the most relevant data segments and patterns. Instead of exhaustively analyzing every data point, the system identifies and processes only the critical touchpoints where friction is likely to occur, achieving high accuracy with reduced computational expenditure.
Data Source
AI summary
Aspects of the present disclosure relate to annotating or tagging customer data. In some embodiments, the annotating can include summarizing touchpoints into k-hot encoding feature vectors, mapping the feature vectors onto an embedding layer, predicting a hierarchical data sequence using the embedding layer and the feature vectors, extracting the feature vectors that are most influential in predicting the embedding layer, and outputting the touchpoints associated with the most influential feature vectors.