Real-Time Alert Classification with Anonymized UGC Data
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Solution Overview
Problem
Existing methods for training machine learning models to predict alerts and incidents on enterprise software platforms face challenges due to restrictions on using user-generated content (UGC) data, leading to inaccurate predictions and compliance issues with privacy regulations.
Innovation Solution
Transform UGC data through non-linear processes to anonymize it, allowing the training of machine learning models that maintain privacy compliance while ensuring predictive accuracy by generating UGC transformed alert data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user generated content (UGC) data is used to train machine learning models, then prediction accuracy improves, but privacy compliance deteriorates
Solution Approach 1:
The patent extracts and removes personally identifiable information (PII) from user-generated content data before training machine learning models. This extraction process separates the useful predictive information from the privacy-sensitive elements, allowing the model to learn from anonymized data that maintains prediction accuracy while complying with privacy regulations.
Solution Approach 2:
The patent introduces an intermediary anonymization layer between the raw UGC data and the machine learning model. This intermediary process transforms identifiable user data into anonymized representations that preserve the predictive value of the data while eliminating privacy risks, serving as a mediator that reconciles the conflict between data utilization and privacy protection.
2Object-affected harmful factors
If UGC data is anonymized through non-linear transformations, then privacy compliance improves, but data utility for training may deteriorate
Solution Approach 1:
The patent applies parameter changes by transforming the structure and format of UGC data through non-linear transformations such as tokenization, embedding, and aggregation. These parameter changes modify the data representation in ways that protect privacy while preserving the essential patterns and relationships needed for effective machine learning training.
Solution Approach 2:
The patent applies different levels and types of anonymization to different portions of the UGC data based on their sensitivity and importance. Critical predictive features are anonymized less aggressively, while highly sensitive personal information undergoes more rigorous transformation, maintaining data utility where possible while ensuring privacy compliance where necessary.
Data Source
AI summary
Apparatus, methods, and computer program products for categorizing a real-time monitoring service alert is provided. An example apparatus may include program code configured to cause the apparatus to retrieve the real-time monitoring service alert, the real-time monitoring service alert including a text string containing user generated content (UGC) text. In addition, the example apparatus may be configured to programmatically parse the text string of the real-time monitoring service alert to segregate the real-time monitoring service alert into an alert message problem component and an alert auxiliary details component. Further, the apparatus may be configured to determine, based on the alert message problem component, the alert auxiliary details component, and using an alert message machine learning model trained based on UGC transformed alert data, an alert message category of the real-time monitoring service alert.