User Interaction Data Compression for ML Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for deploying machine learning models are inefficient due to the high storage and processing requirements of user interaction data, which necessitate large server infrastructure and consume significant resources, making them costly and resource-intensive.
Innovation Solution
The method involves compressing user interaction data into a relevant subset, reducing storage and processing needs, by identifying and discarding irrelevant data, and using the compressed data to train machine learning models, allowing for efficient data access and processing while maintaining privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all user interaction data is stored and processed for machine learning model training, then the model can achieve high accuracy in detecting target category examples, but the storage and processing costs become prohibitively expensive and resource-intensive
Solution Approach 1:
The patent extracts only the most relevant and informative features from user interaction data using automated feature engineering. The system identifies and retains key characteristics that are most predictive of the target category while discarding redundant information, thereby reducing data volume while preserving model accuracy.
Solution Approach 2:
The patent transforms raw user interaction data into optimized feature representations through automated feature engineering processes. By changing the parameters and form of the data (from raw interactions to engineered features), the system reduces storage requirements while maintaining or improving the information content useful for machine learning models.
2Reliability
If all user interaction data is processed for model training, then comprehensive patterns can be detected, but the processing time and computational resources increase significantly
Solution Approach 1:
The system extracts only the most relevant features from user interaction data, eliminating redundant processing of unnecessary information. This selective extraction maintains comprehensive pattern detection capability for critical behaviors while significantly reducing overall processing time.
Solution Approach 2:
The patent performs automated feature engineering in advance to pre-process and organize user interaction data into meaningful features before model training. This preliminary action structures the data in a way that enables faster processing during actual model training and deployment.
3Productivity
If large server infrastructure is deployed to handle all user interaction data, then the system can process data efficiently, but the deployment cost and infrastructure complexity increase
Solution Approach 1:
The system extracts and processes only essential features from user interaction data, reducing the computational burden on servers. This approach maintains data processing efficiency while allowing deployment on less complex and more cost-effective infrastructure.
Solution Approach 2:
The patent transforms raw data into optimized feature sets that reduce the computational complexity required for processing. By changing the data representation parameters, the system achieves efficient processing with simpler server infrastructure requirements.
4Adaptability or versatility
If complete user interaction data is retained and accessed, then full analytical capability is available, but data privacy and security risks increase
Solution Approach 1:
The system extracts only the necessary features required for analytical purposes while leaving out personally identifiable or sensitive information. This selective extraction maintains analytical capability while inherently reducing data privacy and security risks by not storing or processing unnecessary sensitive data.
Data Source
AI summary
A processing system may identify a plurality of user interaction data associated with a target category of a plurality of users, identify a relevant subset of user interaction data, compress the plurality of user interaction data to the relevant subset of user interaction data, train a machine learning model with the relevant subset of user interaction data, obtain additional user interaction data associated with an additional user, identify a relevant subset of the additional user interaction data, apply the relevant subset of the additional user interaction data as an input to the machine learning model, obtain an output of the machine learning model quantifying a measure of which the relevant subset of the additional user interaction data is indicative of the target category, and perform at least one action responsive to the measure of which the relevant subset of the additional user interaction data is indicative of the target category.