Behavior Data Compression via Semi-Parametric Modeling
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Solution Overview
Problem
Processing large amounts of behavior data for authentication and analysis reduces the performance of analytical tools, necessitating efficient compression methods that minimize information loss.
Innovation Solution
The use of semi-parametric or non-parametric modeling techniques to generate a set of parameters that represent data points, allowing for improved analysis by reducing the cardinality of data used, with techniques like the elbow method to determine optimal parameter sets for minimal information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If large amounts of behavior data are collected and processed, then data accuracy and completeness are improved, but processing performance and analytical tool efficiency deteriorate
Solution Approach 1:
The patent extracts essential characteristics from large volumes of behavior data to create compressed representations. Behavioral patterns are decomposed into key features that capture the essence of user actions while discarding redundant information, thereby maintaining analytical accuracy while improving processing performance.
Solution Approach 2:
The patent transforms raw behavior data into compressed parameter representations by changing the state of data from detailed event logs to aggregated behavioral metrics. This parameter transformation reduces data dimensionality while preserving the information necessary for authentication and analysis, resolving the contradiction between data completeness and processing efficiency.
2Productivity
If behavior data is compressed to improve processing performance, then productivity is improved, but data accuracy and information completeness may deteriorate
Solution Approach 1:
The patent employs feedback mechanisms where compressed behavioral representations are continuously validated against authentication outcomes and analytical results. This feedback loop allows the system to refine compression parameters and ensure that essential information is retained, preventing information loss while maintaining processing performance gains.
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with machine learning-based compression models. These intelligent systems automatically identify and retain critical behavioral patterns during compression, substituting rigid data handling with adaptive algorithms that preserve information accuracy while achieving efficient processing.
3Measurement precision
If more behavior data is collected for sophisticated analysis, then measurement precision is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent segments complex behavioral analysis into distinct components: data collection, compression, pattern recognition, and authentication. By dividing the analytical process into manageable segments with specialized functions, the system achieves high measurement precision without proportionally increasing overall system complexity, as each segment can be optimized independently.
Data Source
AI summary
Methods and systems for efficiently compressing information comprising a plurality of data points along a particular dimension are presented. In some embodiments, a model may be generated using a semi-parametric modeling technique or a non-parametric modeling technique to represent the plurality of data points. The model may include a set of parameters that is less in size than the plurality of data points. Once the model is generated, the set of parameters may be stored and subsequently used to represent the information, with a significant reduction in storage space over the original data. In response to a request to analyze the information, the set of parameters may be analyzed to produce an outcome. Since the set of parameters have less cardinality than the plurality of data points in the original information, the efficiency of the analysis tool is enhanced.