Attention Mechanism RNN for User Interaction Pattern Analysis
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Solution Overview
Problem
Analyzing vast amounts of user interaction data in a temporal and longitudinal fashion to identify patterns and derive actionable insights is challenging for computer system operators, limiting their ability to maximize the value of this data.
Innovation Solution
Applying a modified attention mechanism in a recurrent neural network (RNN) deep learning model, such as LSTM, to analyze user interactions, identify correlated patterns, and adjust the user interface to prevent security breaches or optimize user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used to analyze user interaction data, then the analysis process is simple and fast, but the ability to identify patterns and derive actionable insights is limited
Solution Approach 1:
The patent replaces traditional mechanical data analysis methods with a neural network system that uses attention mechanisms. This substitution enables the system to automatically identify complex patterns in user interaction sequences that would be impossible to detect using conventional analysis tools, thereby improving measurement precision while accepting increased system complexity.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary layer between the raw interaction data and the pattern identification process. This intermediary selectively focuses on relevant portions of the interaction sequences, enabling more accurate pattern detection without requiring the system to process all data points equally, thus improving insight derivation while managing computational complexity.
2Loss of information
If detailed data about user interactions is collected, then the amount of information available for analysis increases, but the difficulty of analyzing and processing this data increases
Solution Approach 1:
The patent extracts only the most relevant features and patterns from the vast amount of collected interaction data using the attention mechanism. Instead of attempting to analyze all raw data points, the system selectively extracts meaningful information, thereby reducing processing difficulty while maintaining high information retention for the most critical aspects of user behavior.
Solution Approach 2:
The patent segments the complex interaction data into discrete interaction events that can be processed sequentially by the recurrent neural network. This segmentation transforms the overwhelming volume of raw data into manageable units, each representing a specific user action, making the data processing task more tractable while preserving the temporal relationships between interactions.
3Measurement precision
If recurrent neural networks with attention mechanisms are used to analyze user interactions, then pattern identification accuracy improves, but the computational resources and time required for analysis increase
Solution Approach 1:
The patent performs preliminary processing of interaction data by segmenting it into discrete events and pre-computing feature representations before feeding them to the neural network. This preliminary action reduces the computational burden during the actual analysis phase, enabling faster predictions while maintaining high accuracy through the use of pre-processed, structured input data.
Data Source
AI summary
Users interact with a computer system, which collects data about individual interactions the users have had with the computer system. The users are sorted into one of a first group or a second group. The computer system generates respective user sequence models for the users using information representing the individual interactions. The computer system analyzes the respective user sequence models using a recurrent neural network with an attention mechanism, which produces respective vectors corresponding to the user sequence models. Individual values in the vectors represent respective individual interactions by a given user and correspond to an amount of correlation between the respective individual interactions and the sorting of the given user into the first group or the second group. The computer system identifies a particular type of interaction that is correlated to users being sorted into the first group by analyzing the respective vectors.