Attention-Based Attribution for Null Interaction Filtering
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Solution Overview
Problem
Conventional techniques fail to effectively manage and optimize technical resources for electronic interactions, leading to server congestion and inefficiencies due to null interactions and inadequate analysis of interaction patterns, which consume valuable resources without contributing to defined outcomes.
Innovation Solution
An AI system utilizing a machine learning model to analyze electronic interaction data, identify patterns, and generate a metric representing the contribution of each interaction to a defined outcome, optimizing resource allocation and reducing null interactions through iterative processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional techniques are used to manage electronic interactions, then system operation is maintained, but server resources are wasted due to null interactions and lack of pattern analysis
Solution Approach 1:
The system continuously monitors electronic interaction data, analyzes patterns through machine learning models, and uses this feedback to identify and filter null interactions. This feedback loop enables the system to learn from past performance and improve resource allocation dynamically, reducing waste from ineffective interactions while maintaining system productivity.
Solution Approach 2:
The patent replaces conventional rule-based interaction management with AI-driven machine learning models that automatically detect and filter null interactions. This substitution of mechanical/rules-based systems with intelligent algorithms enables more efficient resource allocation by identifying patterns in interaction data that would be impossible to manually manage.
2Quantity of substance
If all electronic interactions are processed equally, then comprehensive data analysis is achieved, but computational resources are consumed by null interactions
Solution Approach 1:
The system extracts and separates null interactions from valid interactions through pattern recognition. By identifying and removing null interactions from the processing pipeline, the system reduces the computational burden on analyzing ineffective interactions while maintaining comprehensive analysis of meaningful data that drives business decisions.
Solution Approach 2:
The patent applies different processing quality levels to different interaction types. Through local quality analysis, the system intensifies computational resources on interactions showing promising patterns while applying lighter processing to clearly null interactions, optimizing the balance between data comprehensiveness and computational efficiency.
3Device complexity
If interaction patterns are not analyzed, then system simplicity is maintained, but resource allocation efficiency deteriorates
Solution Approach 1:
The system performs self-service through automated machine learning models that continuously analyze interaction patterns and adjust resource allocation without manual intervention. This self-service capability allows the system to improve its own efficiency by learning from historical data, reducing the need for complex manual configuration while maintaining high resource allocation efficiency.
Data Source
AI summary
Embodiments are directed to a method for generating a path embedding representing a decision path comprising a set of touchpoints to obtain a defined outcome, each touchpoint comprising an electronic interaction between electronic devices, generating an attention path embedding based on the path embedding using an attention network of a machine learning model, the attention path embedding comprising a set of aggregated attention weights for the set of touchpoints in the decision path, generating a set of touchpoint contribution values corresponding to the set of touchpoints based on the attention path embedding, a touchpoint contribution value from the set of touchpoint contribution values representing a level of contribution made by a touchpoint from the set of touchpoints to obtain the defined outcome, and providing a recommendation for a connections networking system based on the set of touchpoint contribution values. Other embodiments are described and claimed.