Common Data Repository for Transactional Efficiency
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Solution Overview
Problem
Existing methods for identifying benefits and drawbacks across multiple communication channels are tedious, cumbersome, and typically analyzed on a reactionary or ad-hoc basis due to the volume of user interactions, lacking proactive efficiency in improving transactional services.
Innovation Solution
An information processing system automatically generates a common data repository by analyzing documents to identify parameters and performance metrics, determining correlations, and generating a predictive model to identify actionable items that improve transactional efficiency across communication channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to identify benefits and drawbacks across multiple communication channels, then analysis depth can be thorough, but the process becomes tedious, cumbersome, and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated computer-based systems that use machine learning models and algorithms to analyze communication channel data, thereby eliminating the time-consuming nature of manual analysis while maintaining or improving analysis depth through systematic processing of large datasets
Solution Approach 2:
The system enables self-service analysis by automatically collecting, processing, and analyzing communication channel data without requiring manual intervention, allowing the system to serve its own analytical needs through automated generation of insights and recommendations
2Reliability
If reactive or ad-hoc analysis is performed on user interactions, then response to specific issues can be addressed, but proactive efficiency improvement is lacking
Solution Approach 1:
The patent implements preliminary action by using machine learning models to predict future performance outcomes and identify potential issues before they occur, enabling proactive adjustments to communication channels that improve transactional efficiency rather than merely reacting to problems after they arise
Solution Approach 2:
The system establishes continuous feedback loops where analysis results and performance metrics are fed back into the machine learning models to refine predictions and recommendations, creating a self-improving system that progressively enhances transactional efficiency through learned insights from ongoing data collection
3Measurement precision
If comprehensive data collection across multiple communication channels is implemented, then analysis accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex multi-channel data collection and analysis system into distinct modular components, each handling specific communication channels or analysis functions, thereby managing system complexity through organized separation while maintaining comprehensive data collection capabilities
Solution Approach 2:
The system introduces intermediary processing layers including data normalization modules and feature extraction components that mediate between raw multi-channel data and the machine learning models, simplifying the interface between diverse data sources and analysis algorithms while preserving analysis accuracy
Data Source
AI summary
Examples of the disclosure enable an information processing system to automatically implement a user interaction diagnostic engine. In some examples, the information processing system analyzes documents to identify parameters associated with profile features and performance metrics. Based on the parameters, correlation values between the profile features and one or more performance metrics are determined. Based on the correlation values, at least one profile feature is identified to improve a parameter associated with the performance metrics. A predictive model associated with the profile features and the performance metrics is generated. One or more actionable items configured to modify the at least one profile feature are determined such that, based on the predictive model, a parameter associated with the performance metrics are predicted to improve upon execution of the one or more actionable items.