ML Interaction Insights for Data Analysis Bottlenecks
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Solution Overview
Problem
The abundance of data from various sources poses a challenge in identifying relevant information efficiently, requiring extensive expertise and time, making it incompatible with modern applications that demand rapid and personalized data application.
Innovation Solution
A system that acquires data from diverse sources, extracts user-service interaction events, classifies them using machine learning models, and generates customized insights by determining user intentions and service actions, enabling efficient data application across personalized scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of data sources is performed, then analysis accuracy is improved, but time consumption increases
Solution Approach 1:
The system enables self-service by allowing users to define their own data sources, filters, and analysis parameters through a configuration interface. The machine learning model automatically processes data according to user-defined criteria without requiring manual intervention in the actual analysis execution, thus maintaining accuracy while reducing time consumption.
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with automated machine learning models. The system uses trained models to automatically identify relevant data patterns, filter information, and generate insights, substituting the manual expert evaluation process with computational automation that achieves comparable or superior accuracy faster.
2Measurement precision
If manual data evaluation process is used, then data accuracy is improved, but productivity decreases
Solution Approach 1:
The system maintains continuous operation by automatically processing data streams in real-time. The machine learning model continuously evaluates data against defined criteria without interruption, generating insights on-demand and maintaining high productivity while preserving data accuracy through consistent automated verification.
Solution Approach 2:
The patent enables dynamic parameter changes by allowing users to modify data sources, filters, and analysis parameters through the configuration interface. The system adapts its evaluation criteria based on user-defined parameters, maintaining accuracy for specific use cases while improving overall productivity through flexible, rapid reconfiguration without manual re-evaluation.
3Reliability
If manual analysis process is applied, then result reliability is improved, but adaptability decreases
Solution Approach 1:
The system achieves universality by providing a single platform that can handle multiple data sources, analysis types, and application scenarios through a unified machine learning model. The configuration interface allows the same system to adapt to different domains and requirements without requiring separate specialized processes, thus improving adaptability while maintaining reliability through consistent automated verification.
Solution Approach 2:
The patent implements dynamics by enabling real-time adjustment of analysis parameters and data sources through the configuration interface. The machine learning model dynamically adapts its evaluation criteria based on user-defined parameters, allowing the system to respond to changing requirements and maintain reliability for each specific scenario while improving overall adaptability.
4Productivity
If automated machine learning processing is used, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The system incorporates feedback mechanisms where users can review and validate automated analysis results through the configuration interface. The machine learning model uses feedback from user interactions and defined criteria to continuously refine its predictions, ensuring that productivity gains from automation do not compromise measurement precision through iterative improvement.
Solution Approach 2:
The patent applies preliminary action by allowing users to pre-define data sources, filters, and evaluation criteria before data processing begins. This setup ensures that the automated machine learning model processes data according to predetermined accuracy standards, maintaining measurement precision while achieving high productivity through automated execution of pre-configured analysis.
Data Source
AI summary
Methods, systems, and computer-readable media for the generation of customizable insights using machine learning models. The method acquires data from one or more sources with different formats and data organization to extract events that each represent an interaction between a user and a service and classify the extracted events by adding labels to each of the extracted events. The method next projects the extracted events and generates the customized insights of events representing user interactions.