Personalized Data Assistance via ML Intent Analysis
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Solution Overview
Problem
Traditional data management systems fail to accurately understand user queries, particularly when they are poorly worded, ambiguous, or short, leading to inadequate assistance and inefficient resource utilization.
Innovation Solution
The implementation of a data management system that utilizes supervised and unsupervised machine learning processes to analyze user queries and attributes, such as clickstream data and demographics, to identify the true intent behind user queries and provide personalized assistance through an analysis model that includes natural language clustering, vector clustering, and multiclass classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data management systems use simple search query matching, then the system complexity is low, but the accuracy of understanding user intent is poor
Solution Approach 1:
The patent introduces an analysis model as an intermediary component between the user query and the assistance topic selection. This analysis model processes user queries and user attributes to generate augmented queries that capture true user intent, thereby improving accuracy without requiring complete system redesign
Solution Approach 2:
The system performs self-improvement by automatically analyzing user attributes and query patterns to generate enhanced search queries without human intervention. The analysis model autonomously augments queries based on learned relationships between user attributes and successful assistance topics
2Ease of operation
If the system provides comprehensive self-help features with extensive assistance topics, then the ease of operation improves, but the loss of time for processing fruitless searches increases
Solution Approach 1:
The system performs preliminary analysis of user attributes and query intent before conducting the actual search for assistance topics. By pre-processing queries through the analysis model and generating augmented queries that reflect true user intent, the system filters out fruitless searches in advance, reducing wasted time
Solution Approach 2:
The system incorporates feedback loops where user attributes, query patterns, and assistance topic selections are continuously analyzed to improve the analysis model. This feedback mechanism learns from past interactions to better predict user intent and reduce fruitless searches over time
3Productivity
If traditional systems rely on basic search queries without user attribute analysis, then the device complexity is low, but the productivity of providing accurate assistance is poor
Solution Approach 1:
The patent segments the assistance provision process into distinct components: user attribute collection, query analysis, augmented query generation, and assistance topic selection. This segmentation allows the complex analysis model to be integrated as a modular component, improving productivity without overwhelming system complexity
Data Source
AI summary
A method and system provides personalized assistance to users of a data management system. The method and system trains an analysis model with both a supervised machine learning process and an unsupervised machine learning process to identify relevant assistance topics based on a user query and the attributes of the user that provided the query. The method and system outputs personalized assistance to the user based on the analysis of the analysis model.