Data Analysis System Ranking via User Intent Classification
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Solution Overview
Problem
Conventional methods for extracting information from big data are insufficient as they fail to consider user intent and overall data impression, leading to inefficient and time-consuming information retrieval.
Innovation Solution
A data analysis system that evaluates object data by generating an index based on user input, ranking data according to its relation to a specified case, and allowing users to classify reference data to adjust the ranking, thereby extracting patterns that characterize the data and improve information retrieval efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text search or keyword classification methods are used to extract information from big data, then the system structure remains simple, but the information retrieval precision and ability to understand user intent deteriorate
Solution Approach 1:
The patent introduces classification information as an intermediary element that mediates between the search query and the data objects. This classification information (including classification results and classification weights) acts as a bridge to guide the search process, enabling the system to understand user intent without requiring complex artificial intelligence structures. The intermediary classification information simplifies the retrieval process while improving precision.
Solution Approach 2:
The patent performs preliminary classification of data objects before the actual search operation. By pre-establishing classification information and classification weights for data objects, the system prepares the data in advance, making the subsequent search more efficient and precise. This preliminary action allows the system to quickly filter and rank results based on pre-computed classification data.
2Adaptability or versatility
If users manually classify enormous amounts of information piece by piece, then the classification can be customized to user needs, but the time and effort required increases significantly
Solution Approach 1:
The patent enables the system to automatically generate and update classification information based on search queries and user interactions. Instead of requiring manual classification of all data, the system self-generates classification results and classification weights dynamically. This self-service approach maintains adaptability while dramatically reducing the time and effort required from users.
Solution Approach 2:
The patent implements dynamic classification information that adapts based on search queries and user feedback. The classification weights are not fixed but are updated dynamically based on relevance to the current search query and user interaction patterns. This dynamic approach allows the system to be adaptable to different user needs without requiring manual reclassification, significantly reducing time investment.
3Reliability
If conventional search methods are used without considering user intent, then the processing speed remains fast, but the relevance of search results to user needs deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where classification information is updated based on user interactions with search results. The system learns from user behavior (such as which results are clicked or selected) and adjusts classification weights accordingly. This feedback loop continuously improves search result relevance while maintaining efficient processing, as the system uses pre-computed classification data rather than re-analyzing all data objects.
Data Source
AI summary
The present invention relates to data analysis for evaluating a plurality of pieces of object data; and the evaluation corresponds to the relation between each piece of object data and a specified case. An index that enables ranking of the plurality of pieces of object data is generated by the evaluation and the index changes based on an input entered by a user. A pattern is extracted that characterizes the reference data from the reference data according to the classification information assigned by the input. The index is determined by evaluating the relation between the object data and the specified case based on the extracted pattern and set to the object data. The plurality of pieces of object data are ranked according to the index and reported the user.