Scoring System for Unstructured Data Analysis
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Solution Overview
Problem
Existing methods struggle to effectively analyze and synthesize large volumes of unstructured information, such as texts and speech data, which are heterogeneous and difficult to process automatically, hindering decision-making and operational processes in various industries.
Innovation Solution
A method that assigns a score to elements in a database by converting unstructured data into structured data through a scoring process, using a dimension vector representation and interval subdivision, allowing for the extraction and representation of relevant information in structured form, enabling the synthesis of heterogeneous data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If unstructured data is analyzed directly, then analysis coverage is comprehensive, but analysis efficiency is low and data is not immediately usable
Solution Approach 1:
The patent applies preliminary action by performing pre-processing of unstructured data before analysis. The system converts unstructured data into structured format in advance, extracting relevant information and organizing it into a usable structure. This preliminary transformation enables efficient subsequent analysis while preserving all necessary information, resolving the contradiction between analysis efficiency and data usability.
2Ease of operation
If unstructured data is converted to structured data, then data usability is improved, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex conversion process into distinct modules: pre-processing module, analysis module, and scoring module. Each module handles a specific aspect of the transformation from unstructured to structured data. This modular segmentation reduces processing complexity by making each step manageable and independent, while still achieving comprehensive data transformation and usability improvement.
3Speed
If automatic text analysis techniques are used, then analysis speed is improved, but accuracy of synthesizing heterogeneous information deteriorates
Solution Approach 1:
The patent applies feedback through its multi-stage processing approach with iterative refinement. The system performs initial automatic analysis, then refines results through structured conversion and scoring mechanisms. Each stage provides feedback to improve the next stage's accuracy, maintaining high analysis speed while progressively enhancing synthesis accuracy for heterogeneous information.
Solution Approach 2:
The patent applies parameter changes by transforming data from unstructured format with variable parameters into structured format with standardized parameters. The scoring mechanism assigns numerical values to extracted information, converting qualitative heterogeneous data into quantitative structured data. This parameter standardization maintains analysis speed while significantly improving synthesis accuracy for decision-making processes.
Data Source
AI summary
A system for implementing a scoring method, wherein the system includes at least a data analyzer configured to: determine a plurality of scoring intervals dependent upon the data to be analyzed; assign an integer score and a decimal score within the scoring intervals to each data to be analyzed, the score dependent upon a frequency of appearance; search a database for pairings of (scored element, decimal score); and generate an alert if the pairing is found in the database.