Auto-Generated Table Templates for Text Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems require manual user interaction to define and extract report data from text files into tabular formats, which is inefficient and unreliable, especially when dealing with unstructured or semi-structured data.
Innovation Solution
The development of systems and methods that automatically generate templates to extract data from text files into tables, using auto-define inputs to identify and group lines of text based on matching properties, thereby minimizing user interaction and improving data accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual template definition is used to extract report data, then data extraction can be performed, but the process becomes inefficient and requires extensive user interaction
Solution Approach 1:
The system performs self-service by automatically analyzing report files and generating extraction templates without requiring manual user definition. The template generator autonomously identifies data patterns, field structures, and extraction rules, eliminating the need for users to manually create templates while maintaining accurate data extraction capabilities
Solution Approach 2:
The system performs preliminary action by pre-processing report files to identify and categorize data patterns before actual extraction occurs. The template generator analyzes the report structure in advance, creating ready-to-use extraction templates that can be immediately applied, thus eliminating the need for real-time manual template creation during data extraction operations
2Reliability
If manual template definition is used for data extraction, then extraction can be performed, but reliability decreases due to user error
Solution Approach 1:
The system eliminates user error by replacing manual template definition with automated template generation. The template generator systematically analyzes report structures and generates consistent, error-free extraction templates, ensuring reliable data extraction without being subject to human mistakes in template creation
Solution Approach 2:
The system incorporates feedback mechanisms where the template generator analyzes extracted data quality and adjusts template parameters accordingly. This iterative process ensures that templates continuously improve based on actual extraction results, maintaining high reliability while adapting to variations in report formats
3Adaptability or versatility
If traditional field extraction strategies are used, then data can be converted to tables, but the process requires manual definition of templates for each line type
Solution Approach 1:
The template generator creates universal templates that can handle multiple line types and data formats through a single unified approach. Instead of requiring separate manual templates for each line type, the system generates adaptable templates that automatically adjust to different data structures, reducing template complexity while maintaining versatility across various report formats
Solution Approach 2:
The system segments the template generation process into distinct analytical phases: pattern identification, field detection, relationship mapping, and template synthesis. This segmentation allows the complex task of handling diverse line types to be broken down into manageable steps, reducing overall complexity while maintaining adaptability to different data structures
Data Source
AI summary
Systems and methods are provided for creating tables using auto-generated templates. Reports including lines of text to be extracted into tables are received. An auto define input is received to auto-generate the tables corresponding to the reports. Groups of lines are identified from among the lines of text in the reports. A detail group and relevant groups are selected and identified from among the groups of lines. A final detail group is created by merging the detail group with at least a portion of the relevant groups. Append groups are identified from among the groups of lines not included in the final detail group. Templates corresponding to the final detail group and the append groups are generated. Text is extracted from the reports based on the templates. Tables are generated using the text extracted from the reports, by assigning the text from the text fragments to entries in the tables.