Automated Database Record Update via Text Tagging and Analyst Review
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Solution Overview
Problem
Current systems face challenges in efficiently and automatically updating business-related information in databases with new and altered data from diverse sources, leading to increased editorial staff requirements and inefficiencies in processing and reviewing news, reports, and other forms of information.
Innovation Solution
The system employs automated text analysis and tagging engines, using XML markup to identify relevant terms and relationships, which are then reviewed and confirmed by human analysts through a user interface, allowing for the updating of databases with accurate and relevant information while enabling human analysts to modify or reject automated identifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated text analysis and tagging engines are used to identify relevant information, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system divides the information processing task into two segments: automated text analysis and tagging engines perform initial identification of relevant information to improve productivity, while human analysts review and validate the identified information to ensure measurement precision. This segmentation allows each component to excel at its specific function.
Solution Approach 2:
Human analysts serve as an intermediary between the automated text analysis system and the final database updates. They review the automated identifications, correct errors, and confirm accuracy, thus mediating between automated efficiency and human precision to resolve the contradiction.
2Measurement precision
If human analysts review all identified information, then measurement precision is improved, but loss of time increases
Solution Approach 1:
Human analysts do not review all identified information uniformly, but rather focus their review on cases where automated confidence is lower or where complexity requires human judgment. This partial action approach maintains measurement precision for critical cases while minimizing time loss.
Solution Approach 2:
The automated text analysis and tagging engines perform preliminary identification of relevant information before human review, filtering out obvious cases that don't require human attention. This preliminary action reduces the volume of information requiring human review, thus reducing time loss while maintaining precision.
3Productivity
If automated processing is used to update databases, then productivity is improved, but reliability deteriorates
Solution Approach 1:
The system implements feedback loops where human analysts review automated identifications and provide corrections. This feedback mechanism continuously improves the reliability of automated processing while maintaining high productivity, as the system learns from human corrections and validates its outputs.
Solution Approach 2:
Human analysts act as an intermediary validation layer between automated database updates and the final data store. They verify the accuracy of automated extractions before committing to the database, thus ensuring reliability while allowing automated processing to maintain productivity.
Data Source
AI summary
The invention provides systems, methods, and computer programs to improve the accuracy and efficiency with which data analysts can use news stories, press releases, and other sources of information to maintain databases that contain information about individuals and businesses and other organizations. Documents containing material information are acquired in computer-readable form and optionally may then be reduced to raw text. One or more computerized systems process the text and tag important terms such as proper nouns, job titles, awards, and other terms indicating professional, educational, corporate, or other developments. The invention provides a user interface with which a data analyst can review, confirm, remove, modify, introduce, and link the tags, ultimately adding the information and links to a database and storing the source document in an electronic warehouse for future retrieval.