User Tag Collection for Faster AI Document Dataset Curation
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Solution Overview
Problem
Existing systems face challenges in efficiently and accurately tagging large volumes of document data, particularly in risk-related applications, due to the manual curation of golden datasets and the time-consuming nature of creating high-quality datasets for AI training, which can be costly and error-prone.
Innovation Solution
A system and method for automatically selecting users from a user data store to tag documents, transmitting tag requests, receiving document tags, and storing them in a database, while leveraging AI models for improved tagging efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual curation of golden datasets is performed to train AI models, then the accuracy and quality of AI analysis improves, but the time and cost required increases significantly
Solution Approach 1:
The system enables users to self-tag documents by providing an intuitive tagging interface where users can independently categorize and label documents without requiring manual intervention from data curators. This self-service approach allows the system to generate its own training data at scale.
Solution Approach 2:
The tagging platform serves multiple functions: it acts as both a document management system and a machine learning training data generation system. The same tagging interface serves to organize documents for business use while simultaneously creating labeled datasets for AI model training.
2Adaptability or versatility
If manual management of business logic rules is performed for different applications, then the requirements of each application can be met, but the process becomes time-consuming and error-prone
Solution Approach 1:
The tagging platform provides a universal tagging system that can be applied across multiple different applications and use cases. A single tagging framework supports diverse applications including risk assessment, document classification, and analytics without requiring separate manual configuration for each application.
Solution Approach 2:
The system dynamically adapts to different application requirements through configurable tagging schemas and flexible data models. Business logic rules can be modified and updated without requiring complete system reconfiguration, allowing the platform to evolve with changing application needs.
3Speed
If AI capabilities are implemented to accelerate data analysis, then the analysis speed improves, but the reliance on manually curated golden datasets increases the overall time investment
Solution Approach 1:
The system performs preliminary tagging actions automatically using existing document metadata and content analysis before AI model training is required. This preliminary structuring of data reduces the preparation time needed for golden datasets by having documents pre-organized and partially labeled before intensive AI processing begins.
Data Source
AI summary
In some embodiments, an input document is received at a tagging platform, via a communication device, and associates it with a tag request. The tagging platform automatically selects at least one electronic record associated with a first user from a user data store containing electronic records associated with users (each record including at least a user identifier and a user communication address). The input document and tag request are transmitted to the communication address associated with the first user, and a document tag is received from the first user. The tagging platform may then store the document tag in a document mining result database by adding an entry to the database identifying the received document tag and transmit an indication associated with the document mining result database to a plurality of risk applications.