Document Classifier Generation via Self-Service GUI
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Solution Overview
Problem
Existing document classification systems require extensive expertise and resources, including the involvement of data scientists, and are often outdated when new document classes are added, posing challenges for sensitive data entities that hesitate to share their records for classifier development.
Innovation Solution
A graphical user interface (GUI) resembling a file system is used to enable users to define classes and assign documents, assisted by unsupervised machine learning processes like clustering, allowing non-data scientists to generate and manage document classifiers, reducing the need for extensive model building and data scientists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data scientists are employed to build custom document classifiers, then classification accuracy and reliability are improved, but cost and complexity increase significantly
Solution Approach 1:
The system enables domain experts to self-serve by directly creating and training document classifiers using their own knowledge and data, without requiring data scientists. Users can define classes, upload training documents, and manage classifiers through an intuitive interface, making the system self-sufficient for its primary function.
Solution Approach 2:
The patent introduces an intermediary system that bridges domain experts and machine learning capabilities. This intermediary automatically handles complex tasks like feature extraction, model selection, and training, translating user-friendly inputs into sophisticated classification models without requiring users to understand the underlying complexity.
2Reliability
If data scientists continuously update document classifiers to accommodate new document classes, then classification accuracy is maintained, but time and cost increase
Solution Approach 1:
The system is designed to be dynamic and adaptable, allowing users to easily add new document classes and retrain classifiers as needs change. The architecture supports continuous learning and adaptation without requiring complete system redesign or expert intervention, enabling flexible response to evolving classification requirements.
Solution Approach 2:
The system performs preliminary actions by automatically preparing and preprocessing training data when new document classes are added. It handles feature extraction, data normalization, and model preparation in advance, reducing the time and effort required for classifier updates and enabling rapid adaptation to new requirements.
3Reliability
If data scientists are given access to sensitive documents for classifier development, then classification performance improves, but data security and privacy concerns arise
Solution Approach 1:
Domain experts can directly upload and use their own sensitive documents for training classifiers within their secure environment. The system processes data locally or in secure cloud environments, eliminating the need to share sensitive information with external data scientists while still enabling high-performance classification through expert-curated training data.
Solution Approach 2:
The patent introduces a secure intermediary layer that processes sensitive documents without exposing them to external parties. This intermediary handles all data processing, feature extraction, and model training within secure boundaries, acting as a trusted mediator that protects data confidentiality while enabling effective classifier development.
4Measurement precision
If manual document classification is performed by users familiar with the documents, then classification accuracy improves, but time and labor requirements increase
Solution Approach 1:
The system performs preliminary automated classification and organizing of documents based on user inputs and existing patterns. This preliminary action reduces the manual workload by pre-sorting documents into candidate categories, allowing users to focus only on reviewing and confirming classifications rather than performing complete manual classification from scratch.
Solution Approach 2:
The system incorporates feedback loops where user corrections and classifications are automatically used to improve and retrain the classifier. This feedback mechanism enables the system to learn from user interactions over time, progressively improving accuracy while reducing the effort required for manual classification as the automated system becomes more proficient.
Data Source
AI summary
A method includes performing, by a computing device, a clustering operation to group documents of a document corpus into clusters in a feature vector space. The document corpus includes one or more labeled documents and one or more unlabeled documents. Each of the one or more labeled documents is assigned to a corresponding class in classification data associated with the document corpus, and each of the one or more unlabeled document is not assigned to any class in the classification data. The method also includes generating, by the computing device, a prompt requesting classification of a particular document of the document corpus, where the particular document is selected based on a distance between the particular document and a labeled document of the one or more labeled documents.