Machine Teaching Model for Automated Metadata Extraction
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Solution Overview
Problem
Existing collaborative computing platforms face inefficiencies in document management and content retrieval due to time-consuming and inconsistent tagging processes, leading to inefficient utilization of resources and delays in finding relevant content.
Innovation Solution
Implementing a machine teaching model on the computing platform that uses natural language text to generate input for training, allowing users to upload sample documents, identify concepts, and validate the model for automated labeling and classification of similar documents, reducing the need for manual expertise and improving productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual tagging of documents is performed to enable searching, then content discoverability is improved, but time consumption and inconsistency increase
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically perform tagging and classification of documents without requiring manual human intervention. The model learns from sample documents and independently applies tagging to new content, making the system self-sufficient in content organization tasks.
Solution Approach 2:
The patent replaces the mechanical manual tagging process with an automated machine learning system. Users interact through a simplified graphical interface rather than manually tagging each document, substituting the labor-intensive mechanical process with an intelligent automated system that performs classification and tagging.
2Loss of information
If multiple documents are opened to search for particular context, then information completeness is improved, but resource utilization deteriorates
Solution Approach 1:
The machine learning model acts as an intermediary that processes and understands document content, enabling users to search for context across multiple documents simultaneously without manually opening each one. The model mediates between the user's search intent and the document corpus, retrieving relevant information efficiently.
Solution Approach 2:
The system creates a conceptual copy or representation of document content through extracted features and metadata that the machine learning model processes. This allows the system to analyze and search content without requiring users to physically open and view each document, reducing resource consumption while maintaining information accessibility.
3Extent of automation
If traditional machine learning is used for document classification, then automation capability is improved, but data requirements increase
Solution Approach 1:
The system applies partial action by using only the necessary minimum of training data required to achieve effective classification. Rather than requiring exhaustive datasets, the machine learning model learns from a focused set of sample documents that are sufficient to capture the essential patterns needed for automation.
Solution Approach 2:
The patent changes the parameters of the machine learning approach by using a methodology that requires fewer training samples. This involves adjusting the learning parameters and data requirements to achieve effective automation with reduced data volume, making the system more practical for organizations with limited annotated data.
4Productivity
If automated classification is implemented, then productivity is improved, but system complexity increases
Solution Approach 1:
The system extracts the complex machine learning functionality as a separate, modular component that can be independently managed and configured. By taking out the AI processing from the core document management system, the patent reduces the apparent complexity for end users while maintaining advanced automation capabilities in the background.
Solution Approach 2:
A graphical user interface acts as an intermediary layer between the user and the complex machine learning system. This mediator simplifies user interaction by providing intuitive controls for uploading sample documents, configuring classification parameters, and viewing results, thereby hiding the underlying system complexity from users.
Data Source
AI summary
Techniques configuring a machine learning model include receiving, via a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform, a selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model, which are sent to the machine learning model. As new files are uploaded to the selected directories of the collaborative computing platform, the machine learning model is applied to the uploaded files to classify the files and extract metadata. The extracted metadata and associated classification data are stored in data structures associated with the new files. The data structures are existing data structures of the collaborative computing platform.