Machine Learning Security Classification Using Content and Metadata Weights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack an efficient method to automatically determine accurate security classifications for electronic documents, which is crucial for managing sensitive information in computer networks, especially in environments with large datasets.
Innovation Solution
The use of machine learning logic to generate classification models based on content, metadata, and contextual factors, with weight parameters determined by a model weighting module, to determine security classifications for electronic documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual security classification methods are used, then accuracy can be maintained through human judgment, but productivity is significantly reduced due to time-consuming manual review processes
Solution Approach 1:
The patent introduces a machine learning-based inference model as an intermediary between the electronic document and the security classification decision. The model processes document content, metadata, and contextual factors to generate predicted classifications, serving as a mediator that handles the bulk of classification work while maintaining consistent accuracy standards without requiring manual review of every document
Solution Approach 2:
The system enables electronic documents to self-classify by automatically processing their own content, metadata, and contextual information through the inference model. Documents with high-confidence predictions receive automatic classification without human intervention, while only ambiguous cases require manual review, significantly improving productivity while maintaining reliability
2Measurement precision
If multiple classification factors (content, metadata, context) are analyzed, then classification accuracy is improved, but system complexity increases due to multiple inference models and weight parameters
Solution Approach 1:
The patent segments the classification task into three distinct inference models, each specialized for a specific type of input: content analysis, metadata analysis, and contextual factor analysis. This segmentation allows each model to focus on its specific domain with optimized parameters, improving overall precision while managing complexity through modular design
Solution Approach 2:
The system dynamically adjusts weight parameters for each inference model based on the specific document being classified. The model weighting module modifies the influence of content, metadata, and contextual factors according to document-specific characteristics, enabling adaptive precision without requiring a fixed complex structure for all cases
3Productivity
If automated machine learning classification is implemented, then productivity increases by reducing manual review, but reliability may deteriorate due to potential algorithmic errors
Solution Approach 1:
The patent implements a feedback mechanism where the inference model's predictions are continuously evaluated against actual classification outcomes. The model weighting module uses this feedback to adjust weight parameters and improve prediction accuracy over time, ensuring that automated classification maintains high reliability while processing large volumes of documents
Solution Approach 2:
The system applies automated classification selectively rather than universally. Documents with high-confidence predictions from the inference model receive automatic classification, while ambiguous or high-risk documents are flagged for manual review. This partial automation approach maximizes productivity gains while maintaining reliability through targeted human oversight
Data Source
AI summary
A computer-implemented method that includes a computing system receiving a plurality of data items that include content data. The system then generates a first inference model based on the received content data. The system also receives metadata associated with respective data items and then generates a second inference model based on the received metadata data. The system further determines, based on the first inference model, a first weight parameter associated with the content data, and determines, based on the second inference model, a second weight parameter associated with the metadata. The system then generates a classification model for determining a classification of at least one data item of the plurality of data items. The classification model is generated based on at least one of the first weight parameter or the second weight parameter.