Contextual Classification Using Supervised and Unsupervised Training
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Solution Overview
Problem
Existing methods for contextual classification of objects rely heavily on manual input, which is time-consuming and costly, making it unfeasible for high-volume content providers to classify content quickly and accurately.
Innovation Solution
The use of supervised and unsupervised machine learning techniques, including parallel processing, to automatically classify objects by collecting and preprocessing training data, training models, and identifying optimal models for contextual classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification methods are used, then classification accuracy can be maintained, but classification speed and productivity deteriorate significantly
Solution Approach 1:
The patent segments the classification task into multiple independent classifier components that process different aspects of content simultaneously. Multiple classifiers run in parallel to evaluate different features and parameters, with results aggregated to produce the final classification. This segmentation enables high-speed processing while maintaining accuracy through distributed evaluation.
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated computer-based classification systems. The system uses algorithms and data structures to automatically analyze and classify content without human intervention, substituting the mechanical manual review process with electronic automated processing that achieves both speed and accuracy.
2Reliability
If manual classification is performed to ensure accuracy, then classification quality is maintained, but time consumption and cost increase
Solution Approach 1:
The patent implements preliminary automated classification that processes content before final review. The system pre-evaluates content using multiple classifiers to identify obvious cases that can be automatically classified, reserving manual review only for ambiguous or complex cases. This preliminary action reduces overall time consumption while maintaining quality by focusing human effort where most needed.
Solution Approach 2:
The classification system performs self-service through automated algorithms that independently evaluate and classify content without requiring manual intervention for every item. The system serves itself by using trained models to automatically process the majority of classification tasks, reducing both time consumption and resource requirements while maintaining consistent quality standards.
3Productivity
If a large number of editors are hired to classify high-volume content, then classification capacity increases, but operational cost increases significantly
Solution Approach 1:
The patent uses copied and replicated classifier algorithms that can be instantiated multiple times to handle high-volume content classification. Instead of hiring additional editors, the system creates multiple copies of the same automated classification logic that can process content in parallel, increasing capacity without proportionally increasing operational costs associated with human resources.
Solution Approach 2:
The patent changes the fundamental parameter of classification from human-based to algorithm-based processing. This parameter change transforms the cost structure from labor-intensive to computation-intensive, allowing capacity to be scaled by adding computational resources rather than hiring personnel, thereby increasing productivity while controlling operational costs.
4Productivity
If manual tagging is performed quickly to meet deadlines, then productivity increases, but classification precision deteriorates
Solution Approach 1:
The patent implements continuous automated classification processing that operates without interruption or degradation in quality. The system maintains consistent classification standards throughout high-volume processing by using the same algorithmic logic for every item, ensuring that tagging accuracy does not deteriorate even as productivity increases to meet deadlines.
Data Source
AI summary
Computerized systems and methods are disclosed for performing contextual classification of objects using supervised and unsupervised training. In accordance with one implementation, content reviewers may review training objects and submit supervised training data for preprocessing and analysis. The supervised training data may be preprocessed to identify key terms and phrases, such as by stemming, tokenization, or n-gram analysis, and form vectorized objects. The vectorized objects may be used to train one or more models for subsequent classification of objects. In certain implementations, preprocessing or training, among other steps, may be performed in parallel over multiple machines to improve efficiency. The disclosed systems and methods may be used in a wide variety of applications, such as article classification and content moderation.