Neural Network Data Mining for Autonomous Systems
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Solution Overview
Problem
Current data mining processes for autonomous and semi-autonomous systems rely heavily on human intervention for annotation, which is time-consuming and requires specialized expertise, especially in fields like finance, and lacks efficiency in filtering and identifying important data samples.
Innovation Solution
The implementation of a system using multiple neural networks to automatically generate annotations by processing input data to remove unimportant data samples, retrieve important ones, determine uncertainty classifications, and assign final object classifications, thereby reducing the need for human intervention and accelerating the data mining process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human reviewers annotate data samples to ensure accuracy, then annotation quality is improved, but the time required for data mining increases significantly
Solution Approach 1:
The system uses automated neural networks to perform annotation tasks that traditionally required human reviewers. The neural network processes data samples and generates annotations automatically, eliminating the need for manual human intervention while maintaining annotation quality through machine learning-based classification and uncertainty assessment.
Solution Approach 2:
The patent replaces the mechanical process of human review and annotation with an automated computational system. Neural networks and machine learning models substitute for human cognitive processes, performing data classification, annotation generation, and quality assessment through algorithmic processing rather than manual evaluation.
2Measurement precision
If specialized humans review data samples to ensure domain expertise, then annotation accuracy is improved, but the complexity and cost of the process increases
Solution Approach 1:
The system employs automated neural networks that can be trained on domain-specific data to perform annotation tasks requiring specialized knowledge. The machine learning models self-learn domain expertise from training data, eliminating the need to manually recruit and manage specialized human reviewers while maintaining annotation accuracy through algorithmic pattern recognition.
Solution Approach 2:
The patent changes the fundamental parameter of who performs the annotation from human experts to machine learning models. This parameter change transforms the annotation process from a human-centric workflow requiring specialized knowledge to an automated computational process that learns domain expertise from data, thereby reducing process complexity while maintaining or improving accuracy.
3Quantity of substance
If all data samples are processed manually to ensure completeness, then data coverage is improved, but productivity decreases
Solution Approach 1:
The automated neural network system processes data samples without human intervention, enabling high-volume data processing at machine speeds. The system can automatically review and annotate thousands or millions of data samples in the time it would take a human reviewer to process a fraction of that amount, thereby dramatically increasing productivity while maintaining comprehensive data coverage.
Solution Approach 2:
The patent implements continuous automated processing where the neural network operates without interruption to process data samples. Unlike manual review processes that require human休息 and work in batches, the automated system maintains continuous operation, processing data samples in an unbroken sequence, thereby maximizing productivity and ensuring complete data coverage.
Data Source
AI summary
In various examples, machine learning data mining for autonomous or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use neural networks to perform one or more data mining processes. For instance, a first neural network(s) may process input data (e.g., image data) to remove data samples (e.g., images) that are associated with a first object classification(s) and/or a second neural network(s) may process the input data to retrieve data samples (e.g., images) that are associated with a second classification(s). Next, a third neural network(s) may process filtered input data (e.g., the input data not removed by the first neural network(s) and/or the input data retrieved by the second neural network(s)) to determine uncertainty classifications associated with the data samples and a fourth neural network(s) may process the filtered input data to determine final object classifications associated with the data samples.