IoT Labeled Data Generation for Automated Input-Output Alignment
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Solution Overview
Problem
Existing supervised learning systems face challenges in efficiently gathering and generating labeled data from diverse and distributed IoT sources due to the complexity of data collection and alignment of inputs and expected outputs from varied sources, leading to errors and inefficiencies in data processing.
Innovation Solution
An IoT data labeling system that automatically generates labeled data instances by configuring IoT servers to collect, process, and transform data into formats suitable for supervised learning algorithms, linking inputs with expected outputs, and cleaning the data set to ensure accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data labeling methods are used, then data accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The system enables automated self-labeling of data through machine learning models that automatically process and label IoT data without human intervention. The model trains on available data and performs self-labeling, eliminating the need for manual annotation while maintaining consistency and accuracy.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computational system. Machine learning algorithms and automated processing mechanisms substitute human operators, transforming the labeling task from a manual mechanical process to an automated intelligent system that operates continuously without fatigue or inconsistency.
2Quantity of substance
If data is collected from diverse IoT sources, then data quantity and variety increase, but data alignment complexity and processing difficulty increase
Solution Approach 1:
The system segments the complex data collection and alignment process into distinct modular components: data collection from multiple IoT sources, data preprocessing, feature extraction, labeling, and model training. Each module handles specific tasks independently, making the overall complex process manageable and maintainable.
Solution Approach 2:
The patent introduces intermediate processing layers including data normalization modules, feature extraction components, and alignment algorithms that act as mediators between diverse IoT data sources and the machine learning model. These intermediaries standardize and harmonize data from different sources, reducing alignment complexity.
3Productivity
If automated data labeling is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The system employs universal machine learning frameworks and multi-functional processing modules that can handle various types of IoT data (sensor readings, images, text, audio) through the same automated labeling pipeline. This universality reduces system complexity by avoiding the need for separate specialized systems for each data type.
Solution Approach 2:
The patent utilizes parameter adjustments and transformations in the machine learning models to adapt to different data characteristics without changing the core automated labeling architecture. By modifying model parameters, preprocessing settings, and feature extraction configurations, the system maintains high processing speed across diverse data types while managing complexity through parameter optimization rather than structural complexity.
Data Source
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AI summary
A labeled data generation service provides an Internet-of-Things (IoT) system with a capability whereby users may configure how the system gathers, processes, and generates labeled data instances by: collecting and processing the data into a format required by supervised learning algorithms; generating expected outputs from data available in the IoT system; supporting the linking of collected inputs with generated expected outputs; forming labeled data instances; cleaning the labeled data set appropriately; sending the labeled data set to target nodes; and/or communicating with target nodes regarding improving the data processing and labeling processes, as required.