Image-Based Learning for Technology Activity Assessment
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Solution Overview
Problem
Existing methods for analyzing changes in parameters over time, such as those in web-based services, user accounts, and consumer product pages, are limited by relying on snapshot analyses, which fail to provide a comprehensive understanding of parameter activity development over time.
Innovation Solution
The implementation of image-based learning algorithms, specifically deep convolutional neural networks, to convert data sets of parameters tracked over time into a format suitable for image-based analysis, enabling a deeper understanding of parameter activity changes through the generation of two-dimensional graphical representations and subsequent assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If snapshot analysis methods are used to analyze parameter changes, then the analysis process is simple and quick, but the understanding of parameter activity development over time is incomplete and inaccurate
Solution Approach 1:
The patent introduces an intermediary representation layer that converts temporal parameter data into a visual format suitable for image-based analysis. This intermediary step bridges the gap between traditional temporal data and image processing algorithms, enabling the use of powerful image-based learning methods while maintaining a clear separation between data collection and analysis components.
Solution Approach 2:
The patent replaces traditional mechanical/time-based sequential analysis methods with image-based parallel processing. By converting temporal parameter sequences into spatial image representations, the system can leverage the power of image-based learning algorithms (such as CNNs) that process spatial patterns in parallel, achieving higher accuracy without being constrained by sequential processing limitations.
2Reliability
If image-based learning algorithms are implemented to analyze parameter trends, then prediction accuracy and decision-making capabilities improve, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the analysis system into distinct functional modules: data collection module, image conversion module, image-based analysis module, and result interpretation module. This segmentation allows each component to be optimized independently and facilitates easier maintenance and deployment. The modular architecture reduces overall system complexity by breaking down the complex image-based analysis into manageable, interchangeable components.
Solution Approach 2:
The patent creates a universal analysis framework that can handle multiple types of parameters and time series data through a common image conversion and analysis pipeline. The system is designed to be multi-functional, accommodating various data sources and parameter types while maintaining a consistent analysis approach, thereby reducing complexity through standardization rather than requiring separate systems for different analysis scenarios.
3Loss of information
If comprehensive temporal analysis is performed on parameter data, then insights into progression and regression are enhanced, but the processing time and computational resources increase
Solution Approach 1:
The patent creates a visual copy or representation of temporal parameter data in the form of images. Instead of directly processing raw temporal data sequences, the system generates image-based copies that preserve the essential patterns and trends. This copying approach allows the use of highly optimized image processing hardware and algorithms, achieving comprehensive analysis with reduced processing time compared to direct temporal data processing.
Data Source
AI summary
Techniques are disclosed relating to assessing technology activity using image-based machine learning algorithms. A computer system may access a data set that includes a plurality of parameters (e.g., technologies) for an item (e.g., a web-based interface). The plurality of parameters may correspond to a plurality of time intervals. The computer system may generate a two-dimensional graphical representation of the data set. A first dimension of the graphical representation may be indicative of values of the plurality of parameters at different time intervals and a second dimension of the graphical representation may be indicative of a time period that includes the plurality of time intervals. At least one characteristic of the data set may be determined by inputting the graphical representation of the data set to a trained machine learning module. The trained machine learning module may implement an image-based learning algorithm.