Dynamic Machine Learning Model Generation for Item Listings
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Solution Overview
Problem
Current systems fail to effectively analyze and visualize complex, heterogeneous data sets in real-time, limiting users' ability to understand and leverage data insights for organizational goals, particularly in environments where users lack data science knowledge or motivation.
Innovation Solution
A computing system dynamically generates machine learning models and user interfaces to analyze and visualize data by processing attribute values of new item listings against previous listings, extracting features, and applying machine learning algorithms to optimize listing creation and suggest improvements for better outcomes, such as increased selling price or probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If machine learning models are used to analyze complex heterogeneous data sets, then data insights and organizational goals achievement are improved, but system complexity and data science expertise requirements increase
Solution Approach 1:
The patent introduces an intermediary system that includes a model generator and visualization generator. This intermediary layer automatically creates machine learning models from raw data and generates visual representations of model outputs, mediating between the complex data analysis requirements and the end-user's need for simple insights without requiring users to directly manage model complexity
Solution Approach 2:
The system performs self-service by automatically generating machine learning models and their visualizations without requiring user intervention in the model creation process. The model generator autonomously selects features, trains models, and the visualization generator automatically creates appropriate visual representations, eliminating the need for users to have data science expertise
2Speed
If real-time data analysis is implemented, then responsiveness and decision-making speed are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing data, pre-selecting features, and pre-training models in advance. The model generator creates models beforehand, and the visualization generator prepares visualization templates, so when real-time analysis is needed, the system can quickly apply pre-prepared models and visualizations to new data without extensive processing delays
3Ease of operation
If comprehensive data visualization is provided, then user understanding and motivation are improved, but interface complexity and development effort increase
Solution Approach 1:
The visualization generator dynamically changes visualization parameters based on the model output type, data characteristics, and user preferences. It automatically selects appropriate visualization types (charts, graphs, tables), adjusts colors, scales, and layouts, providing comprehensive visualizations that adapt to different scenarios without requiring complex manual configuration or increasing interface complexity
Data Source
AI summary
Systems and methods provide real-time machine learning modeling, evaluation, and visualization. A computing system can receive input values for attributes of a new item listing. Concurrently, the system can analyze previous related item listings. The system can process the attributes and associated values of the new listing and previous listings to extract features and associated values. The system can apply the features of previous listings to machine learning algorithms to generate a machine learning model directed towards a target objective, such as maximizing a selling price or a selling probability for the new listing. The system can determine a likely outcome of the new listing by applying user-input values to the model and alternative outcomes by substituting one or more user-input values. If the alternative outcomes represent better results, then the system can present suggestions to revise the new listing to replace one or more user-input values with substitute values.