Learning Model for Predicting Image Data Sales
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for evaluating the ease of selling image data lack efficiency in predicting sales prospects and optimizing image selection for photographers/videographers, as they rely on subjective evaluation and do not effectively utilize data from user interactions on e-commerce platforms.
Innovation Solution
A system comprising a server and communication terminals that generate and utilize a learning model to predict the ease of selling image data by analyzing image feature data, user interaction data, and subject scores, allowing for objective evaluation and selection of high-selling images based on calculated scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective evaluation methods are used to assess image data, then the evaluation process is simple to implement, but the prediction accuracy of sales prospects is insufficient
Solution Approach 1:
The patent replaces subjective human evaluation with an automated learning model that processes image data and user interaction data to predict sales prospects. The learning model analyzes features such as image quality, subject composition, and user behavior patterns to generate objective predictions, thereby improving measurement precision while reducing reliance on human judgment.
Solution Approach 2:
The patent introduces a learning model as an intermediary between image data and sales prediction. This intermediary component processes raw image data and user interaction data through multiple layers of analysis (including CNN-based image analysis and RNN-based sequence modeling) to produce sales prospect predictions, enabling accurate forecasting without direct human evaluation.
2Productivity
If all image data is uploaded to e-commerce platforms, then the quantity of available images is maximized, but unnecessary uploads reduce sales efficiency
Solution Approach 1:
The patent performs preliminary evaluation of image data using the learning model before upload to the e-commerce platform. The model predicts sales prospects for each image and filters out images with low predicted performance, ensuring that only high-potential images are uploaded. This preliminary action increases sales efficiency by reducing unnecessary uploads while maintaining a diverse and high-quality image inventory.
3Measurement precision
If user interaction data is collected and analyzed, then the prediction of high-selling images is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing task into distinct modules: image data processing (using CNNs to extract visual features), user interaction data processing (using RNNs to model behavior sequences), and prediction generation. This segmentation allows each component to specialize in specific data types and processing techniques, improving overall prediction accuracy while managing computational complexity through modular architecture.
Data Source
AI summary
A learning apparatus includes a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute an acquisition process of acquiring an image data group, and correct data pertaining to sale of each piece of image data in the image data group; and a generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process.