AI Image Attribute Selection for Faster Data Transformation
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Solution Overview
Problem
Existing methods for determining which data to transform from large datasets are tedious and slow down the transformation process, often requiring external communications and inefficient use of resources.
Innovation Solution
A computing device equipped with an AI model that identifies attributes of images based on user input, selects portions of images for transformation, and determines the appropriate memory storage based on transformation requirements, thereby optimizing the transformation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods are used to determine which data to transform, then external communications and resource usage occur, but the process becomes tedious and slows down transformation
Solution Approach 1:
The system performs preliminary actions by having the AI model analyze and identify candidate data for transformation before the actual transformation process begins. This includes evaluating data attributes, determining transformation suitability, and preparing selection criteria in advance, which eliminates the need for time-consuming external communications during the transformation process itself.
Solution Approach 2:
The AI model performs self-service by autonomously determining which data should be transformed without requiring external communications or manual intervention. The model independently evaluates data characteristics, applies transformation criteria, and makes selection decisions, thereby eliminating delays caused by external system interactions.
2Productivity
If existing methods are used to determine which data to transform, then resource usage occurs, but efficiency is reduced
Solution Approach 1:
The system applies partial action by having the AI model evaluate only the necessary attributes of data that are relevant to transformation suitability, rather than analyzing all possible data characteristics. This selective evaluation approach reduces computational resource consumption while still achieving accurate data selection for transformation.
3Ease of operation
If manual determination of data to transform is performed, then accuracy can be maintained, but the process becomes tedious and slow
Solution Approach 1:
The patent replaces manual mechanical determination processes with an AI-based automated system. The AI model processes data attributes and makes transformation selections automatically, substituting human manual review and decision-making with computational intelligence that operates faster and without fatigue, thereby improving ease of operation while maintaining accuracy.
Data Source
AI summary
Methods, devices, and systems associated with identifying data to transform are described. A method can include receiving, at a model stored on a computing device, data comprising a number of images, receiving, at the model, an input from a user, identifying, via the model, a number of attributes based on the input from the user, and identifying, via the model, a portion of an image of the number of images including at least one of the number of attributes to transform.