AI Image Region Selection for Targeted Data Transformation
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Solution Overview
Problem
Existing systems face challenges in efficiently identifying and transforming data, particularly images, to meet specific user-defined criteria, leading to tedious processes and inefficient use of resources.
Innovation Solution
A computing device equipped with an AI model that receives user inputs to identify attributes in images and selectively transform portions based on these criteria, determining appropriate storage locations for transformed data using tiered and standard memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If all images are processed and transformed, then complete data transformation is achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent segments the image processing task by dividing images into multiple portions and processing only those portions that contain data meeting the transformation criteria. The model identifies and processes specific regions of interest rather than entire images, reducing overall processing time while maintaining transformation completeness for relevant data.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of images that contain transformable data, rather than processing entire images. The AI model identifies and transforms only the specific data portions that meet user-defined criteria, eliminating unnecessary processing of irrelevant image areas.
2Productivity
If AI model processes only necessary image portions, then processing efficiency improves, but complexity of identifying specific portions increases
Solution Approach 1:
The AI model performs self-service by automatically identifying and processing only the necessary portions of images that contain transformable data. The model independently determines which image portions require transformation based on user-defined criteria, eliminating the need for manual specification and reducing overall system complexity despite the model's internal complexity.
3Manufacturing precision
If user-defined criteria are applied for data selection, then transformation precision improves, but ease of operation decreases due to additional configuration steps
Solution Approach 1:
The patent applies dynamics by making the transformation criteria flexible and user-definable. Users can dynamically adjust the criteria for identifying transformable data based on their specific needs, allowing the system to adapt to different transformation requirements while maintaining precision. The model responds to these dynamic criteria by automatically adjusting its processing behavior.
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.