AI Image Classification for Cost-Aware Enterprise Storage
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Solution Overview
Problem
Conventional image data storage approaches are resource-intensive and lack targeted organizational capabilities, leading to costly cloud storage and time-intensive searching efforts.
Innovation Solution
Implementing artificial intelligence techniques, specifically convolutional neural networks, to automatically classify images into enterprise-related categories, enabling intelligent data storage decisions such as archiving, migrating, or deleting images based on priority.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If cloud storage is used to store image data, then storage capacity is increased, but storage cost and resource consumption increase
Solution Approach 1:
The patent segments image data into different categories based on importance and accessibility requirements. By classifying images into distinct categories (e.g., high-priority vs. low-priority), the system can allocate storage resources differently, storing critical images in premium cloud storage while archiving less important images in cheaper, less accessible storage locations, thus reducing overall storage costs.
Solution Approach 2:
The system changes the storage parameters for different image categories. Instead of uniform storage treatment, the patent applies different storage policies based on image characteristics and business requirements, such as retention periods, access frequencies, and storage locations, optimizing the balance between accessibility and cost.
2Ease of operation
If all images are stored in cloud storage, then data accessibility is improved, but searching efficiency deteriorates due to lack of organization
Solution Approach 1:
The patent segments the image collection into organized categories based on content, importance, and usage patterns. By implementing a hierarchical classification system with multiple levels (e.g., business type, project, image type), the system maintains easy access to all images while enabling rapid filtering and searching within specific categories, significantly improving search efficiency.
Solution Approach 2:
The classification system acts as an intermediary between the raw image data and the user's search queries. Instead of searching through unorganized data, the intermediary categorization structure pre-organizes images, allowing users to navigate and search more efficiently through structured groups rather than individual files.
3Ease of manufacture
If manual image classification is performed, then storage organization is improved, but time consumption increases
Solution Approach 1:
The patent implements self-service classification using automated image recognition technology. The system automatically analyzes image content, extracts features, and assigns appropriate categories without human intervention. This self-service approach maintains high-quality storage organization while eliminating the time-consuming manual classification process, allowing the system to organize itself based on visual content analysis.
Solution Approach 2:
The system replaces the mechanical manual classification process with automated computer vision and machine learning algorithms. Instead of human operators visually inspecting and categorizing images, the patent uses computational models that automatically recognize patterns, objects, and contexts in images, substituting human cognitive processing with automated mechanical systems.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically classifying images for storage-related determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one image associated with an enterprise; classifying one or more portions of the at least one image into one or more enterprise-related categories by processing at least a portion of the at least one image using one or more artificial intelligence techniques; and performing, based at least in part on the classifying of the one or more portions of the at least one image, one or more automated actions pertaining to storing the at least one image in at least one of multiple storage options.