Blockchain Digital Image Filtering System
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Solution Overview
Problem
Current systems fail to efficiently locate and purchase specific digital images captured at a specific location, time, and event, as traditional methods lack the ability to filter and deliver professional-quality images based on user-defined criteria, leading to uncertainty in image quality and relevance.
Innovation Solution
A digital image blockchain filtering system that utilizes blockchain technology to associate photographs with metadata such as geolocation, timestamp, and camera angle, allowing users to search and filter images based on specific criteria like location, time, and subject matter, using machine learning and facial recognition to reduce irrelevant images and ensure image authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional distribution models are used to supply digital images, then the supply chain remains simple, but users cannot efficiently locate specific professional-quality images among large volumes of available images
Solution Approach 1:
The system performs preliminary actions by automatically appending metadata to digital images at the time of creation, before users need to search for them. This pre-organization of image data with relevant information (quality markers, event data, location, time) enables efficient later retrieval without requiring users to manually filter through vast quantities of images.
Solution Approach 2:
The patent introduces blockchain technology as an intermediary layer between image creators and users. The blockchain stores and verifies metadata information about digital images, acting as a trusted mediator that provides users with reliable information about image quality, authenticity, and relevance without requiring direct access to the entire image corpus.
2Ease of operation
If all digital images are stored without filtering mechanisms, then complete image archives are maintained, but users experience difficulty locating specific images of interest
Solution Approach 1:
The system pre-processes and organizes images by automatically appending metadata at the time of image creation. This preliminary organization includes tagging images with relevant information such as location, time, event data, and quality markers, enabling users to quickly locate specific images without manual searching through entire archives.
Solution Approach 2:
The patent segments the large corpus of digital images by organizing them according to multiple metadata dimensions (geolocation, timestamp, event, quality markers). This segmentation allows users to filter and narrow down searches across different criteria simultaneously, making image location much easier and faster while maintaining the complete archive.
3Reliability
If professional-quality image verification is implemented, then image quality assurance is improved, but system complexity increases
Solution Approach 1:
The system implements self-service quality verification by having the digital data capturing device automatically append metadata to images at the time of creation. The device itself provides quality assurance information without requiring external verification systems, maintaining reliability while minimizing added complexity.
Solution Approach 2:
The blockchain infrastructure serves multiple functions simultaneously: it stores image metadata, verifies image authenticity, tracks image provenance, and provides quality certification. This multi-functionality achieves reliable quality assurance without proportionally increasing system complexity, as the same underlying technology platform handles multiple tasks.
4Loss of information
If metadata is appended to all digital images at creation, then image information completeness is enhanced, but data processing requirements increase
Solution Approach 1:
The digital data capturing device performs self-service by automatically appending metadata to images at the moment of creation. This eliminates the need for separate post-processing operations to add metadata, as the data is organized and tagged in real-time during capture, reducing overall data processing requirements.
Solution Approach 2:
Metadata appending is performed as a preliminary action during image creation rather than as a subsequent processing step. By organizing and tagging data at the source and time of creation, the system avoids the need for energy-intensive batch processing operations later, reducing overall computational energy requirements.
Data Source
AI summary
A system and method for filtering digital images stored on a blockchain database to locate one or more specific digital images from a corpus of digital images from an event includes receiving a search criteria from a user for searching through the corpus of digital images stored on the blockchain database, filtering the corpus of digital images stored on the blockchain database based on the plurality of factors that match the search criteria, locating the one or more specific digital photographs that match the search criteria among the corpus of digital images stored on the blockchain database, as a function of the filtering, presenting the one or more specific digital photographs to the user for selection and purchase of the one or more specific digital photographs, and processing a purchase order for the one or more specific digital photographs selected for purchase by the user.