Photo Upload Filtering Using ML Attribute Selection
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Solution Overview
Problem
Existing photo upload systems lack the ability to intelligently select and upload photos based on designated attributes, leading to unnecessary data transmission, storage, and battery consumption due to the upload of uninteresting photos.
Innovation Solution
An intelligent photo upload service using machine-learning models to identify and upload photos with specific attributes, allowing users to select or exclude photos based on criteria such as facial recognition, presence of text, or other features, and optionally deleting uninteresting photos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all photos in a selected folder are automatically uploaded to the content management system, then the completeness of photo backup is improved, but unnecessary data transmission and storage resources are consumed
Solution Approach 1:
The patent extracts and identifies specific photos of interest from the complete photo collection using image recognition technology. Only photos containing detected objects, people, or text that match user-defined criteria are selected for upload, separating the useful subset from the entire dataset to avoid unnecessary transmission and storage.
Solution Approach 2:
The system changes the selection parameter from 'all photos' to 'photos matching specific attributes'. By using machine learning models to detect and classify photos based on content attributes (objects, people, text), the system transforms the upload criterion from a blanket approach to a selective one, reducing the volume of uploaded photos while maintaining backup reliability for important images.
2Reliability
If all photos in a selected folder are automatically uploaded to the content management system, then the completeness of photo backup is improved, but data transmission bandwidth is wasted
Solution Approach 1:
The system extracts only the necessary photos from the complete collection by applying image recognition algorithms to identify photos containing user-specified attributes. This extraction process filters out irrelevant photos before upload, significantly reducing the data transmission volume while ensuring that important photos are backed up.
Solution Approach 2:
The patent performs preliminary analysis of photos using machine learning models before the upload process. By pre-identifying and tagging photos with detected attributes (objects, people, text), the system determines which photos should be uploaded in advance, preventing unnecessary data transmission while ensuring completeness of important photo backup.
3Reliability
If all photos in a selected folder are automatically uploaded to the content management system, then the completeness of photo backup is improved, but storage resources are consumed unnecessarily
Solution Approach 1:
The system extracts and uploads only photos that match user-defined criteria using image recognition technology. By identifying and separating photos containing specific objects, people, or text from the complete collection, the system reduces the storage volume required while maintaining backup completeness for photos of actual interest to the user.
Solution Approach 2:
The patent changes the backup parameter from 'all photos' to 'photos with specific attributes'. By using machine learning to detect and classify photo content, the system transforms the storage requirement from storing every photo to storing only photos that meet predefined criteria, optimizing storage space while ensuring reliability of important photo backup.
4Measurement precision
If users manually select each photo individually for upload, then upload precision is improved, but user time and effort are increased
Solution Approach 1:
The system performs automatic photo selection and classification using image recognition technology, eliminating the need for manual user selection. The machine learning models autonomously analyze photos, detect attributes, and determine which photos should be uploaded based on user-defined criteria, providing both high precision and time efficiency simultaneously.
Solution Approach 2:
The patent replaces the manual mechanical process of photo selection with an automated computer vision system. Machine learning models perform the selection task that would otherwise require human time and effort, achieving both high precision in photo selection and significant time savings by substituting automated intelligence for manual user action.
Data Source
AI summary
A system and method are provided for automatically selecting a subset of photos to be backed up by an intelligent photo upload service provided by a content management system. The intelligent photo upload service is provided to a client application on a client device that uses one or more machine-learning models to determine which photos have the one or more attributes. The photos that have been determined to have the one or more attributes may be uploaded. Once uploaded the one or more attributes may be used to label the respective photos with metadata indicating the presence of the one or more attributes. The one or more attributes may be used to filter photos at the content management system.