Metadata Inheritance for Digital Asset Processing

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

The generation of metadata for digital assets, particularly images, is a labor-intensive and costly process that requires significant human input, with manual or editing-based methods being the primary approaches, which is inefficient for large collections.

Innovation Solution

A method that analyzes digital assets to identify attributes, formulates search criteria, and conducts searches to find matching or near-matching assets, allowing for the sharing and importing of metadata between them, thereby reducing the need for manual metadata generation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual metadata generation methods are used, then metadata accuracy and completeness are improved, but labor time and costs increase significantly

Engineering Contradiction:

Improvemetadata accuracyVSAvoidlabor time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by automatically generating candidate metadata using AI models and inheritance mechanisms before manual review, preparing the metadata in advance so that human operators only need to verify and correct rather than create from scratch

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system copies metadata from similar digital assets through inheritance mechanisms, where metadata from source assets is replicated and adapted for target assets, reducing the need for manual creation while maintaining consistency across collections

Inventive Principle:

Principle #26Copying

2Productivity

If automated metadata generation is implemented, then productivity is improved, but metadata quality and reliability may deteriorate

Engineering Contradiction:

Improvemetadata generation speedVSAvoidmetadata quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements feedback loops where generated metadata is evaluated against quality criteria, and incorrect or low-quality metadata triggers re-generation or manual review, ensuring continuous improvement and quality control in the automated process

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system introduces intermediary mechanisms such as confidence scoring and quality filtering layers between automated generation and final metadata adoption, allowing only high-quality generated metadata to be automatically applied while routing uncertain cases for manual verification

Inventive Principle:

Principle #24Intermediary (Mediator)

3Manufacturing precision

If metadata is generated for every digital asset individually, then metadata specificity is improved, but processing time and resources increase

Engineering Contradiction:

Improvemetadata specificityVSAvoidprocessing efficiency

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system merges metadata generation operations by processing groups of similar digital assets together using inheritance and template mechanisms, allowing common metadata to be generated once and applied across multiple assets rather than individually processing each asset

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS10685234B2Automatic and semi-automatic metadata generation via inheritance in homogeneous and heterogeneous environments

Publication Date: 2020.06.16 XEROX CORP

AI summary

A method and system are provided that analyze a first digital asset to identify a set of attributes of the first digital asset. Search criteria are then formulated, and a search is conducted. Once search results are obtained, at least one second digital asset that is substantially identical to the first digital asset is identified. Then, metadata between the first digital asset and the second digital asset is shared.