Frame Slicing Machine for Image Inventory Authentication

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Solution Overview

Problem

Existing digital image processing technologies lack efficient methods to extract individual frames from movies and verify their authenticity and provenance simultaneously.

Innovation Solution

A system comprising a frame slicing and packaging machine that uses artificial intelligence for object detection and recognition, generates cryptographic hashes, and adds watermarks to frame metadata, storing this information in a blockchain for secure and transparent authentication.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If individual frames are extracted from movies using traditional digital image processing, then frame extraction can be achieved, but authentication and provenance verification are lacking

Engineering Contradiction:

Improveauthenticity verificationVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the authentication process into distinct components: cryptographic hash generation for each frame, metadata creation with provenance information, and blockchain registration. This segmentation allows each function to be performed independently and verified separately, enhancing reliability without requiring complete system redesign.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces cryptographic hashes and blockchain technology as intermediary elements between frame extraction and authentication. These intermediaries provide a trusted verification mechanism that connects the extracted frames to their provenance without requiring direct trust in the extraction system alone.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If cryptographic hashes and watermarks are added to each frame with metadata, then authenticity and provenance can be verified, but processing time and computational resources increase

Engineering Contradiction:

Improveprovenance verificationVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent performs cryptographic hash generation and metadata creation during the frame extraction process itself, rather than as a separate post-processing step. This preliminary action ensures that authentication data is ready when frames are extracted, reducing overall processing time while maintaining provenance verification.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Each frame becomes self-containing with its own cryptographic hash and metadata embedded in the file structure. This self-service approach allows individual frames to be verified independently without requiring continuous system resources, reducing ongoing processing time while maintaining verification capability.

Inventive Principle:

Principle #25Self-service

3Reliability

If blockchain technology is used to store frame metadata, then tamper-proof records are achieved, but storage requirements and system complexity increase

Engineering Contradiction:

Improvetamper-proof metadataVSAvoidstorage requirements

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent extracts only the essential authentication data (cryptographic hashes and key metadata) to the blockchain, while storing the actual frame images separately. This extraction reduces the quantity of data stored on the blockchain to minimal necessary information for verification, lowering storage requirements while maintaining tamper-proof records.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent uses cryptographic hashes as digital copies or fingerprints of the original frames. These hash copies are stored on the blockchain instead of the full frame data, providing tamper-proof verification without requiring substantial storage space for the actual image content.

Inventive Principle:

Principle #26Copying

4Loss of information

If AI object detection and recognition are integrated into the frame processing, then object metadata can be generated, but processing complexity and computational load increase

Engineering Contradiction:

Improveobject information retentionVSAvoidprocessing complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent integrates AI object detection and recognition into the existing frame extraction and metadata generation pipeline. This multi-functional approach allows the same system to perform both traditional frame extraction and AI-based object analysis, reducing overall processing complexity by eliminating separate dedicated systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent merges the AI object detection process with the cryptographic hash generation and metadata creation steps. By combining these functions into a unified processing pipeline, the system reduces computational overhead and simplifies the overall architecture while still capturing comprehensive object information.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS10289915B1Manufacture of image inventories

Publication Date: 2019.05.14 EIGHT PLUS VENTURES LLC

AI summary

There are disclosed methods and apparatus for manufacture of image inventories. A frame slicing and packaging machine assigns metadata to each frame of a digital video work. It then detects objects in each frame's image, recognizes the objects and assigns metadata to the objects. The machine then generates a cryptographic hash of the frame's image. Lastly, the machine writes the hash to a node of a transaction processing network.