Semantic Visual Hash Injection for Privacy-Preserving Workflow Analysis
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Solution Overview
Problem
Existing asset-modification applications, such as Adobe Photoshop and Illustrator, face limitations in analyzing user activity streams without deeper contextual knowledge of the asset's visual content, leading to a limited understanding of user behavior and workflow optimization.
Innovation Solution
Generating image hashes as digital fingerprints that can be compared to a library of categorized hashes to determine the visual category of an image, allowing for contextual analysis and optimization of workflows without revealing the actual image content, thus maintaining user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If actual image content is collected and transmitted for analysis, then deeper contextual knowledge about visual content is obtained, but user privacy is compromised
Solution Approach 1:
The patent extracts only the essential visual characteristics of images by generating hash values, separating the useful contextual information from the actual image content. This allows analysis of visual content patterns without transmitting or storing the original images, thus preserving user privacy while gaining contextual knowledge.
Solution Approach 2:
Instead of transmitting actual images, the patent creates hash value copies that represent the visual content. These hash copies contain sufficient information for categorization and pattern recognition but cannot be reverse-engineered to reveal the original image, providing a privacy-preserving representation.
2Loss of information
If image hashes are generated and compared to determine visual categories, then contextual analysis is enabled without revealing actual image content, but the complexity of hash generation and comparison increases
Solution Approach 1:
The patent transforms images into a different parameter space by generating hash values. This parameter transformation simplifies the comparison operation, as hash values can be efficiently compared using simple distance metrics rather than complex image processing algorithms, reducing overall computational complexity.
Solution Approach 2:
The patent replaces complex mechanical image processing and visual analysis systems with a computational hash-based system. Instead of using sophisticated image recognition algorithms, the system substitutes a simpler hash generation and comparison mechanism that achieves the same categorization goal with reduced complexity.
3Quantity of substance
If event logs with high-level user actions are collected, then user activity data is obtained, but the ability to extrapolate meaningful insight is limited without deeper contextual knowledge
Solution Approach 1:
The patent merges event log data with hash-based visual content analysis by associating hash values with user actions in the event logs. This combination preserves the quantity of usage data while adding contextual knowledge about the visual content being manipulated, enabling more meaningful insights without compromising privacy.
Data Source
AI summary
In various implementations, an abstraction is generated from an asset associated with an asset-modifying workflow. The abstraction can be embedded into an activity stream generated from an asset-modification application and communicated to a remote server device for collection and analysis. The remote server device, upon receiving at least the abstraction, can determine a contextual identifier for association with the abstraction and the asset associated with the asset-modifying workflow. The remote server device can conduct usage analysis on data received from the activity stream in association with the contextual identifier, and further send a signal to the asset-modification application to customize the workflow based on the contextual identifier determined to be associated with the abstraction and asset.