Hash-Based Personalized Design Generation with Verifiable Ownership
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for personalized product design are time and cost-intensive, lack scalability, and do not provide a democratized platform for connecting customers with designers, and there is a need for immersive product selection experiences with unique verifiable signatures.
Innovation Solution
A processor-implemented method and system that generates personalized designs by converting user inputs into hash values, determining design parameters using random number generation, and tokenizing the designs as non-fungible tokens in blockchain, allowing users to select generative algorithms and customize designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional methods connect customers with exclusive designers for personalized product creation, then design quality and personalization depth are improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical system of human designer-customer interaction with an automated generative AI system. The AI model generates personalized designs algorithmically based on customer inputs, eliminating the need for time-intensive human design processes while maintaining personalization quality.
Solution Approach 2:
The system enables customers to self-generate personalized designs by providing their own inputs (text, images, audio, video) which the AI system automatically processes into unique design outputs. This self-service approach eliminates dependency on external designers while delivering personalized results.
2Productivity
If conventional methods use pre-defined modular customization options, then scalability is improved, but personalization scope is limited
Solution Approach 1:
The patent transforms the customization approach from selecting from pre-defined discrete options to continuously adjustable parameters. The AI system processes various input types (text, images, audio, video) and converts them into design parameters, allowing unlimited personalization scope while maintaining scalability through automated generation.
Solution Approach 2:
The generative AI system serves multiple functions: it processes diverse input types (text, images, audio, video), generates unique designs, creates digital twins, and enables customization across different product types. This multi-functionality allows the system to scale while providing extensive personalization scope.
3Adaptability or versatility
If a democratized design platform connects multiple ecosystem players, then design diversity and innovation are improved, but system complexity increases
Solution Approach 1:
The patent introduces a centralized platform architecture that acts as an intermediary between multiple ecosystem players (designers, retailers, customers). The platform manages design generation, digital twin creation, and NFT tokenization through standardized interfaces, coordinating complex interactions without requiring direct complex connections between all participants.
Solution Approach 2:
The system creates digital twins (digital copies) of physical products and designs, which can be manipulated, customized, and traded without affecting the original physical items. This copying mechanism simplifies the platform architecture by working with digital representations rather than managing complex physical product interactions.
4Reliability
If unique verifiable signatures are assigned to each design, then ownership verification is improved, but processing overhead increases
Solution Approach 1:
The patent replaces traditional ownership verification mechanisms with blockchain-based NFT tokenization. The system automatically generates and assigns unique NFT tokens to each design and digital twin, providing verifiable ownership through cryptographic proof rather than manual verification processes.
Solution Approach 2:
The system performs ownership verification in advance by minting NFT tokens at the moment of design generation. This preliminary action embeds ownership information into the design data structure itself, eliminating the need for subsequent verification overhead and enabling instant ownership confirmation.
Data Source
AI summary
The disclosure relates generally to methods and systems for generating personalized designs based on a personalized input provided by a user. Conventional techniques for personalized designs lack a democratized design content platform to connect retailers, designers or digital artists or creative coders or generative artists and the customers. According to the present disclosure, the customer or a user provides personalized input. Further, the customer or the user is allowed to choose a generative design of interest by selecting a suitable generative design algorithm from a list of generative design algorithms. The personalized input provided by the customer is then transformed as a hash value which is used to determine a set of design parameters based on a set of design attributes, using a random number generation technique. Finally, the set of design parameters are then used to generate an exclusive personalized design.