Parameter-Based Synthetic Models for Personalized Product Visualization
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Solution Overview
Problem
Existing marketing materials and recommendation systems fail to account for the unique preferences and tastes of individual users, leading to limited demographic appeal and ineffective product visualization, as they rely on static human models and generic recommendations.
Innovation Solution
A system utilizing generative machine learning models to generate parameterized synthetic models and recommendations based on user input and behavioral data, allowing users to specify or infer parameters for personalized image generation and tailored product suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static human models are used in marketing materials, then production cost and time are reduced, but demographic appeal and user engagement are limited
Solution Approach 1:
The patent transforms static human models into dynamic, parameterizable synthetic models that can be adjusted in real-time. The system uses generative machine learning models to create models with varying demographic characteristics (age, ethnicity, body type, etc.), allowing marketing materials to adapt to different user preferences without manual production for each demographic segment.
Solution Approach 2:
The patent implements parameter-based control over synthetic model characteristics. Users can specify parameters such as age range, ethnicity, body type, and other demographic attributes, and the system generates corresponding synthetic models by adjusting these parameters. This enables flexible adaptation of content to different demographic segments while maintaining efficient automated generation.
2Adaptability or versatility
If diverse human models are manually created for different demographics, then demographic appeal is improved, but production cost and time increase significantly
Solution Approach 1:
The patent uses synthetic models generated by machine learning as digital copies that can be infinitely reproduced and modified. Instead of photographing diverse real models (which requires significant resources), the system creates digital representations that can be generated on-demand with specific demographic parameters, eliminating the need for manual content creation for each demographic segment.
Solution Approach 2:
The patent creates a universal synthetic model generation system that can produce models across all demographic segments using a single automated platform. The generative machine learning model serves multiple functions: it can generate models of any age, ethnicity, body type, and other characteristics, replacing the need for separate content creation processes for each demographic category.
3Device complexity
If generic recommendations are provided to all users, then system complexity is reduced, but personalization and user engagement are compromised
Solution Approach 1:
The patent performs preliminary analysis of user behavior and preferences to infer demographic parameters before generating recommendations. The system proactively collects and analyzes user interaction data, browsing patterns, and purchase history to determine user characteristics, then uses these inferred parameters to generate personalized recommendations in advance of user requests.
Solution Approach 2:
The patent implements feedback loops where user responses to recommendations and interactions with synthetic models are continuously analyzed. The system uses this feedback to refine its understanding of user preferences and adjust future recommendations and model generations, creating a dynamic personalization system that improves over time based on actual user behavior.
Data Source
AI summary
Some embodiments described herein relate to systems and methods for parameter-based synthetic model generation and recommendations including an image generation module and a recommendation module. The image generation module can receive one or more parameters and, responsive to receive the one or more parameters, generate a parameterized image using a generative machine learning model. The generative ML model may use the parameters as a seed for generating the parameterized image. The recommendation module may generate a first set of recommendations for a user of the client device and receive the one or more parameters. The recommendation module may determine, based on the one or more parameters, a second set of recommendations for the user of the client device. The second set of recommendations may include at least one element from the first set of recommendations.