AI Content Generation Model for User-Specific Digital Assets

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Solution Overview

Problem

Existing document hosting systems face inefficiencies in content creation, requiring time-intensive user interactions and providing rigid, unintelligent digital graphic design and drawing tools that do not significantly reduce the time needed to create content.

Innovation Solution

A custom content generation system utilizing artificial intelligence and machine learning to generate user-specific content by training a content generation model with user-specific attributes from content collections, allowing for quick creation of custom content items based on user requests.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual content creation tools are provided, then users can create custom content, but the process is time intensive and requires significant user interaction

Engineering Contradiction:

Improvecontent creation easeVSAvoidcontent creation time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical content creation processes with an AI-based system that generates content automatically. Instead of users manually creating content through drawing tools and graphic design software, the system uses machine learning models to generate content items based on user preferences and patterns, thereby reducing time consumption while maintaining ease of operation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service content creation by automatically generating content items based on learned user preferences. The AI model analyzes user interactions and content requests to autonomously create personalized content, eliminating the need for users to spend time on manual creation tasks while still receiving customized content results

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If rigid digital graphic design tools are provided, then users can create content with basic functionalities, but the tools are unintelligent and do not significantly reduce time requirements

Engineering Contradiction:

Improvetool functionalityVSAvoidcontent creation time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent transforms static, rigid graphic design tools into dynamic AI-based tools that adapt to user needs in real-time. The system learns from user preferences and content requests, automatically adjusting content generation parameters to match user expectations. This dynamic adaptation enables the system to reduce content creation time while providing versatile, intelligent functionality that goes beyond basic rigid tools

Inventive Principle:

Principle #15Dynamics

3Productivity

If time intensive user interactions are required for content creation, then content can be created with basic tools, but computational resources are significantly consumed

Engineering Contradiction:

Improvecontent creation speedVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary learning and analysis of user preferences during off-peak times or in advance, building a comprehensive model of user needs before actual content creation requests. This preliminary action allows the AI model to generate content quickly and efficiently when users make requests, thereby increasing productivity while reducing real-time computational resource consumption through pre-computed understanding of user preferences

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240265204A1Generating user-specific synthetic content utilizing machine learning with user created content items

Publication Date: 2024.08.08 DROPBOX INC

AI summary

This disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that can utilize artificial intelligence to generate user-specific content based on content collections associated with a user account. Indeed, in one or more implementations, the disclosed systems utilize machine learning to intelligently generate new, custom content items that emulate user-specific content attributes based on content collections associated with a user account. In some instances, the disclosed systems utilize a content generation model that is trained to generate new content items in response to user requests (e.g., requests that describe one or more features). Furthermore, the disclosed systems can fine tune or modify parameters of the content generation model with content items from the content collections associated with the user account to create a custom content generation model that synthesizes at least one attribute of the user account's content items within generated, new content items.