Neural Network Personalized Story Generation System

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Solution Overview

Problem

Users often struggle to consume content within a set time frame due to its fixed expression and length, leading to incomplete consumption or the need for multiple sessions, highlighting a desire for personalized, derivative content that meets individual needs and desires.

Innovation Solution

A dynamic content generation system utilizing a trained artificial neural network to create personalized derivative stories by modifying existing content, incorporating elements from various sources, formats, and lengths, allowing for radical changes or combinations to suit consumer preferences, while ensuring compliance with metadata constraints and royalty tracking for copyrighted materials.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If content is presented in fixed expression and length, then content integrity and authorial intent are preserved, but user consumption time increases and user satisfaction decreases

Engineering Contradiction:

Improvecontent adaptabilityVSAvoidconsumption time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent implements dynamic content generation where stories are created in real-time based on user preferences, consumption time constraints, and other parameters. The system transitions from static pre-written content to dynamically generated content that adapts to individual user needs, allowing the same source material to be transformed into multiple personalized versions with varying lengths, formats, and details.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system allows users to specify multiple parameters including consumption time, format preferences, detail level, and topic interests. The neural network processes these parameter changes to generate customized stories that match user requirements while maintaining narrative coherence and quality, effectively transforming content based on user-defined parameters.

Inventive Principle:

Principle #35Parameter changes

2Loss of information

If content length is extended to provide comprehensive storytelling, then narrative depth increases, but user attention span is exceeded and completion rate decreases

Engineering Contradiction:

Improvenarrative completenessVSAvoidconsumption efficiency

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The system generates stories with appropriate length and detail level based on user preferences rather than providing complete exhaustive narratives. It produces just the right amount of content needed for each user's consumption capacity and interests, avoiding both information deficiency and excessive length that would reduce completion rates.

Inventive Principle:

Principle #16Partial or excessive action

3Adaptability or versatility

If personalized derivative content is generated using neural networks, then user satisfaction and content adaptability improve, but system complexity and computational resources increase

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system uses neural networks trained on existing stories to generate new personalized content. Instead of requiring complex rule-based systems or manual curation, the neural network learns patterns from training data and reproduces appropriate story elements, structures, and styles automatically, simplifying the overall system architecture while maintaining high personalization capability.

Inventive Principle:

Principle #26Copying

4Loss of time

If traditional auto-summarization tools are used to reduce content length, then consumption time decreases, but narrative quality and engagement deteriorate

Engineering Contradiction:

Improveconsumption timeVSAvoidnarrative quality

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The patent replaces traditional mechanical summarization algorithms with neural network-based generation. Instead of mechanically truncating or condensing existing text, the neural network generates new narrative content that maintains quality, coherence, and engagement while adapting length to user preferences, fundamentally substituting the summarization mechanism with a generative approach.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11501079B1Personalized content creation using neural networks

Publication Date: 2022.11.15 X DEVELOPMENT LLC

AI summary

A technique for dynamic generation of a derivative story includes obtaining content preferences from a content consumer. The content preferences indicate preferences for characteristics of the derivative story. A content data structure is identified based at least in part on the content preferences. The content data structure specifies story elements of a preexisting story. The story elements are defined at one or more different levels of story abstraction and associated with metadata constraints that constrain modification or use of the story elements within the derivative story. At least some of the metadata constraints indicate whether associated ones of the story elements are mutable story elements. One or more of the mutable story elements are adapted to the content preferences of the content consumer as constrained by the metadata constraints to generate the derivative story. The derivative story is then rendered via a user interface.