AI Consumer Appliance With Age-Based Content Personalization

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Solution Overview

Problem

Existing electronic consumer appliances lack personalization and adaptability to individual user preferences and age-specific content generation, particularly in the context of content created using generative AI.

Innovation Solution

A user-specific electronic consumer appliance utilizing a generative model based on artificial intelligence, specifically large language models, generates content tailored to user preferences and age, allowing real-time adaptation and interaction, with features like voice input, haptic feedback, and customizable templates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If generative AI models are used to create personalized content for each user, then content personalization and user engagement are improved, but computational resources and processing time are increased

Engineering Contradiction:

Improvecontent personalizationVSAvoidcomputational resources

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by pre-processing user profile data, preferences, and device characteristics before content generation requests. User profiles are structured and stored in advance with key attributes (age, preferences, device type) that can be quickly retrieved and used as templates during content generation, reducing real-time computational burden while maintaining personalization quality

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies local quality by generating only the specific portions of content that need personalization rather than regenerating entire content pieces. The generative AI model focuses on adapting local elements (story elements, product descriptions, recommendations) based on user profile attributes, while reusing standardized templates and structures for consistent content frameworks

Inventive Principle:

Principle #3Local quality

2Measurement precision

If user profiles contain detailed preferences and characteristics for personalized content generation, then content accuracy and user satisfaction are improved, but data storage requirements and processing complexity are increased

Engineering Contradiction:

Improveuser preference accuracyVSAvoidprofile data structure

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The user profile data structure is segmented into distinct modular components: demographic information (age, location), preference categories (content types, topics, formats), device characteristics, and interaction history. Each segment is independently structured and can be selectively accessed during content generation, reducing overall processing complexity while maintaining comprehensive personalization capabilities

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system uses parameter changes by representing user preferences as structured data parameters with defined ranges and categories (e.g., age groups, preference weights, content categories). These parameters can be efficiently stored, compared, and applied to content generation templates, transforming complex qualitative preferences into quantifiable data structures that simplify processing

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If the system generates content in real-time using generative models, then content freshness and user engagement are improved, but response time and energy consumption are increased

Engineering Contradiction:

Improvecontent freshnessVSAvoidcontent generation time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system prepares content templates, structure frameworks, and style guides in advance before actual content generation. User profile data is pre-processed and matched with appropriate templates beforehand, so that real-time generation only requires filling in personalized content elements rather than creating entire content pieces from scratch, significantly reducing response time while maintaining freshness

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system maintains continuity by keeping generative models in a ready state with pre-loaded user profiles and content templates. Rather than initializing models for each request, the system continuously maintains active sessions with pre-warmed models that can generate content with minimal latency, ensuring both freshness and rapid response times

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS20260050642A1Ai in an electronic appliance

Publication Date: 2026.02.19 INFINITISPARK GMBH

AI summary

A user-specific electronic consumer appliance, in particular a consumer appliance adapted to the user's age, serves for the acoustic and/or optical and/or haptic reproduction of content generated using a generative model based on artificial intelligence. The generated content is adapted to the user, is generated using the generative model on the basis of a user-specific template, and the user-specific template contains a user preference assigned to the user profile and the user age.