Personalized Artificial Entities Using Digital Trait Copying

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Solution Overview

Problem

Existing artificial entities based on Generative Pre-trained Transformer (GPT) architecture and NLP models provide generic responses lacking personalization, failing to mirror the unique cognitive traits and preferences of individuals.

Innovation Solution

Systems and methods for generating personalized artificial entities by receiving information about an individual, creating an artificial entity tailored to their characteristics, and determining responses based on interaction context.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If generic databases and conversation records are used for artificial entities, then the system complexity is low, but the personalization and authenticity of interactions are poor

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by collecting and processing individual data (text, audio, photos, videos) before creating the artificial entity. This advance preparation enables the entity to inherit the source individual's cognitive traits, preferences, and interaction patterns, achieving personalization without requiring complex real-time processing during interactions

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a copy of the source individual's digital footprint by processing their data through deep-learning algorithms. This digital copy captures cognitive traits, preferences, and interaction patterns, allowing the artificial entity to mirror the individual's unique characteristics while maintaining system manageability

Inventive Principle:

Principle #26Copying

2Reliability

If deep-learning algorithms process individual data to create personalized entities, then interaction authenticity is improved, but data processing requirements and system resources increase

Engineering Contradiction:

Improveinteraction authenticityVSAvoiddata processing resources

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system performs intensive data processing in advance during the entity creation phase, when resources can be allocated appropriately. Once the artificial entity is created with inherited cognitive traits and preferences, subsequent interactions require minimal processing, maintaining authenticity without continuous high resource consumption

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The artificial entity serves itself by using the inherited cognitive traits and preferences to generate authentic responses independently. The deep-learning model enables the entity to process interactions autonomously based on the source individual's patterns, reducing the need for external computational resources during operation

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12620262B2Using artificial entities for generating personalized responses

Publication Date: 2026.05.05 INGEL BEN AVI

AI summary

Systems, methods and non-transitory computer readable media for generating and operating artificial entities are provided. Some disclosed embodiments may involve receiving information related to a source individual; generating an artificial entity associated with the source individual based on the received information; receiving data reflecting an interaction with the artificial entity; and determining a manner for the artificial entity to respond to the interaction based on the collected information.