Personalized Artificial Entities With Profile-Based Response Customization
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Solution Overview
Problem
Existing artificial entities based on Generative Pre-trained Transformer (GPT) architecture and NLP models lack personalization, failing to effectively mirror the unique cognitive traits, preferences, and interaction styles of individuals, leading to generic and less engaging interactions.
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 interactions using advanced AI techniques like NLP and computer vision, enabling personalized and engaging interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic databases and NLP models are used for artificial entities, then the system complexity is reduced and ease of manufacture is improved, but personalization capability and engagement quality deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and processing user data beforehand to create a personalized profile. This profile is then used to customize the artificial entity's responses and behavior, allowing the system to start with a generic model and gradually personalize it through pre-collected data about the user's preferences, communication style, and interests.
Solution Approach 2:
The system creates a digital copy or representation of the user's characteristics, preferences, and communication patterns. This copy is then integrated into the artificial entity, allowing it to mimic the user's unique traits while maintaining the underlying generic NLP model structure, thus achieving personalization without complete system redesign.
2Adaptability or versatility
If personalized artificial entities are created using deep-learning algorithms and individual data, then personalization capability and engagement quality are improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system segments the personalization process into distinct modules: data collection module, profile generation module, and response customization module. Each module handles a specific aspect of personalization, allowing the complex deep-learning algorithms to be broken down into manageable components that can be implemented and maintained separately, reducing overall system complexity.
Solution Approach 2:
The system employs a universal deep-learning framework that can handle multiple types of data (text, audio, photos, videos) and apply the same personalization logic across different interaction modalities. This multi-functional approach allows the system to achieve high personalization capability without creating separate complex systems for each data type, thereby managing system complexity more effectively.
3Measurement precision
If comprehensive individual data is collected and processed, then personalization accuracy and authenticity are improved, but data storage requirements and processing time increase
Solution Approach 1:
The system performs preliminary data processing and profile generation during off-peak times or when the user is not actively interacting with the artificial entity. By pre-processing the comprehensive individual data and creating the personalized profile in advance, the system reduces the processing time required during actual interactions while maintaining high personalization accuracy.
Solution Approach 2:
The system applies different levels of data processing intensity to different types of data based on their importance and usage frequency. Critical personalization data receives more thorough processing to ensure accuracy, while less critical data is processed more quickly or selectively, optimizing the balance between personalization accuracy and processing time for each specific data element.
Data Source
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.