AI Entity Advertisement System for Personalized User Interaction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current conversational chat programs, or chatbots, have limited conversational abilities and cannot accurately predict user intentions, restricting their effectiveness in providing personalized interactions and recommendations.
Innovation Solution
An artificial intelligence entity advertisement system that allows business entities to create customizable AI entities to interact with users, parse user queries, and provide relevant information and recommendations based on keyword associations, user preferences, and bidding systems, enabling personalized conversations and improved user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current chatbots are used for user interaction, then basic conversational functions are provided, but the conversational abilities are limited and user intention prediction is inaccurate
Solution Approach 1:
The system segments the chatbot functionality into multiple specialized AI entities, each trained on specific datasets and configured for particular domains or tasks. This allows each entity to excel at its specific function while the system as a whole provides comprehensive conversational capabilities across multiple domains.
Solution Approach 2:
The system dynamically changes parameters such as temperature, top-k, and presence penalty based on the conversation context and user intent. This adaptive parameter adjustment enables the AI entities to balance between creativity and determinism, improving both conversational versatility and intention prediction accuracy.
2Adaptability or versatility
If AI entities are customized for specific business entities, then personalized interactions are improved, but system complexity increases
Solution Approach 1:
The system provides a universal AI entity framework that can be configured for multiple business entities and domains. The same underlying infrastructure supports customized AI entities for different purposes, reducing overall system complexity while maintaining high personalization capability.
Solution Approach 2:
The system uses template-based configurations and pre-trained models that can be copied and adapted for different business entities. This allows rapid deployment of customized AI entities without building each one from scratch, reducing configuration complexity while maintaining personalization.
3Adaptability or versatility
If multiple AI entities are maintained for different subjects, then query coverage is improved, but entity selection and management becomes more complex
Solution Approach 1:
The system implements feedback mechanisms that monitor conversation quality and user satisfaction. This feedback is used to dynamically adjust which AI entity is selected for a given query, improving query coverage while simplifying entity management through data-driven selection rather than manual configuration.
Solution Approach 2:
The AI entity selection is dynamic and context-dependent, changing based on the conversation state, user preferences, and query characteristics. This dynamic selection approach improves query coverage across different subjects while reducing management complexity through automated, context-aware routing.
Data Source
AI summary
Described herein is a system and method for providing a conversation session with an artificial intelligence entity that is associated with a business entity. In some aspects, input is provided to an artificial intelligence entity advertisement system. The input is analyzed to determine the subject matter of the input. An artificial intelligence entity associated with the subject matter is then selected and provided to the user. The artificial intelligence entity recommends products or services that are provided by the business entity to the user.