Capital one services, llc (20240346253). SYSTEMS AND METHODS FOR GENERATING DYNAMIC CONVERSATIONAL RESPONSES THROUGH AGGREGATED OUTPUTS OF MACHINE LEARNING MODELS simplified abstract
SYSTEMS AND METHODS FOR GENERATING DYNAMIC CONVERSATIONAL RESPONSES THROUGH AGGREGATED OUTPUTS OF MACHINE LEARNING MODELS
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SYSTEMS AND METHODS FOR GENERATING DYNAMIC CONVERSATIONAL RESPONSES THROUGH AGGREGATED OUTPUTS OF MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346253 titled 'SYSTEMS AND METHODS FOR GENERATING DYNAMIC CONVERSATIONAL RESPONSES THROUGH AGGREGATED OUTPUTS OF MACHINE LEARNING MODELS
Simplified Explanation: The patent application describes methods and systems for generating dynamic conversational responses, enhancing interactive exchanges with users through specialized data enrichment and machine learning models.
Key Features and Innovation:
- Specialized methods enrich data indicative of user intent before processing through machine learning models.
- Specialized architecture for machine learning models leverages user interface format.
Potential Applications: This technology can be applied in chatbots, virtual assistants, customer service platforms, and interactive voice response systems.
Problems Solved: This technology addresses the challenge of providing dynamic and contextually relevant conversational responses to users in real-time.
Benefits:
- Improved user engagement and satisfaction.
- Enhanced user experience through personalized responses.
- Increased efficiency in handling user queries.
Commercial Applications: Potential commercial applications include customer service automation, virtual sales assistants, and interactive marketing campaigns targeting online users.
Questions about Dynamic Conversational Responses: 1. How does this technology improve user interactions in comparison to traditional conversational systems? 2. What are the key advantages of using specialized data enrichment methods in generating dynamic conversational responses?
Frequently Updated Research: Stay updated on advancements in natural language processing, machine learning models, and user interface design to enhance the capabilities of dynamic conversational response systems.
Original Abstract Submitted
methods and systems are described herein for generating dynamic conversational responses. for example, dynamic conversational responses may facilitate an interactive exchange with users. therefore, the methods and systems used specialized methods to enriched data that may be indicative of a user's intent prior to processing that data through the machine learning model, as well as a specialized architecture for the machine learning models that take advantage of the user interface format.
