View source for 17850282. SYSTEMS AND METHODS FOR GENERATING REAL-TIME DYNAMIC CONVERSATIONAL RESPONSES DURING CONVERSATIONAL INTERACTIONS USING MACHINE LEARNING MODELS simplified abstract (Capital One Services, LLC)

=SYSTEMS AND METHODS FOR GENERATING REAL-TIME DYNAMIC CONVERSATIONAL RESPONSES DURING CONVERSATIONAL INTERACTIONS USING MACHINE LEARNING MODELS=


==Organization Name==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Md Arafat Hossain Khan of Dallas TX (US)|Md Arafat Hossain Khan of Dallas TX (US)]][[Category:Md Arafat Hossain Khan of Dallas TX (US)]]

[[:Category:Isha Chaturvedi of Mountain View CA (US)|Isha Chaturvedi of Mountain View CA (US)]][[Category:Isha Chaturvedi of Mountain View CA (US)]]

[[:Category:Arturo Hernandez Zeledon of Arlington VA (US)|Arturo Hernandez Zeledon of Arlington VA (US)]][[Category:Arturo Hernandez Zeledon of Arlington VA (US)]]

[[:Category:Mohammad Sorower of McLean VA (US)|Mohammad Sorower of McLean VA (US)]][[Category:Mohammad Sorower of McLean VA (US)]]

==SYSTEMS AND METHODS FOR GENERATING REAL-TIME DYNAMIC CONVERSATIONAL RESPONSES DURING CONVERSATIONAL INTERACTIONS USING MACHINE LEARNING MODELS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17850282 titled 'SYSTEMS AND METHODS FOR GENERATING REAL-TIME DYNAMIC CONVERSATIONAL RESPONSES DURING CONVERSATIONAL INTERACTIONS USING MACHINE LEARNING MODELS

==Simplified Explanation==

The patent application describes methods and systems for generating real-time dynamic conversational responses using machine learning models. These models are trained based on historic intents and user-specific interactions. Here are the key points:

* The invention uses machine learning models to generate real-time dynamic conversational responses.
* The models are trained using historic intents and user-specific interactions.
* One model selects a first intent from a range of intents based on historic data.
* Another model selects a first interaction-specific intent from a range of interaction-specific intents based on interaction-specific data for a user.

==Potential Applications==
This technology has potential applications in various fields, including:

* Customer service: It can be used to provide automated and personalized responses to customer queries in real-time.
* Virtual assistants: Virtual assistants can use this technology to generate more natural and context-aware responses during conversations.
* Chatbots: Chatbots can benefit from this technology to improve their conversational abilities and provide more accurate and relevant information.

==Problems Solved==
The technology addresses several problems in conversational interactions, such as:

* Lack of real-time dynamic responses: Traditional conversational systems often provide static and predefined responses, which may not be suitable for dynamic conversations.
* Limited personalization: Without user-specific training, conversational systems may struggle to understand and respond to individual users' needs and preferences.
* Contextual understanding: By considering historic intents and interaction-specific data, the technology enables better understanding of the context and generates more relevant responses.

==Benefits==
The use of this technology offers several benefits, including:

* Improved user experience: Real-time dynamic responses make conversations more engaging and natural, enhancing the overall user experience.
* Personalization: By considering user-specific data, the technology can tailor responses to individual users, making interactions more personalized.
* Enhanced efficiency: Automated and context-aware responses save time and effort for both users and conversational systems.


==Original Abstract Submitted==

Methods and systems are presented for generating real-time dynamic conversational responses during conversational interactions using machine learning models based on historic intents for a plurality of users and user-specific interactions. The machine learning models comprise a neural network trained to select a first intent from a plurality of intents based on historic data accumulated prior to the conversational interaction and a neural network trained to select a first interaction-specific intent from a plurality of interaction-specific intents based on interaction-specific data for a user.



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