20240046922. SYSTEMS AND METHODS FOR DYNAMICALLY UPDATING MACHINE LEARNING MODELS THAT PROVIDE CONVERSATIONAL RESPONSES simplified abstract (Capital One Services, LLC)
SYSTEMS AND METHODS FOR DYNAMICALLY UPDATING MACHINE LEARNING MODELS THAT PROVIDE CONVERSATIONAL RESPONSES
Organization Name
Inventor(s)
Tate Travaglini of New York NY (US)
Andrew Oestreicher of Washington DC (US)
Victor Alvarez Miranda of McLean VA (US)
Parag Jain of Falls Church VA (US)
SYSTEMS AND METHODS FOR DYNAMICALLY UPDATING MACHINE LEARNING MODELS THAT PROVIDE CONVERSATIONAL RESPONSES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240046922 titled 'SYSTEMS AND METHODS FOR DYNAMICALLY UPDATING MACHINE LEARNING MODELS THAT PROVIDE CONVERSATIONAL RESPONSES
Simplified Explanation
Methods and systems for dynamically updating machine learning models that provide conversational responses through the use of a configuration file are disclosed. The configuration file defines modifications and changes to the machine learning model, such as expected behavior and required attributes, which can be implemented using a mutation algorithm.
- The patent application describes a system for updating machine learning models used in conversational response systems.
- The system utilizes a configuration file to define modifications and changes to the machine learning model.
- The configuration file can specify the expected behavior and required attributes for the modifications and changes.
- A mutation algorithm is used to implement the modifications and changes defined in the configuration file.
- The system allows for dynamic updates to the machine learning model, enabling it to adapt and improve over time.
Potential applications of this technology:
- Conversational chatbots and virtual assistants: The technology can be used to improve the conversational capabilities of chatbots and virtual assistants, allowing them to provide more accurate and contextually appropriate responses.
- Customer support systems: The technology can be applied to customer support systems to enhance the quality and efficiency of automated responses, improving customer satisfaction.
- Language translation services: By updating the machine learning models based on user feedback and specific requirements defined in the configuration file, the technology can improve the accuracy and fluency of language translation services.
Problems solved by this technology:
- Outdated or inaccurate responses: The technology addresses the problem of conversational systems providing outdated or inaccurate responses by allowing for dynamic updates to the machine learning models.
- Lack of adaptability: The system solves the issue of conversational systems being unable to adapt to changing user needs and preferences by enabling modifications and changes to the machine learning models based on the defined configuration file.
Benefits of this technology:
- Improved conversational capabilities: By updating the machine learning models, the technology enhances the conversational capabilities of systems, leading to more natural and contextually appropriate responses.
- Increased accuracy and relevance: The dynamic updates based on the configuration file allow for improvements in accuracy and relevance of responses, resulting in a better user experience.
- Time and cost efficiency: The use of a configuration file and mutation algorithm enables efficient updates to the machine learning models, saving time and resources compared to manual updates.
Original Abstract Submitted
methods and systems for dynamically updating machine learning models that provide conversational responses through the use of a configuration file that defines modifications and changes to the machine learning model are disclosed. for example, the configuration file may be used to define an expected behavior and required attributes for instituting modifications and changes (e.g., via a mutation algorithm) to the machine learning model.
