Google llc (20240194180). STRUCTURED DESCRIPTION-BASED CHATBOT DEVELOPMENT TECHNIQUES simplified abstract
Contents
STRUCTURED DESCRIPTION-BASED CHATBOT DEVELOPMENT TECHNIQUES
Organization Name
Inventor(s)
Sasha Goldshtein of Tel Aviv (IL)
Asaf Aharoni of Ramat Hasharon (IL)
STRUCTURED DESCRIPTION-BASED CHATBOT DEVELOPMENT TECHNIQUES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240194180 titled 'STRUCTURED DESCRIPTION-BASED CHATBOT DEVELOPMENT TECHNIQUES
The patent application focuses on creating a chatbot that can engage in conversations with users based on unstructured free-form natural language input. This input helps define dialog states and transitions for the chatbot, either implicitly or explicitly, and can be used to fine-tune a machine learning model for more effective communication.
- Receiving unstructured free-form natural language input
- Generating a chatbot based on the input
- Engaging in conversations with users based on the input
- Defining dialog states and transitions implicitly or explicitly
- Fine-tuning a machine learning model for better communication
Potential Applications: - Customer service chatbots - Virtual assistants - Educational chatbots for interactive learning
Problems Solved: - Improving user interaction with chatbots - Enhancing the efficiency of automated conversations - Providing more personalized responses to users
Benefits: - Quick and efficient deployment of chatbots - Enhanced user experience with more natural conversations - Improved machine learning model performance
Commercial Applications: Title: "Enhancing Customer Engagement with AI Chatbots" This technology can be used in various industries such as e-commerce, healthcare, and education to provide better customer service, streamline communication processes, and increase user satisfaction.
Prior Art: Researchers can explore existing patents related to chatbot technology, natural language processing, and machine learning models to understand the evolution of this innovation.
Frequently Updated Research: Stay updated on advancements in natural language processing, machine learning algorithms, and chatbot development to enhance the capabilities of this technology.
Questions about Chatbot Technology: 1. How does this technology improve user engagement with chatbots? 2. What are the key differences between implicit and explicit dialog state definitions in chatbots?
Original Abstract Submitted
implementations are directed to receiving unstructured free-form natural language input, generating a chatbot based on the unstructured free-form natural language input and in response to receiving the unstructured free-form natural language input, and causing the chatbot to perform engage in corresponding conversations with additional users. in various implementations, the unstructured free-form natural language input implicitly defines a corresponding dialog state map (e.g., defines corresponding dialog states and/or corresponding dialog state transitions) without defining any explicit dialog states and/or explicit dialog state transitions. in other implementations, the unstructured free-form natural language input is assigned to explicit dialog states and/or explicit dialog state transitions. nonetheless, the unstructured free-form natural language input may be utilized to fine-tune and/or primed a machine learning model that is already capable of being utilized in conducting generalized conversations. as a result, the chatbot can be generated and deployed in a quick and efficient manner.
