18172571. QUERY RESPONSE GENERATION USING STRUCTURED AND UNSTRUCTURED DATA FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS simplified abstract (NVIDIA Corporation)
Contents
- 1 QUERY RESPONSE GENERATION USING STRUCTURED AND UNSTRUCTURED DATA FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 QUERY RESPONSE GENERATION USING STRUCTURED AND UNSTRUCTURED DATA FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
QUERY RESPONSE GENERATION USING STRUCTURED AND UNSTRUCTURED DATA FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Shubhadeep Das of Kolkata (IN)
Sumit Kumar Bhattacharya of Pune (IN)
Oluwatobi Olabiyi of Falls Church VA (US)
QUERY RESPONSE GENERATION USING STRUCTURED AND UNSTRUCTURED DATA FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18172571 titled 'QUERY RESPONSE GENERATION USING STRUCTURED AND UNSTRUCTURED DATA FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
Simplified Explanation
The abstract describes a patent application for systems and methods that generate contextual data for conversational AI systems using structured and unstructured data. This contextual data is then used by neural networks to generate responses to user requests.
- Structured and unstructured data are used to generate contextual data for conversational AI systems.
- The contextual data includes text generated from both structured and unstructured data.
- Neural networks process user requests and contextual data to generate responses.
- The technology aims to improve the conversational capabilities of AI systems.
Potential Applications
The technology can be applied in various conversational AI systems, virtual assistants, chatbots, customer service applications, and knowledge management systems.
Problems Solved
1. Enhancing the conversational abilities of AI systems by generating contextual data from structured and unstructured sources. 2. Improving the accuracy and relevance of responses generated by AI systems.
Benefits
1. More accurate and contextually relevant responses to user queries. 2. Enhanced user experience in interacting with AI systems. 3. Increased efficiency in handling user requests and providing information.
Potential Commercial Applications
"Enhancing Conversational AI Systems with Contextual Data" can be used in customer service chatbots, virtual assistants for businesses, knowledge management systems for organizations, and AI-powered search engines.
Possible Prior Art
There may be prior art related to using structured and unstructured data to enhance the capabilities of AI systems in generating responses to user queries. This could include research papers, patents, or existing AI technologies that utilize similar techniques.
Unanswered Questions
The technology described in the patent application focuses on generating contextual data for AI systems, but it is essential to consider how user data privacy is maintained and protected during this process. This aspect could be crucial for ensuring user trust and compliance with data protection regulations.
What are the potential limitations or challenges in implementing this technology in real-world applications?
While the patent application outlines the benefits and capabilities of the technology, there may be practical challenges or limitations in deploying it in various AI systems. Understanding these potential obstacles can help in devising strategies to overcome them and ensure successful implementation.
Original Abstract Submitted
In various examples, contextual data may be generated using structured and unstructured data for conversational AI systems and applications. Systems and methods are disclosed that use structured data (converted to unstructured form) and unstructured data, such as from a knowledge database(s), to generate contextual data. For instance, the contextual data may represent text (e.g., narratives), where a first portion of the text is generated using the structured data and a second portion of the text is generated using the unstructured data. The systems and methods may then use a neural network(s), such as a neural network(s) associated with a dialogue manager, to process input data representing a request (e.g., a query) and the contextual data in order to generate a response to the request. For instance, if the request includes a query for information associated with a topic, the neural network(s) may generate a response that includes the requested information.
