17947491. DIALOGUE SYSTEMS USING KNOWLEDGE BASES AND LANGUAGE MODELS FOR AUTOMOTIVE SYSTEMS AND APPLICATIONS simplified abstract (NVIDIA Corporation)
Contents
- 1 DIALOGUE SYSTEMS USING KNOWLEDGE BASES AND LANGUAGE MODELS FOR AUTOMOTIVE SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DIALOGUE SYSTEMS USING KNOWLEDGE BASES AND LANGUAGE MODELS FOR AUTOMOTIVE 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
DIALOGUE SYSTEMS USING KNOWLEDGE BASES AND LANGUAGE MODELS FOR AUTOMOTIVE SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Peng Xu of Redwood City CA (US)
Mostofa Patwary of Fremont CA (US)
Rajath Shetty of Sunnyvale CA (US)
Niral Lalit Pathak of San Jose CA (US)
Ratin Kumar of Cupertino CA (US)
Bryan Catanzaro of Los Altos Hills CA (US)
Mohammad Shoeybi of Foster City CA (US)
DIALOGUE SYSTEMS USING KNOWLEDGE BASES AND LANGUAGE MODELS FOR AUTOMOTIVE SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17947491 titled 'DIALOGUE SYSTEMS USING KNOWLEDGE BASES AND LANGUAGE MODELS FOR AUTOMOTIVE SYSTEMS AND APPLICATIONS
Simplified Explanation
The abstract describes systems and methods that use dialogue systems associated with machine systems to retrieve question/answer pairs and contextual information to generate prompts and determine outputs related to the text data, such as questions associated with vehicles.
- Dialogue systems associated with machine systems
- Retrieval of question/answer pairs and contextual information
- Generation of prompts and determination of outputs
- Language models used to determine outputs
Potential Applications
The technology described in the patent application could be applied in various fields such as customer service chatbots, virtual assistants for vehicles, and automated helpdesk systems.
Problems Solved
This technology helps in efficiently retrieving relevant information and providing accurate answers to user queries, enhancing user experience and reducing the need for human intervention in certain tasks.
Benefits
The benefits of this technology include improved customer service, faster response times, increased efficiency in information retrieval, and overall enhanced user satisfaction.
Potential Commercial Applications
The technology could be utilized in customer service industries, automotive companies, tech support services, and any other field where automated responses to user queries are required.
Possible Prior Art
One possible prior art could be the use of natural language processing in chatbots and virtual assistants to provide automated responses to user queries.
Unanswered Questions
How does this technology handle multi-language support?
The article does not mention how the system deals with multiple languages and if it can provide accurate responses in languages other than the primary one.
What measures are in place to ensure data privacy and security?
The article does not address the security measures implemented to protect user data and ensure privacy while using the dialogue systems.
Original Abstract Submitted
In various examples, systems and methods that use dialogue systems associated with various machine systems and applications are described. For instance, the systems and methods may receive text data representing speech, such as a question associated with a vehicle or other machine type. The systems and methods then use a retrieval system(s) to retrieve a question/answer pair(s) associated with the text data and/or contextual information associated with the text data. In some examples, the contextual information is associated with a knowledge base associated with or corresponding to the vehicle. The systems and methods then generate a prompt using the text data, the question/answer pair(s), and/or the contextual information. Additionally, the systems and methods determine, using a language model(s) and based at least on the prompt, an output associated with the text data. For instance, the output may include information that answers the question associated with the vehicle.
