18183997. LANGUAGE MODELS FOR READING CHARTS simplified abstract (Adobe Inc.)
Contents
LANGUAGE MODELS FOR READING CHARTS
Organization Name
Inventor(s)
Victor Soares Bursztyn of Chicago IL (US)
Eunyee Koh of Sunnyvale CA (US)
Jane Elizabeth Hoffswell of Seattle WA (US)
Shunan Guo of San Jose CA (US)
LANGUAGE MODELS FOR READING CHARTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18183997 titled 'LANGUAGE MODELS FOR READING CHARTS
The patent application describes systems and methods for natural language processing.
- An answer model generates an answer to a query based on a chart using a machine learning model trained on chart data.
- A description model generates a visual description based on the answer and the chart using a machine learning model trained on a chart specification.
- A response component transmits a response to the query based on the answer and the visual description.
Potential Applications: - Natural language processing in various industries such as customer service, healthcare, and finance. - Enhancing chatbots and virtual assistants with improved ability to understand and respond to queries.
Problems Solved: - Improving the accuracy and efficiency of natural language processing systems. - Enhancing user experience by providing more relevant and detailed responses to queries.
Benefits: - Increased automation and efficiency in handling large volumes of text data. - Enhanced user interaction and satisfaction with intelligent responses.
Commercial Applications: - This technology can be utilized in customer service chatbots, virtual assistants, data analysis tools, and educational platforms to improve communication and information retrieval processes.
Questions about Natural Language Processing: 1. How does the machine learning model improve the accuracy of generating answers to queries? 2. What are the potential limitations of using a chart-based approach in natural language processing systems?
Frequently Updated Research: - Stay updated on advancements in machine learning algorithms and natural language processing techniques to enhance the performance of these systems.
Original Abstract Submitted
Systems and methods for natural language processing are described. Embodiments of the present disclosure obtain a chart and a query via a user interface. An answer model generates an answer to the query based on the chart, wherein the answer model comprises a machine learning model trained based on chart data for the chart. A description model generates a visual description based on the answer and the chart, wherein the description model comprises a machine learning model trained based on a chart specification for the chart. A response component transmits a response to the query based on the answer and the visual description.
