Patents by Inventor Tyler Rasmussen

Tyler Rasmussen has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • TRAINING AND UTILIZING LANGUAGE MACHINE LEARNING MODELS TO CREATE STRUCTURED OUTPUTS FOR BUILDING DIGITAL VISUALIZATIONS FROM ANALYTICS DATABASES AND DIGITAL TEXT PROMPTS
    Publication number: 20250225146
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a digital data visualization from data of an analytics database. In particular, in some embodiments, the disclosed systems receive a digital text prompt that includes a target digital data visualization description. Furthermore, in some embodiments the disclosed systems generate visualization token predictions from the digital text prompt utilizing a language machine learning model. Moreover, in some embodiments the disclosed systems modify the visualization token predictions to generate refined visualization tokens and generates the digital data visualization based on the refined visualization tokens.
    Type: Application
    Filed: January 10, 2024
    Publication date: July 10, 2025
    Inventors: Ryan A Rossi, Sungchul Kim, Tung Mai, Tong Yu, Ritwik Sinha, Prithvi Bhutani, Abhisek Trivedi, Said Kobeissi, Sam Grigg, Tyler Rasmussen, Brandon Mooso, Jordan Walker, William Brandon George
  • Learning-based method for generating visualizations via natural language
    Patent number: 12265557
    Abstract: Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.
    Type: Grant
    Filed: August 31, 2023
    Date of Patent: April 1, 2025
    Assignee: Adobe Inc.
    Inventors: William Brandon George, Wei Zhang, Tyler Rasmussen, Tung Mai, Tong Yu, Sungchul Kim, Shunan Guo, Samuel Nephi Grigg, Said Kobeissi, Ryan Rossi, Ritwik Sinha, Eunyee Koh, Prithvi Bhutani, Jordan Henson Walker, Abhisek Trivedi
  • LEARNING-BASED METHOD FOR GENERATING VISUALIZATIONS VIA NATURAL LANGUAGE
    Publication number: 20250077549
    Abstract: Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.
    Type: Application
    Filed: August 31, 2023
    Publication date: March 6, 2025
    Inventors: William Brandon GEORGE, Wei Zhang, Tyler Rasmussen, Tung Mai, Tong Yu, Sungchul Kim, Shunan Guo, Samuel Nephi Grigg, Said Kobeissi, Ryan Rossi, Ritwik Sinha, Eunyee Koh, Prithvi Bhutani, Jordan Henson Walker, Abhisek Trivedi