Patents by Inventor Neil Blunt Toronto

Neil Blunt Toronto 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).

  • Publication number: 20240232545
    Abstract: The indirect querying of models to determine capabilities possessed by the model. Such indirect queries take the form of model input that potentially includes a natural language input user data. Such model input is structured such that the output of the model is either not natural language at all, or else is natural language that is not semantically responsive to the natural language input. Nevertheless, the output is evaluated to estimate or determine the capability possessed by the model. Thus, models may be more fully utilized to their better potential.
    Type: Application
    Filed: October 20, 2022
    Publication date: July 11, 2024
    Inventors: Benjamin Goth ZORN, Carina Suzana NEGREANU, Neil Blunt TORONTO, Brian Paul SLININGER, Andrew Donald GORDON, Advait SARKAR, Elnaz NOURI, Vu Minh LE, Christian Leopold Bejamin POELITZ, Shraddha Govind BARKE, Sruti Srinivasa RAGAVAN
  • Publication number: 20240143928
    Abstract: The automated generation of a natural language explanation of what code does. The code is structured to perform tasks because the code itself semantically specifies that those tasks are to be performed. A task-centric representation of the code is automatically generated that includes a task representation of each of some or all of the tasks to be performed as specified by the code. Natural language utterances are then automatically generated by generating a corresponding natural language utterance that semantically describes in natural language the corresponding task represented by the corresponding task representation. Controls are rendered for each natural language utterance that each permit a user to edit the corresponding natural language utterance. After editing, the code itself may be automatically modified or regenerated to reflect the changed natural language utterances.
    Type: Application
    Filed: October 28, 2022
    Publication date: May 2, 2024
    Inventors: Benjamin Goth ZORN, Carina Suzana NEGREANU, Advait SARKAR, Andrew Donald GORDON, John Herbert Martin WILLIAMS, Xieyang LIU, Neil Blunt TORONTO, Sruti Srinivasa RAGAVAN, Brian Paul SLININGER
  • Publication number: 20240135113
    Abstract: The indirect querying of models to determine capabilities possessed by the model. Such indirect queries take the form of model input that potentially includes a natural language input user data. Such model input is structured such that the output of the model is either not natural language at all, or else is natural language that is not semantically responsive to the natural language input. Nevertheless, the output is evaluated to estimate or determine the capability possessed by the model. Thus, models may be more fully utilized to their better potential.
    Type: Application
    Filed: October 19, 2022
    Publication date: April 25, 2024
    Inventors: Benjamin Goth ZORN, Carina Suzana NEGREANU, Neil Blunt TORONTO, Brian Paul SLININGER, Andrew Donald GORDON, Advait SARKAR, Elnaz NOURI, Vu Minh LE, Christian Leopold Bejamin POELITZ, Shraddha Govind BARKE, Sruti Srinivasa RAGAVAN
  • Publication number: 20230418815
    Abstract: The generation of a response to a task prompt that represents a task to perform on declarative code. The response is generated with the aid of a language model that was trained on imperative code. The declarative code includes declarations about data. A task prompt represents a task to perform on the declarative code. At least a portion of the declarative code and at least a portion of the task prompt are converted into input imperative code. The input imperative code is then caused to be provided as input to the language model, resulting in the language model generating output imperative code. At least a portion of the output imperative code is then converted into a response to the task prompt.
    Type: Application
    Filed: June 24, 2022
    Publication date: December 28, 2023
    Inventors: Benjamin Goth ZORN, Carina Suzana NEGREANU, Neil Blunt TORONTO, Brian Paul SLININGER, Andrew Donald GORDON, Advait SARKAR, Sruti Srinivasa RAGAVAN
  • Patent number: 10902194
    Abstract: Technology is disclosed herein for handing approximate (or uncertain) values in spreadsheet applications. More specifically, the technology describes spreadsheet applications that support arrays or sets of approximate (or uncertain) values as native entities. An approximate (or uncertain) value may be the value of a cell of the spreadsheet that is resolvable by formula, charts and other functionalities. In some implementations, approximate values may include a range of data and a probability distribution that can be automatically created by the spreadsheet application, generated based on context, input by the user, etc. Because the approximate (or uncertain) value is natively available, it can be incorporated in a spreadsheet like other values. Additionally, the approximate (or uncertain) values can automatically propagate through a spreadsheet calculation to obtain a final approximate result.
    Type: Grant
    Filed: June 11, 2018
    Date of Patent: January 26, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Neil Blunt Toronto, Advait Sarkar, Christian Mendel Canton, Andrew Donald Gordon, Benjamin Edward Rampson, Johnny Campbell, Anusha Iyer
  • Publication number: 20190251158
    Abstract: Technology is disclosed herein for handing approximate (or uncertain) values in spreadsheet applications. More specifically, the technology describes spreadsheet applications that support arrays or sets of approximate (or uncertain) values as native entities. An approximate (or uncertain) value may be the value of a cell of the spreadsheet that is resolvable by formula, charts and other functionalities. In some implementations, approximate values may include a range of data and a probability distribution that can be automatically created by the spreadsheet application, generated based on context, input by the user, etc. Because the approximate (or uncertain) value is natively available, it can be incorporated in a spreadsheet like other values. Additionally, the approximate (or uncertain) values can automatically propagate through a spreadsheet calculation to obtain a final approximate result.
    Type: Application
    Filed: June 11, 2018
    Publication date: August 15, 2019
    Inventors: Neil Blunt Toronto, Advait Sarkar, Christian Mendel Canton, Andrew Donald Gordon, Benjamin Edward Rampson, Johnny Campbell, Anusha Iyer