公开(公告)号：US20240232545A9

公开(公告)日：2024-07-11

申请号：US17969922

申请日：2022-10-20

Applicant: Microsoft Technology Licensing, LLC

Inventor： 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

IPC: G06F40/51 ,  G06F40/35

CPC classification number: G06F40/51 ,  G06F40/35

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.

公开(公告)号：US20240184979A1

公开(公告)日：2024-06-06

申请号：US18075497

申请日：2022-12-06

Applicant: Microsoft Technology Licensing, LLC

Inventor： Mukul SINGH ,  José Pablo CAMBRONERO SÁNCHEZ ,  Sumit GULWANI ,  Vu Minh LE ,  Carina Suzana NEGREANU ,  Mohammad RAZA ,  Daniel Galen SIMMONS ,  Gust Ben Anneloes VERBRUGGEN

IPC: G06F16/35 ,  G06F40/18

CPC classification number: G06F16/355 ,  G06F40/18

Abstract: Some embodiments automatically generate data processing rules based on positive examples of processed data, e.g., formatting rules based on formatted data, filtering rules based on filtered data, or validating rules based on valid data. Some embodiments also use negative examples, e.g., unformatted data. A machine learning rule generation architecture includes a predicate generator, a cell cluster creator, a rule enumerator, and in some versions a rule ranker. Formatting rules written by a user are replaced by simpler autogenerated rules. Spreadsheet formatting rule functionality is enhanced, and surfaced in a user interface.

公开(公告)号：US20240135113A1

公开(公告)日：2024-04-25

申请号：US17969922

申请日：2022-10-19

Applicant: Microsoft Technology Licensing, LLC

Inventor： 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

IPC: G06F40/51 ,  G06F40/35

CPC classification number: G06F40/51 ,  G06F40/35

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.

公开(公告)号：US20240061677A1

公开(公告)日：2024-02-22

申请号：US18500907

申请日：2023-11-02

Applicant: Microsoft Technology Licensing, LLC

Inventor： David Ellis PUGH ,  Mark Alistair WILSON-THOMAS ,  Vu Minh LE

IPC: G06F8/71 ,  G06F40/197

CPC classification number: G06F8/71 ,  G06F40/197

Abstract: Distinguishing pattern differences from non-pattern differences. A set of differences is identified. The set comprises a plurality of differences between first and second versions of a document. A pattern is identified. The pattern explains a transformation from a first string in the first version of the document to a second string in the second version of the document. A subset of differences are identified. The subset comprises a plurality of differences, from among the set, which match the pattern. While presenting a user interface that visually highlights differences between the first and second versions of the document, a first visual treatment is applied to a first difference, based on the first difference being included in the subset. A second visual treatment is also applied to a second difference, based on the second difference being excluded from the subset. The second visual treatment is different than the first visual treatment.

公开(公告)号：US20230289523A1

公开(公告)日：2023-09-14

申请号：US17693285

申请日：2022-03-11

Applicant: Microsoft Technology Licensing, LLC

Inventor： Rohan Jayesh BAVISHI ,  José Pablo CAMBRONERO SÁNCHEZ ,  Anna FARIHA ,  Sumit GULWANI ,  Vu Minh LE ,  Ivan RADICEK ,  Daniel Galen SIMMONS ,  Ashish TIWARI

IPC: G06F40/211 ,  G06F40/284 ,  G06F8/30 ,  G06F16/332

CPC classification number: G06F40/211 ,  G06F40/284 ,  G06F8/31 ,  G06F16/3329

Abstract: Techniques are described herein that are capable of creating a language-agnostic computer program repair engine generator. A context-free grammar is annotated to identify token(s) that are likely to be included in or excluded from a computer program in a manner that violates the context-free grammar. A language-agnostic computer program repair engine generator is created that is configured to generate a parser. The repair engine generator is configured to create a repair engine that: converts the candidate string into repaired strings that neither violate the context-free grammar nor violate a criterion for a valid computer program; calculates differences between the candidate string and the respective repaired strings; and replaces the candidate string with a designated repaired string based at least in part on the difference between the designated repaired string and the candidate string being less than or equal to a difference threshold.