Patents by Inventor Rishabh Singh

Rishabh Singh 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: 20250085942
    Abstract: Implementations are described herein for adapting existing source code snippets to new contexts. In various implementations, a command may be detected to incorporate an existing source code snippet into destination source code. An embedding may be generated based on the existing source code snippet, e.g., by processing the existing source code snippet using an encoder. The destination source code may be processed to identify one or more decoder constraints. Subject to the one or more decoder constraints, the embedding may be processed using a decoder to generate a new version of the existing source code snippet that is adapted to the destination source code.
    Type: Application
    Filed: November 26, 2024
    Publication date: March 13, 2025
    Inventors: Qianyu Zhang, Bin Ni, Rishabh Singh, Olivia Hatalsky
  • Publication number: 20250068837
    Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.
    Type: Application
    Filed: June 5, 2024
    Publication date: February 27, 2025
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Benjamin Goth ZORN, Marc Manuel Johannes BROCKSCHMIDT, Pallavi CHOUDHURY, Oleksandr POLOZOV, Rishabh SINGH, Saswat PADHI
  • Publication number: 20250028995
    Abstract: Disclosed implementations relate to adding “bottleneck” models to machine learning pipelines that already apply domain models to translate and/or transfer representations of high-level semantic concepts between domains. In various implementations, an initial representation in a first domain of a transition from an initial state of an environment to a goal state of the environment may be processed based on a pre-trained first domain encoder to generate a first embedding that semantically represents the transition. The first embedding may be processed based on one or more bottleneck models to generate a second embedding with fewer dimensions than the first embedding. In various implementations, the second embedding may be processed in various ways to train one or more of the bottleneck model(s) based on various different auxiliary loss functions.
    Type: Application
    Filed: July 21, 2023
    Publication date: January 23, 2025
    Inventors: Rishabh Singh, David Andre, Garrett Raymond Honke, Falak Shah, Nisarg Vyas, Jayendra Parmar, Brian M. Rosen, Shaili Trivedi
  • Patent number: 12182555
    Abstract: Implementations are described herein for adapting existing source code snippets to new contexts. In various implementations, a command may be detected to incorporate an existing source code snippet into destination source code. An embedding may be generated based on the existing source code snippet, e.g., by processing the existing source code snippet using an encoder. The destination source code may be processed to identify one or more decoder constraints. Subject to the one or more decoder constraints, the embedding may be processed using a decoder to generate a new version of the existing source code snippet that is adapted to the destination source code.
    Type: Grant
    Filed: October 12, 2023
    Date of Patent: December 31, 2024
    Assignee: GOOGLE LLC
    Inventors: Qianyu Zhang, Bin Ni, Rishabh Singh, Olivia Hatalsky
  • Publication number: 20240394025
    Abstract: Techniques are described herein for iterative code generation using neural language models. In various implementations, an original source code snippet in a first programming language may be processed using a translation machine learning model to generate a first translation of the original source code snippet in a second programming language. The first translation of the original source code snippet may be evaluated to identify error(s) in the first translation. Based on the error(s), respective mask(s) may be inserted to generate a masked first translation of the original source code snippet in the second programming language. The masked first translation of the original source code snippet may be processed using the translation machine learning model to generate a second translation of the original source code snippet in the second language. The second translation may include infill(s) of corrected source code in place of one or more of the masks.
    Type: Application
    Filed: August 1, 2024
    Publication date: November 28, 2024
    Inventors: Giovanni De Toni, Rishabh Singh, Jonathan Malmaud, Navneet Potti
  • Patent number: 12147794
    Abstract: Implementations are described herein for predicting symbolic transformation templates to automate source code transformations. In various implementations, pair(s) of predecessor and successor source code snippets may be processed using a symbolic transformation template prediction (STTP) model to predict a symbolic transformation template that includes a predecessor portion that matches the predecessor source code snippet(s) of the pair(s) and a successor portion that matches the successor source code snippet(s) of the pair(s). At least one additional predecessor source code snippet may be identified that matches the predecessor portion of the predicted symbolic transformation template. Placeholders of the predecessor portion of the predicted symbolic transformation template may be bound to one or more tokens of the at least one additional predecessor source code snippet to create binding(s).
    Type: Grant
    Filed: November 28, 2022
    Date of Patent: November 19, 2024
    Assignee: GOOGLE LLC
    Inventors: Joey Hong, Rishabh Singh, Joel Galenson, Jonathan Malmaud, Manzil Zaheer
  • Publication number: 20240361991
    Abstract: Techniques are described herein for automatically synthesizing programs that include one or more functions in a spreadsheet programming language. A method includes: receiving a first example including input provided in a first cell in a spreadsheet; automatically synthesizing a plurality of candidate programs including a first set of candidate programs consistent with the first example, wherein each candidate program in the first set of candidate programs comprises at least one function in a spreadsheet programming language and, when the candidate program is executed, the candidate program generates output that matches the first example; ranking the plurality of candidate programs; and storing a highest-ranked program of the plurality of candidate programs in association with the first cell in the spreadsheet.
    Type: Application
    Filed: July 9, 2024
    Publication date: October 31, 2024
    Inventors: Rishabh Singh, Aaron Zemach, Chiraag Galaiya, Dima Brezhnev, David Lick, Francisco Velasquez, Max Lin, Neha Bhargava, Peilun Zhang, Rahul Srinivasan, Simon Tong, Victoria Taylor, Vishnu Sivaji, Zifan Xiao
  • Publication number: 20240356954
    Abstract: Described are techniques for characterizing performance of a cybersecurity detection tool. The techniques include generating a cybersecurity result set in response to applying synthetic test data to the cybersecurity detection tool. The techniques further include extracting respective rules from the cybersecurity detection tool. The techniques further include characterizing the performance of the cybersecurity detection tool based on the cybersecurity result set and the respective rules.
    Type: Application
    Filed: April 18, 2023
    Publication date: October 24, 2024
    Inventors: Mahbod Tavallee, Rishabh Singh, Stepan Vovshchuk
  • Patent number: 12093672
    Abstract: Techniques are described herein for iterative code generation using neural language models. In various implementations, an original source code snippet in a first programming language may be processed using a translation machine learning model to generate a first translation of the original source code snippet in a second programming language. The first translation of the original source code snippet may be evaluated to identify error(s) in the first translation. Based on the error(s), respective mask(s) may be inserted to generate a masked first translation of the original source code snippet in the second programming language. The masked first translation of the original source code snippet may be processed using the translation machine learning model to generate a second translation of the original source code snippet in the second language. The second translation may include infill(s) of corrected source code in place of one or more of the masks.
    Type: Grant
    Filed: December 6, 2022
    Date of Patent: September 17, 2024
    Assignee: GOOGLE LLC
    Inventors: Giovanni De Toni, Rishabh Singh, Jonathan Malmaud, Navneet Potti
  • Patent number: 12093671
    Abstract: Techniques are described herein for translating source code using sparse-self attention. In various implementations, a source code snippet in a first programming language may be processed to obtain graph(s) representing snippet tokens, and relationships therebetween. Based on the graph(s), a subset of snippet token pairs may be identified from a superset of all possible token pairs in the source code snippet. Each token pair of the subset may include snippet tokens that are represented by nodes connected by one or more edges of the one or more graphs. A self-attention network of a translation machine learning model may be adapted to sparsely attend across the identified subset of token pairs. The source code snippet may then be processed based on the adapted translation machine learning model to generate a translation of the source code snippet in the second programming language.
    Type: Grant
    Filed: April 28, 2022
    Date of Patent: September 17, 2024
    Assignee: GOOGLE LLC
    Inventors: Rishabh Singh, Bin Ni, Manzil Zaheer
  • Patent number: 12073194
    Abstract: Techniques are described herein for automatically synthesizing programs that include one or more functions in a spreadsheet programming language. A method includes: receiving a first example including input provided in a first cell in a spreadsheet; automatically synthesizing a plurality of candidate programs including a first set of candidate programs consistent with the first example, wherein each candidate program in the first set of candidate programs comprises at least one function in a spreadsheet programming language and, when the candidate program is executed, the candidate program generates output that matches the first example; ranking the plurality of candidate programs; and storing a highest-ranked program of the plurality of candidate programs in association with the first cell in the spreadsheet.
    Type: Grant
    Filed: October 24, 2022
    Date of Patent: August 27, 2024
    Assignee: GOOGLE LLC
    Inventors: Rishabh Singh, Aaron Zemach, Chiraag Galaiya, Dima Brezhnev, David Lick, Francisco Velasquez, Max Lin, Neha Bhargava, Peilun Zhang, Rahul Srinivasan, Simon Tong, Victoria Taylor, Vishnu Sivaji, Zifan Xiao
  • Publication number: 20240273270
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating learned representations of digital circuit designs. One of the systems includes obtaining data representing a program that implements a digital circuit design, the program comprising a plurality of statements; processing the obtained data to generate data representing a graph representing the digital circuit design, the graph comprising: a plurality of nodes representing respective statements of the program, a plurality of first edges each representing a control flow between a pair of statements of the program, and a plurality of second edges each representing a data flow between a pair of statements of the program; and generating a learned representation of the digital circuit design, comprising processing the data representing the graph using a graph neural network to generate a respective learned representation of each statement represented by a node of the graph.
    Type: Application
    Filed: May 31, 2022
    Publication date: August 15, 2024
    Inventors: Shobha Vasudevan, Wenjie Jiang, Charles Aloysius Sutton, Rishabh Singh, David Bieber, Milad Olia Hashemi, Chian-min Richard Ho, Hamid Shojaei
  • Publication number: 20240256484
    Abstract: The disclosed data storage management system enables data owners to model the costs and attributes of archiving their data and to readily capture and implement one or more resultant archiving plans. Modeling enables data owners to make informed choices about cost profiles before data is actually archived. Archiving plans devised according to these choices are intended to save on data storage costs and provide a compliance-ready data archive in cloud storage repository(ies). Armed with archiving simulations supplied by the illustrative data storage management system, a data owner may control data placement to predict costs, free up primary storage, and move inactive data to less expensive archive storage. Preferably, the disclosed system is implemented as a software-as-a-service (SaaS) solution, and the accompanying archive storage is implemented as a cloud storage service, but the invention is not limited to SaaS or to cloud-based data archives.
    Type: Application
    Filed: January 9, 2024
    Publication date: August 1, 2024
    Applicant: Commvault Systems, Inc.
    Inventors: Tanmay GARG, Rishabh SINGH, Richa Dilip KULKARNI
  • Patent number: 12045221
    Abstract: Described herein are techniques for compact representation of table columns via templatization. Column templates can model the collection of columns in a table as a logical entity. The logical entity can be split into two objects. A first object can contain a subset of column attributes (e.g., fields) that can be shared with other tables, such as name and data type. A second object can contain another subset of column attributes that are unique to a table, such as time stamps and created-on information.
    Type: Grant
    Filed: July 31, 2023
    Date of Patent: July 23, 2024
    Assignee: Snowflake Inc.
    Inventors: Rishabh Singh Ahluwalia, Lin Chan, Benoit Dageville, Yi Fang, Yiming Kang, Nithin Mahesh, Subramanian Muralidhar, Vikram Wakade
  • Patent number: 12039257
    Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.
    Type: Grant
    Filed: July 13, 2018
    Date of Patent: July 16, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Benjamin Goth Zorn, Marc Manuel Johannes Brockschmidt, Pallavi Choudhury, Oleksandr Polozov, Rishabh Singh, Saswat Padhi
  • Publication number: 20240211218
    Abstract: The present disclosure provides systems and methods for synthesizing computer-readable code based on the receipt of input and output examples. A computing system in accordance with the disclosure can be configured to receive a given input and output, access and library of operations, and perform a search of a library of operations (e.g., transpose, slice, norm, etc.) that can be applied to the input. By applying the operations to the input and tracking the results, the computing system may identify an expression comprising one or a combination of operations that when applied to the input generates the output. In this manner, implementations of the disclosure may be used to identify one or more solutions that a user having access to the library of operations may use to generate the output from the input.
    Type: Application
    Filed: December 5, 2023
    Publication date: June 27, 2024
    Inventors: Kensen Shi, Rishabh Singh, David J. Bieber
  • Patent number: 12014160
    Abstract: Techniques are described herein for translating a source code snippet from a first programming language to a second programming language independently of sequence-to-sequence decoding. In various implementations, the source code snippet written in the first programming language may be processed using an encoder portion of a transformer network to generate an embedding of the source code snippet. The embedding of the source code snippet may be processed using an all-pair attention layer to generate an attended embedding of the source code snippet. The attended embedding of the source code snippet may be processed using an output layer to generate, by way of a single transformation of the attended embedding of the source code snippet, data indicative of a translation of the source code snippet in the second programming language.
    Type: Grant
    Filed: April 11, 2022
    Date of Patent: June 18, 2024
    Assignee: GOOGLE LLC
    Inventors: Rishabh Singh, Manzil Zaheer
  • Publication number: 20240184555
    Abstract: Techniques are described herein for iterative code generation using neural language models. In various implementations, an original source code snippet in a first programming language may be processed using a translation machine learning model to generate a first translation of the original source code snippet in a second programming language. The first translation of the original source code snippet may be evaluated to identify error(s) in the first translation. Based on the error(s), respective mask(s) may be inserted to generate a masked first translation of the original source code snippet in the second programming language. The masked first translation of the original source code snippet may be processed using the translation machine learning model to generate a second translation of the original source code snippet in the second language. The second translation may include infill(s) of corrected source code in place of one or more of the masks.
    Type: Application
    Filed: December 6, 2022
    Publication date: June 6, 2024
    Inventors: Giovanni De Toni, Rishabh Singh, Jonathan Malmaud, Navneet Potti
  • Publication number: 20240184556
    Abstract: Techniques are described herein for translating between low-level languages and high-level languages. A method includes: receiving first source code in assembly language, the first source code including a plurality of code blocks, and each code block of the plurality of code blocks including a plurality of lines of assembly code; for each of the plurality of code blocks: for each of the plurality of lines of assembly code in the code block, processing the line of assembly code to generate a natural language description of the line of assembly code; and processing the code block and the natural language descriptions of the plurality of lines of assembly code in the code block to generate a natural language description of the code block; and processing the natural language descriptions of the plurality of code blocks to generate a natural language description of the first source code.
    Type: Application
    Filed: December 6, 2022
    Publication date: June 6, 2024
    Inventor: Rishabh Singh
  • Publication number: 20240176604
    Abstract: Implementations are described herein for predicting symbolic transformation templates to automate source code transformations. In various implementations, pair(s) of predecessor and successor source code snippets may be processed using a symbolic transformation template prediction (STTP) model to predict a symbolic transformation template that includes a predecessor portion that matches the predecessor source code snippet(s) of the pair(s) and a successor portion that matches the successor source code snippet(s) of the pair(s). At least one additional predecessor source code snippet may be identified that matches the predecessor portion of the predicted symbolic transformation template. Placeholders of the predecessor portion of the predicted symbolic transformation template may be bound to one or more tokens of the at least one additional predecessor source code snippet to create binding(s).
    Type: Application
    Filed: November 28, 2022
    Publication date: May 30, 2024
    Inventors: Joey Hong, Rishabh Singh, Joel Galenson, Jonathan Malmaud, Manzil Zaheer