Patents by Inventor Sudipta Sengupta

Sudipta Sengupta 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).

  • Patent number: 11886297
    Abstract: When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.
    Type: Grant
    Filed: November 9, 2022
    Date of Patent: January 30, 2024
    Assignee: BMC Software, Inc.
    Inventors: Nigel Slinger, Wenjie Zhu, Catherine Drummond, Roxanne Kallman, Sudipta Sengupta, Jeremy Riegel, John Flournoy
  • Publication number: 20230418566
    Abstract: Evaluation data sets may be programmatically generated for code generation models. An evaluation data set is obtained that includes items that correspond to different evaluation tests for a code generation system. The individual items of the evaluation data set maybe converted, including the conversion of a function signature for the items, the test statements for the items and using a code generation system to generate the body of the function.
    Type: Application
    Filed: June 22, 2022
    Publication date: December 28, 2023
    Applicant: Amazon Technologies, Inc.
    Inventors: Praphruetpong Athiwaratkun, Zixuan Lin, Ramana Keerthi, Zijian Wang, Yuchen Tian, Hantian Ding, Sri Ranga Akhilesh Bontala, Matthew Lee, Yanitsa Donchev, Ramesh M Nallapati, Parminder Bhatia, Andrew Oliver Arnold, Bing Xiang, Sudipta Sengupta, Rama Krishna Sandeep Pokkunuri, Srinivas Iragavarapu, Atul Deo, Ankur Deepak Desai
  • Publication number: 20230418565
    Abstract: Code completion suggestions may be proactively obtained and validated. An event that triggers obtaining a code completion suggestion for inclusion in a code file being edited using an integrated development environment may be detected. The code completion suggestion may be obtained. The characters of the code completion suggestion may be compared with characters added to the code file after the detection of the event that triggered obtaining the code completion suggestion to determine whether the code completion suggestion is valid. A valid code completion suggestion may then be displayed.
    Type: Application
    Filed: June 22, 2022
    Publication date: December 28, 2023
    Applicant: Amazon Technologies, Inc.
    Inventors: Sathish Arumugam Selvaraj, Qiang Yu, Venkat Rakshith Reddy Swamireddy, Matthew Lee, Lei Gao, Wei Fang, Rama Krishna Sandeep Pokkunuri, Ramesh M Nallapati, Srinivas Iragavarapu, Alexander Johannes Smola, Sudipta Sengupta, Wasi Uddin Ahmad, Parminder Bhatia, Atul Deo, Ankur Deepak Desai, Bing Xiang, Andrew Oliver Arnold
  • Publication number: 20230418567
    Abstract: Pre-fix matching may constrain the generation of next token predictions. Input text to perform a next token prediction may be received. Multiple tokens may be determined from the input text, including a partial token. From possible tokens, one or more matching possible tokens with the partial token may be identified. Next token predictions may then be filtered using the identified possible tokens in order to ensure that the partial token is matched.
    Type: Application
    Filed: June 22, 2022
    Publication date: December 28, 2023
    Applicant: Amazon Technologies, Inc.
    Inventors: Praphruetpong Athiwaratkun, Yuchen Tian, Mingyue Shang, Zijian Wang, Ramesh M. Nallapati, Parminder Bhatia, Andrew Oliver Arnold, Bing Xiang, Sudipta Sengupta, Yanitsa Donchev, Srinivas Iragavarapu, Matthew Lee, Vamshidhar Krishnamurthy Dantu, Atul Deo, Ankur Deepak Desai
  • Publication number: 20230419036
    Abstract: Random token segmentation may be implemented for next token prediction. Text data may be received for training a machine learning model to predict a next token given input text tokens. Multiple tokens may be determined from the text data. Different ones of the multiple token may be randomly segmented in to sub-tokens. The machine learning model may then be trained using the multiple tokens including the respective sub-tokens as a training data set.
    Type: Application
    Filed: June 22, 2022
    Publication date: December 28, 2023
    Applicant: Amazon Technologies, Inc.
    Inventors: Zijian Wang, Yuchen Tian, Mingyue Shang, Praphruetpong Athiwaratkun, Ming Tan, Parminder Bhatia, Andrew Oliver Arnold, Ramesh M Nallapati, Sudipta Sengupta, Bing Xiang, Atul Deo, Ankur Deepak Desai
  • Patent number: 11797535
    Abstract: Techniques for batch mode execution for calls to remote services are described. A method of batch mode execution for calls to remote services may include generating, by a query service of a provider network, a query plan to optimize a query for batch processing of data, the query plan including at least a function reference to a function provided by at least one service of the provider network, executing the query plan to invoke the function associated with the function reference, wherein a batch function generates a request including a batch of service calls to be processed by the at least one service, sends the request including the batch of service calls to the at least one service, and obtains a plurality of machine learning responses from the at least one service, and generating a query response based on the plurality of responses.
    Type: Grant
    Filed: November 25, 2020
    Date of Patent: October 24, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Stefano Stefani, Sudipta Sengupta, Julio Delgado Mangas, James Laurence Finnerty, Ronak Bharat Shah, Sumeetkumar V. Maru
  • Publication number: 20230325384
    Abstract: Interactive assistances for executing natural language queries to data sets may be performed. A natural language query may be received. Candidate entity linkages may be determined between an entity recognized in the natural language query and columns in data sets. The candidate linkages may be ranked according to confidence scores which may be evaluated to detect ambiguity for an entity linkage. Candidate entity linkages may be provided to a user via an interface to select an entity linkage to use as part of completing the natural language query.
    Type: Application
    Filed: March 10, 2023
    Publication date: October 12, 2023
    Applicant: Amazon Technologies, Inc.
    Inventors: Ramesh M Nallapati, Zhiguo Wang, Bing Xiang, Patrick Ng, Yung Haw Wang, Mukul Karnik, Nanyan Li, Sharanabasappa Parashuram Revadigar, Timothy Jones, Stephen Michael Ash, Sudipta Sengupta, Gregory David Adams, Deepak Shantha Murthy, Douglas Scott Cerny, Stephanie Weeks, Hanbo Li
  • Patent number: 11775868
    Abstract: Techniques for making machine learning inference calls for database query processing are described. In some embodiments, a method of making machine learning inference calls for database query processing may include generating a first batch of machine learning requests based at least on a query to be performed on data stored in a database service, wherein the query identifies a machine learning service, sending the first batch of machine learning requests to an input buffer of an asynchronous request handler, the asynchronous request handler to generate a second batch of machine learning requests based on the first batch of machine learning requests, and obtaining a plurality of machine learning responses from an output buffer of the asynchronous request handler, the machine learning responses generated by the machine learning service using a machine learning model in response to receiving the second batch of machine learning requests.
    Type: Grant
    Filed: August 10, 2022
    Date of Patent: October 3, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Sangil Song, Yongsik Yoon, Kamal Kant Gupta, Saileshwar Krishnamurthy, Stefano Stefani, Sudipta Sengupta, Jaeyun Noh
  • Patent number: 11726997
    Abstract: Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.
    Type: Grant
    Filed: November 14, 2022
    Date of Patent: August 15, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Jun Wang, Zhiguo Wang, Sharanabasappa Parashuram Revadigar, Ramesh M Nallapati, Bing Xiang, Stephen Michael Ash, Timothy Jones, Sudipta Sengupta, Rishav Chakravarti, Patrick Ng, Jiarong Jiang, Hanbo Li, Donald Harold Rivers Weidner
  • Patent number: 11726994
    Abstract: Query restatements may be provided for explaining natural language query results. A natural language query is received at a natural language query processing system. An intermediate representation of the natural language query is generated for executing the natural language query. The intermediate representation is translated into a natural language restatement of the natural language query. The natural language restatement is provided with a result of the natural language query via an interface of the natural language query processing system.
    Type: Grant
    Filed: March 31, 2021
    Date of Patent: August 15, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Jun Wang, Zhiguo Wang, Sharanabasappa Parashuram Revadigar, Ramesh M Nallapati, Bing Xiang, Sudipta Sengupta, Yung Haw Wang
  • Publication number: 20230196113
    Abstract: Methods and systems for training a neural network are provided. In one example, an apparatus comprises a memory that stores instructions; and a hardware processor configured to execute the instructions to: control a neural network processor to perform a loss gradient operation to generate data gradients; after the loss gradient operation completes, control the neural network processor to perform a forward propagation operation to generate intermediate outputs; control the neural network processor to perform a backward propagation operation based on the data gradients and the intermediate outputs to generate weight gradients; receive the weight gradients from the neural network processor; and update weights of a neural network based on the weight gradients.
    Type: Application
    Filed: February 21, 2023
    Publication date: June 22, 2023
    Inventors: Sudipta Sengupta, Randy Renfu Huang, Ron Diamant, Vignesh Vivekaja
  • Publication number: 20230115166
    Abstract: When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.
    Type: Application
    Filed: November 9, 2022
    Publication date: April 13, 2023
    Inventors: Nigel Slinger, Wenjie Zhu, Catherine Drummond, Roxanne Kallman, Sudipta Sengupta, Jeremy Riegel, John Flournoy
  • Patent number: 11610128
    Abstract: Methods and systems for training a neural network are provided. In one example, an apparatus comprises a memory that stores instructions; and a hardware processor configured to execute the instructions to: control a neural network processor to perform a loss gradient operation to generate data gradients; after the loss gradient operation completes, control the neural network processor to perform a forward propagation operation to generate intermediate outputs; control the neural network processor to perform a backward propagation operation based on the data gradients and the intermediate outputs to generate weight gradients; receive the weight gradients from the neural network processor; and update weights of a neural network based on the weight gradients.
    Type: Grant
    Filed: March 31, 2020
    Date of Patent: March 21, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Sudipta Sengupta, Randy Renfu Huang, Ron Diamant, Vignesh Vivekraja
  • Publication number: 20230078177
    Abstract: Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.
    Type: Application
    Filed: November 14, 2022
    Publication date: March 16, 2023
    Applicant: Amazon Technologies, Inc.
    Inventors: Jun Wang, Zhiguo Wang, Sharanabasappa Parashuram Revadigar, Ramesh M Nallapati, Bing Xiang, Stephen Michael Ash, Timothy Jones, Sudipta Sengupta, Rishav Chakravarti, Patrick Ng, Jiarong Jiang, Hanbo Li, Donald Harold Rivers Weidner
  • Patent number: 11604794
    Abstract: Interactive assistances for executing natural language queries to data sets may be performed. A natural language query may be received. Candidate entity linkages may be determined between an entity recognized in the natural language query and columns in data sets. The candidate linkages may be ranked according to confidence scores which may be evaluated to detect ambiguity for an entity linkage. Candidate entity linkages may be provided to a user via an interface to select an entity linkage to use as part of completing the natural language query.
    Type: Grant
    Filed: March 31, 2021
    Date of Patent: March 14, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Ramesh M Nallapati, Zhiguo Wang, Bing Xiang, Patrick Ng, Yung Haw Wang, Mukul Karnik, Nanyan Li, Sharanabasappa Parashuram Revadigar, Timothy Jones, Stephen Michael Ash, Sudipta Sengupta, Gregory David Adams, Deepak Shantha Murthy, Douglas Scott Cerny, Stephanie Weeks, Hanbo Li
  • Patent number: 11599821
    Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes receiving an application instance configuration, an application of the application instance to utilize a portion of an attached accelerator during execution of a machine learning model and the application instance configuration including: an indication of the central processing unit (CPU) capability to be used, an arithmetic precision of the machine learning model to be used, an indication of the accelerator capability to be used, a storage location of the application, and an indication of an amount of random access memory to use.
    Type: Grant
    Filed: June 27, 2018
    Date of Patent: March 7, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Sudipta Sengupta, Poorna Chand Srinivas Perumalla, Dominic Rajeev Divakaruni, Nafea Bshara, Leo Parker Dirac, Bratin Saha, Matthew James Wood, Andrea Olgiati, Swaminathan Sivasubramanian
  • Patent number: 11526400
    Abstract: When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.
    Type: Grant
    Filed: January 22, 2021
    Date of Patent: December 13, 2022
    Assignee: BMC Software, Inc.
    Inventors: Nigel Slinger, Wenjie Zhu, Catherine Drummond, Roxanne Kallman, Sudipta Sengupta, Jeremy Riegel, John Flournoy
  • Patent number: 11500865
    Abstract: Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.
    Type: Grant
    Filed: March 31, 2021
    Date of Patent: November 15, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: Jun Wang, Zhiguo Wang, Sharanabasappa Parashuram Revadigar, Ramesh M Nallapati, Bing Xiang, Stephen Michael Ash, Timothy Jones, Sudipta Sengupta, Rishav Chakravarti, Patrick Ng, Jiarong Jiang, Hanbo Li, Donald Harold Rivers Weidner
  • Patent number: 11494621
    Abstract: Implementations detailed herein include description of a computer-implemented method.
    Type: Grant
    Filed: June 27, 2018
    Date of Patent: November 8, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: Sudipta Sengupta, Poorna Chand Srinivas Perumalla, Dominic Rajeev Divakaruni, Nafea Bshara, Leo Parker Dirac, Bratin Saha, Matthew James Wood, Andrea Olgiati, Swaminathan Sivasubramanian
  • Patent number: 11475067
    Abstract: Techniques for generation of synthetic queries from customer data for training of document querying machine learning (ML) models as a service are described. A service may receive one or more documents from a user, generate a set of question and answer pairs from the one or more documents from the user using a machine learning model trained to predict a question from an answer, and store the set of question and answer pairs generated from the one or more documents from the user. The question and answer pairs may be used to train another machine learning model, for example, a document ranking model, a passage ranking model, a question/answer model, or a frequently asked question (FAQ) model.
    Type: Grant
    Filed: November 27, 2019
    Date of Patent: October 18, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: Cicero Nogueira Dos Santos, Xiaofei Ma, Peng Xu, Ramesh M. Nallapati, Bing Xiang, Sudipta Sengupta, Zhiguo Wang, Patrick Ng