Patents by Inventor Mukul R. Prasad

Mukul R. Prasad 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: 20240160646
    Abstract: A method may include obtaining a dataset that may include at least a first column and a second column, with a first title and a first value and a second title and a second value, respectively. The operations may further include determining a total similarity value between the first and second columns based on a metadata similarity value, a semantic similarity value, and/or a unit of measurement similarity value; adding the first and second columns to a cluster if the total similarity value is less than a threshold value; generating a new column to add to the cluster using a feature engineering function, where the new column may include a new title and a new value, the new value determined using the feature engineering function by acting on the first value and/or the second value. Further, the operations may include adding the new column to the dataset.
    Type: Application
    Filed: November 16, 2022
    Publication date: May 16, 2024
    Applicant: Fujitsu Limited
    Inventors: Lei LIU, Yinlin DENG, Mehdi BAHRAMI, Mukul R. PRASAD
  • Publication number: 20240160999
    Abstract: A method may include obtaining a dataset that may include one or more columns, wherein each of the one or more columns may include a title and at least one value. The operations may further include extracting, for each of the one or more columns, the title and a sample value from the at least one value. The operations may additionally include, synthesizing a question based on the title and the sample value for each of the one or more columns. Further, the operations may include sending the question to a language model to obtain an answer. The operations may additionally include generating from the answer to the question, a predicted unit of measurement for the at least one value in each of the one or more columns. Systems and devices for performing the method are also disclosed.
    Type: Application
    Filed: November 15, 2022
    Publication date: May 16, 2024
    Applicant: Fujitsu Limited
    Inventors: Mehdi BAHRAMI, Yinlin DENG, Mukul R. PRASAD
  • Publication number: 20230316100
    Abstract: A method may include obtaining a trained machine learning (ML) pipeline skeleton model configured to predict functional blocks within a new ML pipeline based on meta-features of a dataset associated with the new ML pipeline; obtaining parametric templates, each of the parametric templates including fillable portions and static text portions that in combination describe a given functional block; receiving a request to generate the new ML pipeline; determining functional blocks to populate the new ML pipeline based on the pipeline skeleton model; extracting decision-making conditions leading to the functional blocks; generating explanations of the functional blocks using the parametric templates, where at least one of the fillable portions is filled based on the decision-making conditions leading to the functional blocks; instantiating the new ML pipeline including the functional blocks with the generated explanations.
    Type: Application
    Filed: March 29, 2022
    Publication date: October 5, 2023
    Applicant: FUJITSU LIMITED
    Inventors: Ripon K. SAHA, Mukul R. PRASAD
  • Patent number: 11740895
    Abstract: Operations may include obtaining a repair related to correcting an error of source code of a software program, the repair including an edit to make to the source code. The operations may further include determining a change type of the edit. The operations may additionally include identifying an explanation template from a library of explanation templates based on the change type corresponding to the explanation template in the library of explanation templates. In addition, the operations may include generating a change explanation for the edit based on the explanation template. The change explanation may provide a natural language explanation of the changes made by the edit.
    Type: Grant
    Filed: March 31, 2021
    Date of Patent: August 29, 2023
    Assignee: FUJITSU LIMITED
    Inventors: Hiroaki Yoshida, Mukul R. Prasad
  • Patent number: 11650901
    Abstract: Operations include obtaining a first patch that corrects a first error in a first buggy code snippet of first source code based on the first buggy code snippet and a first repaired code snippet. The operations also include generating a second patch based on the first patch and a bug pattern of a bug scenario that corresponds to the first error. In addition, the operations include generating a third patch based on the second patch, the bug pattern, and a second buggy code snippet of second source code, the third patch correcting a second error in the second buggy code snippet. Moreover, the operations include performing one or more repair operations with respect to the second buggy code snippet based on the third patch.
    Type: Grant
    Filed: September 23, 2020
    Date of Patent: May 16, 2023
    Assignee: FUJITSU LIMITED
    Inventors: Sonal Mahajan, Mukul R. Prasad
  • Patent number: 11556455
    Abstract: Operations may include obtaining a buggy code snippet of source code of a software program in which the buggy code snippet includes a particular error. The operations may also include determining a respective first similarity between the buggy code snippet and a plurality of bug patterns of previously identified bug scenarios. In addition, the operations may include selecting a particular bug pattern based on a determined particular first similarity between the particular bug pattern and the buggy code snippet. Moreover, the operations may include determining a respective second similarity between the particular bug pattern and example code snippets obtained from a plurality of posts. The operations may also include selecting a particular post as providing a potential solution to correct the particular error based on a determined particular second similarity between the particular bug pattern and a particular example code snippet of the particular post.
    Type: Grant
    Filed: August 4, 2020
    Date of Patent: January 17, 2023
    Assignee: FUJITSU LIMITED
    Inventors: Sonal Mahajan, Mukul R. Prasad
  • Patent number: 11551151
    Abstract: According to one or more embodiments, operations may include storing existing machine learning (ML) projects in a corpus. The operations may also include generating a search query for a new ML project based on a new dataset and a new ML task for the new ML project. In addition, the operations may include searching through the existing ML projects stored in the corpus, based on the search query, for a set of existing ML projects. Moreover, the operations may include merging the ML pipelines of the set of existing ML projects to generate a new ML pipeline for the new ML project. In addition, the operations may include adapting functional blocks of the new ML pipeline for the new ML project to enable the new ML pipeline to be executed to perform the new ML task on the new dataset.
    Type: Grant
    Filed: September 2, 2020
    Date of Patent: January 10, 2023
    Assignee: FUJITSU LIMITED
    Inventors: Ripon K. Saha, Mukul R. Prasad
  • Publication number: 20220342799
    Abstract: Operations may include obtaining a plurality of posts from one or more web sites, each post including a respective buggy snippet of source code that includes a corresponding error. The operations may also include generating a plurality of bug patterns from the plurality of posts in which each respective bug pattern corresponds to a respective buggy snippet and indicates a corresponding bug scenario that leads to the corresponding error of the respective buggy snippet that corresponds to the respective bug pattern. The operations may also include determining similarities with respect to the respective bug patterns and selecting, based on the similarity determinations, a first bug pattern of the plurality of bug patterns for revision. In addition, the operations may include obtaining a revised bug pattern that is a revised version of the first bug pattern.
    Type: Application
    Filed: April 20, 2021
    Publication date: October 27, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Sonal MAHAJAN, Mukul R. PRASAD
  • Publication number: 20220318005
    Abstract: Operations may include obtaining a repair related to correcting an error of source code of a software program, the repair including an edit to make to the source code. The operations may further include determining a change type of the edit. The operations may additionally include identifying an explanation template from a library of explanation templates based on the change type corresponding to the explanation template in the library of explanation templates. In addition, the operations may include generating a change explanation for the edit based on the explanation template. The change explanation may provide a natural language explanation of the changes made by the edit.
    Type: Application
    Filed: March 31, 2021
    Publication date: October 6, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Hiroaki YOSHIDA, Mukul R. PRASAD
  • Publication number: 20220318602
    Abstract: According to an aspect of an embodiment, operations may include predicting, by a pre-trained DNN, a first class for a first datapoint of a first dataset. A first set of feature scores is determined for the first datapoint based on the first class associated with the first datapoint. A set of confusing class pairs associated with the DNN is identified based on the first class and a predetermined class of the first datapoint. The first dataset is clustered into one of a set of semantic classes based on the first set of feature score, the first class, and the set of confusing class pairs for each datapoint in the first dataset. Each semantic class indicates a prediction accuracy of a dataset clustered in the semantic class. A classifier is trained based on the clustered first dataset, the first set of feature scores, and the set of semantic classes.
    Type: Application
    Filed: March 31, 2021
    Publication date: October 6, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Ripon K. SAHA, Mukul R. PRASAD, Indradeep GHOSH
  • Patent number: 11461650
    Abstract: According to an aspect of an embodiment, operations may include receiving a first data point associated with a real-time application and predicting a first class for the received first data point, by a Deep Neural Network (DNN) pre-trained for a classification task of the real-time application. The operations may further include extracting, from the DNN, a first set of features and a corresponding first set of weights, for the predicted first class. The extracted first set of features may be associated with a convolution layer of the DNN. The operations may further include determining, by a pre-trained classifier associated with the predicted first class, a confidence score for the predicted first class based on the extracted first set of features and the corresponding first set of weights. The operations may further include generating output information to indicate correctness of the predicted first class based on the determined confidence score.
    Type: Grant
    Filed: March 26, 2020
    Date of Patent: October 4, 2022
    Assignee: FUJITSU LIMITED
    Inventors: Ripon K Saha, Mukul R Prasad, Seemanta Saha
  • Publication number: 20220269981
    Abstract: Operations include obtaining a machine learning (ML) pipeline skeleton model configured to generate an ML pipeline skeleton, the pipeline skeleton indicating a set of first functional blocks to use to process a new dataset of the new ML project. For each respective first functional block of the set of first functional blocks, the operations include identifying training data used by the ML pipeline skeleton model to determine the respective first functional block of the pipeline skeleton. The operations further include identifying a code snippet of the existing ML pipeline that is associated with the training data. Moreover, the operations include selecting the code snippet for instantiation of the respective first functional block based on the code snippet being associated with the training data used to determine the first functional block.
    Type: Application
    Filed: February 24, 2021
    Publication date: August 25, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Hiroaki YOSHIDA, Mukul R. PRASAD
  • Patent number: 11403304
    Abstract: According to one or more embodiments, operations may include gathering a set of machine learning (ML) projects from one or more repositories of ML projects based on a filtering criteria. The operations may also include ensuring executability of ML pipelines in the set of ML projects. In addition, the operations may include identifying irrelevant portions of the ML pipelines in the set of ML projects. Moreover, the operations may include generating quality features for the set of ML projects. In addition, the operations may include generating diversity features for the set of ML projects. Moreover, the operations may include selecting a subset of ML projects from the set of ML projects based on the quality features and the diversity features. In addition, the operations may include storing the subset of ML projects in a corpus of ML projects that may be adapted for use in new ML projects.
    Type: Grant
    Filed: September 2, 2020
    Date of Patent: August 2, 2022
    Assignee: FUJITSU LIMITED
    Inventors: Ripon K. Saha, Mukul R. Prasad, Chenguang Zhu
  • Patent number: 11366742
    Abstract: Operations may include obtaining a buggy code snippet from a question included in a post of a discussion forum, the buggy code snippet including an error and a plurality of lines of code. The operations may further include obtaining text from an answer to the question and identifying keywords from the text. In addition, the operations may include marking a particular line from the plurality of lines as being related to the error based on one or more of the keywords corresponding to one or more elements of the particular line. Moreover, the operations may include performing one or more software program repair operations based on the marking of the particular line.
    Type: Grant
    Filed: November 13, 2020
    Date of Patent: June 21, 2022
    Assignee: FUJITSU LIMITED
    Inventors: Sonal Mahajan, Mukul R. Prasad
  • Publication number: 20220156174
    Abstract: Operations may include obtaining a buggy code snippet from a question included in a post of a discussion forum, the buggy code snippet including an error and a plurality of lines of code. The operations may further include obtaining text from an answer to the question and identifying keywords from the text. In addition, the operations may include marking a particular line from the plurality of lines as being related to the error based on one or more of the keywords corresponding to one or more elements of the particular line. Moreover, the operations may include performing one or more software program repair operations based on the marking of the particular line.
    Type: Application
    Filed: November 13, 2020
    Publication date: May 19, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Sonal MAHAJAN, Mukul R. PRASAD
  • Publication number: 20220091963
    Abstract: Operations may include obtaining a first patch that corrects a first error in a first buggy code snippet of first source code based on the first buggy code snippet and the first repaired code snippet. The operations may also include generating a second patch based on the first patch and a bug pattern of a bug scenario that corresponds to the first error. In addition, the operations may include generating a third patch based on the second patch, the bug pattern, and a second buggy code snippet of second source code, the third patch correcting a second error in the second buggy code snippet. Moreover, the operations may include performing one or more repair operations with respect to the second buggy code snippet based on the third patch.
    Type: Application
    Filed: September 23, 2020
    Publication date: March 24, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Sonal MAHAJAN, Mukul R. PRASAD
  • Publication number: 20220076143
    Abstract: According to one or more embodiments, operations may include, extracting first features from existing machine learning (ML) projects and storing the first features in a corpus. In addition, the operations may include performing a first search on the corpus based on a first search query to generate a first ranked set of the existing ML projects. Moreover, the operations may include generating second features based on the first features of the first ranked set of the existing ML projects. Moreover, the operations may include performing a second search on the corpus based on a second search query to generate a second ranked set of the existing ML projects. In addition, the operations may include recommending a highest ranked existing ML project in the second ranked set of the existing ML projects as adaptable for use in a second ML project.
    Type: Application
    Filed: September 4, 2020
    Publication date: March 10, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Ripon K. SAHA, Mukul R. PRASAD
  • Publication number: 20220067054
    Abstract: According to one or more embodiments, operations may include gathering a set of machine learning (ML) projects from one or more repositories of ML projects based on a filtering criteria. The operations may also include ensuring executability of ML pipelines in the set of ML projects. In addition, the operations may include identifying irrelevant portions of the ML pipelines in the set of ML projects. Moreover, the operations may include generating quality features for the set of ML projects. In addition, the operations may include generating diversity features for the set of ML projects. Moreover, the operations may include selecting a subset of ML projects from the set of ML projects based on the quality features and the diversity features. In addition, the operations may include storing the subset of ML projects in a corpus of ML projects that may be adapted for use in new ML projects.
    Type: Application
    Filed: September 2, 2020
    Publication date: March 3, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Ripon K. SAHA, Mukul R. PRASAD, Chenguang ZHU
  • Publication number: 20220067575
    Abstract: According to one or more embodiments, operations may include storing existing machine learning (ML) projects in a corpus. The operations may also include generating a search query for a new ML project based on a new dataset and a new ML task for the new ML project. In addition, the operations may include searching through the existing ML projects stored in the corpus, based on the search query, for a set of existing ML projects. Moreover, the operations may include merging the ML pipelines of the set of existing ML projects to generate a new ML pipeline for the new ML project. In addition, the operations may include adapting functional blocks of the new ML pipeline for the new ML project to enable the new ML pipeline to be executed to perform the new ML task on the new dataset.
    Type: Application
    Filed: September 2, 2020
    Publication date: March 3, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Ripon K. SAHA, Mukul R. PRASAD
  • Publication number: 20220067576
    Abstract: According to one or more embodiments, operations may include normalizing machine learning (ML) pipelines of existing ML projects stored in a corpus of existing ML projects. The operations may also include extracting functional blocks from the normalized ML pipelines. In addition, the operations may include assigning a label to each of the functional blocks in the normalized ML pipelines. Moreover, the operations may include indexing each of the ML pipelines in the corpus based on the labels assigned to the functional blocks. In addition, the operations may include utilizing the labels assigned to the functional blocks in the corpus to generate a new pipeline to perform a new ML task on a new dataset of a new ML project.
    Type: Application
    Filed: September 2, 2020
    Publication date: March 3, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Ripon K. SAHA, Mukul R. PRASAD