Patents by Inventor Mehdi Bahrami

Mehdi Bahrami 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: 12019992
    Abstract: According to an aspect of an embodiment, operations for code enrichment for training language models on tasks related to computer programming are provided. The operations include receiving source code data including a computer-executable code and a natural language (NL) text. The operations further include determining blocks of code from the computer-executable code. The operations further include extracting a set of features related to components of the source code data from the blocks of code. The extraction is performed by parsing the blocks of code using Abstract Syntax Tree (AST) data of the blocks of code. The operations further include revising the AST data. The operations further include updating the source code data based on the revised AST data and generating a dataset of NL and abstracted code features as training data based on the updated source code data and further training a language model on a sequence-to-sequence generation task.
    Type: Grant
    Filed: March 31, 2022
    Date of Patent: June 25, 2024
    Assignee: FUJITSU LIMITED
    Inventors: Mehdi Bahrami, Wei-Peng Chen
  • 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: 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: 20240160852
    Abstract: In an embodiment, a set of texts associated with a domain is received. A set of hypothesis statements associated with the domain is received. A pre-trained natural language inference (NLI) model is applied on each of the received set of texts and on each of the received set of hypothesis statements. A second text corpus associated with the domain is generated. The generated second text corpus corresponds to a set of labels associated with the domain. A few-shot learning model is applied on the generated second text corpus to generate a third text corpus associated with the domain. The generated third text corpus is configured to fine-tune the applied pre-trained NLI model, and the fine-tuned NLI model is configured to label an input text associated with the domain. A display of the labelled input text on a display device is controlled.
    Type: Application
    Filed: November 16, 2022
    Publication date: May 16, 2024
    Applicant: Fujitsu Limited
    Inventors: Wei-Peng CHEN, Mehdi BAHRAMI, Lei LIU
  • Publication number: 20240143702
    Abstract: A method of machine learning algorithm selection may include obtaining a dataset that includes multiple data entries. In some embodiments, each of the data entries may include multiple features and one of the multiple features may be designated as a target variable. The method may further include selecting a subset of the data entries. In some embodiments, selecting the subset of the data entries may include binning the data entries into multiple data bins based on values in the target variable and selecting a subset of the binned data entries from each of the multiple data bins as the subset of the data entries. The method may further include constructing multiple machine learning models using the subset of the data entries and selecting one of the multiple machine learning models based on an evaluation of the multiple machine learning models.
    Type: Application
    Filed: October 31, 2022
    Publication date: May 2, 2024
    Applicant: Fujitsu Limited
    Inventors: Mehdi BAHRAMI, Wei-Peng CHEN, Mukul PRASAD
  • Patent number: 11868731
    Abstract: According to an aspect of an embodiment, operations include receiving a set of NL descriptors and a corresponding set of PL codes. The operations further include determining a first vector associated with each NL descriptor and a second vector associated with each PL code, using language models. The operations further include determining a number of a set of semantic code classes to cluster the set of PL codes into the set of semantic code classes, based on the number, the first vector, and the second vector. The operations further include training a multi-class classifier model to predict a semantic code class, from the set of semantic code classes, corresponding to an input NL descriptor. The operations further include selecting an intra-class predictor model based on the predicted semantic code class. The operations further include training the intra-class predictor model to predict a PL code corresponding to the input NL descriptor.
    Type: Grant
    Filed: March 31, 2022
    Date of Patent: January 9, 2024
    Assignee: FUJITSU LIMITED
    Inventors: Mehdi Bahrami, Wei-Peng Chen
  • Publication number: 20230315442
    Abstract: According to an aspect of an embodiment, operations include receiving, as an output of a code generation tool, a file that includes a computer-executable code and a non-executable description. The operations further include modifying blocks of the computer-executable code into modular code blocks and determining a set of trigger conditions from the modified computer-executable code and the non-executable description. The operations further include matching the set of trigger conditions with template conditions in a set of revision rules and determining, based on the match, a set of changes applicable to portions of the modified computer-executable code and the non-executable description. The operations further include updating the modified computer-executable code and the non-executable description based on the set of changes and generating a tutorial-style code file that includes the updated computer-executable code and the updated non-executable description.
    Type: Application
    Filed: March 31, 2022
    Publication date: October 5, 2023
    Applicant: FUJITSU LIMITED
    Inventors: Mehdi BAHRAMI, Wei-Peng CHEN
  • Patent number: 11750383
    Abstract: A method comprises receiving vehicle data comprising information associated with a plurality of sensors of autonomous vehicle and segmenting the received vehicle data into non-public data and public data. The method further comprises partitioning the public data into a plurality of data partitions and generating a plurality of data levels of the public data. Each data level of the plurality of data levels is generated according to an access level of a plurality of access levels and includes one or more data partitions of the plurality of data partitions in an encrypted form. The method further comprises transmitting the generated plurality of data levels to a group of electronic devices. Each electronic device of the group of electronic devices retrieves, according to one of the plurality of access levels, at least a portion of the public data from the transmitted plurality of data levels.
    Type: Grant
    Filed: March 31, 2021
    Date of Patent: September 5, 2023
    Assignee: FUJITSU LIMITED
    Inventors: Mehdi Bahrami, Takuki Kamiya, Wei-Peng Chen
  • Publication number: 20230266940
    Abstract: Operations may include obtaining a dataset that includes a plurality of unique values and obtaining a plurality of permutations with respect to the plurality of unique values. Additionally, the operations may include, for each respective permutation, obtaining a respective overall permutation probability for the respective permutation based on masked value probabilities determined by a masked language model (MLM). Each masked value probability may be determined with respect to a respective masked version of a plurality of masked versions of the respective permutation. The operations may also include selecting a particular permutation from the plurality of permutations based on a comparison between the respective overall permutation probabilities of the plurality of permutations.
    Type: Application
    Filed: February 23, 2022
    Publication date: August 24, 2023
    Applicant: FUJITSU LIMITED
    Inventors: Mehdi BAHRAMI, Wei-Peng CHEN
  • Patent number: 11651014
    Abstract: A method may include obtaining training code and extracting features from the training code. The extracted features of the training code may be mapped to natural language code vectors by a deep neural network. A natural language search query requesting source-code suggestions may be received, and the natural language search query may be mapped to a natural language search vector by the deep neural network. The method may include mapping the natural language search query to the natural language search vector in the same or a similar method as mapping the extracted features of the training code to natural language code vectors, and the natural language search vector may be compared to the natural language code vectors. Source code responsive to the natural language search query may be suggested based on the comparison between the natural language search vector and the natural language code vectors.
    Type: Grant
    Filed: October 30, 2020
    Date of Patent: May 16, 2023
    Assignee: FUJITSU LIMITED
    Inventors: Mehdi Bahrami, Manisha Mukherjee, Wei-Peng Chen
  • Publication number: 20230106226
    Abstract: According to an aspect of an embodiment, operations for code enrichment for training language models on tasks related to computer programming are provided. The operations include receiving source code data including a computer-executable code and a natural language (NL) text. The operations further include determining blocks of code from the computer-executable code. The operations further include extracting a set of features related to components of the source code data from the blocks of code. The extraction is performed by parsing the blocks of code using Abstract Syntax Tree (AST) data of the blocks of code. The operations further include revising the AST data. The operations further include updating the source code data based on the revised AST data and generating a dataset of NL and abstracted code features as training data based on the updated source code data and further training a language model on a sequence-to-sequence generation task.
    Type: Application
    Filed: March 31, 2022
    Publication date: April 6, 2023
    Applicant: FUJITSU LIMITED
    Inventors: Mehdi BAHRAMI, Wei-Peng CHEN
  • Publication number: 20230107242
    Abstract: According to an aspect of an embodiment, operations for code enrichment through metadata for code synthesis are provided. The operations include acquiring package data that include source code files and package metadata. The operations further include extracting additional metadata associated with software package and preparing metadata features based on the package metadata and the additional metadata. The operations further include identifying a set of target portions of a source code included in the source code files and updating one or more source code files using the metadata features. Such files are updated by performing at least one of a revision of existing code comments, and an addition of new code comments for the target portions. The operations further include generating a dataset of natural language (NL) text features and respective code features and training a language model on a sequence-to-sequence generation task.
    Type: Application
    Filed: July 24, 2022
    Publication date: April 6, 2023
    Applicant: FUJITSU LIMITED
    Inventors: Mehdi BAHRAMI, Wei-Peng CHEN
  • Publication number: 20230100208
    Abstract: According to an aspect of an embodiment, operations include receiving a set of NL descriptors and a corresponding set of PL codes. The operations further include determining a first vector associated with each NL descriptor and a second vector associated with each PL code, using language models. The operations further include determining a number of a set of semantic code classes to cluster the set of PL codes into the set of semantic code classes, based on the number, the first vector, and the second vector. The operations further include training a multi-class classifier model to predict a semantic code class, from the set of semantic code classes, corresponding to an input NL descriptor. The operations further include selecting an intra-class predictor model based on the predicted semantic code class. The operations further include training the intra-class predictor model to predict a PL code corresponding to the input NL descriptor.
    Type: Application
    Filed: March 31, 2022
    Publication date: March 30, 2023
    Applicant: FUJITSU LIMITED
    Inventors: Mehdi BAHRAMI, Wei-Peng CHEN
  • Publication number: 20230096325
    Abstract: According to an aspect of an embodiment, operations for deep parameter learning for code synthesis are provided. The operations may include receiving a source code file and generating an abstract syntax tree (AST). The operations may further include determining a set of classes, and functions/procedures from the computer-executable code and extracting metadata associated to each component. The operations may further include selecting a subset of functions for which descriptions in the extracted metadata satisfy filtering criteria and updating the computer-executable code by filtering lines of code (LoCs) corresponding to the subset of functions/procedures. The operations may further include generating a dataset of code features and respective metadata features that includes a deep connection between parameters and its usage based on the updated computer-executable code and the metadata generation task.
    Type: Application
    Filed: July 24, 2022
    Publication date: March 30, 2023
    Applicant: FUJITSU LIMITED
    Inventors: Mehdi BAHRAMI, Wei-Peng CHEN
  • Patent number: 11609748
    Abstract: A method may include obtaining machine-readable source code. The method may include parsing the source code for one or more code descriptions and identifying a section of the source code corresponding to each of the code descriptions. The method may include determining a description-code pair including a first element representing the code description and a second element representing the section of the source code corresponding to the code description. The method may include generating an augmented programming language corpus based on the description-code pair, the one or more code descriptions, and the source code. The method may include receiving a natural language search query for source-code recommendations, identifying source code from the augmented programming language corpus responsive to the natural language search query, and responding to the natural language search query with the identified source code.
    Type: Grant
    Filed: January 28, 2021
    Date of Patent: March 21, 2023
    Assignee: FUJITSU LIMITED
    Inventors: Mehdi Bahrami, Wei-Peng Chen, Shrikanth Narayanaswamy Chandrasekaran
  • Patent number: 11550937
    Abstract: A method may include providing access to a first application programming interface (API) provided by a first party and a second API provided by a second party. The method may also include collecting a first set of API data sources related to the first API and a second set of API data sources related to the second API. The method may additionally include using a deep learning model to predict a privacy trustworthiness level for the first API and the second API, and disabling access to the first API based on the privacy trustworthiness level of the first API being below a threshold level.
    Type: Grant
    Filed: June 13, 2019
    Date of Patent: January 10, 2023
    Assignee: FUJITSU LIMITED
    Inventors: Mehdi Bahrami, Wei-Peng Chen
  • Patent number: 11526336
    Abstract: Operations may include obtaining source code describing an optimization problem. The operations may include identifying problem parameters associated with the optimization problem such that a specialized computing system may be enabled to solve the optimization problem. The operations may include extracting one or more first parameters of the problem parameters from the source code. The operations may include identifying one or more second parameters of the problem parameters that are not included in the source code. A user may be prompted via a GUI for input relating to the one or more second parameters. The operations may include compiling the extracted first parameters and the user-provided second parameters as input parameters of the specialized computing system. The operations may include providing the input parameters to the specialized computing system such that the specialized computing system is able to solve the optimization problem.
    Type: Grant
    Filed: March 15, 2021
    Date of Patent: December 13, 2022
    Assignee: FUJITSU LIMITED
    Inventors: Mehdi Bahrami, Wei-Peng Chen, Oussama Chafiqui
  • Patent number: 11468180
    Abstract: According to an aspect of an embodiment, operations may include receiving a first input corresponding to a selection of a combinatorial optimization problem and receiving a set of datapoints as input. The operations may further include generating a first quadratic unconstrained binary optimization (QUBO) formulation based on an objective function for the combinatorial optimization problem and the received set of datapoints. The operations may further include selecting a first privacy setting and encoding the first QUBO formulation based on the selected privacy setting to generate a second QUBO formulation. The operations may further include submitting the generated second QUBO formulation to an optimization solver machine and receiving a first solution of the second QUBO formulation. The operations may further include decoding the first solution to produce a second solution and publishing an output of the combinatorial optimization problem on a user device based on the second solution.
    Type: Grant
    Filed: March 31, 2020
    Date of Patent: October 11, 2022
    Assignee: FUJITSU LIMITED
    Inventors: Wei-Peng Chen, Mehdi Bahrami, Junhee Park
  • Publication number: 20220321343
    Abstract: A method comprises receiving vehicle data comprising information associated with a plurality of sensors of autonomous vehicle and segmenting the received vehicle data into non-public data and public data. The method further comprises partitioning the public data into a plurality of data partitions and generating a plurality of data levels of the public data. Each data level of the plurality of data levels is generated according to an access level of a plurality of access levels and includes one or more data partitions of the plurality of data partitions in an encrypted form. The method further comprises transmitting the generated plurality of data levels to a group of electronic devices. Each electronic device of the group of electronic devices retrieves, according to one of the plurality of access levels, at least a portion of the public data from the transmitted plurality of data levels.
    Type: Application
    Filed: March 31, 2021
    Publication date: October 6, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Mehdi BAHRAMI, Takuki KAMIYA, Wei-Peng CHEN
  • Publication number: 20220291906
    Abstract: Operations may include obtaining source code describing an optimization problem. The operations may include identifying a plurality of problem parameters associated with the optimization problem such that a specialized computing system may be enabled to solve the optimization problem. The operations may include extracting one or more first parameters of the plurality of problem parameters from the source code. The operations may include identifying one or more second parameters of the plurality of problem parameters that are not included in the source code. A user may be prompted via a GUI for input relating to the one or more second parameters. The operations may include compiling the extracted first parameters and the user-provided second parameters as input parameters of the specialized computing system. The operations may include providing the input parameters to the specialized computing system such that the specialized computing system is able to solve the optimization problem.
    Type: Application
    Filed: March 15, 2021
    Publication date: September 15, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Mehdi BAHRAMI, Wei-Peng CHEN, Oussama CHAFIQUI