Patents by Inventor Antonios GEORGIADIS

Antonios GEORGIADIS 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: 12159127
    Abstract: Systems and methods for detecting code duplication are disclosed. In one embodiment, a method for detecting exact code snippet duplicates may include: (1) representing, by a code duplication detection computer program, each of a plurality of code snippets in a codebase as an abstract syntax trees; (2) featurizing, by the code duplication detection computer program, the abstract syntax trees into corpus feature vectors by converting the abstract syntax tree into vector representations; (3) generating, by the code duplication detection computer program, dense feature vectors from the corpus feature vectors using a dimension reduction technique; (4) identifying, by the code duplication detection computer program, exact duplicate code snippet matches by apply density-based clustering to the dense feature vectors; and (5) tagging, by the code duplication detection computer program, the exact duplicate code snippets.
    Type: Grant
    Filed: December 12, 2022
    Date of Patent: December 3, 2024
    Assignee: JPMORGAN CHASE BANK, N.A.
    Inventors: Rohan Saphal, Fanny Silavong, Sean Moran, Antonios Georgiadis, Sanat Saha, Gaurav Singh, Pierre Osselin, Rob Otter
  • Publication number: 20240281603
    Abstract: A method may include: receiving a seed topic word distribution; receiving a corpus of documents; generating bag of words representations for the corpus of documents; converting the corpus of documents to vector representations; training a topic modeling system using the seed topic word distribution and concatenated bag of words representations and the vector representations resulting in a topic word distribution and a document word distribution; generating a plurality of new generated topics based on the topic word distribution; precomputing a topic word distribution penalty and a topic word distribution reward for the plurality of topics; penalizing the topic modeling system in response to a divergence and rewarding the topic modeling system in response to a similarity; determining a total loss from a neural network loss, the topic word distribution penalty, and the topic word distribution reward; and training the topic modeling system based on the total loss.
    Type: Application
    Filed: February 15, 2024
    Publication date: August 22, 2024
    Inventors: Fanny SILAVONG, Sae Young MOON, Antonios GEORGIADIS
  • Publication number: 20240264997
    Abstract: Systems and methods for counteracting data-skewness for locality sensitive hashing via feature selection and pruning are disclosed. In one embodiment, a method for feature selection for counteracting data skewness on locality sensitive hashing (LSH)-based search may include: (1) ingesting, by an ingestion computer program and from a plurality of data sources, data; (2) extracting, by the ingestion computer program, a plurality of features from the ingested data; (3) transforming, by the ingestion computer program, each of the plurality of features into a feature vector; (4) selecting, by the ingestion computer program, a subset of the plurality of features; and (5) for each selected feature vector: computing, by the ingestion computer program, a random hash function for the selected feature; and inserting, by the ingestion computer program, an output of the random hash function into a hash table with the selected feature.
    Type: Application
    Filed: April 17, 2024
    Publication date: August 8, 2024
    Inventors: Sean MORAN, Fanny SILAVONG, Robert OTTER, Antonios GEORGIADIS, Brett SANFORD
  • Publication number: 20240264828
    Abstract: Systems and methods for automated code analysis and tagging are disclosed. In one embodiment, a method for automated code analysis and tagging may include: (1) receiving, by a code annotation computer program executed by a computer processor, a training code snippet from a training codebase; (2) parsing, by the code annotation computer program, the training code snippet into a data structure; (3) quantifying, by the code annotation computer program, the data structure, (4) parsing, by the code annotation computer program, a docstring associated with the training code snippet into a plurality of keywords; (5) quantifying, by the code annotation computer program, the plurality of keywords; and (6) training, by the code annotation computer program, a code annotation model based on a similarity between the quantified data structure and a smoothing parameter for a Dirichlet prior smoothing estimate.
    Type: Application
    Filed: April 16, 2024
    Publication date: August 8, 2024
    Inventors: Sean MORAN, Sanat SAHA, Gaurav SINGH, Fanny SILAVONG, Antonios GEORGIADIS, Ganesh CHANDRASEKAR, Andy ALEXANDER, Robert OTTER, Brett SANFORD
  • Patent number: 12008365
    Abstract: Systems and methods for automated code analysis and tagging are disclosed. In one embodiment, a method for automated code analysis and tagging may include: (1) receiving, by a code annotation computer program executed by a computer processor, a training code snippet from a training codebase; (2) parsing, by the code annotation computer program, the training code snippet into a data structure; (3) quantifying, by the code annotation computer program, the data structure, (4) parsing, by the code annotation computer program, a docstring associated with the training code snippet into a plurality of keywords; (5) quantifying, by the code annotation computer program, the plurality of keywords; and (6) training, by the code annotation computer program, a code annotation model based on a similarity between the quantified data structure and a smoothing parameter for a Dirichlet prior smoothing estimate.
    Type: Grant
    Filed: February 14, 2022
    Date of Patent: June 11, 2024
    Assignee: JPMORGAN CHASE BANK , N.A.
    Inventors: Sean Moran, Sanat Saha, Gaurav Singh, Fanny Silavong, Antonios Georgiadis, Ganesh Chandrasekar, Andy Alexander, Rob Otter, Brett Sanford
  • Publication number: 20240062113
    Abstract: Systems and methods for scalable and flexible federated learning frameworks are disclosed. A method may include: (1) receiving, by a computer program executed by an electronic device and from a client, a project for federated learning using a training federation, the training federation comprising a plurality of clients; (2) generating, by the computer program, a configuration file that reflects a set-up for the training federation; (3) receiving, by the computer program, files necessary to build containers, wherein at least some of the files are customized by the client; (4) generating, by the computer program, containers comprising the configuration file and files necessary to build the containers; and (5) deploying, by the computer program, the containers to a client compute environment for the client as a client node, wherein the client node is configured to join the training federation as a server and/or a participant.
    Type: Application
    Filed: August 18, 2023
    Publication date: February 22, 2024
    Inventors: Fanny SILAVONG, Shaltiel ELOUL, Antonios GEORGIADIS, Sanket KAMTHE, Sean MORAN
  • Patent number: 11868768
    Abstract: A method for facilitating identification of secrets in source code by using machine learning is provided. The method includes retrieving a plurality of files from a repository, each of the plurality of files including a source code file; parsing the source code file to identify a training feature; associating a predetermined label with the training feature, the predetermined label corresponding to a secret label and a non-secret label; training a model by using the training feature and the corresponding predetermined label; receiving, via a graphical user interface, a test file, the test file including a set of source codes; parsing the set of source codes to identify a feature; and determining, by using the model, a first characteristic of the feature.
    Type: Grant
    Filed: September 16, 2021
    Date of Patent: January 9, 2024
    Assignee: JPMORGAN CHASE BANK, N.A.
    Inventors: Sean Moran, Ahmad Emami, Fanny Silavong, Joachim Fainberg, Ashish Tiwari, Antonios Georgiadis, Bill Moriarty, Solomon Olaniyi Adebayo, Georgios Papadopoulos, Rohan Saphal, Robert Falconer Keith, Rob Otter, Stephen Hall
  • Publication number: 20230259359
    Abstract: Systems and methods for automated code analysis and tagging are disclosed. In one embodiment, a method for automated code analysis and tagging may include: (1) receiving, by a code annotation computer program executed by a computer processor, a training code snippet from a training codebase; (2) parsing, by the code annotation computer program, the training code snippet into a data structure; (3) quantifying, by the code annotation computer program, the data structure, (4) parsing, by the code annotation computer program, a docstring associated with the training code snippet into a plurality of keywords; (5) quantifying, by the code annotation computer program, the plurality of keywords; and (6) training, by the code annotation computer program, a code annotation model based on a similarity between the quantified data structure and a smoothing parameter for a Dirichlet prior smoothing estimate.
    Type: Application
    Filed: February 14, 2022
    Publication date: August 17, 2023
    Inventors: Sean MORAN, Sanat SAHA, Gaurav SINGH, Fanny SILAVONG, Antonios GEORGIADIS, Ganesh CHANDRASEKAR, Andy ALEXANDER, Rob OTTER, Brett SANFORD
  • Publication number: 20230229786
    Abstract: Systems and methods for federated model validation and data verification are disclosed. A method may include: (1) receiving, by a local computer program executed by client system, a federated machine learning model from a federated model server; (2) testing, by the local computer program and using a policy service, the federated machine learning model for vulnerabilities to attacks; (3) accepting, by the local computer program, the federated machine learning model in response to the federated machine learning model passing the testing; (4) training, by the local computer program, the federated machine learning model using input data comprising local data and outputting training parameters; (5) identifying, by the local computer program using the policy service, accidental leakage and/or contamination by comparing the training parameters to the input data; and (6) providing, by the local computer program, the training parameters to the federated model server.
    Type: Application
    Filed: January 19, 2023
    Publication date: July 20, 2023
    Inventors: Shaltiel ELOUL, Sean MORAN, Fanny SILAVONG, Sanket KAMTHE, Antonios GEORGIADIS
  • Publication number: 20230229930
    Abstract: Systems and methods for locality preserving federated learning are disclosed. In one embodiment, a method for locality preserving federated learning may include: (1) receiving, at an aggregator computer program and from each of a plurality of clients, weights for each client's local machine learning model; (2) generating, by the aggregator computer program, an averaged machine learning model based on the received weights; (3) sharing, by the aggregator computer program, the averaged machine learning model with the plurality of clients; and (4) applying, by each client, a scaling factor to the averaged machine learning model to update its local machine learning model.
    Type: Application
    Filed: January 17, 2023
    Publication date: July 20, 2023
    Inventors: Antonios GEORGIADIS, Fanny SILAVONG, Sean MORAN, Rob OTTER
  • Publication number: 20230185550
    Abstract: Systems and methods for detecting code duplication are disclosed. In one embodiment, a method for detecting exact code snippet duplicates may include: (1) representing, by a code duplication detection computer program, each of a plurality of code snippets in a codebase as an abstract syntax trees; (2) featurizing, by the code duplication detection computer program, the abstract syntax trees into corpus feature vectors by converting the abstract syntax tree into vector representations; (3) generating, by the code duplication detection computer program, dense feature vectors from the corpus feature vectors using a dimension reduction technique; (4) identifying, by the code duplication detection computer program, exact duplicate code snippet matches by apply density-based clustering to the dense feature vectors; and (5) tagging, by the code duplication detection computer program, the exact duplicate code snippets.
    Type: Application
    Filed: December 12, 2022
    Publication date: June 15, 2023
    Inventors: Rohan SAPHAL, Fanny SILAVONG, Sean MORAN, Antonios GEORGIADIS, Sanat SAHA, Gaurav SINGH, Pierre OSSELIN, Rob OTTER
  • Patent number: 11625423
    Abstract: Systems and methods for generating a fusion score between electronic documents. The method includes receiving a first electronic document by a document management system. The method further includes extracting a first set of features from the first electronic document including at least one feature type indicating the hierarchical structure of the first electronic document. The method also includes receiving a second electronic document by the document management server. The method further includes extracting a second set of features from the second electronic document including at least one feature type indicating the hierarchical structure of the second electronic document. The method further includes generating a fusion score based on a comparison of the first set of features and the second set of features.
    Type: Grant
    Filed: January 25, 2021
    Date of Patent: April 11, 2023
    Assignee: JPMORGAN CHASE BANK, N.A.
    Inventors: Sean Moran, Fanny Silavong, Rob Otter, Brett Sanford, Antonios Georgiadis, Sae Young Moon
  • Publication number: 20230070420
    Abstract: A method for facilitating identification of secrets in source code by using machine learning is provided. The method includes retrieving a plurality of files from a repository, each of the plurality of files including a source code file; parsing the source code file to identify a training feature; associating a predetermined label with the training feature, the predetermined label corresponding to a secret label and a non-secret label; training a model by using the training feature and the corresponding predetermined label; receiving, via a graphical user interface, a test file, the test file including a set of source codes; parsing the set of source codes to identify a feature; and determining, by using the model, a first characteristic of the feature.
    Type: Application
    Filed: September 16, 2021
    Publication date: March 9, 2023
    Applicant: JPMorgan Chase Bank, N.A.
    Inventors: Sean MORAN, Ahmad EMAMI, Fanny SILAVONG, Joachim FAINBERG, Ashish TIWARI, Antonios GEORGIADIS, Bill MORIARTY, Solomon Olaniyi ADEBAYO, Georgios PAPADOPOULOS, Rohan SAPHAL, Robert Falconer KEITH, Rob OTTER, Stephen HALL
  • Publication number: 20230064540
    Abstract: Systems and methods for federated secure vocabulary learning. The method may include communicating, by a first client network and a second client network and to a global federation terminal, a first set of encrypted tokens and a second set of encrypted tokens. The method may further include generating, by the global federation terminal, a consolidated vocabulary based on the first set of encrypted tokens and the second set of encrypted tokens. The method may include determining, a first set of weights and a second set of weights for the consolidated vocabulary. The method may include receiving by the global federation terminal the first set of weights and the second set of weights. The method may further include aggregating the first set of weights and the second set of weights. The method may further include distributing, to the first client network and the second client network, an aggregated set of weights.
    Type: Application
    Filed: February 16, 2022
    Publication date: March 2, 2023
    Inventors: Fanny SILAVONG, Antonios GEORGIADIS, Sean MORAN, Brett SANFORD, Rob OTTER
  • Publication number: 20230058972
    Abstract: Systems and methods for noise agnostic federated learning are disclosed. A method may include a client computer program executed by an electronic device in a federated learning computer network comprising a plurality of clients: receiving, from a federated learning computer program, a data format having desirable noise characteristics; transforming a client data set comprising variable noise characteristics to the data format using a client generative adversarial network (GAN); generating client weights for the transformed client data set, wherein the client weights indicate features of the client data set; communicating the client weights to the federated learning computer program; receiving, from the federated learning computer program, adjusted weights, wherein the adjusted weights are based on the client weights and a plurality client weights received from the clients in the federated learning computer network; and updating the client weights for a client machine learning model using the adjusted weights.
    Type: Application
    Filed: August 16, 2022
    Publication date: February 23, 2023
    Inventors: Antonios GEORGIADIS, Fanny SILAVONG, Sean MORAN, Rob OTTER, Varun BABBAR
  • Publication number: 20220237182
    Abstract: Systems and methods for generating a fusion score between electronic documents. The method includes receiving a first electronic document by a document management system. The method further includes extracting a first set of features from the first electronic document including at least one feature type indicating the hierarchical structure of the first electronic document. The method also includes receiving a second electronic document by the document management server. The method further includes extracting a second set of features from the second electronic document including at least one feature type indicating the hierarchical structure of the second electronic document. The method further includes generating a fusion score based on a comparison of the first set of features and the second set of features.
    Type: Application
    Filed: January 25, 2021
    Publication date: July 28, 2022
    Inventors: Fanny SILAVONG, Sean MORAN, Antonios GEORGIADIS, Rob OTTER, Brett SANFORD
  • Publication number: 20220100725
    Abstract: Systems and methods for counteracting data-skewness for locality sensitive hashing via feature selection and pruning are disclosed. In one embodiment, a method for feature selection for counteracting data skewness on locality sensitive hashing (LSH)-based search may include: (1) ingesting, by an ingestion computer program and from a plurality of data sources, data; (2) extracting, by the ingestion computer program, a plurality of features from the ingested data; (3) transforming, by the ingestion computer program, each of the plurality of features into a feature vector; (4) selecting, by the ingestion computer program, a subset of the plurality of features; and (5) for each selected feature vector: computing, by the ingestion computer program, a random hash function for the selected feature; and inserting, by the ingestion computer program, an output of the random hash function into a hash table with the selected feature.
    Type: Application
    Filed: September 27, 2021
    Publication date: March 31, 2022
    Inventors: Sean MORAN, Fanny SILAVONG, Rob OTTER, Antonios GEORGIADIS, Brett SANFORD