Patents by Inventor Bishwaranjan Bhattacharjee
Bishwaranjan Bhattacharjee 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).
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Patent number: 11934922Abstract: A computer system, product, and method are provided. The computer system includes an artificial intelligence (AI) platform operatively coupled to a processor. The AI platform includes tools in the form of a machine learning model (MLM) manager, a metric manager, and a training manager. The MLM manager accesses a plurality of pre-trained source MLMs, and inputs a plurality of data objects of a test dataset into each of the source MLMs. The test dataset includes the plurality of data objects associated with respective labels. For each source MLM, associated labels are generated from the inputted data objects and a similarity metric is calculated. The MLM manager selects a base MLM to be used for transfer learning from the plurality of source MLMs based upon the calculated similarity metric. The training manager trains the selected base MLM with a target dataset for the target domain.Type: GrantFiled: October 9, 2020Date of Patent: March 19, 2024Assignee: International Business Machines CorporationInventors: Parul Awasthy, Bishwaranjan Bhattacharjee, John Ronald Kender, Radu Florian, Hui Wan
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Patent number: 11853877Abstract: Whether to train a new neural network model can be determined based on similarity estimates between a sample data set and a plurality of source data sets associated with a plurality of prior-trained neural network models. A cluster among the plurality of prior-trained neural network models can be determined. A set of training data based on the cluster can be determined. The new neural network model can be trained based on the set of training data.Type: GrantFiled: April 2, 2019Date of Patent: December 26, 2023Assignee: International Business Machines CorporationInventors: Patrick Watson, Bishwaranjan Bhattacharjee, Siyu Huo, Noel Christopher Codella, Brian Michael Belgodere, Parijat Dube, Michael Robert Glass, John Ronald Kender, Matthew Leon Hill
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Patent number: 11853284Abstract: A method includes storing an anchor row vector identification for an anchor row to a local memory. It is determined whether the anchor row vector identification is visible based on isolation requirements. The anchor row vector identification is accessed upon a determination that the anchor row vector identification is visible, and the row vector identification is re-read from the local memory. It is determined whether the anchor row vector identification has not changed since a start of the accessing. Upon a determination that the anchor row vector identification has not changed, read anchor row fields are returned. A first check history is performed on an anchor row history tuple sequence number (TSN) for the anchor row.Type: GrantFiled: August 29, 2019Date of Patent: December 26, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ronald J. Barber, Bishwaranjan Bhattacharjee, Mohammad Sadoghi Hamedani, Guy M. Lohman, Chandrasekaran Mohan, Vijayshankar Raman, Richard S. Sidle, Adam J. Storm, Xun Xue
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Publication number: 20230326186Abstract: An automated data labeling method, system, and computer program product that includes composing a semantically-named anchor vector derived from a source dataset into a sequence that defines a location description for target data items based on a generalization of distances into Cayley-Menger content and outputting a label for a target data item based on the location description.Type: ApplicationFiled: March 28, 2022Publication date: October 12, 2023Inventors: Parijat Dube, John Ronald Kender, Bishwaranjan Bhattacharjee, Brian Michael Belgodere
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Publication number: 20230259716Abstract: A neural architecture search method, system, and computer program product that determines, by a computing device, a best fit language model of a plurality of language models that is a best fit for interpretation of a corpus of natural language and interprets, by the computing device, the corpus of natural language using the best fit language model.Type: ApplicationFiled: February 14, 2022Publication date: August 17, 2023Inventors: Michele Merler, Aashka Trivedi, Rameswar Panda, Bishwaranjan Bhattacharjee, Taesun Moon, Avirup Sil
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Patent number: 11568235Abstract: Embodiments for implementing mixed precision learning for neural networks by a processor. A neural network may be replicated into a plurality of replicated instances and each of the plurality of replicated instances differ in precision used for representing and determining parameters of the neural network. Data instances may be routed to one or more of the plurality of replicated instances for processing according to a data pre-processing operation.Type: GrantFiled: November 19, 2018Date of Patent: January 31, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Zehra Sura, Parijat Dube, Bishwaranjan Bhattacharjee, Tong Chen
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Patent number: 11475032Abstract: A method, system and computer program product for analyzing multidimensional data are disclosed. In embodiments, the method comprises obtaining an original set of data having a sequential order and multiple original dimensions; selecting a topic-based summarization scheme to summarize the original set of data; and applying the selected topic-based summarization scheme to the original set of data to transform the original set of data into a new set of data having fewer dimensions than the original set of data, while preserving, within a defined measure, the sequential order of the original set of data. In embodiments, the selecting a topic-based summarization scheme includes selecting a plurality of topics, each of the topic representing a set of the original dimensions. In embodiments, the applying the topic-based summarization scheme includes performing dimensionality reduction on the original set of data to transform the original dimensions to the topics.Type: GrantFiled: March 13, 2019Date of Patent: October 18, 2022Assignee: International Business Machines CorporationInventors: Vatche Isahagian, Vinod Muthusamy, Phuong Nguyen, Aleksander Slominski, Bishwaranjan Bhattacharjee
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Publication number: 20220114473Abstract: A computer system, product, and method are provided. The computer system includes an artificial intelligence (AI) platform operatively coupled to a processor. The AI platform includes tools in the form of a machine learning model (MLM) manager, a metric manager, and a training manager. The MLM manager accesses a plurality of pre-trained source MLMs, and inputs a plurality of data objects of a test dataset into each of the source MLMs. The test dataset includes the plurality of data objects associated with respective labels. For each source MLM, associated labels are generated from the inputted data objects and a similarity metric is calculated. The MLM manager selects a base MLM to be used for transfer learning from the plurality of source MLMs based upon the calculated similarity metric. The training manager trains the selected base MLM with a target dataset for the target domain.Type: ApplicationFiled: October 9, 2020Publication date: April 14, 2022Applicant: International Business Machines CorporationInventors: Parul Awasthy, Bishwaranjan Bhattacharjee, John Ronald Kender, Radu Florian, Hui Wan
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Patent number: 11295239Abstract: Techniques for distributing the training of machine learning models across a plurality of computing devices are presented. An example method includes receiving, from a computing device in a distributed computing environment, a request for a set of outstanding jobs for training part of a machine learning model. A system transmits, to the computing device, information identifying the set of outstanding jobs. The system receives, from the computing device, a selected job for execution on the computing device from the set of outstanding jobs. A chunk of training data associated with the selected job and one or more parameters associated with the selected job may be transmitted to the computing device, and the system may take one or more actions with respect to the chunk of data associated with the selected job based on a response from the computing device.Type: GrantFiled: April 17, 2019Date of Patent: April 5, 2022Assignee: International Business Machines CorporationInventors: Bishwaranjan Bhattacharjee, Paul C. Castro, Vatche Isahagian, Vinod Muthusamy, Aleksander Slominski
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Patent number: 11151410Abstract: A computer-implemented method for data labeling is provided. The computer-implemented method assigns pseudo-labels to unlabeled examples of data using a similarity metric on an embedding space to produce pseudo-labeled examples. A curriculum learning model is trained using the pseudo-labeled examples. The curriculum learning model trained with the pseudo-labeled examples is employed in in a fine-tuning task to enhance classification accuracy of the data.Type: GrantFiled: September 7, 2018Date of Patent: October 19, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Patrick Watson, Bishwaranjan Bhattacharjee, Siyu Huo, Noel C. Codella, Brian M. Belgodere, Parijat Dube, Michael R. Glass, John R. Kender, Matthew L. Hill
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Publication number: 20210174191Abstract: In an embodiment, a method for fine-tuning a pre-trained neural network for transfer learning, the method comprising obtaining a first target feature vector from a first layer of a pre-trained neural network responsive to a first target data element of a target dataset passing therethrough, obtaining a first source feature vector associated with the first layer of the pre-trained neural network, calculating a first divergence value for the first layer of the pre-trained neural network based at least in part on the first target feature vector and the first source feature vector, and setting a learning rate for the first layer of the pre-trained neural network based at least in part on the first divergence value.Type: ApplicationFiled: December 5, 2019Publication date: June 10, 2021Applicant: International Business Machines CorporationInventors: Parijat Dube, Bishwaranjan Bhattacharjee, Patrick Watson, John Ronald Kender
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Patent number: 11030483Abstract: Aspects include a system, computer program production and computer-implemented method for tagging an image. An image classification engine stored in a memory of a computer device generates a plurality of tags for the image and uses the plurality of tags to generate a relevance subgraph for the image. An embedding engine embeds nodes and edges of the relevance subgraph into fixed dimension vectors of a matrix. A neural network stored in the memory determines a feature vector from the image. A processor applies the feature vector to the matrix to generate a context vector for the image. The context vector is used to tag the image.Type: GrantFiled: August 7, 2018Date of Patent: June 8, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Bishwaranjan Bhattacharjee, Tushar Nagarajan
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Publication number: 20210133596Abstract: A system for ranking machine learning base models for transfer learning purposes is described. The system receives image data in the form an image or an image set and extracts image tags from the images. The image tags are expanded into a set of associated terms using a word embedding database and model. The associated terms are used to query a knowledge database for parent or categorical terms used to rank various matching machine learning base models that may be improved or trained by the image data.Type: ApplicationFiled: October 30, 2019Publication date: May 6, 2021Inventors: MUSTAFA CANIM, Bishwaranjan Bhattacharjee, Alfio Massimiliano Gliozzo
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Publication number: 20200334567Abstract: Techniques for distributing the training of machine learning models across a plurality of computing devices are presented. An example method includes receiving, from a computing device in a distributed computing environment, a request for a set of outstanding jobs for training part of a machine learning model. A system transmits, to the computing device, information identifying the set of outstanding jobs. The system receives, from the computing device, a selected job for execution on the computing device from the set of outstanding jobs. A chunk of training data associated with the selected job and one or more parameters associated with the selected job may be transmitted to the computing device, and the system may take one or more actions with respect to the chunk of data associated with the selected job based on a response from the computing device.Type: ApplicationFiled: April 17, 2019Publication date: October 22, 2020Inventors: Bishwaranjan Bhattacharjee, Paul C. Castro, Vatche Isahagjan, Vinod Muthusamy, Aleksander Slominski
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Publication number: 20200320379Abstract: Whether to train a new neural network model can be determined based on similarity estimates between a sample data set and a plurality of source data sets associated with a plurality of prior-trained neural network models. A cluster among the plurality of prior-trained neural network models can be determined. A set of training data based on the cluster can be determined. The new neural network model can be trained based on the set of training data.Type: ApplicationFiled: April 2, 2019Publication date: October 8, 2020Inventors: Patrick Watson, Bishwaranjan Bhattacharjee, Siyu Huo, Noel Christopher Codella, Brian Michael Belgodere, Parijat Dube, Michael Robert Glass, John Ronald Kender, Matthew Leon Hill
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Publication number: 20200293540Abstract: A method, system and computer program product for analyzing multidimensional data are disclosed. In embodiments, the method comprises obtaining an original set of data having a sequential order and multiple original dimensions; selecting a topic-based summarization scheme to summarize the original set of data; and applying the selected topic-based summarization scheme to the original set of data to transform the original set of data into a new set of data having fewer dimensions than the original set of data, while preserving, within a defined measure, the sequential order of the original set of data. In embodiments, the selecting a topic-based summarization scheme includes selecting a plurality of topics, each of the topic representing a set of the original dimensions. In embodiments, the applying the topic-based summarization scheme includes performing dimensionality reduction on the original set of data to transform the original dimensions to the topics.Type: ApplicationFiled: March 13, 2019Publication date: September 17, 2020Inventors: Vatche Isahagian, Vinod Muthusamy, Phuong Nguyen, Aleksander Slominski, Bishwaranjan Bhattacharjee
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Patent number: 10754842Abstract: Methods and systems for performing database transactions include executing a first transaction request in a preplay mode that locks the requested data with a prefetch-lock and reads one or more requested data items from storage into a main memory buffer; locking the requested data items with a read/write lock after said data items are read into the main memory buffer; and performing the requested transaction on the data items in the main memory buffer using a processor.Type: GrantFiled: June 13, 2014Date of Patent: August 25, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Bishwaranjan Bhattacharjee, Mustafa Canim, Mohammad Sadoghi Hamedani, Kenneth A. Ross
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Publication number: 20200257980Abstract: In an embodiment, a method includes training a neural network model with a first set of training data. In an embodiment, the method includes calculating divergence for a set of layers of the neural network model, the set of layers comprising at least one batch norm layer. In an embodiment, the method includes analyzing, based on the calculated divergence, a stability of each of the set of layers. In an embodiment, the method includes removing, based on the analysis determining a subset of the set of layers fails to meet a threshold stability, the subset of the set of layers of the neural network model.Type: ApplicationFiled: February 8, 2019Publication date: August 13, 2020Applicant: International Business Machines CorporationInventors: Bishwaranjan Bhattacharjee, Parag Chandakkar, John R. Smith
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Patent number: 10731878Abstract: A computer-implemented mitigation and warning method, system, and computer program product including detecting an enclosure temperature via an enclosure temperature probe and activating, via a controller, a thermoelectric device when the enclosure temperature exceeds a predetermined threshold temperature to transfer heat between an enclosure and a phase change material (PCM) to cause the PCM to change phase and cool the enclosure.Type: GrantFiled: November 22, 2017Date of Patent: August 4, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ayushmaan Aggarwal, Bishwaranjan Bhattacharjee, Niharika Bhattacharjee, Aadi Gupta Bhattacharya, Raka Bose, Anshul Gupta, Deepta Bhattacharya Gupta, Renuka Muralidhar, Elina Rani
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Publication number: 20200160169Abstract: Embodiments for implementing mixed precision learning for neural networks by a processor. A neural network may be replicated into a plurality of replicated instances and each of the plurality of replicated instances differ in precision used for representing and determining parameters of the neural network. Data instances may be routed to one or more of the plurality of replicated instances for processing according to a data pre-processing operation.Type: ApplicationFiled: November 19, 2018Publication date: May 21, 2020Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Zehra SURA, Parijat DUBE, Bishwaranjan BHATTACHARJEE, Tong CHEN