Patents by Inventor Rajesh Vellore ARUMUGAM

Rajesh Vellore ARUMUGAM 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: 20250252554
    Abstract: Methods, systems, and computer-readable storage media for a defect detection system that generates synthetic defect data that is representative of real-world defects in products. The synthetic defect data is included in training data for training a defect detection model. The defect detection model is deployed to a production environment to detect defects in products.
    Type: Application
    Filed: February 6, 2024
    Publication date: August 7, 2025
    Inventors: Yinan He, Xinyan Chen, Rajesh Vellore Arumugam, Anantharaman Ravi
  • Publication number: 20250147989
    Abstract: Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.
    Type: Application
    Filed: January 9, 2025
    Publication date: May 8, 2025
    Inventors: Sundeep Gullapudi, Rajesh Vellore Arumugam, Matthias Frank, Wei Xia
  • Patent number: 12277148
    Abstract: Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.
    Type: Grant
    Filed: April 19, 2022
    Date of Patent: April 15, 2025
    Assignee: SAP SE
    Inventors: Sundeep Gullapudi, Rajesh Vellore Arumugam, Matthias Frank, Wei Xia
  • Publication number: 20250117663
    Abstract: Methods, systems, and computer-readable storage media for training a global matching ML model using a set of enterprise data associated with a set of enterprises, receiving a subset of enterprise data associated with an enterprise that is absent from the set of enterprises, fine tuning the global matching ML model using the subset of enterprise data to provide a fine-tuned matching ML model. deploying the fine-tuned matching ML model for inference, receiving feedback to one or more inference results generated by the fine-tuned matching ML model, receiving synthetic data from a LLM system in response to at least a portion of the feedback, and fine tuning one or more of the global matching ML model and the fine-tuned ML model using the synthetic data.
    Type: Application
    Filed: October 4, 2023
    Publication date: April 10, 2025
    Inventors: Rajesh Vellore Arumugam, Donglin Ruan, Matthias Frank, Yi Quan Zhou
  • Publication number: 20250077773
    Abstract: Methods, systems, and computer-readable storage media for receiving, by an entity matching ML model, a query and target pair including a query entity and a target entity, providing, by the entity matching ML model, a query-target prediction by processing the query entity and the target entity, the query-target prediction indicating a match type between the query entity and the target entity, generating a prompt by populating a prompt template with at least a portion of the query-target prediction, inputting the prompt into a large language model (LLM), and receiving, from the LLM, an explanation that is responsive to the prompt and that describes one or more reasons for the query-target prediction output by the entity matching ML model.
    Type: Application
    Filed: July 25, 2023
    Publication date: March 6, 2025
    Inventors: Rajesh Vellore Arumugam, Anantharaman Ravi, Matthias Frank, Sundeep Gullapudi, Yi Quan Zhou
  • Publication number: 20250068965
    Abstract: Methods, systems, and computer-readable storage media for receiving a real data table, providing a synthetic structured table based on the real data table, providing a sampled data table comprising a sub-set of real data of the real data table, transmitting a prompt to a LLM system, the prompt being generated based on the real data table and the synthetic structured data table, receiving synthetic unstructured data from the LLM system, providing an aggregate synthetic table that includes at least a portion of the synthetic unstructured data, and training a ML model using the aggregate synthetic table.
    Type: Application
    Filed: August 25, 2023
    Publication date: February 27, 2025
    Inventors: Matthias Frank, Sundeep Gullapudi, Rajesh Vellore Arumugam, Anantharaman Ravi, Prawira Putra Fadjar, Yi Quan Zhou
  • Publication number: 20250036974
    Abstract: Methods, systems, and computer-readable storage media for providing, for a set of ML models, a set of training metrics determined using test data during a training phase, providing, for a production-use ML model, a set of inference metrics based on predictions generated by the production-use ML model, generating, by a prompt generator, a set of few-shot examples using the set of training metrics and the set of inference metrics, inputting, by the prompt generator, the set of few-shot examples to a LLM as prompts, transmitting, to the LLM a query, displaying, to a user, a recommendation that is received from the LLM and responsive to the query, receiving input from a user indicating a user-selected ML model responsive to the recommendation, and deploying a user-selected ML model to an inference runtime for production use.
    Type: Application
    Filed: July 25, 2023
    Publication date: January 30, 2025
    Inventors: Rajesh Vellore Arumugam, Anantharaman Ravi, Isaac New Yi Qing, Sundeep Gullapudi, Yi Quan Zhou
  • Patent number: 12093300
    Abstract: Methods, systems, and computer-readable storage media for receiving a first document including structured data and unstructured data, providing a first sub-document and a second sub-document, the first sub-document including the structured data of the first document, the second sub-document including the unstructured data of the first document, generating a prompt using the second sub-document and a second document, inputting the prompt to a LLM, receiving a response from the LLM, providing a calibrated first document by merging the response into the first sub-document, and processing the calibrated first document and the second document using a ML model to provide a prediction, the prediction indicating a matching class between the first document and the second document.
    Type: Grant
    Filed: September 8, 2023
    Date of Patent: September 17, 2024
    Assignee: SAP SE
    Inventors: Yi Quan Zhou, Rajesh Vellore Arumugam, Raja Sekhar Juluri, Xingce Bao, Eshwin Sukhdeve
  • Publication number: 20240177053
    Abstract: Methods, systems, and computer-readable storage media for receiving query data representative of query entities and target data representative of target entities, determining, by an attention ML model, a set of character-level embeddings, providing, by a sub-word-level tokenizer, a set of sub-word-level tokens, each sub-word-level token including a string of multiple characters, generating, by the attention ML model, a set of sub-word-level embeddings based on the set of sub-word-level tokens, providing, by the attention ML model, at least one attention matrix including attention scores, each attention score representative of a relative importance of a respective sub-word-level token in a predicted match, the predicted match including a match between a query entity and a target entity, and outputting an explanation based on the at least one attention matrix.
    Type: Application
    Filed: November 29, 2022
    Publication date: May 30, 2024
    Inventors: Sundeep Gullapudi, Rajesh Vellore Arumugam, Abhinandan Padhi
  • Publication number: 20240045890
    Abstract: Methods, systems, and computer-readable storage media for a machine learning (ML) system for matching a query entity to one or more target entities, the ML system that reducing a number of query-target entity pairs from consideration as potential matches during inference.
    Type: Application
    Filed: August 4, 2022
    Publication date: February 8, 2024
    Inventors: Hoang-Vu Nguyen, Li Rong Wang, Matthias Frank, Rajesh Vellore Arumugam, Stefan Klaus Baur, Sundeep Gullapudi
  • Publication number: 20230334070
    Abstract: Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.
    Type: Application
    Filed: April 19, 2022
    Publication date: October 19, 2023
    Inventors: Sundeep Gullapudi, Rajesh Vellore Arumugam, Matthias Frank, Wei Xia
  • Publication number: 20230222147
    Abstract: Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.
    Type: Application
    Filed: January 10, 2022
    Publication date: July 13, 2023
    Inventors: Hoang-Vu Nguyen, Rajesh Vellore Arumugam, Matthias Frank, Stefan Klaus Baur
  • Publication number: 20230214456
    Abstract: Methods, systems, and computer-readable storage media for receiving a first set of predictions generated by a ML model during execution of a training pipeline to train the ML model, each prediction in the first set of predictions being associated with a confidence, determining a set of confidence bins based on confidences of the first set of predictions, for each confidence bin in the set of confidence bins, providing an accuracy, processing the set of confidence bins and accuracies through a regression model to provide one or more regressions, each regression representing a confidence-to-accuracy relationship, defining a set of confidence thresholds based on at least one regression of the one or more regressions, and during an inference phase, applying the set of confidence thresholds to selectively filter predictions from a second set of predictions generated by the ML model.
    Type: Application
    Filed: January 4, 2022
    Publication date: July 6, 2023
    Inventors: Sundeep Gullapudi, Rajesh Vellore Arumugam, Anantharaman Ravi, Prawira Putra Fadjar, Wei Xia
  • Patent number: 11687575
    Abstract: Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.
    Type: Grant
    Filed: January 10, 2022
    Date of Patent: June 27, 2023
    Assignee: SAP SE
    Inventors: Hoang-Vu Nguyen, Rajesh Vellore Arumugam, Matthias Frank, Stefan Klaus Baur
  • Publication number: 20230128485
    Abstract: Methods, systems, and computer-readable storage media for receiving IRF data sets, the IRF data sets including a set of records including inference results determined by the ML model during production use of the ML model and at least one correction to an inference result, executing incremental training of the ML model to provide an updated ML model by selectively filtering one or more records of the set of records to adjust a negative sample to positive sample proportion of a sub-set of records based on a negative sample to positive sample proportion of initial training of the ML model, for each record in the sub-set of records, determining a weight, and during incremental training, applying the weight of a respective record being in a loss function in determining an accuracy of the ML model based on the respective record, and deploying the updated ML model for production use.
    Type: Application
    Filed: October 27, 2021
    Publication date: April 27, 2023
    Inventors: Rajesh Vellore Arumugam, Andrew Ivan Soegeng, Stefan Klaus Baur
  • Patent number: 10996641
    Abstract: Embodiments provide a system for controlling HVAC/ACMV system of a building, including an occupancy pattern extractor configured to generate at least one facility-based occupancy pattern for each facility type based on historical occupancy data and spatial information of the building; a zone occupancy predictor configured to predict zone occupancy variation of each zone after a predetermined time period, based on the facility-based occupancy patterns and real-time occupancy data; a similar zone matcher configured to match each zone with one or more pre-stored zones and determine air handler configurations based on the matched pre-stored zones; a configuration generator configured to determine configuration combinations by combining the air handler configurations for a plurality of zones of the building, each configuration combination including one of the air handler configurations for each zone; and a configuration optimizer configured to determine an optimal configuration combination based on one or more key
    Type: Grant
    Filed: October 2, 2017
    Date of Patent: May 4, 2021
    Assignee: HITACHI, LTD.
    Inventors: Yang Yan, Rajesh Vellore Arumugam, Wujuan Lin
  • Publication number: 20210080915
    Abstract: Embodiments provide a system for controlling HVAC/ACMV system of a building, including an occupancy pattern extractor configured to generate at least one facility-based occupancy pattern for each facility type based on historical occupancy data and spatial information of the building; a zone occupancy predictor configured to predict zone occupancy variation of each zone after a predetermined time period, based on the facility-based occupancy patterns and real-time occupancy data; a similar zone matcher configured to match each zone with one or more pre-stored zones and determine air handler configurations based on the matched pre-stored zones; a configuration generator configured to determine configuration combinations by combining the air handler configurations for a plurality of zones of the building, each configuration combination including one of the air handler configurations for each zone; and a configuration optimizer configured to determine an optimal configuration combination based on one or more key
    Type: Application
    Filed: October 2, 2017
    Publication date: March 18, 2021
    Applicant: Hitachi, Ltd.
    Inventors: Yang YAN, Rajesh Vellore ARUMUGAM, Wujuan LIN
  • Publication number: 20200193511
    Abstract: Methods, systems, and computer-readable storage media for receiving, by a machine learning (ML) platform, a set of invoices including two or more invoices, processing, by the ML platform, each invoice through a neural network to provide respective invoice embeddings, each invoice embedding including a multi-dimensional vector, comparing, by the ML platform, invoice embeddings to define two or more super-invoices, each super-invoice including a sub-set of the set of invoices, and matching a bank statement to a super-invoice of the two or more super-invoices.
    Type: Application
    Filed: December 12, 2018
    Publication date: June 18, 2020
    Inventors: Sean Saito, Chaitanya Krishna Joshi, Rajalingappaa Shanmugamani, Truc Viet Le, Rajesh Vellore Arumugam
  • Patent number: 10440679
    Abstract: According to various embodiments, there is provided a passenger load prediction system including: a component configured to detect a wireless device carried by a passenger on a train or a train station platform, the train including a plurality of train cars; a passenger to train car mapper configured to determine a location of the passenger, based on a location of the wireless device; a destination predictor configured to predict a destination of the passenger, based at least in part on an identifier code of the wireless device; and a train car load level estimator configured to predict a respective passenger load of each train car of the plurality of train cars, based on the predicted destination and further based on the determined location of the passenger.
    Type: Grant
    Filed: July 27, 2016
    Date of Patent: October 8, 2019
    Assignee: HITACHI, LTD.
    Inventors: Rajesh Vellore Arumugam, Wujuan Lin, Yutaka Kudo, Su Hnin Wut Yi
  • Publication number: 20190124619
    Abstract: According to various embodiments, there is provided a passenger load prediction system including: a component configured to detect a wireless device carried by a passenger on a train or a train station platform, the train including a plurality of train cars; a passenger to train car mapper configured to determine a location of the passenger, based on a location of the wireless device; a destination predictor configured to predict a destination of the passenger, based at least in part on an identifier code of the wireless device; and a train car load level estimator configured to predict a respective passenger load of each train car of the plurality of train cars, based on the predicted destination and further based on the determined location of the passenger.
    Type: Application
    Filed: July 27, 2016
    Publication date: April 25, 2019
    Applicant: Hitachi, Ltd.
    Inventors: Rajesh Vellore ARUMUGAM, Wujuan LIN, Yutaka KUDO, Su Hnin Wut YI