Patents by Inventor Samuel Pierce Burns
Samuel Pierce Burns 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: 20240160698Abstract: System and methods for generating synthetic data according to a plurality of options are provided. According to a first option, synthetic data is generated based on an indication of one or more target distributions. According to a second option, a one or more target distributions are determined based on a sample data set, and synthetic data is generated based on the determined target distribution(s). According to a third option, a temporary synthetic data set is generated based on an indication of one or more target distributions, matches between the temporary synthetic data set and a sample data set are then identified, and a synthetic data set is then generated based on the identified matches. A system may automatically determine which option to leverage based on whether a user provides a sample data set, one or more target distributions, or both.Type: ApplicationFiled: November 7, 2022Publication date: May 16, 2024Applicant: PwC Product Sales LLCInventors: Zhen QI, Mansi ARORA, Bo LI, Yifeng WANG, Samuel Pierce BURNS, Joseph David VOYLES, Anand Srinivasa RAO
-
Publication number: 20240118867Abstract: Disclosed herein are methods and systems for generating a merged dataset, comprising: accessing data comprising a core dataset and an additional dataset; identifying a plurality of common attributes between the core dataset and the additional dataset; determining a plurality of similarity scores between an inquiring entity in the core dataset and a plurality of candidate entities in the additional dataset, including, for each candidate entity of the plurality of candidate entities: calculating a similarity score for the candidate entity based at least in part on a distance-based score and a weight influence score; selecting one or more matches for the inquiring entity in the core dataset from the plurality of candidate entities in the additional dataset based at least in part on the plurality of similarity scores; and generating the merged dataset by adding the one or more selected matches for the inquiring entity to the core dataset.Type: ApplicationFiled: September 30, 2022Publication date: April 11, 2024Applicant: PricewaterhouseCoopers LLPInventors: Zhen QI, Xingyi YU, Samuel Pierce BURNS, Sierra HAWTHORNE, Shannon SMITH, Joseph David VOYLES, Anand Srinivasa RAO
-
Publication number: 20230052327Abstract: Described are methods and systems for calibrating simulation models to generate digital twins for physical entities. In some embodiments, a method includes receiving a plurality of datasets for a plurality of corresponding physical entities. A calibration request is enqueued to a calibration requests queue for each received dataset and includes information indicating a dataset and a corresponding physical entity. A plurality of calibration engines and a plurality of corresponding simulation clusters for generating a plurality of calibration results for a plurality of calibration requests dequeued from the calibration requests queue can be deployed.Type: ApplicationFiled: November 3, 2022Publication date: February 16, 2023Applicant: PricewaterhouseCoopers LLPInventors: Sai Phanindra VENKATAPURAPU, Mrinal Kanti MANDAL, Jerome Patrick OFFNER, Rakesh Vidya Chandra KAPILA, Gaurav DWIVEDI, Qian CHEN, Julia Hui-ling CHEN, Samuel Pierce BURNS, Paul M. D'ALESSANDRO
-
Patent number: 11564637Abstract: Systems and methods for health and body simulations in order to predict numerous physiological parameters in a subject or a population of subjects based on the input of limited physiological data.Type: GrantFiled: August 1, 2019Date of Patent: January 31, 2023Assignee: PricewaterhouseCoopers LLPInventors: Paul M. D'Alessandro, Mark Paich, Samuel Pierce Burns, Joydeep Sarkar, Gaurav Dwivedi, Colleen Chelini
-
Patent number: 11564635Abstract: Systems and methods for health and body simulations in order to predict numerous physiological parameters in a subject or a population of subjects based on the input of limited physiological data.Type: GrantFiled: August 1, 2019Date of Patent: January 31, 2023Assignee: PricewaterhouseCoopers LLPInventors: Paul M. D'Alessandro, Mark Paich, Samuel Pierce Burns, Joydeep Sarkar, Gaurav Dwivedi, Colleen Chelini
-
Patent number: 11564638Abstract: Systems and methods for health and body simulations in order to predict numerous physiological parameters in a subject or a population of subjects based on the input of limited physiological data.Type: GrantFiled: August 1, 2019Date of Patent: January 31, 2023Assignee: PricewaterhouseCoopers LLPInventors: Paul M. D'Alessandro, Mark Paich, Samuel Pierce Burns, Joydeep Sarkar, Gaurav Dwivedi, Colleen Chelini
-
Patent number: 11564636Abstract: Systems and methods for health and body simulations in order to predict numerous physiological parameters in a subject or a population of subjects based on the input of limited physiological data.Type: GrantFiled: August 1, 2019Date of Patent: January 31, 2023Assignee: PricewaterhouseCoopers LLPInventors: Paul M. D'Alessandro, Mark Paich, Samuel Pierce Burns, Joydeep Sarkar, Gaurav Dwivedi, Colleen Chelini
-
Patent number: 11520957Abstract: Described are methods and systems for calibrating simulation models to generate digital twins for physical entities. In some embodiments, a method includes receiving a plurality of datasets for a plurality of corresponding physical entities. A calibration request is enqueued to a calibration requests queue for each received dataset and includes information indicating a dataset and a corresponding physical entity. A plurality of calibration engines and a plurality of corresponding simulation clusters for generating a plurality of calibration results for a plurality of calibration requests dequeued from the calibration requests queue can be deployed.Type: GrantFiled: May 14, 2020Date of Patent: December 6, 2022Assignee: PricewaterhouseCoopers LLPInventors: Sai Phanindra Venkatapurapu, Mrinal Kanti Mandal, Jerome Patrick Offner, Rakesh Vidya Chandra Kapila, Gaurav Dwivedi, Qian Chen, Julia Hui-ling Chen, Samuel Pierce Burns, Paul M. D'Alessandro
-
Publication number: 20210357556Abstract: Described are methods and systems for calibrating simulation models to generate digital twins for physical entities. In some embodiments, a method includes receiving a plurality of datasets for a plurality of corresponding physical entities. A calibration request is enqueued to a calibration requests queue for each received dataset and includes information indicating a dataset and a corresponding physical entity. A plurality of calibration engines and a plurality of corresponding simulation clusters for generating a plurality of calibration results for a plurality of calibration requests dequeued from the calibration requests queue can be deployed.Type: ApplicationFiled: May 14, 2020Publication date: November 18, 2021Applicant: PricewaterhouseCoopers LLPInventors: Sai Phanindra VENKATAPURAPU, Mrinal Kanti MANDAL, Jerome Patrick OFFNER, Rakesh Vidya Chandra KAPILA, Gaurav DWIVEDI, Qian CHEN, Julia Hui-ling CHEN, Samuel Pierce BURNS, Paul M. D'ALESSANDRO
-
Patent number: 10398389Abstract: Systems and methods for health and body simulations in order to predict numerous physiological parameters in a subject or a population of subjects based on the input of limited physiological data.Type: GrantFiled: April 11, 2016Date of Patent: September 3, 2019Assignee: PricewaterhouseCoopers LLPInventors: Paul M. D'Alessandro, Mark Paich, Samuel Pierce Burns, Joydeep Sarkar, Gaurav Dwivedi, Colleen Chelini