Patents by Inventor Devavrat D. Shah
Devavrat D. Shah 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: 11775608Abstract: A system and method model a time series from missing data by imputing missing values, denoising measured but noisy values, and forecasting future values of a single time series. A time series of potentially noisy, partially-measured values of a physical process is represented as a non-overlapping matrix. For several classes of common model functions, it can be proved that the resulting matrix has a low rank or approximately low rank, allowing a matrix estimation technique, for example singular value thresholding, to be efficiently applied. Applying such a technique produces a mean matrix that estimates latent values, of the physical process at times or intervals corresponding to measurements, with less error than previously known methods. These latent values have been denoised (if noisy) and imputed (if missing). Linear regression of the estimated latent values permits forecasting with an error that decreases as more measurements are made.Type: GrantFiled: July 7, 2022Date of Patent: October 3, 2023Assignee: Massachusetts Institute of TechnologyInventors: Devavrat D. Shah, Anish Agarwal, Muhammad Amjad, Dennis Shen
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Publication number: 20220414483Abstract: A computer-implemented method includes: identifying, from first and second data, interventions common to a target unit and one or more of a plurality of donor units as filtered donor units, the first data corresponding to the target unit under one or more interventions, the second data corresponding to the plurality of donor units each under one or more interventions; identifying, from the first data, third data corresponding to the target unit under the common interventions; identifying, from the second data, fourth data corresponding to the filtered donor units under the common interventions; identifying, from the second data, fifth data corresponding to the filtered donor units under a subject intervention; generating, from the third and fourth data, a learned model representing to a relationship between the target unit and the filtered donor units; applying the learned model to the fifth data to generate the synthetic data; and outputting the synthetic data.Type: ApplicationFiled: April 11, 2022Publication date: December 29, 2022Applicant: Massachusetts Institute of TechnologyInventors: Dennis Shen, Devavrat D. Shah, Anish Agarwal
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Publication number: 20220366009Abstract: A system and method model a time series from missing data by imputing missing values, denoising measured but noisy values, and forecasting future values of a single time series. A time series of potentially noisy, partially-measured values of a physical process is represented as a non-overlapping matrix. For several classes of common model functions, it can be proved that the resulting matrix has a low rank or approximately low rank, allowing a matrix estimation technique, for example singular value thresholding, to be efficiently applied. Applying such a technique produces a mean matrix that estimates latent values, of the physical process at times or intervals corresponding to measurements, with less error than previously known methods. These latent values have been denoised (if noisy) and imputed (if missing). Linear regression of the estimated latent values permits forecasting with an error that decreases as more measurements are made.Type: ApplicationFiled: July 7, 2022Publication date: November 17, 2022Applicant: Massachusetts Institute of TechnologyInventors: Devavrat D. SHAH, Anish AGARWAL, Muhammad AMJAD, Dennis SHEN
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Patent number: 11423118Abstract: A system and method model a time series from missing data by imputing missing values, denoising measured but noisy values, and forecasting future values of a single time series. A time series of potentially noisy, partially-measured values of a physical process is represented as a non-overlapping matrix. For several classes of common model functions, it can be proved that the resulting matrix has a low rank or approximately low rank, allowing a matrix estimation technique, for example singular value thresholding, to be efficiently applied. Applying such a technique produces a mean matrix that estimates latent values, of the physical process at times or intervals corresponding to measurements, with less error than previously known methods. These latent values have been denoised (if noisy) and imputed (if missing). Linear regression of the estimated latent values permits forecasting with an error that decreases as more measurements are made.Type: GrantFiled: January 7, 2019Date of Patent: August 23, 2022Assignee: Massachusetts Institute of TechnologyInventors: Devavrat D. Shah, Anish Agarwal, Muhammad Amjad, Dennis Shen
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Patent number: 11055157Abstract: A method is disclosed including: receiving a graph-based program that identifies a bipartite graph and one or more update function sets, the bipartite graph including a plurality of graph nodes and a plurality of edges, such that each graph node corresponds to one of the update function sets; associating each of a plurality of computing units with a different respective one of the graph nodes; instantiating, by a Publisher Subscriber platform, a plurality of channels, the plurality of channels defining a topology that matches a topology of the bipartite graph; and executing the graph-based program based on the plurality of channels to produce a result.Type: GrantFiled: November 14, 2019Date of Patent: July 6, 2021Assignee: Massachusetts Institute of TechnologyInventors: Devavrat D. Shah, Vinayak Ramesh
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Publication number: 20200218776Abstract: A system and method model a time series from missing data by imputing missing values, denoising measured but noisy values, and forecasting future values of a single time series. A time series of potentially noisy, partially-measured values of a physical process is represented as a non-overlapping matrix. For several classes of common model functions, it can be proved that the resulting matrix has a low rank or approximately low rank, allowing a matrix estimation technique, for example singular value thresholding, to be efficiently applied. Applying such a technique produces a mean matrix that estimates latent values, of the physical process at times or intervals corresponding to measurements, with less error than previously known methods. These latent values have been denoised (if noisy) and imputed (if missing). Linear regression of the estimated latent values permits forecasting with an error that decreases as more measurements are made.Type: ApplicationFiled: January 7, 2019Publication date: July 9, 2020Inventors: Devavrat D. SHAH, Anish AGARWAL, Muhammad AMJAD, Dennis SHEN
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Publication number: 20200167214Abstract: A method is disclosed including: receiving a graph-based program that identifies a bipartite graph and one or more update function sets, the bipartite graph including a plurality of graph nodes and a plurality of edges, such that each graph node corresponds to one of the update function sets; associating each of a plurality of computing units with a different respective one of the graph nodes; instantiating, by a Publisher Subscriber platform, a plurality of channels, the plurality of channels defining a topology that matches a topology of the bipartite graph; and executing the graph-based program based on the plurality of channels to produce a result.Type: ApplicationFiled: November 14, 2019Publication date: May 28, 2020Inventors: Devavrat D. Shah, Vinayak Ramesh
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Patent number: 10565038Abstract: A method is disclosed including: receiving a graph-based program that identifies a bipartite graph and one or more update function sets, the bipartite graph including a plurality of graph nodes and a plurality of edges, such that each graph node corresponds to one of the update function sets; associating each of a plurality of computing units with a different respective one of the graph nodes; instantiating, by a Publisher Subscriber platform, a plurality of channels, the plurality of channels defining a topology that matches a topology of the bipartite graph; and executing the graph-based program based on the plurality of channels to produce a result.Type: GrantFiled: November 27, 2018Date of Patent: February 18, 2020Assignee: MASSACHUSETTS INSTITUTE OF TECHNOLOGYInventors: Devavrat D. Shah, Vinayak Ramesh
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Patent number: 9720975Abstract: An engine and method for tracking the influence of an entity operating within a social network are presented. A query containing social network content is received. A database for entries referencing the social network content is searched, and interactions between an entity participating within the social network and the social network content are identified. A dynamic interaction network (DIN) of a plurality of the entities is identified and a dynamic influence score for an entity in the query specific DIN is calculated.Type: GrantFiled: January 30, 2013Date of Patent: August 1, 2017Assignee: Massachusetts Institute of TechnologyInventors: Tauhid Rashed Zaman, Devavrat D. Shah
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Patent number: 9270412Abstract: Described herein are new error-correction (channel) codes: permute codes, iterative ensembles of permute and spinal codes, and graphical hash codes. In one aspect, a wireless system includes an encoder configured to encode data using one of the aforementioned channel codes. The wireless system also includes a decoder configured to decode the encoded data.Type: GrantFiled: June 25, 2014Date of Patent: February 23, 2016Assignee: Massachusetts Institute of TechnologyInventors: Jonathan Perry, Hari Balakrishnan, Devavrat D. Shah
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Publication number: 20150003557Abstract: Described herein are new error-correction (channel) codes: permute codes, iterative ensembles of permute and spinal codes, and graphical hash codes. In one aspect, a wireless system includes an encoder configured to encode data using one of the aforementioned channel codes. The wireless system also includes a decoder configured to decode the encoded data.Type: ApplicationFiled: June 25, 2014Publication date: January 1, 2015Applicant: Massachusetts Institute of TechnologyInventors: Jonathan Perry, Hari Balakrishnan, Devavrat D. Shah