Patents by Inventor Mehdi Maasoumy
Mehdi Maasoumy 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: 20240078618Abstract: A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.Type: ApplicationFiled: October 23, 2023Publication date: March 7, 2024Inventors: Adrian Albert, Mehdi Maasoumy Haghighi
-
Publication number: 20240022483Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.Type: ApplicationFiled: September 22, 2023Publication date: January 18, 2024Inventors: Jeremy Kolter, Giuseppe Barbaro`, Mehdi Maasoumy Haghighi, Henrik Ohlsson, Umashankar Sandilya
-
Patent number: 11823291Abstract: A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.Type: GrantFiled: November 17, 2020Date of Patent: November 21, 2023Assignee: C3.ai, Inc.Inventors: Adrian Albert, Mehdi Maasoumy Haghighi
-
Publication number: 20230344724Abstract: Provided herein are methods and systems for determining a historical state of a dynamic network. The methods may comprise continuously obtaining data associated with a system from a plurality of different data sources; constructing a full history dynamic network (FHDN) of the system using the data; and providing a state of the system for a historical time instance in response to a query of the FHDN for the historical time instance.Type: ApplicationFiled: June 28, 2023Publication date: October 26, 2023Inventors: Henrik Ohlsson, Umashankar Sandilya, Mehdi Maasoumy Haghighi
-
Patent number: 11784892Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.Type: GrantFiled: September 20, 2021Date of Patent: October 10, 2023Assignee: C3.ai, Inc.Inventors: Jeremy Kolter, Giuseppe Barbaro, Mehdi Maasoumy Haghighi, Henrik Ohlsson, Umashankar Sandilya
-
Patent number: 11777813Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.Type: GrantFiled: May 4, 2022Date of Patent: October 3, 2023Assignee: C3.AI, Inc.Inventors: Jeremy Kolter, Giuseppe Barbaro, Mehdi Maasoumy Haghighi, Henrik Ohlsson, Umashankar Sandilya
-
Patent number: 11729066Abstract: Provided herein are methods and systems for determining a historical state of a dynamic network. The methods may comprise continuously obtaining data associated with a system from a plurality of different data sources; constructing a full history dynamic network (FHDN) of the system using the data; and providing a state of the system for a historical time instance in response to a query of the FHDN for the historical time instance.Type: GrantFiled: April 30, 2021Date of Patent: August 15, 2023Assignee: C3.AI, Inc.Inventors: Henrik Ohlsson, Umashankar Sandilya, Mehdi Maasoumy Haghighi
-
Publication number: 20230101023Abstract: A method includes identifying, using at least one processor, uncertainty distributions for multiple variables. The method also includes identifying, using the at least one processor, one or more hyperparameters. The method further includes performing, using the at least one processor, multiple simulations to simulate effects of future requests using the one or more hyperparameters and at least one of the uncertainty distributions. The simulations involve sampling of the at least one uncertainty distribution to simulate at least one uncertainty associated with at least one of the variables on the future requests. In addition, the method includes selecting, using the at least one processor, one or more of the simulated future requests.Type: ApplicationFiled: September 16, 2022Publication date: March 30, 2023Inventors: Zhaoyang Jin, Mehdi Maasoumy Haghighi, Zeshi Zheng
-
Publication number: 20220261695Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.Type: ApplicationFiled: May 4, 2022Publication date: August 18, 2022Inventors: Jeremy Kolter, Giuseppe Barbaro, Mehdi Maasoumy Haghighi, Henrik Ohlsson, Umashankar Sandilya
-
Publication number: 20220247644Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.Type: ApplicationFiled: September 20, 2021Publication date: August 4, 2022Inventors: Jeremy Kolter, Giuseppe Barbaro, Mehdi Maasoumy Haghighi, Henrik Ohlsson, Umashankar Sandilya
-
Publication number: 20220215295Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to train a Bayesian network model based on a given set of data. Information associated with a user can be received. The information can include aggregated energy consumption data at one or more low frequency time intervals. At least a portion of the information can be inputted into the Bayesian network model. A plurality of energy consumption values for a plurality of energy consumption sources associated with the user can be inferred based on inputting the at least the portion of the information into the Bayesian network model.Type: ApplicationFiled: March 22, 2022Publication date: July 7, 2022Inventors: Zico Kolter, Nikhil Krishnan, Mehdi Maasoumy, Henrik Ohlsson
-
Patent number: 11301771Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to train a Bayesian network model based on a given set of data. Information associated with a user can be received. The information can include aggregated energy consumption data at one or more low frequency time intervals. At least a portion of the information can be inputted into the Bayesian network model. A plurality of energy consumption values for a plurality of energy consumption sources associated with the user can be inferred based on inputting the at least the portion of the information into the Bayesian network model.Type: GrantFiled: November 21, 2014Date of Patent: April 12, 2022Assignee: C3.AI, INC.Inventors: Zico Kolter, Nikhil Krishnan, Mehdi Maasoumy, Henrik Ohlsson
-
Publication number: 20220108407Abstract: A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.Type: ApplicationFiled: November 17, 2020Publication date: April 7, 2022Inventors: Adrian Albert, Mehdi Maasoumy Haghighi
-
Publication number: 20220067849Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to identify a set of features associated with at least one of a collection of residences or an energy billing period. Measured energy consumption information and a plurality of feature values can be acquired for each residence in the collection of residences. Each feature value in the plurality of feature values can correspond to a respective feature in the set of features. A regression model can be trained based on the measured energy consumption information and the plurality of features values for each residence in the collection of residences. At least one expected consumption value and at least one efficient consumption value can be determined based on the regression model.Type: ApplicationFiled: April 22, 2021Publication date: March 3, 2022Inventors: Mehdi Maasoumy Haghighi, Jeremy Kolter, Henrik Ohlsson
-
Publication number: 20220025765Abstract: A method of waterflood management for reservoir(s) having production hydrocarbon-containing well(s) including injector well(s). A reservoir model has model parameters in a mathematical relationship relating a water injection rate to a total production rate of the production well including at least one of a hydrocarbon production rate and water production rate. A solver implements automatic differentiation utilizing training data regarding the reservoir including operational data that includes recent sensor and/or historical data for the water injection rate and the hydrocarbon production rate, and constraints for the model parameters. The solver solves the reservoir model to identify values or value distributions for the model parameters to provide a trained reservoir model. The trained reservoir model uses water injection schedule(s) for the injector well to generate predictions for the total production rate.Type: ApplicationFiled: July 21, 2021Publication date: January 27, 2022Inventors: Amir Hossein Delgoshaie, Mehdi Maasoumy Haghighi, Riyad Sabir Muradov, Sina Khoshfetratpakazad, Henrik Ohlsson, Philippe Ivan S. Wellens
-
Publication number: 20210359915Abstract: Provided herein are methods and systems for determining a historical state of a dynamic network. The methods may comprise continuously obtaining data associated with a system from a plurality of different data sources; constructing a full history dynamic network (FHDN) of the system using the data; and providing a state of the system for a historical time instance in response to a query of the FHDN for the historical time instance.Type: ApplicationFiled: April 30, 2021Publication date: November 18, 2021Inventors: Henrik OHLSSON, Umashankar SANDILYA, Mehdi Maasoumy HAGHIGHI
-
Patent number: 11010847Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to identify a set of features associated with at least one of a collection of residences or an energy billing period. Measured energy consumption information and a plurality of feature values can be acquired for each residence in the collection of residences. Each feature value in the plurality of feature values can correspond to a respective feature in the set of features. A regression model can be trained based on the measured energy consumption information and the plurality of features values for each residence in the collection of residences. At least one expected consumption value and at least one efficient consumption value can be determined based on the regression model.Type: GrantFiled: May 30, 2019Date of Patent: May 18, 2021Assignee: C3.ai, Inc.Inventors: Mehdi Maasoumy Haghighi, Jeremy Kolter, Henrik Ohlsson
-
Patent number: 10872386Abstract: A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.Type: GrantFiled: December 15, 2016Date of Patent: December 22, 2020Assignee: C3.ai, Inc.Inventors: Adrian Albert, Mehdi Maasoumy Haghighi
-
Publication number: 20200058084Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to identify a set of features associated with at least one of a collection of residences or an energy billing period. Measured energy consumption information and a plurality of feature values can be acquired for each residence in the collection of residences. Each feature value in the plurality of feature values can correspond to a respective feature in the set of features. A regression model can be trained based on the measured energy consumption information and the plurality of features values for each residence in the collection of residences. At least one expected consumption value and at least one efficient consumption value can be determined based on the regression model.Type: ApplicationFiled: May 30, 2019Publication date: February 20, 2020Inventors: Mehdi Maasoumy, Zico Kolter, Henrik Ohlsson
-
Patent number: 10346933Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to identify a set of features associated with at least one of a collection of residences or an energy billing period. Measured energy consumption information and a plurality of feature values can be acquired for each residence in the collection of residences. Each feature value in the plurality of feature values can correspond to a respective feature in the set of features. A regression model can be trained based on the measured energy consumption information and the plurality of features values for each residence in the collection of residences. At least one expected consumption value and at least one efficient consumption value can be determined based on the regression model.Type: GrantFiled: February 12, 2015Date of Patent: July 9, 2019Assignee: C3 IoT, Inc.Inventors: Mehdi Maasoumy, Zico Kolter, Henrik Ohlsson