Patents by Inventor Michael Hind
Michael Hind 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).
-
Patent number: 12263139Abstract: The present invention relates to the use of cannabidiol (CBD) for the treatment of tumours associated with Tuberous Sclerosis Complex (TSC). In particular the CBD was able to decrease the number and size of marker cells, pS6, in a zebrafish model of TSC. This is suggestive of a disease modifying effect whereby treatment with CBD could result in the reduction or prevention of the benign tumours that occur in TSC patients. Preferably the CBD used is in the form of a highly purified extract of cannabis such that the CBD is present at greater than 98% of the total extract (w/w) and the other components of the extract are characterised. In particular the cannabinoid tetrahydrocannabinol (THC) has been substantially removed, to a level of not more than 0.15% (w/w) and the propyl analogue of CBD, cannabidivarin, (CBDV) is present in amounts of up to 1%. Alternatively, the CBD may be a synthetically produced CBD. In use the CBD is given concomitantly with one or more other drugs used in the treatment of TSC.Type: GrantFiled: January 30, 2023Date of Patent: April 1, 2025Assignee: JAZZ PHARMACEUTICALS RESEARCH UK LIMITEDInventors: Benjamin Whalley, William Hind, Royston Gray, Michael Bazelot, Ines De Silva Serra, Claire Williams, Andrew Tee
-
Publication number: 20250094541Abstract: Techniques regarding the governing of use by a consumer of an artificial intelligence technology are described. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include an analyzing component that can, based on an artificial intelligence usage policy, analyze a proposed use by a consumer of an artificial intelligence technology, resulting in an analyzed use of the artificial intelligence technology. The computer executable components can further include a governing component that can, based on the analyzing, govern use by the consumer of the artificial intelligence technology.Type: ApplicationFiled: September 15, 2023Publication date: March 20, 2025Inventors: John Thomas Richards, Manish Anand Bhide, Michael Hind, Aleksandra Mojsilovic, Jacquelyn Martino, David John Piorkowski
-
Publication number: 20240362337Abstract: One or more systems, devices, computer program products and/or computer-implemented methods provided herein relate to risk assessment for artificial intelligence models, and more specifically, to the generation of customized risk scores and converted comparable scores. In an embodiment, the customized risk assessment scores can be based on a risk profile determined from risk assessment requirements and measurements of an artificial intelligence model. In another embodiment, one or more customized risk assessment scores can be converted to a converted risk assessment score that is comparable to a customized risk assessment score or another converted risk assessment score.Type: ApplicationFiled: April 28, 2023Publication date: October 31, 2024Inventors: Abigail Goldsteen, Michael Hind, Jacquelyn Martino, David John Piorkowski, Orna Raz, John Thomas Richards, Moninder Singh, Marcel Zalmanovici
-
Publication number: 20240330577Abstract: Provided are techniques for dynamic fact contextualization in support of AI model development. A template from a plurality of templates is selected, where the template includes definitions for identifying facts. The facts are retrieved from a facts repository based on the definitions. It is determined that that the facts are valid based on one or more policies. A FactSheet is generated using the template and the facts. A machine learning model is used to identify one or more deficient facts from the FactSheet. The FactSheet is displayed in a preview with the one or more deficient facts. One or more facts corresponding to the one or more deficient facts are located. The FactSheet is updated to correct the one or more deficient facts with the corresponding facts.Type: ApplicationFiled: March 30, 2023Publication date: October 3, 2024Inventors: John Thomas Richards, Thomas Hampp-Bahnmueller, Michael Hind, David John Piorkowski
-
Patent number: 12056585Abstract: A computer implemented method of performing large-scale machine learning experiments includes expanding on one or more input datasets by systematically generating several data set drift splits. A set of experimental jobs corresponding to the generated data set drift splits are executed to generate experimental results. The experimental results are processed, consolidated, and clustered according to the generated data set drift splits.Type: GrantFiled: December 2, 2020Date of Patent: August 6, 2024Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Evelyn Duesterwald, Anupama Murthi, Michael Hind, Matthew Richard Arnold, Benjamin Tyler Elder, Jiri Navratil
-
Patent number: 11940978Abstract: An example operation may include one or more of generating a plurality of successive data points of an iterative simulation based on predefined checkpoints, each data point identifying an evolving state of the iterative simulation with respect to a previous data point among the successive data points, transmitting a blockchain request for validating state data within a first data point among the plurality of successive data points to a first subset of endorsing nodes of a blockchain network, and transmitting a blockchain request for validating state data within a second data point among the plurality of successive data points to a second subset of endorsing nodes which are mutually exclusive from the first subset of endorsing nodes of the blockchain network for parallel endorsement of the first and second data points.Type: GrantFiled: September 19, 2018Date of Patent: March 26, 2024Assignee: International Business Machines CorporationInventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K Pissadaki, Nelson K. Bore
-
Publication number: 20240095575Abstract: Techniques regarding determining sufficiency of one or more machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in memory. The computer executable components can comprise a measurement component that measures maximum deviation of a supervised learning model from a reference model over a certification set and an analysis component that determines sufficiency of the supervised learning model based at least in part on the maximum deviation.Type: ApplicationFiled: September 13, 2022Publication date: March 21, 2024Inventors: Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush Raj Varshney, Elizabeth Daly, Moninder Singh, Michael Hind
-
Publication number: 20230412360Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.Type: ApplicationFiled: August 29, 2023Publication date: December 21, 2023Inventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K. Pissadaki, Nelson K. Bore
-
Patent number: 11784789Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.Type: GrantFiled: April 30, 2021Date of Patent: October 10, 2023Assignee: International Business Machines CorporationInventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K. Pissadaki, Nelson K. Bore
-
Publication number: 20220172109Abstract: A computer implemented method of performing large-scale machine learning experiments includes expanding on one or more input datasets by systematically generating several data set drift splits. A set of experimental jobs corresponding to the generated data set drift splits are executed to generate experimental results. The experimental results are processed, consolidated, and clustered according to the generated data set drift splits.Type: ApplicationFiled: December 2, 2020Publication date: June 2, 2022Inventors: Evelyn Duesterwald, Anupama Murthi, Michael Hind, Matthew Richard Arnold, Benjamin Tyler Elder, Jiri Navratil
-
Publication number: 20220121648Abstract: An example operation may include one or more of generating a data frame storing content of a simulation, compressing the simulation content within the data frame based on previous simulation content stored in another data frame to generate a compressed data frame, and transmitting the compressed data frame via a blockchain request to one or more endorsing peer nodes of a blockchain network for inclusion of the compressed data frame within a hash-linked chain of blocks of the blockchain network.Type: ApplicationFiled: January 3, 2022Publication date: April 21, 2022Inventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K Pissadaki, Nelson K. Bore
-
Patent number: 11306699Abstract: Provided is a method for adjusting a pitch angle of a rotor blade connected to a rotor of a wind turbine, the method includes: pitching the rotor blade towards a target blade pitch angle, the manner of pitching depending on a load on a pitch bearing and/or an azimuthal position of the rotor.Type: GrantFiled: November 27, 2017Date of Patent: April 19, 2022Assignee: Siemens Gamesa Renewable Energy A/SInventors: Julian Ehlers, Michael Hind, Drew Eisenberg, Alejandro Gomez Gonzalez, Peder Bay Enevoldsen, Lasse Gilling
-
Patent number: 11263188Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.Type: GrantFiled: November 1, 2019Date of Patent: March 1, 2022Assignee: International Business Machines CorporationInventors: Matthew R. Arnold, Rachel K. E. Bellamy, Kaoutar El Maghraoui, Michael Hind, Stephanie Houde, Kalapriya Kannan, Sameep Mehta, Aleksandra Mojsilovic, Ramya Raghavendra, Darrell C. Reimer, John T. Richards, David J. Piorkowski, Jason Tsay, Kush R. Varshney, Manish Kesarwani
-
Patent number: 11212076Abstract: An example operation may include one or more of generating a data frame storing content of a simulation, compressing the simulation content within the data frame based on previous simulation content stored in another data frame to generate a compressed data frame, and transmitting the compressed data frame via a blockchain request to one or more endorsing peer nodes of a blockchain network for inclusion of the compressed data frame within a hash-linked chain of blocks of the blockchain network.Type: GrantFiled: September 19, 2018Date of Patent: December 28, 2021Assignee: International Business Machines CorporationInventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K Pissadaki, Nelson K. Bore
-
Publication number: 20210273781Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.Type: ApplicationFiled: April 30, 2021Publication date: September 2, 2021Inventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K. Pissadaki, Nelson K. Bore
-
Patent number: 11032063Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.Type: GrantFiled: September 19, 2018Date of Patent: June 8, 2021Assignee: International Business Machines CorporationInventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K Pissadaki, Nelson K. Bore
-
Publication number: 20210133162Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.Type: ApplicationFiled: November 1, 2019Publication date: May 6, 2021Inventors: Matthew R. Arnold, Rachel K.E. Bellamy, Kaoutar El Maghraoui, Michael Hind, Stephanie Houde, Kalapriya Kannan, Sameep Mehta, Aleksandra Mojsilovic, Ramya Raghavendra, Darrell C. Reimer, John T. Richards, David J. Piorkowski, Jason Tsay, Kush R. Varshney, Manish Kesarwani
-
Publication number: 20200092086Abstract: An example operation may include one or more of generating a data frame storing content of a simulation, compressing the simulation content within the data frame based on previous simulation content stored in another data frame to generate a compressed data frame, and transmitting the compressed data frame via a blockchain request to one or more endorsing peer nodes of a blockchain network for inclusion of the compressed data frame within a hash-linked chain of blocks of the blockchain network.Type: ApplicationFiled: September 19, 2018Publication date: March 19, 2020Inventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K Pissadaki, Nelson K. Bore
-
Publication number: 20200092082Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.Type: ApplicationFiled: September 19, 2018Publication date: March 19, 2020Inventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K. Pissadaki, Nelson K. Bore
-
Publication number: 20200089791Abstract: An example operation may include one or more of generating a plurality of successive data points of an iterative simulation based on predefined checkpoints, each data point identifying an evolving state of the iterative simulation with respect to a previous data point among the successive data points, transmitting a blockchain request for validating state data within a first data point among the plurality of successive data points to a first subset of endorsing nodes of a blockchain network, and transmitting a blockchain request for validating state data within a second data point among the plurality of successive data points to a second subset of endorsing nodes which are mutually exclusive from the first subset of endorsing nodes of the blockchain network for parallel endorsement of the first and second data points.Type: ApplicationFiled: September 19, 2018Publication date: March 19, 2020Inventors: Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K. Pissadaki, Nelson K. Bore