Patents by Inventor Thomas H. Osiecki
Thomas H. Osiecki 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: 10725206Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: GrantFiled: October 14, 2018Date of Patent: July 28, 2020Assignee: International Business Machines CorporationInventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Patent number: 10705256Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: GrantFiled: November 2, 2017Date of Patent: July 7, 2020Assignee: International Business Machines CorporationInventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Patent number: 10234598Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: GrantFiled: November 2, 2017Date of Patent: March 19, 2019Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Publication number: 20190049627Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: ApplicationFiled: October 14, 2018Publication date: February 14, 2019Inventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Patent number: 9999788Abstract: Simulating particle beam interactions includes identifying a set of n functions F1, F2, . . . , Fn corresponding to a plurality of different physical aspects of a particle beam, performing simulations of each Fi using a full physics model, selecting for each Fi a distribution function fi that models relevant behavior and reducing computation of the full physics model for each Fi by replacing Fi with a distribution function fi. The computation reduction includes comparing a set of simulations wherein each fi replaces its respective Fi to determine if relevant behavior is accurately modeled and selecting one of fi or Fi for each n, for a Monte Carlo simulation based on a runtime and accuracy criteria.Type: GrantFiled: January 14, 2015Date of Patent: June 19, 2018Assignee: International Business Machines CorporationInventors: Anne E. Gattiker, Damir A. Jamsek, Sani R. Nassif, Thomas H. Osiecki, William E. Speight, Chin Ngai Sze, Min-Yu Tsai
-
Patent number: 9952353Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: GrantFiled: September 13, 2014Date of Patent: April 24, 2018Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Patent number: 9945981Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: GrantFiled: October 11, 2014Date of Patent: April 17, 2018Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Publication number: 20180059285Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: ApplicationFiled: November 2, 2017Publication date: March 1, 2018Inventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Publication number: 20180059286Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: ApplicationFiled: November 2, 2017Publication date: March 1, 2018Inventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Publication number: 20160077238Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: ApplicationFiled: October 11, 2014Publication date: March 17, 2016Inventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Publication number: 20160078112Abstract: Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.Type: ApplicationFiled: September 13, 2014Publication date: March 17, 2016Inventors: Hans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real
-
Publication number: 20150352374Abstract: Simulating particle beam interactions includes identifying a set of n functions F1, F2, . . . , Fn corresponding to a plurality of different physical aspects of a particle beam, performing simulations of each Fi using a full physics model, selecting for each Fi a distribution function fi that models relevant behavior and reducing computation of the full physics model for each Fi by replacing Fi with a distribution function fi. The computation reduction includes comparing a set of simulations wherein each fi replaces its respective Fi to determine if relevant behavior is accurately modeled and selecting one of fi or Fi for each n, for a Monte Carlo simulation based on a runtime and accuracy criteria.Type: ApplicationFiled: January 14, 2015Publication date: December 10, 2015Inventors: Anne E. Gattiker, Damir A. Jamsek, Sani R. Nassif, Thomas H. Osiecki, William E. Speight, Chin Ngai Sze, Min-Yu Tsai