Patents by Inventor Andrew. Cardno
Andrew. Cardno 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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Publication number: 20220163960Abstract: A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.Type: ApplicationFiled: November 30, 2021Publication date: May 26, 2022Applicant: STRONG FORCE IOT PORTFOLIO 2016, LLCInventors: Charles H. Cella, Gerald William Duffy, JR., Jeffrey P. McGuckin, Teymour S. El-Tahry, Andrew Cardno, Jenna Parenti
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Publication number: 20220163959Abstract: A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.Type: ApplicationFiled: November 30, 2021Publication date: May 26, 2022Applicant: STRONG FORCE IOT PORTFOLIO 2016, LLCInventors: Charles H. Cella, Gerald William Duffy, JR., Jeffrey P. McGuckin, Teymour S. El-Tahry, Andrew Cardno, Jenna Parenti
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Patent number: 11308754Abstract: An entertainment gaming system and method for transforming a non-random number outcome into an entertainment gaming outcome. The entertainment gaming system comprises a central server, non-random number outcome generator machine, entertainment gaming machine, and database. The central server communicatively coupled with non-random number outcome generator machine and entertainment gaming machine. The entertainment gaming system collects non-random number based outcome from real-world events and transmitting it to the central server. The central server further utilizes an algorithm to process the received non-random number outcome into an entertainment gaming outcome. Further, the present entertainment gaming system comprises a database to store the received real-world non-random number based outcome and processed entertainment gaming outcome generated from the received non-random number based outcome.Type: GrantFiled: February 4, 2020Date of Patent: April 19, 2022Assignee: Quick Custom Intelligence, LLCInventors: Andrew Cardno, Daniel Cardno, Ralph W Thomas, Ralph J Thomas
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Publication number: 20220108262Abstract: An industrial plant operation management platform integrating a set of executive digital twins that take data from an intelligent data and networking pipeline to provide role-specific features, including AI-enabled expert agent features and enhanced collaboration features, and salient views of the entities and workflows of an industrial plant operation, thereby enabling executives to monitor and control entities and workflows to an unprecedented degree at appropriate levels of granularity and using familiar taxonomies and decision-making frameworks.Type: ApplicationFiled: October 4, 2021Publication date: April 7, 2022Applicant: STRONG FORCE IOT PORTFOLIO 2016, LLCInventors: Charles H. Cella, Richard Spitz, Gerald William Duffy, JR., Jeffrey P. McGuckin, Brent Bliven, Teymour S. El-Tahry, Andrew Cardno, Jenna Parenti
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Publication number: 20220058569Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: August 30, 2021Publication date: February 24, 2022Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Teymour S. EL-TAHRY, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20220051171Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: August 30, 2021Publication date: February 17, 2022Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Teymour S. EL-TAHRY, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20220051361Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: August 30, 2021Publication date: February 17, 2022Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Teymour S. EL-TAHRY, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20220051184Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: August 30, 2021Publication date: February 17, 2022Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Teymour S. EL-TAHRY, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20220044204Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: August 30, 2021Publication date: February 10, 2022Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Teymour S. EL-TAHRY, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20220036275Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: August 30, 2021Publication date: February 3, 2022Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Teymour S. EL-TAHRY, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20220036301Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: August 30, 2021Publication date: February 3, 2022Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Teymour S. EL-TAHRY, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20210375091Abstract: Embodiments of the present invention provide entertainment gaming systems and methods comprising, among other things, a real world non-random number outcome generator machine and an entertainment gaming machine wherein the non-random outcome from the real world non-random number generator machine is processed into an entertainment gaming machine outcome. The entertainment gaming system and method of embodiments of the invention may be configured to collect non-random number based outcome from a real-world event and transmitting it to the entertainment gaming machine. The non-random number outcome from the real-world is processed into an entertainment gaming machine outcome event by the entertaining game machine processor or by the central server.Type: ApplicationFiled: August 17, 2021Publication date: December 2, 2021Inventors: Andrew Cardno, Daniel Cardno, Ralph W. Thomas, Ralph J. Thomas
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Publication number: 20210357850Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: May 28, 2021Publication date: November 18, 2021Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20210357422Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: May 28, 2021Publication date: November 18, 2021Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20210357823Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: May 28, 2021Publication date: November 18, 2021Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20210357827Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: May 28, 2021Publication date: November 18, 2021Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20210357959Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: May 28, 2021Publication date: November 18, 2021Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20210357838Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.Type: ApplicationFiled: May 28, 2021Publication date: November 18, 2021Applicant: STRONG FORCE VCN PORTFOLIO 2019, LLCInventors: Charles Howard CELLA, Richard SPITZ, Andrew CARDNO, Jenna PARENTI, Brent BLIVEN, Joshua DOBROWITSKY
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Publication number: 20210342836Abstract: Systems and methods for controlling rights related to digital knowledge are disclosed. A sample system may include an input system to receive digital knowledge from a user, a tokenization system to tokenize the digital knowledge and a ledger management system to create, manage, and store things on a distributed ledger and provide provable access to the digital knowledge. A smart contract system may create a smart contract including triggering action is and respond with a defined smart contract action on an occurrence of the triggering event. The smart contract system may also process commitments to the smart contract.Type: ApplicationFiled: July 16, 2021Publication date: November 4, 2021Inventors: Charles Howard Cella, Andrew Cardno, Taylor D. Charon, Teymour S. El-Tahry
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Publication number: 20210287459Abstract: A method for updating one or more properties of one or more transportation system digital twins includes receiving a request to update the one or more transportation system digital twins; retrieving the one or more transportation system digital twins to fulfill the request from a digital twin datastore; and retrieving one or more dynamic models to fulfill the request from a dynamic model datastore. The method further includes selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as input data to determine one or more output values; and updating the one or more properties of the one or more transportation system digital twins based on the one or more output values of the one or more dynamic models.Type: ApplicationFiled: May 28, 2021Publication date: September 16, 2021Inventors: Charles Howard Cella, Andrew Cardno