View source for 17531857. DATA MODEL BASED SIMULATION UTILIZING DIGITAL TWIN REPLICAS simplified abstract (International Business Machines Corporation)

=DATA MODEL BASED SIMULATION UTILIZING DIGITAL TWIN REPLICAS=


==Organization Name==

[[:Category:International Business Machines Corporation|International Business Machines Corporation]]


[[Category:International Business Machines Corporation]]

==Inventor(s)==

[[:Category:Balaji Ganesan of Bengaluru (IN)|Balaji Ganesan of Bengaluru (IN)]][[Category:Balaji Ganesan of Bengaluru (IN)]]

[[:Category:Soma Shekar Naganna of Bangalore (IN)|Soma Shekar Naganna of Bangalore (IN)]][[Category:Soma Shekar Naganna of Bangalore (IN)]]

[[:Category:Pranay Kumar Lohia of Bhagalpur (IN)|Pranay Kumar Lohia of Bhagalpur (IN)]][[Category:Pranay Kumar Lohia of Bhagalpur (IN)]]

[[:Category:Priyanka Telang of Bengaluru (IN)|Priyanka Telang of Bengaluru (IN)]][[Category:Priyanka Telang of Bengaluru (IN)]]

[[:Category:Sameep Mehta of Bangalore (IN)|Sameep Mehta of Bangalore (IN)]][[Category:Sameep Mehta of Bangalore (IN)]]

==DATA MODEL BASED SIMULATION UTILIZING DIGITAL TWIN REPLICAS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17531857 titled 'DATA MODEL BASED SIMULATION UTILIZING DIGITAL TWIN REPLICAS

==Simplified Explanation==

The patent application describes computer hardware and/or software that performs several operations related to digital twin technology. Here is a simplified explanation of the abstract:

* The system receives a data model that represents different types of information and their relationships.
* It generates a set of digital twin replicas, where each replica corresponds to a node in the data model.
* The set of digital twin replicas is used to generate simulated data that corresponds to the types of information represented by the nodes in the data model.
* The simulated data generated by the digital twin replicas is combined into a combined set of simulated data based on the relationships represented by the edges in the data model.

Potential applications of this technology:

* Industrial manufacturing: The system can be used to create digital twin replicas of physical assets and generate simulated data to optimize production processes and predict maintenance needs.
* Healthcare: Digital twin replicas of patients can be created to simulate different treatment scenarios and improve personalized medicine.
* Smart cities: The technology can be applied to create digital twin replicas of urban infrastructure and generate simulated data to optimize resource allocation and improve sustainability.

Problems solved by this technology:

* Lack of real-time data: The system provides a way to generate simulated data based on digital twin replicas, allowing for continuous monitoring and analysis even when real-time data is not available.
* Cost and scalability: Digital twin replicas can be generated and utilized at a large scale, providing a cost-effective solution for simulating complex systems.
* Risk mitigation: By simulating different scenarios, the system helps identify potential issues and risks before they occur in the real world, enabling proactive decision-making.

Benefits of this technology:

* Improved decision-making: The combined set of simulated data provides insights into the behavior and performance of complex systems, enabling better decision-making and optimization.
* Predictive analytics: By utilizing digital twin replicas and simulated data, the system can predict future outcomes and trends, helping businesses and organizations plan and prepare accordingly.
* Enhanced efficiency and productivity: The technology allows for continuous monitoring, analysis, and optimization of systems, leading to improved efficiency and productivity.


==Original Abstract Submitted==

Computer hardware and/or software that perform the following operations: (i) receiving a data model, the data model including nodes representing types of information and edges representing relationships between the types of information; (ii) generating a set of digital twin replicas, where a digital twin replica of the set of digital twin replicas corresponds to a respective node of the data model; (iii) utilizing the set of digital twin replicas to generate simulated data corresponding to the types of information represented by the nodes of the data model; and (iv) combining the simulated data generated by the set of digital twin replicas into a combined set of simulated data based, at least in part, on the edges of the data model.



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