View source for Bank of America Corporation (20240345890). Real Time Optimization Apparatus for Dynamic Code Evolution using Quantum Machine Learning with Non-Fungible Tokens simplified abstract

=Real Time Optimization Apparatus for Dynamic Code Evolution using Quantum Machine Learning with Non-Fungible Tokens=


==Organization Name==

[[:Category:Bank of America Corporation|Bank of America Corporation]]


[[Category:Bank of America Corporation]]

==Inventor(s)==

[[:Category:Venugopala Rao Randhi of Telangana (IN)|Venugopala Rao Randhi of Telangana (IN)]][[Category:Venugopala Rao Randhi of Telangana (IN)]]

[[:Category:Pitti Venkateswarlu of Chengalpattu District (IN)|Pitti Venkateswarlu of Chengalpattu District (IN)]][[Category:Pitti Venkateswarlu of Chengalpattu District (IN)]]

[[:Category:Rama Venkata Kavali of Frisco TX (US)|Rama Venkata Kavali of Frisco TX (US)]][[Category:Rama Venkata Kavali of Frisco TX (US)]]

[[:Category:Jyothi Gaddam of Telangana (IN)|Jyothi Gaddam of Telangana (IN)]][[Category:Jyothi Gaddam of Telangana (IN)]]

==Real Time Optimization Apparatus for Dynamic Code Evolution using Quantum Machine Learning with Non-Fungible Tokens - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240345890 titled 'Real Time Optimization Apparatus for Dynamic Code Evolution using Quantum Machine Learning with Non-Fungible Tokens

The abstract of the patent application describes a quantum computing platform that can generate optimal container configurations for processing workloads based on historical data and current workload information.

* The quantum computing platform trains a container configuration generation model using historical workload information.
* The platform receives current workload information from a workload processing system.
* It inputs the current workload information into the container configuration generation model to produce an optimal batch configuration for processing the data feed.
* The optimal batch configuration optimizes between computing resources and processing speed.
* The platform sends the container configuration output and commands to the workload processing system to process the data feed using the optimal batch configuration.

Potential Applications:
- Cloud computing optimization
- Data processing efficiency improvement
- Resource allocation in large-scale computing systems

Problems Solved:
- Optimizing container configurations for workload processing
- Balancing computing resources and processing speed

Benefits:
- Increased efficiency in data processing
- Cost savings through optimized resource allocation
- Enhanced performance in computing systems

Commercial Applications:
Title: Quantum Computing Platform for Container Configuration Optimization
This technology can be applied in cloud computing services, data centers, and large-scale computing environments to improve efficiency and performance.

Prior Art:
Researchers can explore prior art related to quantum computing platforms, workload optimization, and containerization technologies to understand the evolution of this innovation.

Frequently Updated Research:
Stay updated on advancements in quantum computing, workload optimization algorithms, and containerization techniques to enhance the efficiency of computing systems.

Questions about Quantum Computing Platform for Container Configuration Optimization:
1. How does this technology compare to traditional methods of workload optimization?
2. What are the potential challenges in implementing this quantum computing platform in real-world computing environments?


==Original Abstract Submitted==

a quantum computing platform may train, using historical workload information, a container configuration generation model. the computing platform may receive, from a workload processing system, a data feed indicating current workload information. the computing platform may input, into the container configuration generation model, the current workload information, which may cause the container configuration generation model to produce a container configuration output, where the container configuration output may be an optimal batch configuration for processing the data feed, and where the optimal batch configuration may be a configuration that optimizes between computing resources and processing speed. the computing platform may send, to the workload processing system, the container configuration output and one or more commands directing the workload processing system to process the data feed using the optimal batch configuration, which may cause the workload processing system to process the data feed using the optimal batch configuration.



[[Category:G06F9/50]]
[[Category:G06N10/00]]
[[Category:CPC_G06F9/505]]