Uipath, inc. (20240184271). AUTOSCALING STRATEGIES FOR ROBOTIC PROCESS AUTOMATION simplified abstract
Contents
AUTOSCALING STRATEGIES FOR ROBOTIC PROCESS AUTOMATION
Organization Name
Inventor(s)
Bogdan Constantin Ripa of Bucharest (RO)
Andrei Robert Oros of Timisoara (RO)
Cristian Pufu of Bucharest (RO)
Clement B. Fauchere of Sammamish WA (US)
Tarek Madkour of Sammamish WA (US)
AUTOSCALING STRATEGIES FOR ROBOTIC PROCESS AUTOMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240184271 titled 'AUTOSCALING STRATEGIES FOR ROBOTIC PROCESS AUTOMATION
Simplified Explanation
Simplified Explanation: The patent application describes systems and methods for allocating computing environments to complete an RPA workload efficiently. This involves receiving a workload request, calculating the number of computing environments needed based on selected autoscaling strategies, and allocating these environments for RPA robots to complete the workload.
- The system receives a request for completing an RPA workload.
- It calculates the number of computing environments required based on selected autoscaling strategies.
- The calculated environments are allocated for RPA robots to complete the workload.
- The computing environments may be virtual machines.
Key Features and Innovation:
- Efficient allocation of computing environments for completing RPA workloads.
- Dynamic calculation of the number of environments based on autoscaling strategies.
- Allocation of virtual machines for RPA robots to complete tasks.
Potential Applications: This technology can be applied in various industries where RPA is used for automating repetitive tasks, such as finance, healthcare, and manufacturing.
Problems Solved:
- Efficient allocation of resources for completing RPA workloads.
- Optimizing computing environments based on workload requirements.
Benefits:
- Improved efficiency in completing RPA tasks.
- Cost-effective allocation of computing resources.
- Scalability to handle varying workloads.
Commercial Applications: Optimizing resource allocation for RPA workloads can benefit companies using automation technologies, leading to increased productivity and cost savings in various industries.
Prior Art: Information on prior art related to this technology is not provided in the abstract.
Frequently Updated Research: There is no specific information on frequently updated research related to this technology in the abstract.
Questions about Resource Allocation for RPA Workloads: Question 1: How does the system determine the number of computing environments needed for completing an RPA workload efficiently? The system calculates the number of computing environments based on selected autoscaling strategies to ensure optimal resource allocation.
Question 2: What are the potential challenges in dynamically allocating computing environments for RPA robots to complete tasks? One potential challenge could be ensuring seamless integration and coordination between the allocated virtual machines and the RPA robots to efficiently complete the workload.
Original Abstract Submitted
systems and methods for allocating computing environments for completing an rpa (robotic process automation) workload are provided. a request for completing an rpa workload is received. a number of computing environments to allocate for completing the rpa workload is calculated based on a selected one of a plurality of rpa autoscaling strategies. the calculated number of computing environments is allocated for allocating one or more rpa robots to complete the rpa workload. the computing environments may be virtual machines.
