# US 12,181,997 B1 · Apparatus and method for virtual integration environments > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,181,997 B1 - Document ID: US12181997B1 - Status: Granted - Kind: B1 - Application number: 18/607,206 - Filed: 2024-03-15 - Granted: 2024-12-31 - Earliest claimed: 2024-03-15 - Assignees: Parry Labs, LLC - Applicants: Parry Labs, LLC - Inventors: David Walsh; Charles Adams; David Morse - Web page: https://patentgrep.com/patent/US12181997B1 ## CPC - G06F 11/3409: {for performance assessment} - G06F 11/301: {where the computing system is a virtual computing platform, e.g. logically partitioned systems (virtual machines G06F9/45533; logical partitioning of resources G06F9/5077)} - G06F 11/328: {Computer systems status display (G06F11/327 takes precedence)} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract An apparatus for virtual integration environments, the apparatus including computing device configured to receive a software package for deployment, determine one or more recipients of the software package, select one or more operating environments as a function of the software package and the one or more recipients, wherein each operating environment of the one or more operating environments is a virtual representation of a system associated with each recipient of the one or more recipients, execute the software package within the one or more operating environments, generate performance data for each operating environment of the one or more operating environments, compare each performance data of the one or more performance data to one or more performance thresholds and graphically display at least the one or more performance data through a graphical user interface. ## Claims ### Claim 1 An apparatus for virtual integration environments, the apparatus comprising: a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to: receive a software package for deployment; determine one or more recipients of the software package; select one or more operating environments as a function of the software package and the one or more recipients of the software package, wherein each operating environment of the one or more operating environments comprises a virtual representation of a system associated with each recipient of the one or more recipients of the software package; execute the software package within the one or more operating environments; generate one or more performance data for each operating environment of the one or more operating environments; compare each performance data of the one or more performance data to one or more performance thresholds; generate feedback data as a function of the comparison of the one or more performance data to the one or more performance thresholds, wherein generating the feedback data comprises: receiving feedback training data comprising a plurality of performance data and a plurality of performance thresholds as inputs correlated to a plurality of feedback data as outputs; training a feedback machine learning model as a function of the feedback training data; and generating the feedback data as a function of the feedback machine learning model; and graphically display at least the one or more performance data through a graphical user interface. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein at least one operating environment of the one or more operating environments comprises emulated hardware. ### Claim 3 (depends on 1) The apparatus of claim 1, wherein at least one operating environment of the one or more operating environments comprises a simulated avionics system. ### Claim 4 (depends on 1) The apparatus of claim 1, wherein: at least one operating environment of the one or more operating environments comprises a design assurance level classification; and comparing the one or more performance data to the one or more performance thresholds comprises comparing the one or more performance data to the one or more performance thresholds as a function of the design assurance level classification. ### Claim 5 (depends on 1) The apparatus of claim 1, wherein generating the one or more performance data for each operating environment of the one or more operating environments comprises generating the one or more performance data using a system profiler. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein generating the feedback data further comprises iteratively training the feedback machine learning model as a function of a user input, and wherein the user input comprises information associated with an accuracy of one or more outputs of the feedback machine learning model. ### Claim 7 (depends on 1) The apparatus of claim 1, wherein selecting the one or more operating environments as a function of the software package and the one or more recipients of the software package comprises instantiating the one or more operating environments on a cloud network. ### Claim 8 (depends on 1) The apparatus of claim 1, wherein at least one operating environment of the one or more operating environments comprises a digital twin associated with at least one recipient of the one or more recipients of the software package. ### Claim 9 A method for virtual integration environments, the method comprising: receiving, by at least a processor, a software package for deployment; determining, by the at least a processor, one or more recipients of the software package; selecting, by the at least a processor, one or more operating environments as a function of the software package and the one or more recipients of the software package, wherein each operating environment of the one or more operating environments comprises a virtual representation of a system associated with each recipient of the one or more recipients of the software package; executing, by the at least a processor, the software package within the one or more operating environments; generating, by the at least a processor, one or more performance data for each operating environment of the one or more operating environments; comparing, by the at least a processor, each performance data of the one or more performance data to one or more performance thresholds; generating, by the at least a processor, feedback data as a function of the comparison of the one or more performance data to the one or more performance thresholds, wherein generating the feedback data comprises: receiving feedback training data comprising a plurality of performance data and a plurality of performance thresholds as inputs correlated to a plurality of feedback data as outputs; training a feedback machine learning model as a function of the feedback training data; and generating the feedback data as a function of the feedback machine learning model; and graphically displaying, by the at least a processor, at least the one or more performance data through a graphical user interface. ### Claim 10 (depends on 9) The method of claim 9, wherein at least one operating environment of the one or more operating environments comprises emulated hardware. ### Claim 11 (depends on 9) The method of claim 9, wherein at least one operating environment of the one or more operating environments comprises a simulated avionics system. ### Claim 12 (depends on 9) The method of claim 9, wherein: at least one operating environment of the one or more operating environments comprises a design assurance level classification; and comparing, by the at least a processor, the one or more performance data to the one or more performance thresholds comprises comparing the one or more performance data to the one or more performance thresholds as a function of the design assurance level classification. ### Claim 13 (depends on 9) The method of claim 9, wherein generating, by the at least a processor, the one or more performance data for each operating environment of the one or more operating environments comprises generating the one or more performance data using a system profiler. ### Claim 14 (depends on 9) The method of claim 9, wherein generating, by the at least a processor, the feedback data further comprises iteratively training the feedback machine learning model as a function of a user input, and wherein the user input comprises information associated with an accuracy of one or more outputs of the feedback machine learning model. ### Claim 15 (depends on 9) The method of claim 9, wherein selecting, by the at least a processor, the one or more operating environments as a function of the software package and the one or more recipients of the software package comprises instantiating the one or more operating environments on a cloud network. ### Claim 16 (depends on 9) The method of claim 9, wherein at least one operating environment of the one or more operating environments comprises a digital twin associated with at least one recipient of the one or more recipients of the software package.