DETAILED ACTION
Claims 1-20 are presented for examination.
This office action is in response to submission of application on 30-MAR-2023.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/30/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 10/02/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 09/09/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 06/30/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
obtaining at least one virtual representation of an infrastructure, wherein the virtual representation represents the infrastructure;
The limitation is interpreted in view of paragraph [0034] of the specification as published. When “obtaining” the data, the “virtual representation of an infrastructure” is based on configuration-related metadata. A person of ordinary skill in the art can observe the meta data when “obtaining” the data.
applying, in a plurality of iterations, a plurality of simulated workloads to the virtual representation to artificially advance the virtual representation to generate a plurality of datasets respectively representing the infrastructure in a plurality of states;
The limitation is interpreted in view of paragraph [0028] of the specification as published. The “simulated workloads” encompasses for forms, such as customer, patters, and casual variables. A person of ordinary skill in the art would be able to observe the underlying configuration and then estimate a performance based on judgment or opinion using previous experience. This could be done for a “plurality of states” where a component is adjusted.
applying one or more gamification processes, between one or more of the plurality of iterations, to alter the corresponding one or more datasets generated by the virtual representation, wherein the one or more altered datasets are applied to the virtual representation along with one or more of the plurality of simulated workloads to represent the infrastructure in one or more subsequent states of the plurality of states; and
The limitation is interpreted in view of paragraph [0038] of the specification as published. When performing the “gamification”, different datasets are adjusted to determine where points of failure could develop. Different configurations or “altered datasets” allow for the different predictions to be formed. A person of ordinary skill in the art would be able to perform these predictions and form opinions/judgements based on previous experience.
obtaining gamification results representing the infrastructure, responsive to applying the one or more gamification processes;
When “obtaining gamification results”, a person of ordinary skill in the art would be performing an evaluation. The “infrastructure” is part of the result and determined based on an opinion or judgement based on previous experience.
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
wherein the steps are performed by at least one processor and at least one memory storing executable computer program instructions.
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is a general-purpose computer and does not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
2. The method of claim 1, further comprising generating one or more recommendations for the infrastructure, responsive to obtaining the gamification results.
The “recommendation” is a type of opinion that is determined from performing the evaluation. The “obtaining” is an observation. (Step 2A Prong 1).
3. The method of claim 2, wherein the one or more recommendations comprise at least one of a design recommendation and an operational recommendation for the infrastructure.
Further specifying the “recommendations” as “a design recommendation” or “an operational recommendation” does not the “recommendations” from being an opinion. (Step 2A Prong 1).
4. The method of claim 1, wherein the one or more gamification processes further comprise one or more processes configured to randomly alter the one or more datasets generated by artificially advancing the virtual representation.
A person of ordinary skill can observe the “one or more datasets” and “randomly alter” one of the sets by performing an evaluation. (Step 2A Prong 1).
5. The method of claim 1, wherein the one or more gamification processes further comprise at least one of a what-if analysis and a chaos engineering analysis.
The “what-if analysis” and “chaos engineering analysis” are known forms of analysis and evaluation. (Step 2A Prong 1).
6. The method of claim 1, wherein each of at least a portion of the plurality of datasets corresponds to at least one of a potential hardware configuration, a potential software configuration, and a potential data configuration for the infrastructure.
The “datasets” can reasonably be observed and remains the same for “a potential hardware configuration, a potential software configuration, and a potential data configuration”. (Step 2A Prong 1).
7. The method of claim 6, wherein the one or more gamification processes alter at least one of the potential hardware configuration, the potential software configuration, and the potential data configuration of each dataset at each of the plurality of iterations.
A person of ordinary skill can observe the “potential hardware configuration” or “potential software configuration” and “randomly alter” one of the sets by performing an evaluation. (Step 2A Prong 1).
8. The method of claim 1, wherein the gamification results are indicative of at least one predicted failure associated with the infrastructure, and a severity of the failure, resulting from execution of the plurality of iterations.
The “predicted failure” is based on an evaluation and the “severity” is based on the opinion or judgement formed by a person of ordinary skill in the art. (Step 2A Prong 1).
9. The method of claim 1, wherein the virtual representation comprises a combination of a physics-based model and an artificial intelligence-driven model.
The limitation is interpreted in view of paragraphs [0020]-[0021] of the specification as published. The “physics-based model” is interpreted as a model of any physical system. The “physics-based model” is an observation or abstraction of a physical object. The “artificial intelligence-driven model” can reasonably be interpreted as performing an evaluation of the abstraction. (Step 2A Prong 1).
10. The method of claim 1, wherein the virtual representation comprises at least one digital twin.
The “digital twin” is an abstract representation of an object and used in performing evaluations. (Step 2A Prong 1).
Claims 11-18 are system claims, containing substantially the same elements as method Claims 1-8, respectively, and are rejected on the same grounds under 35 U.S.C. 101 as Claims 1-8, respectively, Mutatis mutandis.
Claims 19-20 are medium claims, containing substantially the same elements as method Claims 1-2, respectively, and are rejected on the same grounds under 35 U.S.C. 101 as Claims 1-2, respectively, Mutatis mutandis.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Poltronieri et al., “ChaosTwin: A Chaos Engineering and Digital Twin Approach for the Design of Resilient IT Services” [2021] (hereinafter ‘Poltronieri’) in view of
Moyal et al., United States Patent 11,216,261 B1 (hereinafter ‘Moyal’).
Regarding Claim 1: A method, comprising:
Poltronieri teaches obtaining at least one virtual representation of an infrastructure, wherein the virtual representation represents the infrastructure; (Pg. 235 right col 2nd paragraph Poltronieri “…ChaosTwin leverages the BDMaaS+ capabilities to create a digital twin version of a Cloud-based IT system, evaluate its business-level performance, and find the optimal configuration for that system, extending those capabilities to explicitly consider fault injection and management strategies…”)
Poltronieri teaches applying, in a plurality of iterations, a plurality of simulated workloads to the virtual representation to artificially advance the virtual representation… (Fig. 2 and pg. 235 right col last paragraph Poltronieri “…We configured ChaosTwin to run this experiment for 30 iterations of the memetic optimization algorithm to analyze the resulting IT service configurations…”)
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Poltronieri teaches applying one or more gamification processes, between one or more of the plurality of iterations, to alter the corresponding one or more datasets generated by the virtual representation, (Pg. 236 right col 2nd paragraph Poltronieri “…Towards this goal, we designed different events for simulating chaos practices on the Digital Twin: VM Outage (VMO), DC Outage (DCO), and Latency Variation (LV). These are representative Chaos-like faults that we implemented within ChaosTwin to enable their reproducibility on the digital twin. VMO events are to simulate errors/faults in VMs installed in Cloud data centers that make them unavailable for a configurable period of time E<D¹Cº. ChaosTwin leverages random variables with configurable distributions for modeling the rate of VMO events during the simulation. When a VMO event occurs, ChaosTwin marks the VM as not running and then reboots the VM. It is worth noting that while the VM is unavailable it would not serve requests, which will be served by another VM (if available)…”)
Poltronieri teaches obtaining gamification results representing the infrastructure, responsive to applying the one or more gamification processes; (Pg. 238 left col 2nd paragraph Poltronieri “…These results prove that ChaosTwin implements a valuable approach in minimizing the number of failed requests, which are almost distributed around 0.05% in the final stages of the optimization process. Furthermore, the ChaosTwin optimization policy also minimizes the associated operational costs, which in the very first iterations are distributed around 550,000 USD/day to reach about 30,000 USD/day in the final iterations…”)
Poltronieri does not appear to explicitly disclose
to generate a plurality of datasets respectively representing the infrastructure in a plurality of states;
wherein the one or more altered datasets are applied to the virtual representation along with one or more of the plurality of simulated workloads to represent the infrastructure in one or more subsequent states of the plurality of states; and
wherein the steps are performed by at least one processor and at least one memory storing executable computer program instructions.
However, Moyal teaches to generate a plurality of datasets respectively representing the infrastructure in a plurality of states; (Col 11 lines 9-25 Moyal “…The digital replica system 402 can generate one or more deployment scenarios based on the simulation(s) of the new 10 deployment, the generated metrics 410, and/or the like. For example, the digital replica system 402 can determine and/or create internal and/or external impediments for the new deployment (e.g., overall deployment process) with respect 15 to the infrastructure, resources, and/or the like. The digital replica system 402 can identify and/or generate "what-if' scenarios for the identified user and/or user context based, at least in part, on the new deployment request and data feeds (e.g., IoT feeds, etc.) from various resources/endpoints, 20 and/or the like. In some embodiments, digital replica system 402 can determine one or more deployment tasks and/or divide up deployment tasks (e.g., functional, non-functional, etc.) associated with the new deployment, for example, as part of the deployment scenario generation…”)
Moyal teaches wherein the one or more altered datasets are applied to the virtual representation along with one or more of the plurality of simulated workloads to represent the infrastructure in one or more subsequent states of the plurality of states; and (Col 11 33-46 Moyal “…The digital replica system 402 can determine a deployment plan 412 (e.g., optimal/best deployment plan, etc.) to complete and/or queue deployment tasks based on the deployment scenarios (e.g., likely success/failure, deployment tasks, security issues, etc.) and generated metrics 410. that may allow for improved deployment success rates. The deployment plan 412 for a new deployment request (e.g., cloud deployment, etc.) may allow for achieving higher deployment success ratios and/or minimizing deployment failure scenarios. In some embodiments, the deployment plan 412 may identify deployment level distribution tasks, independent tasks, areas for improvement, and/or the like associated with the new deployment request in advance of deployment…”)
Moyal teaches wherein the steps are performed by at least one processor and at least one memory storing executable computer program instructions. (Col 4 lines 33-41 “…FIG. 1 is a functional block diagram illustrating various portions of networked computers system 100, including: server sub-system 102; client sub-systems 104, 106, 108, 110, 112; communication network 114; server computer 200; communication unit 202; processor set 204; input/output (I/O) interface set 206; memory device 208; persistent storage device 210; display device 212; external device set 214; random access memory (RAM) devices 230; cache memory device 232; and program 300…”)
Poltronieri and Moyal are analogous art because they are from the same field of endeavor, digital twin implementations.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the applying, in a plurality of iterations, a plurality of simulated workloads to the virtual representation to artificially advance the virtual representation as disclosed by Poltronieri by to generate a plurality of datasets respectively representing the infrastructure in a plurality of states and wherein the one or more altered datasets are applied to the virtual representation along with one or more of the plurality of simulated workloads to represent the infrastructure in one or more subsequent states of the plurality of states and wherein the steps are performed by at least one processor and at least one memory storing executable computer program instructions as disclosed by Moyal.
One of ordinary skill in the art would have been motivated to make this modification in order to improve the success or failure of a deployment as discussed in Col 2 lines 14-31 by Moyal “…In general, cloud deployments can be highly useful for the dynamic nature of business, allowing for serving requirements quickly with ease and resilience. With increasing demand for cloud deployment, there are different architectures that may be used (e.g., micro-services-based deployment docker/KS deployment, etc.) for faster, high scale deployment without any dependencies on run-time environments. However, with known and unknown changes in platforms, infrastructure, software services, and the like, run time failures for deployments may occur in the cloud environment. Accordingly, embodiments of the present disclosure can provide for using digital replicas (e.g., digital twins) to simulate a new deployment request with respect to real-time data associated with infrastructure and resources in advance of any deployment. Such embodiments can allow for determining likely deployment success/failure rates, deployment tasks, metrics, and/or the like in advance of actual deployment…”
Regarding Claim 2: Poltronieri and Moyal teach The method of claim 1, further comprising
Moyal teaches generating one or more recommendations for the infrastructure, responsive to obtaining the gamification results. (Col 7 lines 16-29 Moyal “…In some embodiments, the deployment request module 335 can provide for any data associated with the new deployment that may assist in the simulation of the new deployment with respect to the available resources (e.g., cloud resources, etc.), infrastructure, environment, and/or the like. In some embodiments, the deployment request module 335 and/or the like may provide data associated with the new deployment request to a digital twin modeling module 320, a digital twin simulation engine 330, and/or the like for use in modeling and simulating a new deployment to allow for determining deployment success/failure scenarios, deployment tasks, recommended actions, upgrades, solutions, and/or the like in advance of the deployment execution…”)
Regarding Claim 3: Poltronieri and Moyal teach The method of claim 2, wherein the one or more recommendations comprise
Moyal teaches at least one of a design recommendation and an operational recommendation for the infrastructure. (Col 8 lines 54-64 Moyal “…In some embodiments, the metrics can be used in generating deployment scenarios based on simulation of the new deployment ( e.g., deployment operations simulation, etc.). In some embodiments, the metrics may be used in determining an optimal deployment plan and/or deployment action plans that may assist in achieving higher deployment success ratios and/or minimize deployment failure scenarios. In some embodiments, the generated metrics may assist in design development, for example, based on classification done for run time environment of deployment tasks…”)
Regarding Claim 4: Poltronieri and Moyal teach The method of claim 1, wherein the one or more gamification processes further comprise
Poltronieri teaches one or more processes configured to randomly alter the one or more datasets generated by artificially advancing the virtual representation. (Pg. 237 right col 4th paragraph Poltronieri “…To implement this experiment, we describe a severe fault profile that would affect the video streaming application illustrated in the previous section. More specifically, this experiment schedules VMOs for VMs running within the Amazon EC2 data center ir using a random variable with exponential distribution with a rate of λ = 0.8. To increase the randomness of the simulation we chose to terminate VM randomly without specifying the software component type. When a VMO occurs, the VM is marked as unavailable for 2 seconds, which is the time we configured to reenact a VM reboot. Along with VMOs, we simulate the complete blackout of two data centers for 15 seconds. The first one is a private data center, while the second one is the Amazon EC2 data center located in California, USA…”)
Regarding Claim 5: Poltronieri and Moyal teach The method of claim 1,
Poltronieri teaches wherein the one or more gamification processes further comprise at least one of … and a chaos engineering analysis. (Pg. 235 left col last paragraph – right col 1st paragraph Poltronieri “…This paper adopts an higher level perspective and embraces the principle of Chaos Engineering for the development of a management framework that enables service providers to explore the best configurations for IT services that minimize the effect of adverse faults…”
Moyal teaches wherein the one or more gamification processes further comprise at least one of a what-if analysis… (Col 2 lines 6-13 Moyal “…In some embodiments, the systems and methods of the present disclosure can provide for creating internal and/or external impediments with respect to infrastructure, resources, and/or the like associated with an overall deployment process and generating what-if deployment scenarios, for example associated with the identified requirements and user context based on feed data from multiple resources and/or end points…”)
Regarding Claim 6: Poltronieri and Moyal teach The method of claim 1,
Moyal teaches wherein each of at least a portion of the plurality of datasets corresponds to at least one of a potential hardware configuration, a potential software configuration, and a potential data configuration for the infrastructure. (Col 10 lines 14-18 Moyal “…The computing system may allow for generating one or more recommended solutions, deployment best practices, recommended upgrades for hardware, software, or services, references to ingest into the model, and/or the like…”)
Regarding Claim 7: Poltronieri and Moyal teach The method of claim 6,
Moyal teaches wherein the one or more gamification processes alter at least one of the potential hardware configuration, the potential software configuration, and the potential data configuration of each dataset at each of the plurality of iterations. (Col 10 lines 10-18 Moyal “…Additionally, in some embodiments, a computing system ( e.g., server computer 200 of FIG. 1 or the like) may provide for performing feedback ingestion of data based on the simulation of the new deployment and the generation of deployment scenarios. The computing system may allow for generating one or more recommended solutions, deployment best practices, recommended upgrades for hardware, software, or services, references to ingest into the model, and/or the like…” Col 11 lines 15-21 “…The digital replica system 402 can identify and/or generate “what-if” scenarios for the identified user and/or user context based, at least in part, on the new deployment request and data feeds (e.g., IoT feeds, etc.) from various resources/endpoints, and/or the like…”)
Regarding Claim 8: Poltronieri and Moyal teach The method of claim 1,
Moyal teaches wherein the gamification results are indicative of at least one predicted failure associated with the infrastructure, and a severity of the failure, resulting from execution of the plurality of iterations. (Col 9 lines 41-56 Moyal “…Processing proceeds to operation S264, where the computing system (e.g., server computer 200 of FIG. 1 or the like) can determine an optimal deployment plan based, at least in part, on the generated deployment scenarios ( e.g., likely success/failure, deployment tasks, security issues, etc.). An optimal deployment plan for a new deployment request (e.g., cloud deployment, etc.) may allow for achieving higher deployment success ratios and/or minimizing deployment failure scenarios. As an example, a deployment plan generator 340 and/or the like can determine an optimal new deployment plan (e.g., action plan(s), etc.) from the generated deployment scenarios. In some embodiments, an optimal deployment plan may identify deployment level distribution tasks, independent tasks, areas for improvement, and/or the like associated with the new deployment request 55 in advance of actual deployment…”)
Regarding Claim 9: Poltronieri and Moyal teach The method of claim 1, wherein the virtual representation comprises
Moyal teaches a combination of a physics-based model and an artificial intelligence-driven model. (Col 10 lines 28-49 Moyal “…In some embodiments, the digital replica ( e.g., digital twin) system 402 can obtain data associated with the deployment requirements 404 (e.g., for a new deployment request, etc.). For example, a user can provide data associated with a new deployment request. The new deployment request can include one or more deployment requirements 404, such as requested resources, dependent services, and/or the like. In some embodiments, the digital replica system 402 may also automatically identify additional deployment requirements, user preferences, user context and/or the like. The digital replica system 402 can obtain data feeds 406. The data feeds 406 can include real-time data feeds that are associated with one or more resources (e.g., cloud resources, network resources, etc.), endpoints, and/or the like. In some embodiments, the data feeds 406 can include IoT data feeds associated with the resources, infrastructure, environment, services, and/or the like. Additionally, in some embodiments, the digital replica system 402 can obtain output of machine learning models 408. The machine learning models 408 can provide data associated with deployment success/ failure rates, prior deployment data, mapping of resources, and/or the like…”)
Regarding Claim 10: Poltronieri and Moyal teach The method of claim 1,
Poltronieri teaches wherein the virtual representation comprises at least one digital twin. (Pg. 235 right col 4th paragraph Poltronieri “…ChaosTwin was designed to maximize the accuracy of the digital twin. To this end, it leverages sophisticated functions that build models of the digital twin at the workload, network, and service level…”)
Claims 11-18 are system claims, containing substantially the same elements as method Claims 1-8, respectively, and are rejected on the same grounds under 35 U.S.C. 103 as Claims 1-8, respectively, Mutatis mutandis.
Claims 19-20 are medium claims, containing substantially the same elements as method Claims 1-2, respectively, and are rejected on the same grounds under 35 U.S.C. 103 as Claims 1-2, respectively, Mutatis mutandis.
Conclusion
Claims 1-20 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN E JOHANSEN whose telephone number is (571)272-8062. The examiner can normally be reached M-F 9AM-3PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached at 5712723652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN E JOHANSEN/Examiner, Art Unit 2187
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