DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 20 February 2026 has been entered.
Status of the Claims
The amendment received on 20 January 2026 has been acknowledged and entered.
Claims 1, 3-4, 9, 11, 16, and 17 have been entered.
Claims 2, 5, 10, and 12 have been canceled.
New claims 23 and 24 have been added.
Claims 1, 3-4, 6-9, 11, and 13-24 are currently pending.
Response to Amendments and Arguments
Applicant's arguments filed 120 January 2026 with respect to claims 1-4, 6-11, and 13-22 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues (in REMARKS, pages 14-15) that Claims 1, 9, and 16 have been amended to recite features that emphasize their patent eligibility under 35 U.S.C. § 101. In the Office Action, the Examiner characterized claims 1, 9, and 16 as allegedly being directed to a judicial exception and, more specifically, to managing personal behavior or relationships or interactions between people and/or mental processes. See Office Action, pg. 14. Applicant respectfully disagrees. In McRO V. Bandai, the Federal Circuit emphasized "claims are considered in their entirety to ascertain whether their character as a whole is directed to excluded subject matter Like the claims at issue in McRO, the present claims include a combined order of specific rules for "generating the estimated GHG emissions of the application when deployed in the virtualization environment." For example, independent claim 1 recites "receiving a request from a deployment pipeline of a software deployment platform to determine estimated greenhouse gas (GHG) emissions of the application when deployed in a virtualization environment By the present response, independent claim 1 has also been amended to recite "extracting a resource definition of the application from the IAC file that is to be provisioned in the virtual environment for execution of the application The infrastructural components and the operational parameters associated with the application are provided to an Al model that is "configured to generate the estimated GHG emissions by determining GHG emissions patterns associated with the provided infrastructural components and operational parameters of the application from the global set of infrastructural components and operational parameters" These recitations include a combined order specific rules that creates a novel output. That is, the claims do not merely reorganize components or data within an IAC file. In sharp contrast, these steps in independent claim 1 extract information from the IAC file, analyze the information based on analysis rules, and generate an estimate of greenhouse gas emissions based on the IAC file and the analysis rules. Similarly to McRO, these claims provide a technological improvement in a software deployment pipeline that may enable software developers to reduce the environmental impact of applications before they are deployed. For at least these reasons, Applicant respectfully submits that independent claims 1, 9, and 16 are not directed to excluded subject matter and are, therefore, patent eligible at the first prong of the patent eligibility framework.
In response to Applicant's argument, the Examiner respectfully disagrees and notes that first, unlike McRO, in which the claims were directed to an improvement in computer animation and thus did not recite a concept similar to previously identified abstract ideas, Applicant's claims are directed to a generating the estimated GHG emissions of an application when deployed in a virtualization environment which does not appear to provide a technical improvement to a technical problem or automates a process to produce a specific, tangible, and superior "output", for instance, like realistic animation.
Applicant argues (in REMARKS, page 16) that assuming, arguendo, that claims 1, 9, 16, and 17 are directed at an abstract idea, the combination of features recited by the present claims recite significantly more than any alleged abstract idea. For example, the present claims clearly provide improvements in a technical field-namely, an application deployment pipeline for virtualization environments. In Amdocs (Israel) Ltd. V. Openet Telecom Inc., the Federal Circuit held that a patent that generally related to correlating a first network accounting record with accounting information from a second source to enhance the first network accounting record using a computer code is patent eligible Like the claims in Amdocs, the present claims solve the problem of estimating greenhouse gas emissions in a virtualization environment by reciting an unconventional use of IAC files. As noted in the specification, IAC files "are used by the software deployment platform to define the various infrastructural components of the data center that an application is designed to use during operation within the virtualization environment." Indeed, the specification further clarifies that IAC files "are industry standard for application deployment within a virtual environment." Id. The claimed approach does not just use IAC files for deploying the applications, but instead includes specific steps for extracting information (e.g., a resource definition) from the IAC files and using that information in a process for estimating greenhouse gas emissions. The claims recite clear steps for performing this process, such as analyzing the resource definition for infrastructure components and operational parameters. Although these claims recite arguably conventional components (e.g., at least one memory and at least one processor), the ordered combination of the instructions executed by the processor provides an unconventional solution to a technological problem. For at least these reasons independent claims 1, 9, and 16 are patent eligible at the second prong of the patent eligibility framework. For at least the preceding reasons, Applicant submits that independent claims 1, 9, and 16 are patent eligible at both prongs of the patent eligibility framework. Thus, Applicant respectfully requests that the Examiner withdraw the rejection of independent claims 1, 9, and 16 17 under 35 U.S.C. § 101.
In response to the Applicant's argument, the Examiner respectfully disagrees and notes that first, the while the specification teaches "receiving" a resource definition, rather than "extracting," which is high level and void of an explanation of how the extracting is performed. As described, the extracting and analyzing could be mental processes and/or organizing human activity. Secondly, unlike Amdocs which provided a specific technical solution to a network problem and described a specific, unconventional, distributed architecture that enhanced the performance of the computer/network system itself, rather than merely implementing an abstract idea on a computer, Applicant's claims do not provide a technical solution to a technical problem by generating the estimated GHG emissions. The Examiner asserts that Amdoc's reducing congestion in network bottlenecks is not the equivalent to the abstract idea of determining GHG emissions. Therefore, the Examiner suggests that Applicant incorporate the mechanism that actually provides the improvement into the claims or establish a clear nexus between the claim language and the improvement to technology where both the claims and the specification supports the asserted technical improvement.
Applicant argues (in REMARKS, pages 17-18) that Claim 17 has been amended to recite features that emphasize its patent eligibility under 35 U.S.C. § 101. Like independent claims 1, 9, and 16, independent claim 17 is believed to be eligible at both prongs of the patent eligibility framework for at least the reasons described above. Additionally, claim 17 includes features, which further emphasize its allowability under 35 U.S.C. § 101. For example, independent claim 17 recites, inter alia, training the Al model to encode relationships between respective infrastructural components and operational parameters of a set of deployed applications and respective greenhouse gas (GHG) emissions of the set of deployed applications, yielding a trained-Al model, wherein the respective infrastructural components of the set of deployed applications comprise components of data centers that each respective deployed application used during operation within a respective virtualization environment, and the respective operational parameters of the set of deployed applications correspond to how each respective deployed application used the respective infrastructural components during operation within the respective virtualization environment Indeed, independent claim 17 recites a specific step for training an AI model. The AI model is then used for "generating estimated GHG emissions of the application when deployed in the virtualization environment." In at least these ways, independent claim 17 is similar to Example VII provided in Section 2106.04(a)(1) of the MPEP. In Example VII, the Patent Office notes that "a method of training a neural network for facial detection" does not recite an abstract idea. Like Example VII, independent claim 17 includes a clear training step to "to encode relationships between respective infrastructural components and operational parameters of a set of deployed applications and respective greenhouse gas (GHG) emissions of the set of deployed applications Because training an Al model is not a human mental process (e.g., not a process that can be performed via pen and paper) nor a method of organizing human activity, claim 17 is not directed to an abstract idea. Accordingly, Applicant submits that independent claims 17 is patent eligible at both prongs of the patent eligibility framework.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, claims that do no more than apply established methods of machine learning to a new data environment, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101. Applicant has not provided a specific, non-generic improvement to the underlying AI technology itself. Lastly, the computer components in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to “apply” the exception using a generic computer component. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, pages 18-19) that dependent Claims 8, 15, and 24 recite additional features that further overcome 35 U.S.C. § 101. Claims 8, 15, and 24 depend from independent claims 1, 9, and 17, respectively. Thus claims 8, 15, and 24 are believed to be allowable for at least the reasons described above. Further, claims 8, 15, and 24 recite additional features which integrate any alleged abstract idea into a practical application. These steps may be used to compare two similar applications and generate a recommendation between the two similar applications to limit greenhouse gas emissions within the virtualization environment. In at least these ways, claims 8 and 15 further provide improvements to an application deployment pipeline for virtualization environments (i.e., a technical field) by generating a recommendation that limits greenhouse gas emissions within the virtualization environment Claim 24 adds an additional step related to training the Al model as described with reference to independent claim 17. For example, claim 24 recites "
generating associations among different infrastructure components and operational parameters corresponding to different virtualization environment service providers." The specification details that GHG emission data may be specific to a virtual environment service provider. As such, when developing the training dataset, the processor may receive infrastructural components and parameters associated with different virtual environment service providers." For at least the preceding reasons, Applicant submits that independent claims 1, 9, 16, and 17 as well as their respective dependent claims are allowable at both prongs of the patent eligibility framework.
In response to Applicant's arguments, the Examiner respectfully disagrees and notes that first, "comparing two similar applications and generating a recommendation between the two similar applications" and/or" generating associations among different infrastructure components and operational parameters corresponding to different virtualization environment service providers" do not appear to provide a technical solution to a technical problem. Instead, the steps which can be performed in the human mind, appear to implemented by generic computer components used as tools to perform the abstract idea. Therefore, the Examiner suggests that Applicant incorporate the mechanism that actually provides the improvement into the claims or establish a clear nexus between the claim language and the improvement to technology where both the claims and the specification supports the asserted technical improvement.
Applicant’s arguments, see REMARKS, pages 20-22, filed 20 January 2026, with respect to the rejection of claims 17-20 under 35 U.S.C. 103 have been fully considered and are persuasive. The rejection of claims 17-20 under 35 U.S.C. 103 has been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1, 3-4, 6-9, 11, and 13-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claims 1, 9, and 16, the Examiner is unable to locate in the specification the step of “extracting a resource definition of the application from the IAC file that is to be provisioned in the virtual environment for execution of the application”
Paragraph [0026] of the Specification recites:
For example, in certain embodiments, the processor 26 may execute the application analyzer 50 to apply the analysis rules 52 to the IAC files 42 and/or source code 40 of the application to determine the infrastructural components and operational parameters 76 of the deployed application. In other embodiments, in block 74, the processor 26 may receive inputs from a user manually defining the infrastructural components and operational parameters of the deployed application.
However, the Examiner is unable to locate how and where the “extracting” is implemented in the Specification. Therefore, appropriate correction is required.
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, 3-4, 6-9, 11, and 13-24 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Step 1
Claims 1, 3-4, 6-9, 11, and 13-24 recite a method for estimating GHG emissions when deploying an application in a virtualization environment. Claims 1, 3-4 and 6-8 are directed to a system (i.e., a machine). Claims 9, 11 and 13-15 are directed to a method (i.e., a process). Claims 16 and 21-22 are directed to a non-transitory, computer-readable medium (i.e., a manufacture). Claims 17-20 and 23-24 are directed to a system (i.e., a machine). Therefore, claims 1, 3-4, 6-9, 11, and 13-24 all fall within the one of the four statutory categories of invention.
Step 2A Prong 1
Independent claim 1 substantially recites:
store an artificial intelligence (AI) model;
during deployment of an application, receiving a request to determine estimated greenhouse gas (GHG) emissions of the application when deployed in a virtualization environment, wherein the request indicates at least one application file of the application, and the request is part of a deployment process of the application;
extracting a resource definition of the application from the file that is to be provisioned in the virtual environment for execution of the application;
analyzing the resource definition of the application to determine infrastructural components of a data center that the application will use during operation within the virtualization environment by comparing a first set of one or more portions of the resource definition of the at least one file to a first set of analysis rules corresponding to a set of infrastructural components and aggregating one or more infrastructural components of the set of infrastructural components that satisfy a condition of at least one analysis rule of the first set of analysis rules associated with the set of infrastructural components;
analyzing the resource application of the application to determine operational parameters of the application corresponding to how the infrastructural components are used by the application during operation within the virtualization environment by comparing a second set of one or more portions of the at least one file to a second set of analysis rules corresponding to a set of operational parameters and aggregating one or more operational parameters that satisfy a condition of at least one analysis rule of the second set of analysis rules associated with the set of operational parameters:
generating the estimated GHG emissions of the application when deployed in the virtualization environment by providing the infrastructural components and operational parameters of the application to the Al model that is trained using a global set of GHG emissions corresponding to a global set of infrastructural components and operational parameters of a respective global set of applications deployed in the virtualization environment, wherein the Al model is configured to generate the estimated GHG emissions by determining GHG emissions patterns associated with the provided infrastructural components and operational parameters of the application from the global set of infrastructural components and operational parameters; and
providing a response to the request that includes the estimated GHG emissions of the application when deployed in the virtualization environment. The aforementioned limitations which may be interpreted as at least as “Managing Personal Behavior or Relationships or Interactions Between People” (including social activities, teaching, and following rules or instructions) and/or a Mental Process (including an observation, evaluation, judgment, opinion) in claim 1. That is, nothing in the claim elements preclude the step from practically being performed by the human mind (analyzing) and by managing behavior or relationships or interactions between people (store, receiving, extracting, analyzing, analyzing, generating, and providing).
Independent claims 9 and 16 substantially recites:
during deployment of an application, receiving a request from a deployment pipeline to determine estimated greenhouse gas (GHG) emissions of the application when deployed in a virtualization environment, wherein the request indicates at least one file of the application, and the request is part of a deployment process of the application;
extracting a resource definition of the application from the file that is to be provisioned in the virtual environment for execution of the application;
analyzing the resource definition of the application to determine infrastructural components of a data center that the application will use during operation within the virtualization environment by comparing a first set of one or more portions of the at least one file to a first set of analysis rules corresponding to a set of infrastructural components and aggregating one or more infrastructural components of the set of infrastructural components that satisfy a condition of at least one analysis rule of the first set of analysis rules associated with the set of infrastructural components:
analyzing the resource definition of the application to determine operational parameters of the application corresponding to how the infrastructural components are used by the application during operation within the virtualization environment by comparing a second set of one or more portions of the at least one application file to a second set of analysis rules corresponding to a set of operational parameters and aggregating one or more operational parameters that satisfy a condition of at least one analysis rule of the second set of analysis rules associated with the set of operational parameters;
generating the estimated GHG emissions of the application when deployed in the virtualization environment by providing the infrastructural components and operational parameters of the application to an artificial intelligence (AI) model that is trained using a global set of GHG emissions corresponding to a global set of infrastructural components and operational parameters of a respective global set of applications deployed in the virtualization environment, wherein the Al model is configured to generate the estimated GHG emissions by determining GHG emissions patterns associated with the provided infrastructural components and operational parameters of the application from the global set of infrastructural components and operational parameters, and
providing a response to the request that includes the estimated GHG emissions of the application when deployed in the virtualization environment. The aforementioned limitations which may be interpreted as at least as “Managing Personal Behavior or Relationships or Interactions Between People” (including social activities, teaching, and following rules or instructions) and/or a Mental Process (including an observation, evaluation, judgment, opinion) in claims 9 and 16. That is, nothing in the claim elements preclude the step from practically being performed by the human mind (analyzing in claims 9 and 16) and by managing behavior or relationships or interactions between people (receiving, extracting, analyzing, analyzing, generating, and providing in claims 9 and 16).
Independent claim 17 substantially recites:
store an artificial intelligence (AI) model;
training the AI model to encode relationships between respective infrastructural components and operational parameters of a set of deployed applications and respective greenhouse gas (GHG) emissions of the set of deployed applications, yielding a trained AI model, wherein the respective infrastructural components of the set of deployed applications comprise components of data centers that each respective deployed application used during operation within a respective virtualization environment, and the respective operational parameters of the set of deployed applications correspond to how each respective deployed application used the respective infrastructural components during operation within the respective virtualization environment;
receiving a file associated with an application to be deployed in a virtualization environment;
identifying, using the trained AI model, infrastructural components and operational parameters of the application based on the IAC file; and
generating, using the trained AI model, estimated GHG emissions of an application when deployed in a virtualization environment, based on infrastructural components and operational parameters of the application by determining GHG emissions patterns associated with the infrastructural components and operational parameters of the application from the respective infrastructural components and operational parameters of the set of deployed applications. The aforementioned limitations which may be interpreted as at least as “Managing Personal Behavior or Relationships or Interactions Between People” (including social activities, teaching, and following rules or instructions) and/or a Mental Process (including an observation, evaluation, judgment, opinion) in claim 17. That is, nothing in the claim elements preclude the step from practically being performed by the human mind (identifying) and by managing behavior or relationships or interactions between people (store, training, receiving, identifying, and generating).
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements (e.g. “a computing system,” “at least one memory,” “an artificial intelligence (AI) model,” “at least one processor,” “instructions,” “an application,” “a software deployment platform,” “at least one IAC file,” and “a set of infrastructural components,” “a global set of infrastructural components” and “an artificial intelligence (AI) model”); claim 9 recites the additional elements (e.g. “an application,” “a software deployment platform,” “at least one IAC file,” “an artificial intelligence (AI) model,” “a set of infrastructural components” and “a global set of infrastructural components”), claim 16 recites the additional element (e.g. “a computer-readable medium,” “instructions,” “a processor,” “a computing system,” “an application,” “a software deployment platform,” “at least one IAC file,” “an artificial intelligence (AI) model,” “a set of infrastructural components” and “a global set of infrastructural components”), and claim 17 recites the additional element (e.g. “a computing system,” “at least one memory,” “an artificial intelligence (AI) model,” “at least one processor,” “instructions,” “a set of deployed applications,” and “at least one IAC file”) – using the memory to perform the “store” in claims 1 and 17; using the processor to perform the “receiving/receive”; “extracting/extract,” “analyzing/analyze”; “analyzing/analyze, ”generating/generate“; and “providing/provide” in claims 1 and 16 and using the at least one processor to perform the “training” and “generating” in claim 17. The “processor” in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “receiving/receive”; “extracting/extract,” “analyzing/analyze”; “analyzing/analyze,” “generating/generate“; and “providing/provide” in claims 1 and 16 and/or “training” and “generating” in claim 17) such that it amounts no more than mere instructions to “apply” the exception using a generic computer component. That is, the aforementioned limitations merely invoke the generic components as a tool to perform the abstract idea, e.g. see MPEP 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
Independent claims 1, 9, 16 and 17, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the “computing system,” “at least one memory,” “artificial intelligence (AI) model,” “at least one processor,” “instructions,” “application,” “at least one IAC file,” “set of infrastructural components,” and “global set of infrastructural components” in claim 1; the additional element of using the “application,” “a software deployment platform,” “at least one IAC file,” “set of infrastructural components,” “artificial intelligence (AI) model,” and “global set of infrastructural components” in claim 9; the additional element of using the “computer-readable medium,” “instructions,” “processor,” “computing system,” “application,” “a software deployment platform,” “at least one IAC file,” “set of infrastructural components,” “artificial intelligence (AI) model,” and “global set of infrastructural components” in claim 16; and the additional element of using the “computing system,” “at least one memory,” “artificial intelligence (AI) model,” “at least one processor,” “instructions,” “set of deployed applications,” and “at least one IAC file” in claim 17 to perform the “store and/or “receiving/receive”; “extracting/extract,” “analyzing/analyze”; “analyzing/analyze,” “generating/generate“; and “providing/provide” steps in claims 1 and 16, and to perform the “store,” “training,” “receiving,” “identifying” and “generating” steps in claim 17) amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e. inventive concept) to the abstract idea. The claims are ineligible.
As per dependent claim 3, the recitation of “a source code file” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claim 1, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claim 4, the recitations of “store…a set of rules…” and “applying each analysis rule of the set of analysis rules …” are further directed to a method of organizing human activity as described in claim 1. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Further, the recitation, “an application analyzer” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claim 1, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 6 and 13, the recitations of “decision tree model, a random forest (RF) model, a support vector machine (SVM) model, a linear regression model, an artificial neural network (ANN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model” are other computer components recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claim 1 and 9, respectively, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 7 and 14, the recitation of “before providing the response, analyzing the infrastructural components and operational parameters… “ is further directed to a method of organizing human activity and/or a mental process as described in claims 1 and 9, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 8 and 15, the recitations of “receiving a second request to determine a second estimated GHG emissions…”; “analyzing… to determine second infrastructural components and operational parameters…”; “generating the second estimated GHG emissions…”; “providing a second response to the second request…” and “comparing the estimated GHG emissions of the application and the second estimated GHG emissions…” are further directed to a method of organizing human activity and/or a mental process as described in claims 1 and 9, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Similar to above, the "receiving a second request…” is just mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Further, the recitations of “a second application” and “at least one second application file” are other computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 9, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claim 11, the recitation of “applying each analysis rule of the set of analysis rules …” are further directed to a method of organizing human activity as described in claim 9. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 18, the recitation of “store a training dataset having a plurality of entries, each indicating the respective infrastructural components and operational parameters…”is further directed to a method of organizing human activity and/or a mental process as described in claim 17. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Further, the recitations of “a deployed application” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claim 17, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claim 19, the recitations of “for each entry of the plurality of entries: (A) providing, as input to the AI model, the respective infrastructural components and operational parameters…” and “in response receiving, as output, estimated GHG emissions of the deployed application”; “(B) calculating a difference between the estimated GHG emissions of the deployed application and the respective GHG emissions…”; and “(C) in response to determining that the difference is greater than a predefined threshold value, modifying one or more parameters of the AI model and returning to step (A)”; and “storing the one or more parameters as the trained AI model” are further directed to a method of organizing human activity as described in claim 17. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Similar to above, the "receiving as output, estimated GHG emissions of the deployed application…” is just mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more.
As per dependent claim 20, the recitations of “for each deployed application of a set of deployed applications: determining the respective infrastructural components and operational parameters of the deployed application”; “determining the respective GHG emissions of the deployed application”; and “generating an entry of the training dataset that includes the respective infrastructural components and operational parameters…” are further directed to a method of organizing human activity and/or a mental process as described in claim 17. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 21, the recitation of “determine one or more recommendations to lower the estimated GHG emissions …” is further directed to a method of organizing human activity and/or a mental process as described in claim 16. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 22, the limitation merely narrows the previously recited abstract idea limitations. Dependent claim 22 recites the estimated GHG emissions comprise a volume or mass of carbon dioxide (CO2) equivalent for a time period that the application is deployed within the virtualization environment based on a predefined number of active users. For the reasons described above with respect to claim 16, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 23, the recitation of “receiving the file associated with the application to be deployed in the virtualization environment…” is further directed to a method of organizing human activity and/or a mental process as described in claim 17. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Similar to above, the "receiving the file …” is just mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more.
As per dependent claim 23, the recitation of “train the model to encode relationships between the respective infrastructural components and operational parameters…” is further directed to a method of organizing human activity and/or a mental process as described in claim 17. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Dependent Claims 3-4, 6-8, 11, 13-15, and 18-24 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent claims 3-4, 6-8, 11, 13-15, and 18-24, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. Accordingly, dependent claims 3-4, 6-8, 11, 13-15, and 18-24 are rejected as being ineligible for patenting under 35 U.S.C. 101 based upon the same analysis.
Examiner’s Note: The Examiner suggests that Applicant incorporate the mechanism providing the improvement into the claims or establish a clear nexus between the claim language and the improvement to technology where both the claims and the specification supports the asserted technical improvement. For instance, the claims should do more than apply established methods of machine learning to a new data environment, by disclosing improvements to the machine learning models to be applied.
Prior Art Discussion
As per claims 1, 9, and 16, the best prior art:
1) Aurongzeb et al. (US PG Pub. 20240037564 A1) discloses a system and method for limiting carbon dioxide footprint caused by installation of software and firmware updates; and
2) Bachman et al. (US PG Pub. 20230359970 A1) discloses an automated estimation and capture of greenhouse gas (GHG) emissions from performance of software-based processes using cloud-based integration platform.
However, neither Aurongzeb et al. nor Bachman et al. discloses or fairly teaches:
during deployment of an application, receiving a request from a deployment pipeline of a software deployment platform to determine estimated greenhouse gas (GHG) emissions of the application when deployed in a virtualization environment, wherein the request indicates at least one infrastructure-as-code (IAC) file of the application, and the request is part of a deployment process of the application;
analyzing the resource definition of the application to determine infrastructural components of a data center that the application will use during operation within the virtualization environment by comparing a first set of one or more portions of the resource definition of the at least one IAC file to a first set of analysis rules corresponding to a set of infrastructural components and aggregating one or more infrastructural components of the set of infrastructural components that satisfy a condition of at least one analysis rule of the first set of analysis rules associated with the set of infrastructural components
As per claim 17, the best prior art:
1) Aurongzeb et al. (US PG Pub. 20240037564 A1) discloses a system and method for limiting carbon dioxide footprint caused by installation of software and firmware updates; and
2) Bachman et al. (US PG Pub. 20230359970 A1) discloses an automated estimation and capture of greenhouse gas (GHG) emissions from performance of software-based processes using cloud-based integration platform.
However, neither Aurongzeb et al. nor Bachman et al. discloses or fairly teaches:
training the AI model to encode relationships between respective infrastructural components and operational parameters of a set of deployed applications and respective greenhouse gas (GHG) emissions of the set of deployed applications, yielding a trained AI model, wherein the respective infrastructural components of the set of deployed applications comprise components of data centers that each respective deployed application used during operation within a respective virtualization environment, and the respective operational parameters of the set of deployed applications correspond to how each respective deployed application used the respective infrastructural components during operation within the respective virtualization environment; and
generating, using the trained AI model, estimated GHG emissions of the application when deployed in the virtualization environment, based on the infrastructural components and operational parameters of the application by determining GHG emissions patterns associated with the infrastructural components and operational parameters of the application from the respective infrastructural components and operational parameters of the set of deployed applications.
As per claim 1, 9, and 16, the best Foreign Prior Art:
1) Ginsberg et al. (AU 2021106485 A4) discloses a computer implemented system for measuring greenhouse gas emitting activities of a user
However, Ginsberg et al. fails to disclose or fairly teach:
during deployment of an application, receiving a request from a deployment pipeline of a software deployment platform to determine estimated greenhouse gas (GHG) emissions of the application when deployed in a virtualization environment, wherein the request indicates at least one infrastructure-as-code (IAC) file of the application, and the request is part of a deployment process of the application;
analyzing the resource definition of the application to determine infrastructural components of a data center that the application will use during operation within the virtualization environment by comparing a first set of one or more portions of the resource definition of the at least one IAC file to a first set of analysis rules corresponding to a set of infrastructural components and aggregating one or more infrastructural components of the set of infrastructural components that satisfy a condition of at least one analysis rule of the first set of analysis rules associated with the set of infrastructural components
As per claim 17, the best Foreign Prior Art,
1) Ginsberg et al. (AU 2021106485 A4) discloses a computer implemented system for measuring greenhouse gas emitting activities of a user
However, Ginsberg et al. fails to disclose or fairly teach:
training the AI model to encode relationships between respective infrastructural components and operational parameters of a set of deployed applications and respective greenhouse gas (GHG) emissions of the set of deployed applications, yielding a trained AI model, wherein the respective infrastructural components of the set of deployed applications comprise components of data centers that each respective deployed application used during operation within a respective virtualization environment, and the respective operational parameters of the set of deployed applications correspond to how each respective deployed application used the respective infrastructural components during operation within the respective virtualization environment; and
generating, using the trained AI model, estimated GHG emissions of the application when deployed in the virtualization environment, based on the infrastructural components and operational parameters of the application by determining GHG emissions patterns associated with the infrastructural components and operational parameters of the application from the respective infrastructural components and operational parameters of the set of deployed applications.
As per claim 1, 9, and 16, the Best NPL:
1) Mytton, David; “Assessing the suitability of the Greenhouse Gas Protocol for calculation of emissions from public cloud computing workloads”, 08/08/2020, Journal of Cloud Computing: Advances, Systems and Applications, 11 pages discloses how the Greenhouse Gas Protocol method of assessment of IT emissions does not work for public cloud environments and suggests how this can be tackled by the cloud vendors themselves.
However, Mytton fails to disclose or fairly teach:
during deployment of an application, receiving a request from a deployment pipeline of a software deployment platform to determine estimated greenhouse gas (GHG) emissions of the application when deployed in a virtualization environment, wherein the request indicates at least one infrastructure-as-code (IAC) file of the application, and the request is part of a deployment process of the application;
analyzing the resource definition of the application to determine infrastructural components of a data center that the application will use during operation within the virtualization environment by comparing a first set of one or more portions of the resource definition of the at least one IAC file to a first set of analysis rules corresponding to a set of infrastructural components and aggregating one or more infrastructural components of the set of infrastructural components that satisfy a condition of at least one analysis rule of the first set of analysis rules associated with the set of infrastructural components
As per claim 17, the Best NPL:
1) Mytton, David; “Assessing the suitability of the Greenhouse Gas Protocol for calculation of emissions from public cloud computing workloads”, 08/08/2020, Journal of Cloud Computing: Advances, Systems and Applications, 11 pages discloses how the Greenhouse Gas Protocol method of assessment of IT emissions does not work for public cloud environments and suggests how this can be tackled by the cloud vendors themselves.
However, Mytton fails to disclose or fairly teach:
training the AI model to encode relationships between respective infrastructural components and operational parameters of a set of deployed applications and respective greenhouse gas (GHG) emissions of the set of deployed applications, yielding a trained AI model, wherein the respective infrastructural components of the set of deployed applications comprise components of data centers that each respective deployed application used during operation within a respective virtualization environment, and the respective operational parameters of the set of deployed applications correspond to how each respective deployed application used the respective infrastructural components during operation within the respective virtualization environment; and
generating, using the trained AI model, estimated GHG emissions of the application when deployed in the virtualization environment, based on the infrastructural components and operational parameters of the application by determining GHG emissions patterns associated with the infrastructural components and operational parameters of the application from the respective infrastructural components and operational parameters of the set of deployed applications.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Bierlaire, Olivier, “Carbon Aware Cloud”, Mar 7, 2023, medium.com, 19 pages discloses Infrastructure as Code (IaC) tools like Terraform enable infrastructure automation, where your infrastructure is described in a machine-readable file, which can be analyzed and generated (templates, variables).
2) “Carbonifer: estimate carbon footprint Terraform projects”, February 3, 2023, reddit.com, 7 pages.
3) Carbonifer.io. , May 7, 2023, Wayback Machine.
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/F.A.N/Examiner, Art Unit 3628 /SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628