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 .
Status of Claims
Claims 1 and 21 have been amended and are hereby entered.
Claims 6 and 11 – 20 were cancelled.
Claims 1 - 5, 7 - 10 and 21 - 29 are pending and have been examined.
This action is made FINAL.
The Examiner would like to note that this application is now being handled by examiner Ivonnemary Rivera González.
Response to Arguments
Applicant arguments filed July 20, 2026 have been fully considered but they are not persuasive.
Regarding the applicant's arguments against the 101 rejection of pending claims on pages 13-15: Applicant’s arguments directed to Step 2A prong 2 from the 101 analysis were considered. However, these arguments are not persuasive and the examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 2 and Step 2B starting in p. 14: The Applicant alleges that the claims integrate, the judicial exception identified, into a practical application and further alleges that “present claims do not merely recite applying a cloud remediation at a high level of generality” because the claim “limitations describe specific mechanisms by which the remediation is implemented within a cloud computing environment itself” and further compares the claimed invention with BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51 (Fed. Cir. 2016). However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Because such “claimed mechanisms” are simply improving and further defining the abstract idea itself. Rather, the claims further narrows the abstract idea identified and their specificity in the limitations of claim does not necessarily equate to eligibility. For instance, In buySAFE, Inc. v. Google, Inc. (Fed. Cir. 2014), the court stated that "abstract ideas, no matter how groundbreaking, innovative, or even brilliant, are outside what the statute means by "new and useful process, machine, manufacture, or composition of matter", and reference is made to Myriad by the court for this position. Also stated in buySAFE is "In defining the excluded categories, the Court has ruled that the exclusion applies if a claim involves a natural law or phenomenon or abstract idea, even if the particular natural law or phenomenon or abstract idea at issue is narrow. Mayo, 132 S. Ct. at 1303. The Court in Mayo rejected the contention that the very narrow scope of the natural law at issue was a reason to find patent eligibility, explaining the point with reference to both natural laws and one kind of abstract idea, namely, mathematical concepts. Additionally, these claim limitations describe the abstract idea for the end-result of executing operations to efficiently optimize and manage cloud computing environment’ infrastructure, energy consumption and capacity when handling the user’s digital assets and avoid digital waste while increasing carbon credits without reciting sufficient details of the computer functioning improvements or provide any meaningful limitations that, in an ordered combination provide “significantly more” or provide any integration into a practical application in the claims. Thus, these claim limitations invoked the use of a computer while using an AI models that are generally/broadly recited as a tool to perform an abstract idea(s) of determining cloud management solutions/actions that can still be done mentally and manually with the aid of a computer to further execute a code to perform the solution or action determined (see MPEP 2106.04(d)(I) and MPEP 2106.05(f)).
Thus, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC § 103 for the pending claims on pages 15 – 19: Applicant’s arguments regarding these amended limitation steps in the pending claims are not persuasive. Firstly, because Applicant is focusing on each prior art teaching, rather than focusing on the actual language claimed in each claim limitation and how their corresponding limitation steps are different from the prior art teachings. Thus, these alleged features are not actually claimed in this exact manner. Rather, the steps disclose a broader language that the prior art combination of Chen and Gebhart, still reasonably satisfies and teaches in light of the broadest reasonable interpretation (BRI) of the claim language. Specifically, because the Applicant is arguments about the limitations of “generating” an “executable code” that is generated in response to and after determining a cloud management action to further “execute” it while considering specific and different limitations addressed when determining and utilizing the cloud management action, that upon review, are still taught by Chen in view of Gebhart due to broader language the claim language discloses which still is satisfied by Gebhart for the execution of an executable code or “a command” to “take an action changing a state of the cloud system” wherein such command to take action (i.e. directed to a cloud management action) can be “suspend operation of the software or a portion (e.g., specific functionality, component) thereof”, but can “depend upon factors other than cost” such as security/compliance policies see ¶0035 - 36; Gebhart). Also, different commands for action can be issued that can depend “upon the result of processing the inputs according to the ruleset” as well as from direct “user instructions” to “handle operations in a particular manner” (see ¶0037 and ¶0048; Gebhart) which is another example of an executable code. Further, Chen attends the analysis of a request and determination a “power usage” attributable to that request (see ¶0060 – 61; Chen) as well as, upon a service’s power consumption determination, “sustainability impact may be determined” which the “may depend at least in part on the efficiency of the infrastructure” and “power consumption efficiencies may be indicated by the power usage efficiency (PUE) metric” (see ¶0064 – 65; Chen). Therefore, the Examiner respectfully disagrees and maintains 35 USC § 103 rejection for these pending claims.
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 - 5, 7 - 10 and 21 - 29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of this claimed invention recited in the claims begins in view of independent claim 1, the most representative claim of the independent claims set 1 and 21, as follows:
At Step 1: Claims 1-5 and 7-10 falls under statutory category of a process, while claims 21-29 are directed to a machine.
At Step 2A Prong 1: Claim 1 (representative of claim 21) recites an abstract idea in the following limitations:
receiving…at least one request from at least one user;
receiving…cloud information obtained…associated with the one or more clouds of the user…
analyzing…the cloud information by providing the cloud usage information, the resource allocation information, and the resource utilization information as input…
determining…at least one cloud management action for managing the at least one cloud…based on the analyzing…
generating…at least one management information based on the determining of the at least one cloud management action…; and
transmitting…the at least one management information; and
generating…executable code configured to implement the at least one cloud management action…to address the one or more limitations…; and
executing…the executable code to implement the at least one cloud management action….
Generally, and as disclosed in the specification in ¶0009 and ¶0004, this claimed invention provides a system and a “method for facilitating managing clouds for a user” for providing “cost-effective solutions to optimally utilize cloud computing platforms”, “recycling unused digital assets” and “optimal data storage using cloud computing platforms” and the like, respectively. Further, the claimed invention is intended for “digital recycling of unused, redundant, and/or unrequired digital assets” (i.e. deduplicating and removing redundant data; see ¶0006 from Applicant disclosure). However, the abstract idea(s) of mental processes that can be practically performed in the human mind or in pen and paper are/is recited in claim 1 (See MPEP 2106.04(a)(2), subsection III). Specifically, the abstract idea is recited in the step(s) directed in part to “receiving” requests and “cloud information obtained”, “analyzing…the cloud information…”, “determining…at least one cloud management action for managing the at least one cloud provided”, “generating…at least one management information based on the determining of the at least one cloud management action …” and “transmitting…the at least one management information”. Because receiving cloud-related data and requests to analyze the data and determine cloud management actions to further generate management information to later transmit it at least encompasses concepts that can be performed in the human mind as these steps require observation, evaluation and judgement. Also, these steps can either be done with the help of physical aid such as pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. Thus, the steps do not negate and further still reads in the mental nature of the limitation(s), when obtaining and analyzing such cloud-related information to determine actions, as well as the concept is merely claimed to be performed on a generic computer and is merely using a computer as a tool to perform the concept of analyzing cloud-related data and its actions in order to resolve the user request and de-duplicate data so it can be removed or recycled (see MPEP 2106.04(a)(2)(III)(B & C)).
At Step 2A Prong 2: For independent claims 1 and 21, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of using a communication device, scanning cloud devices, one or more artificial intelligence models and at least one cloud platform. These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). Specifically, these steps are recited as being performed by the computer. The computer which further uses the AI models is recited at a high level of generality that is being used as a tool to perform the generic computer functions for determining the cloud management actions to manage and optimize the cloud data storage and infrastructure for storage efficiency. Thus, these steps mentioned above are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components can perform.
Therefore, this analysis is indicative of the fact that even when viewed in combination, the claims’ additional elements do not integrate the abstract idea or judicial exception into a practical application.
Step 2B: For independent claims 1 and 21, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: using a communication device, scanning cloud devices, one or more artificial intelligence models and at least one cloud platform, including the step “determining” cloud management actions using the AI models claimed. These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 2 - 5, 7 - 10 and 22 - 29, the same analysis is incorporated. Due to their dependency to the independent claims analyzed, these claims cover or fall under the same abstract idea(s) of a mental process. They describe additional limitations steps of:
Claims 2 - 5, 7 - 10 and 22 - 29: further describes the abstract idea of the method for facilitating and managing one or more clouds for a user and determinations of spending/pricing information, “digital waste” analysis and “cloud metrics” to generate “spending optimization recommendations”, “management information” (i.e. optimization reports) and “questionnaires” for cloud data analysis to determine corresponding management actions as well as calculations of “reduced energy consumption and/or reduced carbon emission” which are also encompassing mental processes performed mentally and/or in pen and paper that further require observation, evaluation and judgement.
Step 2A Prong 2 and Step 2B: For dependent claims 2 - 5, 7 - 10 and 22 - 29, are merely reciting further embellishments of the abstract idea and do not amount to anything that is significantly more than the abstract idea itself. In other words, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limitations that, in an ordered combination provide “significantly more” or provide any integration into a practical application. Rather, the dependent claims are merely further reciting features that are just as abstract as independent Claims 1 and 21. Nothing additional is claimed that is not part of the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1 - 5, 7 - 10 and 21 - 29 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (U.S. Pub No. 20140324407 A1) in view of Gebhart (U.S. Pub No. 20210349851 A1).
Regarding claims 1 and 21:
This independent claim set is represented by claim 1.
Chen discloses “systems and methods” to “help identify dependencies, and relate these dependencies to infrastructure, to provide a better understanding of power consumption at the service-level” wherein such services are “individual services executing in data center(s), including cross-platform data centers such as the cloud” (see ¶0014; Chen). Thus, Chen teaches:
receiving, using a communication device, at least one request from at least one user device associated with the user; (In ¶0059 – 60; Fig. 6: teaching determining a received request from a client; see also Figure 6; Further in ¶0021 teaching inputs into the computer environment.)
receiving, using the communication device, cloud information obtained by scanning cloud devices associated with the one or more clouds of the user, wherein the cloud information including cloud usage information associated with the one or more clouds, resource allocation information associated with the one or more clouds, and resource utilization information associated with the one or more clouds, wherein the one or more clouds are provided to the user by at least one cloud platform; (In ¶0023; Fig. 3 (310): teaching that the invention relates to data center activities such as external, cloud device activities and data can be accessed via a “source 115 of information for the data center(s) including cross-platform data centers such as an internal or external cloud”. Noting that Figure 3 teaches feature 310, which is the infrastructure provided by the data center that is being analyzed.)
analyzing, using a processing device, the cloud information by providing the cloud usage information, the resource allocation information, and the resource utilization information as input to one or more artificial intelligence models (In ¶0059 – 60: teaching analyzing the request and determining a “power usage” attributable to that request based on different modeling such as a “direct acyclic graph” (DAG). Regarding the input to one more artificial intelligence models, this is further addressed below.)
determining, using the processing device and based on execution of the one or more artificial intelligence models, at least one cloud management action for managing the at least one cloud provided by the at least one cloud platform…; generating, using the processing device, at least one management information based on the determining of the at least one cloud management action, wherein the at least one management information facilitates a performing of the at least one cloud management action for the managing of the at least one cloud; and (In ¶0060 – 61: teaching analyzing the request and determining a “power usage” attributable to that request. Regarding that the input is to one more artificial intelligence models that include the analyzing cloud management actions claimed, this is further addressed below. Further, in ¶0064 – 65 teaching that “after determining the power consumption of a service, the sustainability impact may be determined” which the “may depend at least in part on the efficiency of the infrastructure” and “power consumption efficiencies may be indicated by the power usage efficiency (PUE) metric”. Regarding the input to one more artificial intelligence models, this is further addressed below.)
transmitting, using the communication device, the at least one management information; and (In ¶0077: teaching providing the data to an end-user interface; see also ¶0040 teaching display of the results in a report format for management or a customer.)
generating, using the processing device, executable code configured to implement the at least one cloud management action on the at least one cloud platform to address the one or more limitations, (In ¶0030: teaching that the modules of the invention “may be implemented as agents that run on top of an existing program code,” and see ¶0071 teaching performing adjustments for improving the power consumption.)
executing, using the processing device or the at least one cloud platform, the executable code to implement the at least one cloud management action at the least one cloud platform. (In ¶0025: teaching using management tools to ensure that the service satisfies sustainability goals, including adjustments based on given power consumption such as changing particular hosts that provide the service (see ¶0071 also). Further, in ¶0026, teaching using the management tools to cap power consumption.)
However, Chen fails to expressly teach the abilities of having the AI models with an executable code generated in response to and after determining a cloud management action as well as the specific and different limitations addressed when determining and utilizing the cloud management action. Thus, Gebhart which discloses “methods and apparatuses implementing intelligent control over software programs being hosted by remote cloud providers and private clouds” (see ¶0004; Gebhart), which teaches:
…based on the analyzing by the one or more artificial intelligence models, (In ¶0020; Fig. 2 (212): teaching an intelligent control application side 116 that includes “a processing engine 118” and “a neural network artificial intelligence 172” that comprises the “data layer 170” and “intelligent control application side 116” that performs the invention; See also Figure 2 feature 212 and ¶0060 teaching the artificial intelligence neural network making an actuator commanded to act upon the software.)
wherein the at least one cloud management action is utilized to address limitations corresponding to one or more of (1) redundant data, (2) inefficient storage methods, and (3) underutilized cloud computing resources, wherein the at least one cloud management action comprising modifying a deployment configuration, reallocating cloud resources, resizing infrastructure components, or migrating workloads between cloud resources (In ¶0105: teaching automatically de-provisioning or moving resources based on prices, carbon footprint, or both; see also, e.g., ¶0107 – 112 providing a determination of waste and a more efficient setup of migrating an operation from cloud provider 1 to cloud provider 2 to save costs.)
wherein the executable code is generated in response to and after determining, from execution of the one or more artificial intelligence models, the at least one cloud management action for managing the at least one cloud; and (In ¶0035 – 36; Fig. 1 (118) and Fig. 5 (505): teaching “based upon the query result (including the cost value) and the ruleset and/or the neural network, the processing engine issues a command 136 to an actuator 138” (i.e. directed to executable code), to “take an action changing a state of the cloud system” wherein such command to take action (i.e. directed to a cloud management action) can be “suspend operation of the software or a portion (e.g., specific functionality, component) thereof”, but can “depend upon factors other than cost” such as security/compliance policies and different commands for action can be issued that depend “upon the result of processing the inputs according to the ruleset” as well as from direct “user instructions” to “handle operations in a particular manner”(see ¶0037 and ¶0048) which is another example of an executable code. Refer to ¶0101 – 102 and ¶0105 – 106 for details and examples of the “ICO Controller” triggering “intelligent cloud operations” based on. Finally, see ¶0117 wherein the “computer-readable storage medium has stored thereon code 505 corresponding to an engine” (e.g. the “processing engine 118”) wherein the “code 504 corresponds to data, for example operational and/or experience data” and “may be configured to reference data stored in a database of a non-transitory computer-readable storage medium, for example as may be present locally or in a remote database server”.)
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of using artificial intelligence with an executable code generated in response to and after determining a cloud management action and considering the specific and different limitations addressed when determining and utilizing the cloud management action to perform a data migration to a different cloud (as disclosed by Gebhart) to the known method and system of quantifying cloud computing actions to determine cost and carbon footprint savings (as disclosed by Chen). One of ordinary skill in the art would have been motivated to apply the known technique of using artificial intelligence to perform this analysis because a trained neural network could “automatically analyze large amounts of data” (see Gebhart ¶0065) better than a human and would do it to save costs or reduce a carbon footprint to “continuously and/or automatically adjust the real infrastructure consumption to the needed infrastructure via automatic de-provisioning” (see Gebhart ¶0105). See also MPEP 2143.I.G. and MPEP § 2143(I)(D).
Regarding claims 2 and 22:
The combination of Chen and Gebhart, as shown in the rejection above, discloses the limitations of claims 1 and 21, respectively.
Chen further teaches:
determining, using the processing device, actual spending corresponding to the usage information; obtaining, using the processing device, platform pricing information corresponding to a plurality of cloud platforms; and analyzing, using the processing device, the platform pricing information and the actual spending using the one or more artificial intelligence models to determine a potential spending corresponding to the cloud usage information for each of the plurality of cloud platforms; and (In ¶0038 – 39: teaching determining costs (i.e. analyzing “sustainability metrics”) based, e.g., on the “power consumption” attributable to the request and outputting, at a dashboard, “information in terms of economic cost 410 for different services 405 and/or ecological cost 415, e.g., based on quantified power consumption”. See ¶0015, ¶0026, and ¶0031 further teaching the determination of costs.)
comparing the potential spending with the actual spending, wherein determining the at least one management action is further based on the comparison. (In ¶0038 – 39; Fig. 4: teaching determining costs (i.e. analyzing “sustainability metrics”) based, e.g., on the “power consumption” attributable to the request. Noting that Figure 4, as explained in ¶0039, provides for comparisons by color for economic costs 410.)
Regarding claims 3 and 23:
The combination of Chen and Gebhart, as shown in the rejection above, discloses the limitations of claims 1 and 21, respectively.
Chen further teaches:
determining, using the processing device, one or more cloud metrics of at least one cloud server of the one or more clouds; and (In ¶0059 – 60: teaching analyzing the request and determining a “power usage” attributable to that request.)
generating, using the processing device, at least one recommendation for optimizing a spending associated with the one or more clouds based on the one or more cloud metrics, wherein generating the at least one management information is further based on the at least one recommendation (In ¶0026: teaching using the invention to compare differences and reduce costs.)
Regarding claims 4 and 24:
The combination of Chen and Gebhart, as shown in the rejection above, discloses the limitations of claims 1 and 21, respectively.
Chen further teaches:
determining, using the processing device, at least one current cloud infrastructure information identifying deployed cloud resources and configuration parameters of a current cloud infrastructure associated with one or more clouds based on the analyzing of the cloud information; analyzing, using the processing device, the at least one current cloud infrastructure information; (In ¶0059 – 60: teaching analyzing the request and determining a “power usage” attributable to that request. Noting that in ¶0014, ¶0023, and ¶0037, the processing environment 300 can be cloud services/resources.)
determining, using the processing device, a digital waste associated with the one or more clouds based on the analyzing of the at least one current cloud infrastructure information; and (In ¶0059 – 60: teaching analyzing the request and determining a “power usage” attributable to that request. Noting that in ¶0014, ¶0023, and ¶0037, the processing environment 300 can be cloud services/resources as well as in ¶0013 and ¶0025 – 26 teaching comparing different services or sites to determine how to do the service most sustainably.)
determining, using the processing device, at least one optimized cloud infrastructure information of an optimized cloud infrastructure associated with the one or more clouds based on the at least one current cloud infrastructure information, the digital waste, and the at least one cloud management action, (In ¶0059 – 60: teaching analyzing the request and determining a “power usage” attributable to that request. Noting that in ¶0014, ¶0023, and ¶0037, the processing environment 300 can be cloud services/resources.)
wherein generating the at least one management information is further based on the at least one optimized cloud infrastructure. (In ¶0026: teaching comparing differences to “reduce costs”.)
However, Chen fails to expressly teach the determination of a “digital waste,” as noted in the rejection of Claim 1 above. Thus, Gebhart teaches the migration of data or processing from one cloud to another (see Gebhart ¶0108 - 113) including for the purpose of cost savings or carbon footprint reduction (see Gebhart ¶0105).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of using artificial intelligence and determining of a “digital waste,” to perform a data migration to a different cloud (as disclosed by Gebhart) to the known method and system of quantifying cloud computing actions to determine cost and carbon footprint savings (as disclosed by Chen). One of ordinary skill in the art would have been motivated to apply the known technique of using artificial intelligence to perform this analysis because a trained neural network could “automatically analyze large amounts of data” (see Gebhart ¶0065) better than a human and would do it to save costs or reduce a carbon footprint to “continuously and/or automatically adjust the real infrastructure consumption to the needed infrastructure via automatic de-provisioning” (see Gebhart ¶0105). See also MPEP 2143.I.G. and MPEP § 2143(I)(D).
Regarding claims 5 and 25:
The combination of Chen and Gebhart, as shown in the rejection above, discloses the limitations of claim 4 and 24, respectively.
Chen further teaches:
determining, using the processing device, at least one of an energy consumption and a carbon emission associated with the at least one current cloud infrastructure based on analyzing of the at least one current cloud infrastructure information; (In ¶0038: teaching that energy, i.e., power consumption, is one of the main metrics analyzed for the datacenters and correlates with carbon emissions attributable to the data center actions. Further, in ¶0064 and ¶0066 – 68, teaching the determination of power consumption for a particular service and then an associated carbon emission for that service.)
calculating, using the processing device, a reduced energy consumption and/or reduced carbon emission based on (1) the at least one of the energy consumption and the carbon emission associated with the at least one current cloud infrastructure and (2) the at least one optimized cloud infrastructure information; and (In ¶0026: teaching comparing differences to “reduce costs”, including ecological costs such as carbon emissions. See ¶0038 – 39 also.)
converting, using the processing device, the reduced energy consumption and/or the reduced carbon emission into a carbon credit based on the reduced energy consumption and/or the reduced carbon emission using at least one standard, wherein generating the at least one management information is further based on the carbon credit to prioritize implementation of the at least one cloud management action. (In ¶0077: teaching providing the data to an end-user interface. See also ¶0040 teaching display of the results in a report format for management or a customer.)
However, Chen fails to expressly teach that the carbon emission reduction is converted into a carbon credit. Nevertheless, Gebhart teaches that the determination and taking action that reduces carbon emissions “could even form the basis for an exchange of carbon credits” (see Gebhart ¶0055).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of using artificial intelligence and the conversion of carbon emission reduction into a carbon credit to perform a data migration to a different cloud (as disclosed by Gebhart) to the known method and system of quantifying cloud computing actions to determine cost and carbon footprint savings (as disclosed by Chen). One of ordinary skill in the art would have been motivated to apply the known technique of using artificial intelligence to perform this analysis because a trained neural network could “automatically analyze large amounts of data” (see Gebhart ¶0065) better than a human and would do it to save costs or reduce a carbon footprint to “continuously and/or automatically adjust the real infrastructure consumption to the needed infrastructure via automatic de-provisioning” (see Gebhart ¶0105). See also MPEP 2143.I.G. and MPEP § 2143(I)(D).
Regarding claims 7 and 26:
The combination of Chen and Gebhart, as shown in the rejection above, discloses the limitations of claims 1 and 21, respectively.
Chen further teaches:
wherein at least one of the one or more clouds is synchronized with at least one external device, wherein the method further comprising: determining, using the processing device, at least one device management action for the at least one external device based on the determining of the at least one cloud management action, (In ¶0037; Fig. 3: teaching that the invention relates to data center activities such as external cloud management actions, including, as shown in Figure 3, various external devices such as vendor databases or cloud storage. Further, ¶0026 is teaching providing information comparing different potential actions (i.e. “cap power consumption to meet sustainability goals, reduce costs, and/or meet regulator requirements”) so that the more sustainable action can be selected.)
wherein the generating of the at least one management information is further based on the determining of the at least one device management action for the at least one external device, wherein the at least one management information facilitates a performing of the at least one device management action for managing of the at least one external device. (In ¶0026; Fig. 3: teaching providing information comparing different potential actions (i.e. “cap power consumption to meet sustainability goals, reduce costs, and/or meet regulator requirements”) so that the more sustainable action can be selected, including, as shown in Figure 3 and in ¶0023 teaching various external devices as “sources” such as vendor databases or cloud storage.)
Regarding claims 8 and 27:
The combination of Chen and Gebhart, as shown in the rejection above, discloses the limitations of claims 7 and 26, respectively.
Chen further teaches:
further comprising: obtaining, using the processing device, at least one device data associated with the at least one external device; (In ¶0023: teaching that the invention relates to data center activities such as external, cloud device activities and data can be accessed via a “source 115 of information for the data center(s) including cross-platform data centers such as an internal or external cloud”. See ¶0038 teaching that energy, i.e., power consumption, is one of the main metrics analyzed for the datacenters and correlates with carbon emissions attributable to the data center actions. Further, in ¶0064 and ¶0066 – 68, teaching the determination of power consumption for a particular service and then an associated carbon emission for that service.)
analyzing, using the processing device, the at least one device data; (In ¶0038: teaching that energy, i.e., power consumption, is one of the main metrics analyzed for the datacenters and correlates with carbon emissions attributable to the data center actions.)
However, Chen fails to expressly teach the abilities of determining a digital waste, calculating a reduced energy consumption, converting the reduced energy consumption into a carbon credit to further generate carbon credit information. Thus, Gebhart further teaches:
determining, using the processing device, a digital waste associated with the at least one external device based on the analyzing of the at least one device data, wherein the determining of the at least one device management action is further based on the digital waste associated with the at least one external device; calculating, using the processing device, a reduced energy consumption of the at least one external device based on the at least one device data, the digital waste, and the at least one device management action; (In ¶0107 – 112: providing a determination of waste and a more efficient setup of migrating an operation from cloud provider 1 to cloud provider 2 to save costs.)
converting, using the processing device, the reduced energy consumption into a carbon credit based on the calculating of the reduced energy consumption; and generating, using the processing device, at least one carbon credit information based on the converting of the reduced energy consumption into the carbon credit (In ¶0055: teaching that this data can be used as the basis for the generation of carbon credits.)
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of using artificial intelligence as well as determining a digital waste, calculating a reduced energy consumption, converting the reduced energy consumption into a carbon credit to further generate carbon credit information and perform a data migration to a different cloud (as disclosed by Gebhart) to the known method and system of quantifying cloud computing actions to determine cost and carbon footprint savings (as disclosed by Chen). One of ordinary skill in the art would have been motivated to apply the known technique of using artificial intelligence to perform this analysis because a trained neural network could “automatically analyze large amounts of data” (see Gebhart ¶0065) better than a human and would do it to save costs or reduce a carbon footprint to “continuously and/or automatically adjust the real infrastructure consumption to the needed infrastructure via automatic de-provisioning” (see Gebhart ¶0105). See also MPEP 2143.I.G. and MPEP § 2143(I)(D).
Regarding claims 9 and 28:
The combination of Chen and Gebhart, as shown in the rejection above, discloses the limitations of claims 1 and 21, respectively.
Chen further teaches:
further comprising: generating, using the processing device, at least one initial management information distinct from the executable code, wherein the at least one initial management information comprises a report associated with at least one cloud of the one or more clouds, the report including at least one optimization parameter or configuration option for the at least one cloud; and (In ¶0035: teaching outputting the data in the “format of a report” (see ¶0040 also). See ¶0026 teaching using that data to “reduce costs”, i.e., optimize costs.)
receiving, using the communication device, at least one user response selecting or modifying at least one of the optimization parameters or configuration options, and wherein generating the at least one management information is further based on the at least one initial management information and the at least one user response. (In ¶0026: teaching using the data to “reduce costs” including by the user using the “management tools”.)
Regarding claims 10 and 29:
The combination of Chen and Gebhart, as shown in the rejection above, discloses the limitations of claims 1 and 21, respectively.
Gebhart teaches the generation of a questionnaire or set of questions which is directed to receiving “Experiential data characterizing a quality of a user interaction with the software program” as “user feedback (e.g., in response to an explicit question)” (see ¶0005 and ¶0027; Gebhart). But Chen further teaches:
further comprising: generating, using the processing device, at least one questionnaire and at least one instruction associated with the at least one questionnaire based on the analyzing of the cloud information and the at least one request; (In ¶0030: teaching analyzing the service in question when “the program code executes the function of the architecture of machine readable instructions as self-contained modules” wherein “the architecture of machine readable instructions may include an input module 210 to receive input data 205 (e.g., from source 115 in FIG. 1) for analysis” such as “data corresponding to service-level factor(s) or metrics for the service in question”. See ¶0041 wherein “the analysis characterizes the service in question” in various ways as shown in Fig. 5. Refer at least to ¶0014 and ¶0037 teaching that the invention relates to data center activities such as cloud activities.)
transmitting, using the communication device, the at least one questionnaire and the at least one instruction to the at least one user device; and (In ¶0077: teaching providing the data to an “end-user interface” wherein the data can be the display of “results in a report format” for management or a customer (see ¶0040).)
receiving, using the communication device, an input command from the at least one user device based on the at least one questionnaire, wherein analyzing the cloud information is further constrained based on the input command, the at least one questionnaire, and the at least one instruction. (In ¶0071: teaching that a user can provide input commands to adjust the data consumptions services to reduce, e.g., power consumption. See ¶0026 wherein “Consumers of the service may also use the management to compare the sustainability of different services.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen - b (U.S. Pub No. 20250013500 A1) is pertinent because it “relates to a deep reinforcement learning-based cloud data center adaptive efficient resource allocation method.”
Wong (U.S. Pub No. 20230138727 A1) is pertinent because it is “relates to managing cloud resources, and more particularly to controlling cloud resource usage based on carbon emission.”
Smith (U.S. Patent No. US 11755372 B2) is pertinent because it is about “methods, systems, and apparatus, including computer-readable media, for environment monitoring and management. In some implementations, information indicating a planned usage level for usage of cloud computing services is accessed by a group of multiple computing environments over a period of time.”
Lang (U.S. Pub No. US 20210064431 A1) is pertinent because teaches that “during training, once the machine learning model generates the likelihood of the system or user usage, the computing system can take one or more additional steps to improve the prediction of the machine learning model. By communicating with the cloud computing system represented by cloud computing system, the computing system efficiently manage cloud computing resources and predictively avoid running idle cloud-hosted environments, while also not shutting down environments prematurely. Thus, the computing system can anticipate client device action and minimize overall cost for the client devices for using the services provided by the server environment.”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626 /DENNIS W RUHL/Primary Examiner, Art Unit 3626
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