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. Because 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 28, 2026 has been entered.
Status of Claims
This action is in reply to the request for continued examination filed on January 28, 2026.
Claims 1-5 and 7-10 are currently amended.
Claims 6 and 11-20 have been canceled.
Claims 21-29 are new.
Claims 1-5, 7-10, and 21-29 are currently pending and have been examined.
Applicant’s remarks and arguments are addressed below.
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 non-statutory subject matter. When considering subject matter eligibility under 35 U.S.C. § 101, there are multiple steps that may need to be assessed. First, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined in step 2A prong 1 whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim is directed toward a judicial exception, it must then be determined in step 2A prong 2 whether the judicial exception is integrated into a practical application. Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in step 2B whether the claim recites “significantly more” than the abstract idea. See “2019 Revised Patent Subject Matter Eligibility Guidance,” 84 Fed. Reg. (4): 50-57 (Jan. 7, 2019).
In the instant case, Claims 1-5 and 7-10 are directed toward a method, i.e., process, and Claims 21-29 are directed toward a system, i.e., apparatus. Thus, each of the claims falls within one of the four statutory categories as required by step 1. Nevertheless, the claims are directed toward the judicial exception of an abstract idea in step 2A prong 1. Independent Claim 1 recites as follows:
Claim 1. A method for facilitating managing one or more clouds for a user, the method comprising:
receiving, using a communication device, at least one request from at least one user device associated with the user;
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;
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;
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 based on the analyzing by the one or more artificial intelligence models, 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;
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
transmitting, using the communication device, the at least one management information; and
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;
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.
The bold language above corresponds to the abstract ideas recited in Claim 1 (whereas the underlined language is language that is addressed in step 2A prong 2 and step 2B). As the bold language above demonstrates, Applicant’s claims are directed toward analyzing what computing actions in a cloud environment might save costs or be more efficient. Examiner notes that one embodiment of the invention is in deduplicating and removing redundant data (see, e.g., Applicant’s specification paragraph 6), which is something that can be performed by the human mind because it relates to the observations, evaluations, and judgments of what things are duplicates and thus can be removed or recycled. Because these steps are based on observations, evaluations, and judgments that could be performed in the human mind, they are abstract mental processes. See MPEP § 2106.04(a)(2)(III).
Finding the claims to be directed toward an abstract idea, however, is not the end of the inquiry. Rather, the next step is to determine whether the judicial exception is integrated into a practical application (step 2A prong 2). The revised guidance provides exemplary considerations that are indicative that an additional element or combination of elements may have integrated the exception into a practical application: 1) an additional element reflecting an improvement in the functioning of a computer or an improvement to another technology or technical field, 2) an additional element that implements the judicial exception with a particular machine or manufacture that is integral to the claim, 3) an additional element that effects a transformation or reduction of a particular article to a different state or thing, or 4) an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.04(d). Examples where a judicial exception has not been integrated into a practical application include: 1) use of “apply it” or the equivalent, i.e., merely using a computer to implement or perform an abstract idea, 2) an additional element that adds insignificant extra-solution activity to the judicial exception, and 3) an additional element that does no more than generally link the use of the judicial exception to a particular technological environment or field of use. See id.
Applying these considerations to the claims in the instant application, the claims do not integrate the judicial exception into a practical application. The claims fail to recite an improvement of a computer, any improvement to a technology or technical field, any particular machine, any transformation or reduction of a particular article to a different state or thing, or any additional element that uses the judicial exception in a meaningful way. Instead, the claims are merely reciting instructions to implement the abstract idea on a computer, which is insufficient to provide a practical application of the claims and provide subject matter eligibility. See id. Examiner notes that the recitation of “at least one artificial intelligent autonomous agent based on the determining of the at least one cloud management action” is something that is “created,” i.e., programmed by a user and thus is also based on mental processes to create the agent. Therefore, there is no integration of the abstract idea into a practical application.
If the claims are not integrated into a judicial exception, the Examiner must consider whether there is “significantly more” recited in the claim in step 2B. See MPEP § 2106.05. There is nothing unconventional or inventive in Applicant’s claims for the purpose of analysis under step 2B, e.g., any combination of elements that provide an advance over any technological state of the art. Rather, as noted above, an abstract mental process is merely implemented by a general-purpose computer. Other than the limitations that are abstract for the reasons articulated above, Applicant has merely recited a generic computer that facilitates the steps of the invention. Thus, Applicant’s claims merely recite a computer to implement the abstract idea, which fails to provide “significantly more” than the abstract idea.
As the MPEP states, Examiners may consider the following three factors when determining whether the claim recites mere instructions to implement an abstract idea on a computer: 1) whether the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished; 2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and 3) the particularity or generality of the application of the judicial exception. See MPEP § 2106.05(f). Applying those factors to the instant application: 1) the claims do not recite how the computer performs any of the steps other than just stating that they do it, whether with a processor, an “artificial intelligence analyst,” or both; 2) the claims invoke the computer to perform a process of efficient storage techniques or deduplication of data that has been performed without computers and before the ubiquity of computers (i.e., this was an issue with hard copy filing cabinets or pictures and other stored data such as music); and 3) the claims are generic and not recited in much particularity because it can apply to any way of performing the analysis.
The dependent claims 2-5, 7-10, and 21-29 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. Therefore, Claims 1-5, 7-10, and 21-29 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 U.S.C. § 101.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7-10, and 21-29 are rejected under 35 U.S.C. § 103 as being unpatentable over Chen et al. (US 2014/0324407, hereinafter “Chen”) in view of Gebhart et al. (US 2021/0349851 A1, hereinafter “Gebhart”).
Claim 1. Chen teaches: A method for facilitating managing one or more clouds for a user, the method comprising:
receiving, using a communication device, at least one request from at least one user device associated with the user (see, e.g., ¶s 59 and 60 teaching determining a received request from a client; see also Figure 6; see further ¶ 21 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 (see, e.g., ¶s 14, 23, and 37 teaching that the example processing environment is a cross-platform data center or cloud and its various services used to provide infrastructure, 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 (see, e.g., ¶s 59-63 teaching analyzing the request and determining a power usage attributable to that request based on different modeling such as a direct acyclic graph; regarding that the input is 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 based on the analyzing by the one or more artificial intelligence models, 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 (see, e.g., ¶s 59-63 teaching analyzing the request to determine what computing is required and determining a power usage attributable to that request; regarding that the input is to one more artificial intelligence models, or that this is to address 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, this is further addressed below);
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 (see, e.g., ¶s 59-63 teaching analyzing the request and determining a power usage attributable to that request; regarding that the input is to one more artificial intelligence models, this is further addressed below);
transmitting, using the communication device, the at least one management information (see, e.g., ¶ 77 teaching providing the data to an end-user interface; see also ¶ 40 teaching display of the results in a report format for management or a customer); and
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 (see, e.g., ¶ 30 teaching that the modules of the invention “may be implemented as agents that run on top of an existing program code,” and ¶ 71 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 (see, e.g., ¶s 25 and 71 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 further ¶ 26 teaching using the management tools to cap power consumption).
Regarding the limitation of the use of one or more artificial intelligence models or that the cloud mitigation action is 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, Chen fails to expressly teach these features. Nevertheless, analogous reference Gebhart teaches: the use of one or more artificial intelligence models to perform the requisite cloud processing analyses (see, e.g., Gebhart ¶ 20 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 ¶ 60 teaching the artificial intelligence neural network making an actuator commanded to act upon the software). Gebhart further teaches that the cloud mitigation action is 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 (see ¶ 105 teaching automatically de-provisioning or moving resources based on prices, carbon footprint, or both; see also, e.g., ¶s 107-112 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). Gebhart is similar to the instant application and Chen because it relates to attempting to “strive for minimal cost and a low carbon footprint in running [complex] software solutions” (see ¶ 3).
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 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 analyze the large amounts of data (see Gebhart ¶ 65) better than a human and would do it to save costs or reduce a carbon footprint (see Gebhart ¶ 105).
Regarding Claim 21, this claim is essentially identical to claim 1 except that it recites a system. The rejection of Claim 1 utilizing the combination of Chen and Gebhart to render the limitations obvious is incorporated herein. Likewise coextensive dependent claims will be addressed together below for the sake of brevity.
Claims 2 and 22. The combination of Chen and Gebhart teaches the limitations of Claim 1. Chen further teaches: The method of claim 1 further comprising:
determining, using the processing device, actual spending corresponding to the usage information (see, e.g., at least ¶s 38-39 teaching determining costs based, e.g., on the power consumption attributable to the request; see also ¶s 15, 26, and 31 further teaching the determination of costs);
obtaining, using the processing device, platform pricing information corresponding to a plurality of cloud platforms (see, e.g., at least ¶s 38-39 teaching determining costs based, e.g., on the power consumption attributable to the request; see also ¶s 15, 26, and 31 further teaching the determination of costs); 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 (see, e.g., at least ¶s 38-39 teaching determining costs based, e.g., on the power consumption attributable to the request; see also ¶s 15, 26, and 31 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 (see, e.g., at least ¶s 38-39 teaching determining costs based, e.g., on the power consumption attributable to the request, noting that Figure 4, as explained in ¶ 39, provides for comparisons by color for economic costs 410; see also ¶s 15, 26, and 31 further teaching the determination of costs).
Claims 3 and 23. The combination of Chen and Gebhart teaches the limitations of Claim 1. Chen further teaches: The method of claim 1 further comprising:
determining, using the processing device, one or more cloud metrics of at least one cloud server of the one or more clouds (see, e.g., ¶s 59-63 teaching analyzing the request and determining a power usage attributable to that request); and
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 (see, e.g., ¶ 26 teaching using the invention to compare differences and reduce costs).
Claims 4 and 24. The combination of Chen and Gebhart teaches the limitations of Claim 1. Chen further teaches: The method of claim 1 further comprising:
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 (see, e.g., ¶s 59-63 teaching analyzing the request and determining a power usage attributable to that request, noting that, e.g., ¶s 14, 23, and 37 note that the processing environment 300 can be cloud services/resources);
analyzing, using the processing device, the at least one current cloud infrastructure information (see, e.g., ¶s 59-63 teaching analyzing the request and determining a power usage attributable to that request, noting that, e.g., ¶s 14, 23, and 37 note that 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 (see, e.g., ¶s 59-63 teaching analyzing the request and determining a power usage attributable to that request, noting that, e.g., ¶s 14, 23, and 37 note that the processing environment 300 can be cloud services/resources as well as ¶s 13 and 25-26 teaching comparing different services or sites to determine how to do the service most sustainably); and
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 (see, e.g., ¶s 59-63 teaching analyzing the request and determining a power usage attributable to that request, noting that, e.g., ¶s 14, 23, and 37 note that the processing environment 300 can be cloud services/resources), wherein the generating of the at least one management information is further based on the at least one optimized cloud infrastructure (see ¶ 26 teaching comparing differences to reduce costs).
Examiner notes that while Chen fails to expressly teach the determination of a “digital waste,” as noted in the rejection of Claim 1 above, Gebhart teaches the migration of data or processing from one cloud to another (see Gebhart ¶s 108-113) including for the purpose of cost savings or carbon footprint reduction (see Gebhart ¶ 105). The rationale for combining Chen and Gebhart is provided in Claim 1 above.
Claims 5 and 25. The combination of Chen and Gebhart teaches the limitations of Claim 1. Chen further teaches: The method of claim 4 further comprising:
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 (see, e.g., ¶s 4, 14, 23, and 37 teaching that the invention relates to data center activities such as cloud activities; see further ¶s 15, 31, and 38 teaching that energy, i.e., power consumption, is one of the main metrics analyzed for the data centers and correlates with carbon emissions attributable to the data center actions; see further ¶s 15, 31, 64, and 66-68 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 (see, e.g., ¶ 26 teaching comparing differences to reduce costs, including ecological costs such as carbon emissions);
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 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 (see, e.g., ¶ 77 teaching providing the data to an end-user interface; see also ¶ 40 teaching display of the results in a report format for management or a customer).
Examiner notes that 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 ¶ 55). The rationale for combining Chen and Gebhart is provided in Claim 1 above.
Claims 7 and 26. The combination of Chen and Gebhart teaches the limitations of Claim 1. Chen further teaches: The method of claim 1, wherein at least one of the one or more clouds is synchronized with at least one external device (see, e.g., ¶ 77 or ¶s 24 or 35 teaching synchronizing with an external device for display of the results of the analysis), 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 (see, e.g., ¶s 4, 14, 23, and 37 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; see further, e.g., ¶ 26 teaching providing information comparing different potential actions 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 (see, e.g., ¶ 26 teaching providing information comparing different potential actions so that the more sustainable action can be selected, including, as shown in Figure 3 and taught in ¶ 23, various external devices such as vendor databases or cloud storage), 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 (see, e.g., ¶ 26 teaching providing information comparing different potential actions so that the more sustainable action can be selected, including, as shown in Figure 3 and taught in ¶ 23, various external devices such as vendor databases or cloud storage).
Claims 8 and 27. The combination of Chen and Gebhart teaches the limitations of Claim 7. Chen further teaches: The method of claim 7 further comprising:
obtaining, using the processing device, at least one device data associated with the at least one external device (see, e.g., ¶s 4, 14, 23, and 37 teaching that the invention relates to data center activities such as external, cloud device activities; see further ¶s 15, 31, and 38 teaching that energy, i.e., power consumption, is one of the main metrics analyzed for the data centers and correlates with carbon emissions attributable to the data center actions; see further ¶s 15, 31, 64, and 66-68 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 (see, e.g., ¶s 4, 14, 23, and 37 teaching that the invention relates to data center activities such as cloud activities; see further ¶s 15, 31, and 38 teaching that energy, i.e., power consumption, is one of the main metrics analyzed for the data centers and correlates with carbon emissions attributable to the data center actions; see further ¶s 15, 31, 64, and 66-68 teaching the determination of power consumption for a particular service and then an associated carbon emission for that service);
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 (see, e.g., ¶s 107-112 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);
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 (see, e.g., ¶s 107-112 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 using at least one standard based on the calculating of the reduced energy consumption (see, e.g., ¶ 55 teaching that this data can be used as the basis for the generation of carbon credits); 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 (see, e.g., ¶ 55 teaching that this data can be used as the basis for the generation of carbon credits).
The rationale for combining Chen and Gebhart is provided in Claim 1 above.
Claims 9 and 28. The combination of Chen and Gebhart teaches the limitations of Claim 1. Chen further teaches: The method of claim 1 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 reports including at least one optimization parameter or configuration option for the at least one cloud (see, e.g., ¶s 13, 35, 40 teaching outputting the data in the format of a report and ¶ 26 teaching using that data to reduce, i.e., optimize costs; see further ¶s 4, 14, 23, and 37 teaching that the invention relates to data services such as cloud storage);
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 (see, e.g., ¶ 26 teaching using the data to reduce costs including by the user using the management tools).
Claims 10 and 29. The combination of Chen and Gebhart teaches the limitations of Claim 1. Chen further teaches: The method of claim 1 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 (see, e.g., ¶s 14, 30, 41, and 60 teaching analyzing the service in question; see further, e.g., ¶s 4, 14, 23, and 37 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 (see, e.g., ¶ 77 teaching providing the data to an end-user interface; see also ¶ 40 teaching display of the results in a report format for management or a customer); and
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 (see, e.g., ¶s 26 and 71 teaching that a user can provide input commands to adjust the data consumptions services to reduce, e.g., power consumption).
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, Applicant specifically addresses the following:
Claim Rejections - 35 U.S.C. § 101:
Claims 1-5, 7-15, and 17-20 were rejected under § 101 as being directed toward the judicial exception of an abstract idea without any integration into a practical application or significantly more. Applicant argues that the claims, as amended, cannot be performed in the human mind because the claims recite “generating executable code and executing executable code” (see Remarks pages 13-14). This argument is not persuasive because the generating and the execution of the code is not part of the abstract idea. The abstract idea, as described in the § 101 rejection above, is the analysis of cloud resource usage and allocation information and a cloud action to take. The generating of executable code and the executing of the code is merely the “apply it” of the abstract idea on a computer, which is insufficient to provide any integration into a practical application or significantly more. See MPEP § 2106.05(f).
Applicant further argues that the claims recite an improvement to technology (see Remarks pages 13-16), which according to step 2A prong 2 analysis provides an integration into a practical application. This argument is not persuasive for the same reasons as above: the alleged improvement is merely just the abstract analysis of what cloud action to take, which is the result of analyses that are based on mental evaluations, judgments, and observations. Similarly, Applicant argues that the claims provide “significantly more” than the abstract idea in step 2B because they “reflect a concrete improvement over conventional systems in the field of cloud computing management and optimization” (see Remarks pages 16-18). This argument is not persuasive because the alleged improvement is merely just the abstract analysis of what cloud action to take, which is the result of analyses that are based on mental evaluations, judgments, and observations. Because the arguments regarding subject matter eligibility are not persuasive, the rejection is maintained.
Claim Rejections - Prior Art:
Regarding the application of the prior art to the claims, Applicant argues that Chen and Gebhart fail to teach the limitations of 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 and 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 (see Remarks pages 18-19). This argument is not persuasive because both Chen and Gebhart do teach executable code that implements cloud management actions on the cloud platform. For example, Chen teaches that the modules of the invention “may be implemented as agents that run on top of an existing program code” (see, e.g., ¶ 30; see also, e.g., Figures 1 and 2 teaching program code 150 and ¶s 18, 20, and 27-29 further describing that the program code executes the invention) as well as that what the code is doing is performing adjustments in the cloud processing resources that improve the power consumption (see ¶ 71; see further, e.g., ¶ 25 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 further ¶ 26 teaching using the management tools to cap power consumption). Likewise, Gebhart teaches that its functions are performed by executable code (see, e.g., ¶s 117-118), and, as noted in the rejection of Claim 1 above, Gebhart teaches the implementation of that code to “address the one or more limitations,” as in this case of moving cloud providers as taught in 107-112 to save costs or carbon footprint. Thus, Applicant’s arguments have been considered but are not considered persuasive. The rejection is maintained.
Conclusion
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/JAN P MINCARELLI/Primary Examiner, Art Unit 3626
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