DETAILED ACTION
This office action is in response to claims filed 5 April 2026.
Claims 1-20 are pending.
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 .
Response to Arguments
Applicant's arguments filed 5 April 2026 have been fully considered but they are not persuasive.
On page 11, the applicant argues the following in the remarks:
“The Office Action evaluates the pending claims under 35 U.S.C. § 101 by characterizing the recited operations at a high level of generality as activities that could be performed by a human. See Office Action at pgs. 2-8. However, the USPTO's August 4, 2025 Memorandum instructs that claims are to be evaluated as a whole and that examiners should avoid overgeneralizing or oversimplifying claim limitations when applying the subject matter eligibility framework.”
The examiner respectfully disagrees. The Office action evaluated the claims “as a whole”, including as “an ordered combination without ignoring the requirements of the individual steps”, as required in MPEP 2106.05(a). This is evident from the fact that each claim limitation was addressed in full, both individually and in ordered combination in the rejection. Further, the Office action was careful to “avoid oversimplifying the claims…and failing to account for the specific requirements of the claims,” as required in MPEP 2106.05(a). While the Office action simplified the explanations, the analysis itself was not performed on simplified claims. The applicant’s argument is not persuasive.
On pages 11-12, the applicant argues the following in the remarks:
“Amended claim 1 recites a structured framework for evaluating and comparing alternative configurations of a cloud workload using multiple predictors. In particular, the claim recites predicting strategies, technologies, and deployments associated with a cloud workload, determining first and second technology and deployment strategic potential values, and identifying second technologies and deployments having comparatively greater strategic potential than corresponding first technologies and deployments.
“The claim further recites updating a machine learning model included in at least one of the strategic technology predictor or the strategic deployment predictor based on discrepancies between predicted and actual results or manual adjustments provided by the user, including adjusting one or more model parameters to improve decision-making process over time.
“These limitations are not directed to a generalized concept of evaluating or selecting options. Rather, the claims require coordinated operation of multiple predictors to determine strategic potential values and perform comparative evaluation across alternative configurations of a cloud workload, together with feedback-driven updating of machine learning models based on discrepancies and user-provided adjustments. See Spec. 1 [0008], [0059], and [0060].
“The Office Action asserts that the recited operations correspond to a mental process under the broadest reasonable interpretation. However, the Office Action does not explain how the claimed operations-particularly (i) coordinated prediction using multiple predictors, (ii) determination of strategic potential values, and (iii) updating of machine learning models based on discrepancies between predicted and actual results and manual adjustments provided by a user-correspond to the identified human activities. The characterization of these limitations at a high level of generality does not address the specific predictive, comparative, and model- updating framework recited in the claims. See Office Action at pgs. 2-8.
“When considered as a whole, the claims recite a sequence of operations implemented within a cloud workload evaluation system, including coordinated prediction of strategies, technologies, and deployments, multi-factor determination of strategic potential values, comparative identification of alternative configurations, and feedback-driven updating of machine learning models through adjustment of model parameters. These operations reflect a specific implementation within a computing environment, rather than a disembodied mental process. Therefore, the amended claims integrate any alleged abstract concept into a practical application under Step 2A, Prong Two.”
The examiner respectfully disagrees. Regarding (i) the coordinated prediction using multiple predictors, the Office action clearly sets forth this concept as a simple mental process of making two separate predictions. Certainly the human mind is capable of making two or more predictions at once, and using those predictions in a coordinated manner. The claim does not even mention “coordination”, much less go into any detail whatsoever of what steps or actions would be involved in the coordination of predictors. Absent of any detail precluding the multiple predictions from being performed and coordinated in the human mind, the applicant’s argument is not persuasive. Regarding (ii) determination of strategic values, the Office action clearly sets forth this concept as a mental process of evaluating predictions and making a judgment of a strategic value in order to identify technologies for a workload. For example, a person may make a mental prediction that a certain technology will have a certain strategic value based on evaluated criteria. No where does the claim contain limitations that would preclude determination of the strategic value from being performed in the human mind, and the applicant’s argument is not persuasive. Regarding (iii), the applicant is correct in that updating a machine learning model would not be considered a human activity. However, training and retraining a machine learning model recites concepts integral to the application of a machine learning model as a tool, and therefore the use of the machine model, including training and retraining the model, to produce an output, represents “mere instructions to apply an exception” as described in MPEP 2106.05(f). Since updating a machine learning model does not provide eligibility, the applicant’s argument is not persuasive.
Further, regarding the argument that the claims recite a “specific implementation”, the examiner respectfully points out that, as explained in the rejection below, the claim recites steps which, but for the recitation of generic computer components, include performance within the human mind. For example, as discussed above, the human mind is capable of making two or more predictions at once, and using those predictions in a coordinated manner,. This does not represent an implementation that is specific to the software arts, but rather is an abstract idea to which general purpose computers (the aforementioned “predictors”) are added post-hoc, as discussed in MPEP 2106.05(a)(I). Since the claim does not represent a “specific implementation”, the applicant’s argument is not persuasive.
On pages 12-13, the applicant argues the following in the remarks:
“The Office Action further asserts that the recited predictors and models are generic components. However, the claims do not rely on the mere presence of such components. Rather, the claims require specific use of these components to perform coordinated prediction, multi-factor value determination, comparative evaluation, and feedback-driven model updating based on discrepancies and user-provided adjustments.
“For similar reasons, the Step 2B analysis is not supported by the record. The Office Action concludes that the claimed elements amount to generic automation of a human process. However, this conclusion does not address the specific manner in which the claims require updating machine learning models based on discrepancies between predicted and actual results and manual adjustments provided by a user, including adjusting model parameters to improve decision-making process over time. The analysis therefore reduces the claims to a generalized concept without addressing the claimed implementation. See Office Action at pgs. 2-8. For these reasons, and those below, claims 1-20 are patent-eligible and the § 101 rejection should be withdrawn.”
The examiner respectfully disagrees. While the previous office action did not address the newly added claim limitations directed toward updating machine learning models, the current office action rejects these limitations (see below). As such, the applicant’s argument is not persuasive.
On pages 13-14, the applicant argues the following in the remarks:
“The application on appeal involved training machine-learning models, and reflected improvements in artificial intelligence (AI) technology that "'us[e] less of their storage capacity,' enables 'reduced system complexity,"' and "'effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Desjardins, supra.
“The ARP cited Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), which recognized that advancements in computer technology "may not be defined by particular physical features but rather by logical structures and processes." The ARP further noted that the claims were also subject to prior art analysis, reinforcing that §§ 102, 103, and 112 remain the traditional statutory tools for evaluating claim scope, rather than § 101.
“The amended claims here, including independent claims 1, 11 and 16, similarly recite structured, computer-implemented processing operations within a cloud workload evaluation system. In particular, claim 1 recites coordinated prediction of strategies, technologies, and deployments associated with a cloud workload, determination of first and second technology and deployment strategic potential values, identification of alternative technologies and deployments having comparatively greater strategic potential, and transmission of a strategic alert responsive to such determination. of 23
“The claim further recites updating a machine learning model included in at least one of the strategic technology predictor or the strategic deployment predictor based on discrepancies between predicted and actual results or manual adjustments provided by a user, including adjusting one or more model parameters to improve decision-making process over time. These recited operations define a coordinated sequence of prediction, evaluation, comparison, and feedback-driven model refinement within a cloud workload evaluation system. See, e.g., Spec. 1 [0008], [0059], [0060], [0133]-[0134], and [0152]-[0157].
“As in Desjardins, the claims are not directed to an abstract idea in isolation, but to a specific implementation of processing operations within a computer-based system. The recited limitations define how multiple predictors are used to evaluate alternative configurations of a cloud workload, determine strategic potential values based on multiple factors, and iteratively update machine learning models through parameter adjustment based on discrepancies and user- provided adjustments. This reflects a structured approach to system-level processing and model refinement rather than a generalized mental process.
“To the extent the Office Action characterizes the claims as directed to abstract evaluation or selection, such characterization does not address the claimed feedback-driven updating of machine learning models based on discrepancies between predicted and actual results or manual adjustments provided by a user. These limitations are directed to the operation and refinement of machine learning models and are not reasonably performed as mental steps.”
The examiner respectfully disagrees. Despite highlighting alleged superficial similarities between the claims of ex parte Desjardins and the instant application, the applicant’s argument fails to actually establish which of these similarities results in either integration of the judicial exception into a practical application, or significantly more than the judicial exception. At best, the argument implies that because the claim is directed to a specific implementation of processing operations within a computer-based system, or that the claim reflects a structured approach to system-level processing and model refinement, rather than a generalized mental process, that the claim is eligible. However, as discussed above, and below in the rejection itself, each and every step of the claim has been analyzed both individually, and collectively in view of the other limitations, and eligibility has not been found.
Specifically regarding the findings of the courts in ex parte Desjardins, it is important to note that the decision of eligibility hinged on the fact that “the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’ encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation ‘adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task”. In other words, the improvement is directed to a specific implementation where a machine learning model is trained, and retrained while protecting some aspect of a machine learning task from being forgotten. In other words, it isn’t enough that parameters of the machine learning model are adjusted to optimize performance, but also that the performance of the first machine learning task is protected, that reflects the improvement to the functioning of the machine learning model itself. In the instant application, simply training and retraining a machine learning model recite concepts integral to the application of a machine learning model as a tool, and therefore the use of the machine model, including training and retraining the model, to produce an output, represents “mere instructions to apply an exception” as described in MPEP 2106.05(f). Since updating a machine learning model does not provide eligibility, the applicant’s argument is not persuasive.
Further, regarding the claimed feedback driven updating of machine learning models, the current office action addresses the claim limitations newly added in the current amendment, and the applicant’s argument is not persuasive.
On page 14, the applicant argues the following in the remarks:
“The Office Action also includes rejections under §§ 102 and 103, underscoring that the claims are amenable to prior art analysis. Consistent with the ARP's reasoning in Ex parte Desjardins and the Federal Circuit's guidance in Enfish, these considerations support evaluation of the claims under the traditional statutory provisions rather than § 101.”
The examiner respectfully disagrees. The MPEP is clear is that a patent application is examined according to “all statutory requirements” including “35 U.S.C. 101, 112, 102, and 103”, described at least in MPEP 2163. The applicant’s argument is not persuasive.
On pages 14-18, the applicant argues the following in the remarks:
“the amended claim 1 includes limitations that integrate the alleged abstract ideas into practical applications. For example, the claimed invention is directed to a specific improvement in computer-implemented cloud workload evaluation systems, including coordinated prediction of strategies, technologies, and deployments, structured determination of strategic potential values based on multiple favors, and feedback-driven updating of machine learning models.”
The examiner respectfully disagrees. As discussed above, the claimed invention is directed to training and retraining a machine learning model, which is a concept integral to the application of machine learning models as tools, to produce outputs, and represents “mere instructions to apply an exception” as described in MPEP 2106.05(f). In claim 3 of example 47 of the AI-related SME examples 47-49 issued in 2024, the claim was found to be eligible, not because of the fact that the machine learning model was trained, but rather that in steps (d)-(f), the claim specifies “remedial actions that are executed to remediate or prevent network intrusions”, thereby reflecting an improvement in the technical field of network intrusion detection. In other words, it wasn’t enough to simply detect the intrusions; remedial actions needed to be taken for the improvement to be reflected by the claims. In the instant application, the claim merely transmits a strategic alert, which is found to be both insignificant extra solution activity, and well-understood, routine and conventional activity. However, no “remedial action”, or any action for that matter, is taken responsive to the strategic alert. Therefore, no improvement can be reflected in the claim, and the claim remains ineligible. Applicant’s argument is not persuasive.
On page 17-19, the applicant argues the following in the remarks:
“Moreover, the claim recites limitations that require manipulation of computer data structures and outputting and so are analogous to the technological improvements recognized in Research Corp. Techs. V. Microsoft Corp., 627 F.3d 859 (Fed. Cir. 2010)…i.e., modifying internal model parameters based on feedback derived from discrepancies between predicted and actual results and user-provided manual adjustments, thereby transforming the internal state of the predictive model.”
The examiner respectfully disagrees. Despite highlighting alleged superficial similarities between the claims of Research Corp. Techs. V. Microsoft Corp and the instant application, the applicant’s argument fails to actually establish which of these similarities results in either integration of the judicial exception into a practical application, or significantly more than the judicial exception. At best, the argument implies that because the claim is directed to generic manipulation of data structures and output. However, it is not clear what role the alleged generic manipulation of data structures and output in Research Corp. Techs. V. Microsoft Corp played in a determination of eligibility, and whether that determination has any bearing on the instant application. Furthermore, Research Corp. Techs. V. Microsoft Corp differs in many key aspects: it does not discuss machine learning models, let alone modifying model parameters based on feedback, does not determine discrepancies between results, and does not transform a state of a model. Therefore, the applicant’s argument is not persuasive.
On page 19, the applicant argues the following in the remarks:
“The claims recite transformation of machine-generated data and feedback-driven modification of model parameters, thereby improving system operation through refinement of predictive behavior and generation of improved outputs over time. This constitutes meaningful integration of any alleged abstract idea into a practical application under step 2A Prong Two. Accordingly, the claims as a whole are directed to patent-eligible subject matter. The rejection under 35 U.S.C. 101 should therefore be withdrawn.”
The examiner respectfully disagrees. As discussed above, the claims do not improve the functioning of the computer through refinement of predictive behavior and generation of improvement outputs over time. The claim merely transmits a strategic alert in response to an output of a strategic predictor. The accuracy of that prediction does not cause the system to be improved in response to the more accurate strategic alert. Therefore, the applicant’s argument is not persuasive.
On pages 19-23, the applicant’s arguments are moot because they do not specifically challenge the new reference (LILLO, cited below) used to reject the limitations at issue in the current rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites a method that transmits a strategic alert in response to predicting that a second deployment of a second technology has greater potential value over a first deployment of a first technology. A method is one of the four statutory categories of invention.
In step 2A, prong 1 of the 101 analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
i. “predicting… a plurality of strategies associated with a cloud workload” (a person can mentally predict a cloud workload strategy by simply evaluating the cloud workload, and making a judgement of a particular strategy (MPEP 2106.04(a))).
ii. “selecting a selected strategy as the strategy selected by the user from the plurality of strategies” (a person can mentally select a strategy by simply evaluating strategies and making a judgement to select one (MPEP 2106.04(a))).
iii. “predicting…a first technology for the cloud workload having a first technology strategic potential value based on the selected strategy” (a person can mentally predict a technology by simply evaluating potential values of technologies, and making a judgement of a particular technology (MPEP 2106.04(a))).
iv. “predicting…a first deployment for the cloud workload having a first deployment strategic potential value based on the selected strategy” (a person can mentally predict a deployment by simply evaluating potential values of deployments, and making a judgement of a particular deployment (MPEP 2106.04(a))).
v. “identify a second technology for the cloud workload having a second technology strategic potential value based on the selected strategy, the second technology strategic potential value of the second technology indicating a greater technology strategic potential than the first technology strategic potential value of the first technology” (a person can mentally identify a technology by simply evaluating potential values of technologies compared to first potential values, and making a judgement of a particular technology (MPEP 2106.04(a))).
vi. “identify a second deployment for the cloud workload having a second deployment strategic potential value based on the selected strategy, the second deployment strategic potential value of the second deployment indicating a greater deployment strategic potential than the first deployment strategic potential value of the first deployment” (a person can mentally identify a deployment by simply evaluating potential values of deployment compared to first potential values, and making a judgement of a particular deployment (MPEP 2106.04(a))).
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A, prong 2 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
vii “using a workload strategy predictor”, “using a strategic technology predictor”, “using a strategic deployment predictor”, “executing the strategic technology predictor to”, “executing the strategic deployment predictor to” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
viii. “presenting the plurality of strategies to a user for selection of a strategy” (insignificant extra-solution activity of mere data output (MPEP 2106.05(g))).
ix. “transmitting, responsive to the executing the strategic technology predictor and the executing the strategic deployment predictor, a strategic alert” (insignificant extra-solution activity of mere data output (MPEP 2106.05(g))).
x. “updating, by at least one of the strategic technology predictor or the strategic deployment predictor, a machine learning model included in the at least one of the strategic technology predictor or the strategic deployment predictor based on at least one of (i) discrepancies between predicted and actual results, or (ii) manual adjustments provided by the user, wherein the updating includes adjusting one or more model parameters to improve a decision-making process over time of the at least one of the strategic technology predictor or the strategic deployment predictor” (common ways of implementing machine learning models including training/retraining models are equivalent to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined through reanalysis of the following limitations considered in step 2A prong 2, that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
vii. “using a workload strategy predictor”, “using a strategic technology predictor”, “using a strategic deployment predictor”, “executing the strategic technology predictor to”, “executing the strategic deployment predictor to” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
viii. “presenting the plurality of strategies to a user for selection of a strategy” (well-understood, routine, and conventional activity of transmitting data over a network (MPEP 2106.05(d)(II))).
ix. “transmitting, responsive to the executing the strategic technology predictor and the executing the strategic deployment predictor, a strategic alert” (well-understood, routine, and conventional activity of transmitting data over a network (MPEP 2106.05(d)(II))).
x. “updating, by at least one of the strategic technology predictor or the strategic deployment predictor, a machine learning model included in the at least one of the strategic technology predictor or the strategic deployment predictor based on at least one of (i) discrepancies between predicted and actual results, or (ii) manual adjustments provided by the user, wherein the updating includes adjusting one or more model parameters to improve a decision-making process over time of the at least one of the strategic technology predictor or the strategic deployment predictor” (common ways of implementing machine learning models including training/retraining models are equivalent to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, the additional element “the strategic technology predictor comprises the machine learning model configured to” does not render the claim patent eligible because under step 2A prong 2, it does not integrate the judicial exception into a practical application (adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))), and under step 2B it does not amount to significantly more than the judicial exception (adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further. the additional element “identify a plurality of technologies based on the cloud workload and the selected strategy” does not render the claim patent eligible because under step 2A prong 1, it recites a judicial exception (mental process) (a person can mentally identify technologies by simply evaluating workloads and strategies making a judgement of particular technologies (MPEP 2106)).
Regarding claim 3, the additional element “the strategic deployment predictor comprises the machine learning model configured to” does not render the claim patent eligible because under step 2A prong 2, it does not integrate the judicial exception into a practical application (adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))), and under step 2B it does not amount to significantly more than the judicial exception (adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further. the additional element “identify a plurality of deployments based on the cloud workload and the selected strategy” does not render the claim patent eligible because under step 2A prong 1, it recites a judicial exception (mental process) (a person can mentally identify deployments by simply evaluating workloads and strategies making a judgement of particular deployments (MPEP 2106)).
Regarding claim 4, the additional element “generating a strategic report depicting an overview of the cloud workload, the selected strategy, the first technology, and the first deployment” does not render the claim patent eligible because under step 2A prong 1, it recites a judicial exception (mental process) (a person can mentally generate a report by simply evaluating information about the cloud workload and making a judgement of information of particular relevance (MPEP 2106)).
Regarding claim 5, the additional element “the strategic alert includes one of an application notification, an email message, and a mobile phone message” does not render the claim patent eligible because under step 2A prong 2, it does not integrate the judicial exception into a practical application (generally links the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)), and under step 2B it does not amount to significantly more than the judicial exception (generally links the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
Regarding claim 6, the additional element “storing a strategic decision comprising the cloud workload, the selected strategy, the first technology, and the first deployment” does not render the claim patent eligible because under step 2A prong 2, it does not integrate the judicial exception into a practical application (insignificant extra-solution activity of mere data storage (MPEP 2106.05(g)), and under step 2B it does not amount to significantly more than the judicial exception (well-understood, routine and conventional activity of storing information in memory (MPEP 2106.05(d)(II)).
Regarding claim 7, the additional element “a technology strategic potential value is based at least in part on technology capability and technology cost” does not render the claim patent eligible because under step 2A prong 2, it does not integrate the judicial exception into a practical application (generally links the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)), and under step 2B it does not amount to significantly more than the judicial exception (generally links the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
Regarding claim 8, the additional element “a deployment strategic potential value is based at least in part on deployment capability and deployment cost” does not render the claim patent eligible because under step 2A prong 2, it does not integrate the judicial exception into a practical application (generally links the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)), and under step 2B it does not amount to significantly more than the judicial exception (generally links the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
Regarding claim 9, the additional element “the executing the strategic technology predictor and executing the strategic deployment predictor is performed on a pre-determined schedule” does not render the claim patent eligible because under step 2A prong 2, it does not integrate the judicial exception into a practical application (generally links the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)), and under step 2B it does not amount to significantly more than the judicial exception (generally links the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
Regarding claim 10, the additional element “presenting a strategic cloud view including a percentage of cloud workloads in alignment with the selected strategy” does not render the claim patent eligible because under step 2A prong 2, it does not integrate the judicial exception into a practical application (insignificant extra-solution activity of mere data output (MPEP 2106.05(g)), and under step 2B it does not amount to significantly more than the judicial exception (well-understood, routine and conventional activity of transmitting data over a network (MPEP 2106.05(d)(II)).
Regarding claims 11-15, and 16-20, they comprise limitations similar to claims 1-3, and 7-8 respectively, and are therefore rejected for similar rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-12, 14-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over BIJANI et al. Pub. No.: US 2017/0017505 A1 (hereafter BIJANI), in view of LILLO Patent No.: US 12,614,057 B2 (hereafter LILLO).
BIJANI was cited previously
Regarding claim 1, BIJANI teaches:
A computer-implemented method comprising:
predicting, using a workload strategy predictor, a plurality of strategies associated with a cloud workload ([0026] FIG. 1 illustrates an exemplary environment 100 for decommissioning a production computer system. Illustrated are a decommissioning system 105, a group of servers 110, and a cloud computing system 115. [0027] The cloud computing system 115 corresponds to a collection of computing resources that may be shared by various entities. [0029] The servers 110, on the other hand, generally correspond to computers systems that may be located in-house and managed by a local IT group or an IT consultant (i.e., exemplary environment 100 presents, or “predicts” at least two different computing systems or strategies for executing applications: a local server strategy and a cloud based strategy));
presenting the plurality of strategies to a user for selection of a strategy, thereby selecting a selected strategy as the strategy selected by the user from the plurality of strategies; predicting, using a strategic technology predictor, a first technology for the cloud workload having a first technology strategic potential value based on the selected strategy; predicting, using a strategic deployment predictor, a first deployment for the cloud workload having a first deployment strategic potential value based on the selected strategy ([0032] The decommissioning system 105 deploys a discovery tool 130 to a server 110 applications. The discovery tool 130 includes modules for performing an analysis of the applications running on the server 110, an in turn transmits application information 135 back to the decommissioning system 105 regarding applications running on the server. [0031] Constantly having to maintain a computer system can be costly. In some cases, IT personal have to be available 24/7 to be able to address unexpected emergencies. Having to upgrade hardware can be cost prohibitive for some companies. [0059] Total cost of ownership data is collected on these applications including run and hosting costs from the questionnaire 140 (i.e., local servers execute deployed applications prior to the deployment of the discovery tool indicating that a user or administrator has made a first choice of “strategy” (local server strategy) based on a first “technology” (local servers) having a total cost of ownership including a first “technology strategic potential value” (costs associated with the technology strategy (local servers) itself, such as hardware maintenance costs, and staffing, as described in TABLE 1) and a first “deployment strategic potential value (costs associated with deployment of the application on the technology (local servers), such as software licensing costs, and capital expenses due to hardware purchase as described in TABLE 1)));
executing the strategic technology predictor to identify a second technology for the cloud workload having a second technology strategic potential value based on the selected strategy, the second technology strategic potential value of the second technology indicating a greater technology strategic potential than the first technology strategic potential value of the first technology ([0057] At block 220, the decommissioning system 105 may receive the information 135 from the discovery tool 130 and generate a recommendation. For example, the properties of the application under evaluation, as determined by the discovery tool 130, may be compared with the properties of applications stored in the training data store 107. Applications in the training data store 107 having properties similar to those of the target application may be selected. The recommendations regarding whether to migrate those applications to the cloud (i.e., a “second identified technology” for the workload), template types for deployment to use, etc., that were specified by the SME are retrieved and form the basis of the recommendation as to cloud migration, template type, etc. for the target application);
executing the strategic deployment predictor to identify a second deployment for the cloud workload having a second deployment strategic potential value based on the selected strategy, the second deployment strategic potential value of the second deployment indicating a greater deployment strategic potential than the first deployment strategic potential value of the first deployment ([0062] A business value column 620 provides an indication of the potential cost savings that may be realized if the application was migrated using the recommended parameters. In this regard, the business value may be based on user 118 feedback provided in the questionnaire 140 regarding a given target application. For example, the SME for the target application may have indicated in the corresponding questioner that the total cost associated with operating the target application on the current server is $150000. On the other hand, operating the application in the cloud using the recommended provider, recommended template, etc. may be known to cost $80,000. Thus, a savings of $70,000 may be realized by migrating the application to the cloud (i.e., cost of operating the target application in the cloud as a “second deployment” based on a second technology (cloud computing system) represents a total cost ($80,000) including a “second technology potential value” (costs associated with a recommended deployment template that specifies virtualized resources to be used to operate the application, as shown in Fig. 7A) and “second deployment potential value” (costs associated with operating the deployed application in the cloud environment, such as cloud solution, cloud model, No. of instance, as shown in Fig. 7A) that is greater (more cost effective) than the first technology potential value ($150,000))); and
transmitting, responsive to the executing the strategic technology predictor and the executing the strategic deployment predictor, a strategic alert ([0057] At block 220, the decommissioning system 105 may receive the information 135 from the discovery tool 130 and generate a recommendation. For example, the properties of the application under evaluation, as determined by the discovery tool 130, may be compared with the properties of applications stored in the training data store 107. Applications in the training data store 107 having properties similar to those of the target application may be selected. The recommendations regarding whether to migrate those applications to the cloud, template types for deployment to use, etc., that were specified by the SME are retrieved and form the basis of the recommendation as to cloud migration, template type, etc. for the target application (i.e., recommendations for cloud migration represent “strategic alerts”)).
While BIJANI discusses predicting technologies and deployments for cloud workloads, BIJANI does not explicitly teach:
updating, by at least one of the strategic technology predictor or the strategic deployment predictor, a machine learning model included in the at least one of the strategic technology predictor or the strategic deployment predictor based on at least one of (i) discrepancies between predicted and actual results, or (ii) manual adjustments provided by the user, wherein the updating includes adjusting one or more model parameters to improve a decision-making process over time of the at least one of the strategic technology predictor or the strategic deployment predictor.
However, in analogous art that similarly teaches prediction methods, LILLO teaches:
updating, by at least one of the strategic technology predictor or the strategic deployment predictor, a machine learning model included in the at least one of the strategic technology predictor or the strategic deployment predictor based on at least one of (i) discrepancies between predicted and actual results, or (ii) manual adjustments provided by the user, wherein the updating includes adjusting one or more model parameters to improve a decision-making process over time of the at least one of the strategic technology predictor or the strategic deployment predictor ([Column 11, Lines 23-47] The modification of parameter values may be performed through a process referred to as “back propagation.” Back propagation includes determining the difference between the expected model output (e.g., the reference data output vectors 122) and the obtained model output (e.g., output vectors 118), and then determining how to modify the values of some or all parameters of the model to reduce the difference between the expected model output and the obtained model output. In some embodiments, a computing system may compute the difference using a loss function, such as a cross-entropy loss function, a L2 Euclidean loss function, a logistic loss function, a hinge loss function, a square loss function, or a combination thereof. The computing system can compute a derivative, or “gradient,” that corresponds to the direction in which each parameter of the machine learning model is to be adjusted in order to improve the model output (e.g., to produce output that is closer to the correct or preferred output for a given training data input vector 120, as represented by the reference data output vector 122). The computing system can update one or more parameters of the machine learning model based on the gradient. For example, the computing system can update some or all parameters of the machine learning model using a gradient descent method. The adjustments may be propagated back through the NN 100 layer-by-layer (i.e., model output is improved by changing model parameter values based on a difference between expected model output, representing “predicted” results, and obtained output, representing “actual” results)).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined LILLO’s teaching of refining a machine learning model that produces predictions, with BIJANI’s teaching of predicting technologies and deployments for cloud workloads, to realize, with a reasonable expectation of success, a system that predicts technologies and deployments for cloud workloads, as in BIJANI, based on output of a refined a machine learning model, as in LILLO. A person having ordinary skill would have been motivated to make this combination to improve prediction accuracy (LILLO Column 11, Lines 23-47).
Regarding claim 2, BIJANI further teaches:
the strategic technology predictor comprises the machine learning model configured to identify a plurality of technologies based on the cloud workload and the selected strategy ([0058] The application is given a rating of 1-5 for each attribute depending on the answers to the question. Algorithmic evaluations using classification algorithms (e.g., Native Bayes) are then performed to determine the recommendations. [0060] Based on the business value, technical fitment and risk/compliance sensitivities of the application as well as it's dependencies, an alternating least squares (ALS) algorithm is utilized to determine the recommended cloud model for the client as one of: SaaS, PaaS, and IaaS, and its cloud nativity as one of: public, private, and on premise (i.e., machine learning classification algorithms identify a plurality of technologies including SaaS, PaaS, and IaaS based on attributes of the application and the selection of a strategy to utilize cloud based technologies)).
Regarding claim 4, BIJANI further teaches:
generating a strategic report depicting an overview of the cloud workload, the selected strategy, the first technology, and the first deployment ([0044] FIGS. 4A and 4B illustrate dialog boxes that may be displayed when the user selects a data center 305. In addition to the previously discovered information listed above, in FIG. 4A, the dialog box 400 displays the number of applications that have been stabilized, deployed, verified, and decommissioned. In this regard, being deployed indicates that the application is set up on the cloud but it's data is yet to be migrated. Being migrated indicates that the application has been set up on the cloud (i.e., deployed) and that the data associated with the application has been migrated. The number of decommissioned applications correspond to the number of applications in the data center than have been shut down and/or deployed to the cloud computing system 115 (i.e., dialog box in Fig. 4a gives an overview of the applications representing the (cloud workload), including number of applications deployed on servers according to the first technology based on the selected strategy)).
Regarding claim 5, BIJANI further teaches:
the strategic alert includes one of an application notification ([0061] FIG. 6 illustrates a screen shot 600 that displays recommendations regarding several applications that may be generated by the decommissioning system 105 (i.e., an application generates and displays the recommendation, representing an “application notification”)), an email message, and a mobile phone message ([0095] The decommissioning system may be configured to send notifications (i.e., emails, text messages, etc.) to stakeholders pertaining to various stages of the decommissioning and cloud migration process, such as “Being Assessed”, “Ready to Deploy”, “Deployed,” etc. The notifications could also provide other status updates, escalations, alerts, approval notices, reverse sign off mailers etc (i.e., the recommendation leads to notifications and alerts sent via email or mobile phone text)).
Regarding claim 6, BIJANI further teaches:
storing a strategic decision comprising the cloud workload, the selected strategy, the first technology, and the first deployment ([0044] FIGS. 4A and 4B illustrate dialog boxes that may be displayed when the user selects a data center 305. In addition to the previously discovered information listed above, in FIG. 4A, the dialog box 400 displays the number of applications that have been stabilized, deployed, verified, and decommissioned. In this regard, being deployed indicates that the application is set up on the cloud but it's data is yet to be migrated. Being migrated indicates that the application has been set up on the cloud (i.e., deployed) and that the data associated with the application has been migrated. The number of decommissioned applications correspond to the number of applications in the data center than have been shut down and/or deployed to the cloud computing system 115 (i.e., an overview of the applications representing the (cloud workload), including number of applications deployed on servers according to the first technology based on the selected strategy is stored for display in the dialog boxes of FIGS. 4a and 4B)).
Regarding claim 7, BIJANI further teaches:
a technology strategic potential value is based at least in part on technology capability and technology cost ([0032] The decommissioning system 105 deploys a discovery tool 130 to a server 110 applications. The discovery tool 130 includes modules for performing an analysis of the applications running on the server 110, an in turn transmits application information 135 back to the decommissioning system 105 regarding applications running on the server. [0031] Constantly having to maintain a computer system can be costly. In some cases, IT personal have to be available 24/7 to be able to address unexpected emergencies. Having to upgrade hardware can be cost prohibitive for some companies. [0059] Total cost of ownership data is collected on these applications including run and hosting costs from the questionnaire 140 (i.e., local servers execute deployed applications prior to the deployment of the discovery tool indicating that a user or administrator has made a first choice of “strategy” (local server strategy) based on a first “technology” (local servers) having a total cost of ownership including a first “technology strategic potential value” (costs associated with the technology strategy (local servers) itself, such as hardware maintenance costs, and staffing, as described in TABLE 1)).
Regarding claim 8, BIJANI further teaches:
a deployment strategic potential value is based at least in part on deployment capability and deployment cost ([0032] The decommissioning system 105 deploys a discovery tool 130 to a server 110 applications. The discovery tool 130 includes modules for performing an analysis of the applications running on the server 110, an in turn transmits application information 135 back to the decommissioning system 105 regarding applications running on the server. [0031] Constantly having to maintain a computer system can be costly. In some cases, IT personal have to be available 24/7 to be able to address unexpected emergencies. Having to upgrade hardware can be cost prohibitive for some companies. [0059] Total cost of ownership data is collected on these applications including run and hosting costs from the questionnaire 140 (i.e., local servers execute deployed applications prior to the deployment of the discovery tool indicating that a user or administrator has made a first choice of “strategy” (local server strategy) based on a first “technology” (local servers) having a total cost of ownership including a first “deployment strategic potential value (costs associated with deployment of the application on the technology (local servers), such as software licensing costs, and capital expenses due to hardware purchase as described in TABLE 1))).
Regarding claim 9, BIJANI further teaches:
the executing the strategic technology predictor and executing the strategic deployment predictor is performed on a pre-determined schedule ([0049] Other Information collected by the first module may include the amount of processor usage associated with a given application and/or a number of users that utilize the application. In this regard, the first module may be left to run on the server for a predetermined amount of time, such as a week, month, etc. to provide a better assessment as to the overall usage of the application (i.e., generating the recommendation is scheduled for executed after a predetermined amount of time, representing a “pre-determined schedule”, used to collect information)).
Regarding claim 10, BIJANI further teaches:
presenting a strategic cloud view including a percentage of cloud workloads in alignment with the selected strategy ([0044] FIGS. 4A and 4B illustrate dialog boxes that may be displayed when the user selects a data center 305. In addition to the previously discovered information listed above, in FIG. 4A, the dialog box 400 displays the number of applications that have been stabilized, deployed, verified, and decommissioned. In this regard, being deployed indicates that the application is set up on the cloud but it's data is yet to be migrated. Being migrated indicates that the application has been set up on the cloud (i.e., deployed) and that the data associated with the application has been migrated. The number of decommissioned applications correspond to the number of applications in the data center than have been shut down and/or deployed to the cloud computing system 115 (i.e., dialog box in Fig. 4a gives a strategic view of the applications representing the (cloud workload), including number of applications deployed on servers according to the selected strategy vs number of applications migrated to the second technology))).
Regarding claims 11-12, 14-17, and 19-20, they comprise limitations similar to claims 1-2, and 7-8, and are therefore rejected for similar rationale.
Claims 3, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over BIJANI, in view of LILLO, as applied to claims 1, 11, and 16 above, and in further view of SANDLER et al. Pub. No.: US 2024/0103493 A1 (hereafter SANDLER).
SANDLER was cited previously
Regarding claim 3, while BIJANI and LILLO teaches allocating resources to workloads, they dont explicitly teach:
the strategic deployment predictor comprises a machine learning model configured to identify a plurality of deployments based on the cloud workload and the selected strategy.
However, in analogous art that similarly allocates resources to workloads, SANDLER teaches:
the strategic deployment predictor comprises a machine learning model configured to identify a plurality of deployments based on the cloud workload and the selected strategy ([0082] At block 168, the process 160 generates a distributed data processing flow, which defines how the data processing tasks from block 164 are distributed among the computing resources identified in block 166. The data processing flow may be defined by one or more deployment configuration files. In some embodiments, the data processing tasks and the identified computing resources may be provided to a machine learning model that assigns data processing tasks to computing resources).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined SANDLER’s teaching of using a machine learning model to determine deployments of data processing tasks to computing resources, with BIJANI and LILLO’s teaching of deploying tasks to resources, to realize, with a reasonable expectation of success, a system that deploys tasks to resources, as in BIJANI and LILLO, based on machine learning model analysis, as in SANDLER. A person having ordinary skill would have been motivated to make this combination to more efficiently deploy tasks to resources (SANDLER [0024]).
Regarding claims 13, and 18, they comprise limitations similar to claim 3, and are therefore rejected for similar rationale.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W AYERS whose telephone number is (571)272-6420. The examiner can normally be reached M-F 8:30-5 PM.
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/MICHAEL W AYERS/Primary Examiner, Art Unit 2195
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