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 07/02/2026 with respect to the rejection of claims 1-20 under 35 U.S.C. § 101 have been fully considered but they are not persuasive. The rejection of claims 1-20 under 35 U.S.C. § 101 is maintained for the reasons of record set forth at pages 3-9 of the Office action mailed 04/02/2026, as supplemented by the rebuttal set forth below and by the analysis set forth in the 35 U.S.C. 101 rejection that follows for the amended claims.
Applicant states that the independent claims are eligible even in unamended form (Remarks, pp. 18). The rebuttal below is accordingly directed to the grounds of rejection already of record and to the arguments Applicant has raised against them. To the extent the analysis below addresses the limitations newly added to the claims by the amendment filed 07/02/2026, that portion of the rejection constitutes a new ground necessitated by Applicant's amendment. See MPEP § 706.0?(a).
Applicant argues (Remarks, pp. 9) argues that the machine learning aspects in claim 1, 16, and 19 do not fall within the mental process grouping. However, they are not interpreted as a mental process and are addressed in the later steps.
Step 2A, Prong One
Applicant argues that the Office failed to identify the specific limitations believed to recite an abstract idea, as required by MPEP § 2106.04(a). This argument is not persuasive. The limitations of each claim believed to recite the judicial exception are identified verbatim, claim by claim, in the rejection set forth below, and each identified limitation is matched to the "mental processes" grouping of MPEP § 2106.04(a)(2)(III).
II. Step 2A, Prong Two
Applicant argues (Remarks, pp. 19-20) that the Office evaluated the additional elements in a vacuum and failed to consider the claim as a whole, citing MPEP § 2106.04(d)(II) and pages 3-4 of the August 4, 2025 memorandum. This argument is not persuasive. The Prong Two analysis set forth below evaluates the additional elements individually and as an ordered combination, and evaluates them together with the limitations reciting the judicial exception, in accordance with MPEP § 2106.04(d)(II). The conclusion does not rest on any element considered in isolation, and no additional element has been disregarded on the ground that it is conventional. See MPEP § 2106.04(d).
Applicant’s arguments with respect to rejection of claims 1, 16, 19 have been fully considered, but they are not deemed to be persuasive.
In response to applicant’s arguments on Page 14 that Desai does not teach or suggest “automated resources” in claims 1, 16 and 19, the arguments have been considered but are not deemed persuasive.
The claims recite ‘automated resources’ and do not recite ‘self-service automation applications”. Desai directly discloses automated resources [Desai, pg. 8, para. 0049 "computing resource 224 includes a group of cloud resources, such as one or more applications ("APPs") 224-1, one or more virtual machines ("VMs") 224-2, virtualized storage ("VSs") 224-3, one or more hypervisors ("HYPs") 224-4, and/or the like."]. The broadest reasonable interpretation of automated resources is any computing resource that performs a task without requiring human intervention, which encompasses hosted applications, virtual machines, virtualized storage, and hypervisors are all automated resources.
Applicants own specification (Specification, pg. 6, lines 5-23) discloses "[t]he PaaS resources 105 comprise, for example, automated resources such as self-service automation (SSA) applications,…the PaaS resources 105 may further comprise other types of applications, computing devices, software components, firmware components or other resources". There is no lexicographic definition of automated resources in the specification and no disavowal of scope. Under MPEP 2111.01 and Thorner, the term therefore keeps its full ordinary meaning.
Applicant asserts that Desai's resources are confined to processing, memory and networking resources, citing an expressly open-ended paragraph: "resources (e.g., processing resources, memory resources, networking resources, and/or the like)" (Desai, paragraph [0010]). Desai clarifies in the specification at paragraph [0049] what the "and/or the like" covers: “As further shown in Fig. 2, computing resource 224 includes a group of cloud resources, such as one or more applications ("APPs") 224-1, one or more virtual machines ("VMs") 224-2, virtualized storage ("VSs") 224-3, one or more hypervisors ("HYPs") 224-4, and/or the like.” As stated above, these are all automated resources.
Applicant argues (Remarks, pg. 14) that Desai does not disclose management and integration of automated resources. Desai explicitly teaches this, stating: "providing, to a resource, instructions that cause the at least one of the resources to reboot, power off, or power on based on the projected resource usage data…generating a recommendation to modify an allocation of the at least one of the resources" (Desai, paragraph [0032]).
Applicant's argument that Desai is directed to a different field is therefore not persuasive.
In response to applicant’s arguments on Page 15 that Desai does not disclose the four clauses in claims 1, 16 and 19, the arguments have been considered but are not deemed persuasive.
The argument is derivative and conclusory, each clause is disclosed by Desai:
collecting usage data (Desai, paragraph [0009]);
computing the utilization score (Desai, paragraphs [0017] and [0018]);
executing the machine learning process to predict future utilization (Desai, paragraph [0003], with Joshi at page 729 for the regressor and decision tree structures);
controlling the integration (Desai, paragraph [0032]).
Claim 1 requires control based at least in part on one or more of the respective utilization score and the respective predicted future utilization. Desai's reliance on projected resource usage data alone satisfies the limitation.
In response to Applicant's argument at page 15 of the Remarks that Desai's usage deviation data is not the claimed utilization score, the argument has been considered but is not persuasive.
The claims do not define utilization score, and the specification supplies no definition, describing only that "in a non-limiting illustrative embodiment, the following formula (1) is used [to] compute resource utilization score" (specification, page 10). A non-limiting illustrative embodiment does not narrow a claim term. See MPEP 2111.01. The broadest reasonable interpretation of a respective utilization score is accordingly a computed numeric measure of the extent to which a given resource is utilized.
Desai teaches such a measure. Desai teaches that "the model may determine a deviation between a quantity of resources requested or estimated by a particular customer and the quantity of resources actually utilized by the particular customer" (Desai, paragraph [0018]), and that the platform uses the model "to determine a degree to which historical usage of resources matched a quantity of resources allocated" (Desai, paragraph [0019]). A per-resource numeric measure of actual utilization derived from usage data reads on a respective utilization score.
As to a number of adopted instances of the given automated resource, Desai teaches grouping the data "based on a quantity of transient virtual machines utilized by the customers ..., a quantity of virtual machines continuously being utilized (e.g., continuously on), a quantity of unutilized virtual machines (e.g., continuously off)" (Desai, paragraph [0015]), and teaches computing the deviation against "the quantity of resources actually utilized by the particular customer" (Desai, paragraph [0018]). A count of instances of a given resource that are in use is a number of adopted instances of the given automated resource. Claim 1 requires only that the respective utilization score be computed based at least in part on the usage data of the given automated resource and that count. Desai is not required to reproduce the formula disclosed in the specification. Applicant's argument is therefore not persuasive.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-15 are directed to a method (i.e., a process); claims 16-18 are directed to an apparatus (i.e., a machine/apparatus); and claims 19-20 are directed to an article of manufacture (i.e., a product); therefore, all pending claims are directed to one of the four categories of invention.
Independent Claims
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, independent claim 1 recites an abstract idea in the form of mental processes. A mental process is a process that “can be performed in the human mind, or by a human using a pen and paper” (MPEP§ 2106.04(a)(2)(III), paragraph 1). Examples of mental processes include “observations, evaluations, judgments, and opinions” (MPEP § 2106.04(a)(2)(III), paragraph 2).
The following limitations of claim 1 are mental processes:
computing a respective utilization score for each of the one or more automated resources of the plurality of automated resources, wherein the respective utilization score of a given automated resource is computed based at least in part on the usage data of the given automated resource and a number of adopted instances of the given automated resource in the information processing system; [This is a mental process that can be performed by observations, evaluations, judgments, and opinions. No specific methodology for computing a utilization score is recited in the claim; therefore, it broadly encompasses processing that can be performed as a mental process.]
executing a machine learning process which analyzes the collected usage data using at least one machine learning model to predict a respective future utilization for each of the one or more automated resources,… [This is a mental process that can be performed by observations, evaluations, judgments, and opinions. No specific methodology for predicting a utilization is recited in the claim; therefore, it broadly encompasses processing that can be performed as a mental process.]
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No. The judicial exception recited in the above discussed claims is not integrated into a
practical application.
collecting usage data for a plurality of automated resources that are integrated and executing in the information processing system; [Collecting usage data represents an insignificant extra-solution activity of data gathering, being pre-solution activity. See MPEP 2106.05(g).]
implementing a resource management system which executes on at least one processing platform to manage an integration of automated resources in an information processing system by performing a process which comprises: [This additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). This element merely indicates a field of use or technological environment in which a judicial exception is applied, namely the resource management system includes managing automated resources].
…wherein the at least one machine learning model comprises at least one machine learning regressor model that includes one or more decision tree structures which are trained to predict transaction volumes of the one or more automated resources based on the collected usage data; and [Executing a machine learning process are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). Additionally, this is a description of how the abstract idea is performed, using a machine learning model. As such, this merely describes a technological environment. See MPEP 2106.05(h).]
controlling an integration of each of the one or more automated resources in the information processing system based at least in part on one or more of the respective utilization score and the respective predicted future utilization of each of the one or more automated resources; [Controlling integration of resources are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f).]
wherein the steps of the method are executed by a processing device operatively coupled to a memory. [A processing device coupled to memory are components recited at a high level are construed as generic computer components used to implement the abstract idea. See MPEP 2106.05(f)(2). As such, the limitations do not integrate the abstract idea into a practical application. Nor to do they amount to significantly more.]
Therefore, under MPEP 2106.04(d), the additional elements of the claims do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No. The claims do not include additional elements that are sufficient for the claims to amount to significantly more than the judicial exception.
Additional elements that are mere instructions to apply an exception or merely generally linking or generally linking the use of a judicial exception to a particular technological environment or field of use do not constitute significantly more than a judicial exception under MPEP§2106.05(I)(A). Since the additional elements in the independent claims are all mere instructions to apply an exception or are merely generally linking or generally linking the use of a judicial exception to a particular technological environment or field of use, they do not constitute significantly more than a judicial exception.
Therefore, the additional elements identified in the Step 2A Prong Two analysis do not
constitute significantly more than a judicial exception.
Independent claims 16 and 19 recite the same relevant limitations and a similar analysis applies.
Claim 16 recites the additional elements of “An apparatus comprising: a processing device operatively coupled to a memory which stores program code, wherein the processing device executes the program code to implement a resource management system is configured to manages an integration of automated resources in an information processing system by performing a process in which the resource management system operates to:” [A processing device coupled to memory are components recited at a high level are construed as generic computer components used to implement the abstract idea. See MPEP 2106.05(f)(2). As such, the limitations do not integrate the abstract idea into a practical application. Nor to do they amount to significantly more.]
Claim 19 recites the additional limitations of " An article of manufacture comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes said at least one processing device to perform the steps of:" – [An article of manufacture comprising a non-transitory processor-readable storage medium are components recited at a high level are construed as generic computer components used to implement the abstract idea. See MPEP 2106.05(f)(2). As such, the limitations do not integrate the abstract idea into a practical application. Nor to do they amount to significantly more.]
Therefore, the independent claims are not patent eligible.
Dependent Claims
The remaining dependent claims being rejected do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 2
the plurality of automated resources comprise self-service automation applications. [This additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). This element merely indicates a field of use or technological environment in which a judicial exception is applied, namely the automated resources include self-service automation applications.].
Claim 3
the information processing system comprises a platform-as-a-service computing environment. [This additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). This element merely indicates a field of use or technological environment in which a judicial exception is applied, namely the information processing system includes a platform-as-as-service computing environment.].
Claim 4
the usage data comprises transaction volume data for respective ones of the plurality of automated resources. [This additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). This element merely indicates a field of use or technological environment in which a judicial exception is applied, namely the usage data includes transaction volume data].
Claim 5
the controlling comprises at least one of maintaining activation of the one or more automated resources in the information processing system, deactivating the one or more automated resources from the information processing system, and activating the one or more automated resources in the information processing system. [This additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). This element merely indicates a field of use or technological environment in which a judicial exception is applied, namely the controlling includes maintaining activation, deactivating, and activating.].
Claim 6
the at least one machine learning model is trained with historical automated resource transaction data. [Training a model are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]
Claim 7
the at least one machine learning regressor model comprises a random forest regressor model which includes a plurality of decision trees, and the plurality of decision trees are respectively trained with different portions of the historical automated resource transaction data. [This additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). This element merely indicates a field of use or technological environment in which a judicial exception is applied, namely the machine learning regressor model includes a random forest regressor model.].
Claim 8 and 17
the usage data is collected in real-time in response to performance of one or more transactions by the one or more automated resources. [This is a mental process that can be performed by observations, evaluations, judgments, and opinions. No specific methodology for usage data collection recited in the claim; therefore, it broadly encompasses processing that can be performed as a mental process.]
Claim 9 and 18
dynamically re-computing a respective utilization score for each of the one or more automated resources based at least in part on real-time changes in a volume of the one or more transactions performed by the one or more automated resources. [Dynamically re-computing a score are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]
Claim 10
generating one or more visualizations of the usage data in real-time in response to at least one of the collection of the usage data, the computing of the respective utilization score and the re-computing of the respective utilization score, wherein the one or more visualizations are displayed on a user interface of at least one user device. [Generating visualizations are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]
Claim 11
generating at least one user interface for submission of one or more features to be added to at least one of an existing automated resource and a new automated resource. [Generating a user interface are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]
Claim 12
generating at least one additional user interface for one of approval and rejection of the one or more features to be added to at least one of the existing automated resource and the new automated resource. [Generating a user interface are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]
Claim 13
automatically adding the one or more features to be added to at least one of the existing automated resource and the new automated resource to a software development backlog in response to the approval [Adding a feature to a backlog are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]; and
automatically generating an electronic communication indicating the addition of the one or more features to the software development backlog, wherein the electronic communication is transmitted to a user device associated with a user that submitted the one or more features. [Generating a communication are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]
Claim 14
identifying, in response to the rejection of the one or more features, one or more reasons for the rejection [This is a mental process that can be performed by observations, evaluations, judgments, and opinions. No specific methodology for identifying a reason is recited in the claim; therefore, it broadly encompasses processing that can be performed as a mental process.]; and
automatically generating an electronic communication indicating the rejection and the one or more reasons for the rejection, wherein the electronic communication is transmitted to a user device associated with a user that submitted the one or more features. [Generating a communication are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]
Claim 15
automatically integrating the one or more features into the information processing system in response to the approval, wherein the one or more features are integrated via at least one of the existing automated resource and the new automated resource [Integrating features are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]; and
computing a utilization score for at least one of the existing automated resource and the new automated resource following the integrating of the one or more features. [This is a mental process that can be performed by observations, evaluations, judgments, and opinions. No specific methodology for computing a utilization score is recited in the claim; therefore, it broadly encompasses processing that can be performed as a mental process.]
Claim 20
the usage data is collected in real-time in response to performance of one or more transactions by the one or more automated resources [This is a mental process that can be performed by observations, evaluations, judgments, and opinions. No specific methodology for usage data collection is recited in the claim; therefore, it broadly encompasses processing that can be performed as a mental process.]; and
the program code further causes said at least one processing device to perform the step of dynamically re-computing a utilization score for each of the one or more automated resources based at least in part on real-time changes in a volume of the one or more transactions performed by the one or more automated resources. [Dynamically re-computing a score are mere instructions to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)]
The prior art used for rejections are provided below:
1. EP3719719Al (July 10, 2020) to Desai et al. (hereinafter Desai)
2. Improving Classification Accuracy Using Ensemble Learning Technique (Using Different Decision Trees) (May 2014) to Joshi et al. (hereinafter Joshi)
3. Bandwidth Distributing Method, Device And Data Centre, And Storage Medium (February 8, 2022) to Wang et al. (hereinafter Wang)
4. Workload Forecasting and Resource Management Models Based on Machine Learning for Cloud Computing Environments (June 30, 2021) to Saxena et al. (hereinafter Saxena)
5. A Statistical and Distributed Packet Filter Against DDoS Attacks in Cloud Environment (March 14, 2018) to Pandey et al. (hereinafter Pandey)
6. US11892933B2 (November 28, 2018) to Cannata et al. (hereinafter Cannata)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 16-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Desai in view of Joshi.
Per claim 1, Desai discloses:
A method comprising: implementing a resource management system which executes on at least one processing platform to manage an integration of automated resources in an information processing system by performing a process which comprises: [Desai, pg. 2, para. 0009 "Some implementations described herein provide a cloud resource prediction platform that utilizes a machine learning model to predict a quantity of cloud resources to allocate to a customer (e.g., an organization)." (note: the cloud resource prediction platform is a system that runs on a processing platform.)]
collecting usage data for a plurality of automated resources that are integrated and executing in the information processing system; [Desai, pg. 3, para. 0009 "the cloud resource prediction platform may receive historical cloud data associated with resources of a cloud computing environment...and…historical customer data associated with requested resource usage by customers of the cloud computing environment." (note: historical cloud data is usage data.)]
computing a respective utilization score for each of the one or more automated resources of the plurality of automated resources, wherein the respective utilization score of a given automated resource is computed based at least in part on the usage data of the given automated resource [Desai, pg. 10, para. 0070 "the cloud resource prediction platform…may determine, based on the historical cloud data and the historical customer data, usage deviation data indicating deviations between actual resource usage and planned resource usage of the cloud computing environment." (note: usage deviation data is the utilization score, deviation data is computed per-resource from the historical (usage) data);
pg. 4, para. 0015 "the usage growth profile may group the historical cloud data and the historical customer data based on a quantity of transient virtual machines utilized by the customers..., a quantity of virtual machines continuously being utilized..., a quantity of unutilized virtual machines..., a quantity of times virtual machines change from one customer to another customer..." (note: usage growth profile does track counts of resource instances by utilization category , "continuously being utilized" (adopted) vs. "unutilized" (not adopted) VM instances for a given resource);
pg. 3, para. 0009 “The cloud resource prediction platform may train a machine learning model, with the usage growth profile and the usage deviation data, to generate a trained machine learning model,” (note: the model trains on the growth profile and the deviation data together)] and a number of adopted instances of the given automated resource in the information processing system; [Desai, pg. 4, para. 0015 "the usage growth profile may group the historical cloud data and the historical customer data based on a quantity of transient virtual machines utilized by the customers..., a quantity of virtual machines continuously being utilized..., a quantity of unutilized virtual machines..., a quantity of times virtual machines change from one customer to another customer..." (note: usage growth profile does track counts of resource instances by utilization category , "continuously being utilized" (adopted) vs. "unutilized" (not adopted) VM instances for a given resource)]
executing a machine learning process which analyzes the collected usage data using at least one machine learning model to predict a respective future utilization for each of the one or more automated resources, [Desai, pg. 1 "The device may process the request for the new resource usage, with the trained model, to generate projected resource usage data, and may perform actions based on the projected resource usage data." (note: projected resource usage data is future utilization. Desai trains a machine learning model on the growth profile and deviation data and applies it to generate a prediction)]
controlling an integration of each of the one or more automated resources in the information processing system based at least in part on one or more of the respective utilization score and the respective predicted future utilization of each of the one or more automated resources; [Desai, pg. 6, para. 0032 "the cloud resource prediction platform providing, to a resource, instructions that cause the resource to reboot, power off, or power on based on the projected resource usage data... generating a recommendation to modify an allocation of a resource for the new customer based on the projected resource usage data." (note: this shows controlling an integration based on utilization score and future utilization.)]
wherein the steps of the method are executed by a processing device operatively coupled to a memory. [Desai, pg. 9, para. 0058 "device 300 may include a bus 310, a processor 320, a memory 330, a storage component 340, an input component 350, an output component 360, and a communication interface 370." (note: Desai's device architecture discloses a processor coupled to memory)]
Desai does not expressly disclose, but Desai combined with Joshi does teach:
wherein the at least one machine learning model comprises at least one machine learning regressor model that includes one or more decision tree structures which are trained to predict transaction volumes of the one or more automated resources based on the collected usage data; and
[Joshi, pg. 729 "Decision tree learning is one of the most popular technique in classification as it is fast and produces models having fair performance. Based on given input variables, a decision tree creates a model that predicts the value of a target variable."; "…is used for regression analysis with the help of regression trees,"; "Leaves of Regression trees predict a real number instead of a class" (note: Joshi teaches classification accuracy via bagging and shows classification as teaching regression. The decision trees Joshi discloses are used for regression)]
Desai and Joshi are analogous art because they are from the same field of endeavor of predictive modeling from historical data to inform an automated decision. They are also reasonably pertinent to the same problem of improving the accuracy of a predictive model trained on historical data.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to substitute Joshi’s decision-tree ensemble architecture for the machine-learning model Desai discloses.
The suggestion/motivation for doing so would have been to improve predictive accuracy. [Joshi, pg. 727 “Using ensemble methods is one of the general strategies to improve the accuracy of classifier and predictor.”]
Per claim 2, Desai-Joshi disclose claim 1.
Desai further teaches: wherein the plurality of automated resources comprise self-service automation applications. [Desai, pg. 8, para. 0049 "computing resource 224 includes a group of cloud resources, such as one or more applications ("APPs") 224-1, one or more virtual machines ("VMs") 224-2, virtualized storage ("VSs") 224-3, one or more hypervisors ("HYPs") 224-4, and/or the like." (note: automated resources under the broadest reasonable interpretation include hosted resources.)]
Per claim 3, Desai-Joshi disclose claim 1.
Desai further teaches: wherein the information processing system comprises a platform-as-a-service computing environment. [Desai, pg. 8, para. 0047 "Cloud computing environment 222 includes an environment that hosts cloud resource prediction platform 220. Cloud computing environment 222 may provide computation, software, data access, storage, etc., services that do not require end-user knowledge of a physical location and configuration of system(s) and/or device(s) that hosts cloud resource prediction platform 220." (note: a cloud environment hosting a platform that provides computation/software/data access/storage services abstracted from physical infrastructure is a platform-as-a-service)]
Per claim 4, Desai-Joshi disclose claim 1.
Desai further teaches: wherein the usage data comprises transaction volume data for respective ones of the plurality of automated resources. [Desai, pg. 4, para. 0012 "quantities of the resources used, times of day or days of the week when the resources are used" (note: quantities of the resources used is transaction volume data per resource over time)]
Per claim 5, Desai-Joshi disclose claim 1.
Desai further teaches: wherein the controlling comprises at least one of maintaining activation of the one or more automated resources in the information processing system, deactivating the one or more automated resources from the information processing system, and activating the one or more automated resources in the information processing system. [Desai, pg. 6, para. 0032 "the cloud resource prediction platform providing, to a resource, instructions that cause the resource to reboot, power off, or power on based on the projected resource usage data." (note: power off is deactivating, power on is activating, reboot necessarily includes a period of continued activation, and is therefore maintaining activation]
Per claim 6, Desai-Joshi disclose claim 1.
Desai further teaches: wherein the at least one machine learning model is trained with historical automated resource transaction data. [Desai, pg. 5, para. 0020 "the cloud resource prediction platform may train a machine learning model, with the usage growth profile and the usage deviation data, to generate a trained machine learning model." (note: the usage growth profile and usage deviation data comprise the historical automated resource transaction data.);
pg. 4, para. 0012 "quantities of the resources used, times of day or days of the week when the resources are used" (note: quantities of the resources used is transaction volume data per resource over time (historical automated resource transaction data))] ]
Claims 16 and 19 are substantially similar in scope and spirit to claim 1. Therefore, the rejection of claim 1 is applied accordingly. Desai further shows the method being implemented by an apparatus [Desai, pg. 2, para. 0004 "a device may include one or more memories, and one or more processors to receive a request for new resource usage by a customer associated with a cloud computing environment, wherein the cloud computing environment includes resources”)] Desai also shows the method being implemented by an article of manufacture [Desai, pg. 2, para. 0005 "a non-transitory computer-readable medium may store one or more instructions that, when executed by one or more processors of a device, may cause the one or more processors to…'')].
Claims 17 is substantially similar in scope and spirit to claim 8. Therefore, the rejection of claim 8 is applied accordingly. Desai further shows the method being implemented by an apparatus [Desai, pg. 2, para. 0004 "a device may include one or more memories, and one or more processors to receive a request for new resource usage by a customer associated with a cloud computing environment, wherein the cloud computing environment includes resources”)]
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Desai in view of Joshi, and further in view of Cannata.
Per claim 7, Desai-Joshi disclose claim 6.
Desai does not fully disclose, but with Cannata does teach: wherein the at least one machine learning regressor model comprises a random forest regressor model which includes a plurality of decision trees, and the plurality of decision trees are respectively trained with different portions of the historical automated resource transaction data. [Cannata, column 3, line 48 "A random forest algorithm, for example, may be executed to generate a random forest model for the throughput model 180. The random forest model may be built as a sequence of decision trees with each new tree constructed by forcing the decision split to consider a random subset of the predictors and thereby decorrelating the trees." (note: Cannata discloses random forest, describing the per-split random-subset feature, and applies it to a continuous prediction target (throughput), which is the transaction-volume prediction. Random forest is, by definition, bagging plus per-split feature randomization. Therefore, this shows that each tree in the plurality is trained on a different bootstrap sample (different portion) of the same dataset.)]
Desai, Joshi, and Cannata are analogous art because they are from the same field of endeavor of prediction of resource usage from historical resource usage data. They are further reasonably pertinent to the same problem of accurately predicting a continuous usage related quantity from operational data.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to substitute Cannata's named random-forest architecture for Desai’s generic model, and Joshi’s bootstrap sampling supplying the data partitioning.
The suggestion/motivation for doing so would have been to obtain greater prediction accuracy as explicitly stated in Cannata and Joshi [Cannata, column 3, line 39 "Random forests and boosting are techniques that are added to simple tree-based methods to produce multiple trees that are combined to yield a single consensus prediction with greater prediction accuracy.”;
Joshi, pg. 727 “Using ensemble methods is one of the general strategies to improve the accuracy of classifier and predictor.”].
Claims 8-10, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Desai in view of Joshi, and further in view of Wang.
Per claim 8, Desai-Joshi disclose claim 1.
Desai does not fully disclose, but with Wang does teach: wherein the usage data is collected in real-time in response to performance of one or more transactions by the one or more automated resources. [Wang, para. 36 “obtaining the bandwidth of user usage history data, according to bandwidth using historical data to determine primary forecast demand bandwidth of the user, real-time collecting bandwidth usage data of the user.” (note: Wang collects the user's bandwidth usage data in real time, alongside the historical data used for the initial forecast, and bandwidth consumed is a measure of the work the resource performs, therefore Wang's real-time collection constitutes collecting usage data in real time in response to the resource's performance of transactions,)]
Desai, Joshi, and Wang are analogous art because they are from the same field of endeavor of computational prediction and adaptive resource management from usage data. They are further reasonably pertinent to the same problem of keeping a computed usage metric accurate as usage changes over time.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Wang's real-time correction technique to the utilization metric of Desai and Joshi.
The suggestion/motivation for doing so would have been improved bandwidth utilization rate and improved user experience. [Wang, para. 104 "so as to improve the prediction accuracy ... more accurately the user bandwidth need change, and performs the dynamic adjustment of bandwidth allocation in time, improves the bandwidth utilization rate, improving the user experience; carrying out bandwidth distribution of intelligent dynamic adaptation, solves the configuration period is long, the problem of complex process ... the difficult to guarantee the important tenant service experience, and further improves the flexibility of the system.”]. Incorporating Joshi leads to improved predictive accuracy. [Joshi, pg. 727 “Using ensemble methods is one of the general strategies to improve the accuracy of classifier and predictor.”]
Per claim 9, Desai-Joshi-Wang disclose claim 8.
Desai does not fully disclose, but with Wang does teach: further comprising dynamically re-computing a respective utilization score for each of the one or more automated resources based at least in part on real-time changes in a volume of the one or more transactions performed by the one or more automated resources. [Wang, para. 39 "real-time collecting the user currently assigned bandwidth and the actually used bandwidth, obtaining the optimum bandwidth utilization ratio threshold value, based on the optimum bandwidth utilization ratio threshold value and the actually used bandwidth determines the ideal bandwidth threshold value, the difference value of the ideal bandwidth threshold and the current allocated bandwidth is determined as the bandwidth correction value. " (note: Wang discloses continuously monitoring real-time usage against an allocated (predicted) value and generating a correction value from the deviation, is a real-time re-computation of a resource allocation metric driven by usage-volume change)]
Desai, Joshi, and Wang are analogous art because they are from the same field of endeavor of computational prediction and adaptive resource management from usage data. They are further reasonably pertinent to the same problem of keeping a computed usage metric accurate as usage changes over time.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Wang's real-time correction technique to the utilization metric of Desai and Joshi.
The suggestion/motivation for doing so would have been improved bandwidth utilization rate and improved user experience. [Wang, para. 104 "so as to improve the prediction accuracy ... more accurately the user bandwidth need change, and performs the dynamic adjustment of bandwidth allocation in time, improves the bandwidth utilization rate, improving the user experience; carrying out bandwidth distribution of intelligent dynamic adaptation, solves the configuration period is long, the problem of complex process ... the difficult to guarantee the important tenant service experience, and further improves the flexibility of the system.”]. Incorporating Joshi leads to improved predictive accuracy. [Joshi, pg. 727 “Using ensemble methods is one of the general strategies to improve the accuracy of classifier and predictor.”]
Per claim 10, Desai-Joshi-Wang disclose claim 9.
Desai further teaches: further comprising generating one or more visualizations of the usage data in real-time in response to at least one of the collection of the usage data, the computing of the respective utilization score and the re-computing of the respective utilization score, wherein the one or more visualizations are displayed on a user interface of at least one user device. [Desai, pg. 6, para. 0031 "the one or more actions may include the cloud resource prediction platform providing the projected resource usage data for display... the cloud resource prediction platform may provide, for display, information identifying the new usage growth profile and/or the new usage deviation data." (note: the projected resource usage data displayed is the visualization of the usage data)]
Claim 18 is substantially similar in scope and spirit to claim 9. Therefore, the rejection of claim 9 is applied accordingly. Wang further shows the method being implemented by an apparatus [Wang, para. 30 "bandwidth distributing method, device and data centre and the storage medium"].
Per claim 20, Desai-Joshi disclose claim 19.
Desai combined with Wang further teaches: the usage data is collected in real-time in response to performance of one or more transactions by the one or more automated [Wang, para. 36 “obtaining the bandwidth of user usage history data, according to bandwidth using historical data to determine primary forecast demand bandwidth of the user, real-time collecting bandwidth usage data of the user.” (note: Wang collects the user's bandwidth usage data in real time, alongside the historical data used for the initial forecast, and bandwidth consumed is a measure of the work the resource performs, therefore Wang's real-time collection constitutes collecting usage data in real time in response to the resource's performance of transactions,)]; and
the program code further causes said at least one processing device to perform the step of dynamically re-computing a utilization score for each of the one or more automated resources based at least in part on real-time changes in a volume of the one or more transactions performed by the one or more automated resources. [Wang, para. 39 "real-time collecting the user currently assigned bandwidth and the actually used bandwidth, obtaining the optimum bandwidth utilization ratio threshold value, based on the optimum bandwidth utilization ratio threshold value and the actually used bandwidth determines the ideal bandwidth threshold value, the difference value of the ideal bandwidth threshold and the current allocated bandwidth is determined as the bandwidth correction value. " (note: Wang discloses continuously monitoring real-time usage against an allocated (predicted) value and generating a correction value from the deviation, is a real-time re-computation of a resource allocation metric driven by usage-volume change)]
Desai, Joshi, and Wang are analogous art because they are from the same field of endeavor of computational prediction and adaptive resource management from usage data. They are further reasonably pertinent to the same problem of keeping a computed usage metric accurate as usage changes over time.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Wang's real-time correction technique to the utilization metric of Desai and Joshi.
The suggestion/motivation for doing so would have been improved bandwidth utilization rate and improved user experience. [Wang, para. I 04 "so as to improve the prediction accuracy ... more accurately the user bandwidth need change, and performs the dynamic adjustment of bandwidth allocation in time, improves the bandwidth utilization rate, improving the user experience; carrying out bandwidth distribution of intelligent dynamic adaptation, solves the configuration period is long, the problem of complex process ... the difficult to guarantee the important tenant service experience, and further improves the flexibility of the system.”]. Incorporating Joshi leads to improved predictive accuracy. [Joshi, pg. 727 “Using ensemble methods is one of the general strategies to improve the accuracy of classifier and predictor.”]
Claims 11-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Desai in view of Joshi, and further in view of Saxena.
Per claim 11, Desai-Joshi disclose claim 1.
Desai does not fully disclose, but with Saxena does teach: further comprising generating at least one user interface for submission of one or more features to be added to at least one of an existing automated resource and a new automated resource. [Saxena, pg. 4, Fig. 2 "m users have requested different applications to be executed at the data center... Resource Management System (RMS) is deployed at the data center to receive application requests from cloud clients and generate satisfactory responses for them, by allocating required capacity of resources (as per application demand) in the form of VMs." (note: m users submitting application requests via the RMS is generating a UI for submission of one or more features to be added to a resource (VM).)]
Desai and Saxena are analogous art because they are from the same field of endeavor of resource management in a cloud computing environment. They are further reasonable pertinent to the same problem of efficiently managing automated computing resources in response to user demand.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate Saxena's user-facing request submission workflow into Desai and Joshi’s resource management platform,
The suggestion/motivation for doing so would have been adapting to demand automatically, as explicitly stated by Saxena [Saxena, pg. 3 “This emphasizes the need for automatic resource management techniques that enable systems to auto-adapt according to the dynamic resource demands by using the existing resources more efficiently”].
Per claim 12, Desai-Joshi-Saxena disclose claim 11.
Desai further teaches: further comprising generating at least one additional user interface for one of approval and rejection of the one or more features to be added to at least one of the existing automated resource and the new automated resource. [Desai, pg. 6, para. 0031 "the cloud resource prediction platform providing the projected resource usage data for display." (note: the communication interface necessarily accepts all the data for transmission to the display).);
pg. 10, para. 0068 "the cloud resource prediction platform (e.g., using computing resource 224, processor 320, input component 350, communication interface 370, and/or the like) may receive the historical customer data associated with requested resource usage by customers of the cloud computing environment." (note: the cloud resource prediction platform allows customers (users) to request data. The requested data by the customer is the feature that is then added to the existing automated resource, the cloud computing environment)]
Per claim 13, Desai-Joshi-Saxena disclose claim 12.
Desai combined with Saxena further teaches: automatically adding the one or more features to be added to at least one of the existing automated resource and the new automated resource to a software development backlog in response to the approval [Saxena, pg. 4 and fig. 2 "RMS consists of VM Management Unit (VMU) and Task Management Unit (TMU)... TMU works on applications, received from cloud clients/users, divide them into tasks, schedule and assign them on selected VMs for execution... Resource Management System (RMS) is deployed at the data center to receive application requests from cloud clients and generate satisfactory responses for them, by allocating required capacity of resources (as per application demand) in the form of VMs... The traditional task scheduling methods includes First-In-First-Out (FIFO), Last-In-First-Out (LIFO), Shortest Job First (SJF), Round-Robin (RR) scheduling etc." (note: this describes an active dispatcher assigning tasks for execution, a scheduler that operates FIFO, SJF, or RR necessarily holds tasks in an ordered wait-state before dispatch, which is a backlog. The generation of satisfactory responses is interpreted as approval)]; and
automatically generating an electronic communication indicating the addition of the one or more features to the software development backlog, wherein the electronic communication is transmitted to a user device associated with a user that submitted the one or more features. [Desai, pg. 9, para. 0006 "Output component 360 includes a component that provides output information from device 300 (e.g., a display, a speaker, and/or one or more light-emitting diodes (LEDs))." (note: the output component supplies the electronic communication transmitted to a user device)]
[Saxena, pg. 4 and fig. 2 "to receive application requests from cloud clients and generate satisfactory responses for them." (note: m users receive a response, which equates to an electronic communication, after sending an application request (feature))]
Desai and Saxena are analogous art because they are from the same field of endeavor of resource management in a cloud computing environment. They are further reasonable pertinent to the same problem of efficiently managing automated computing resources in response to user demand.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate Saxena's user-facing request submission workflow into Desai and Joshi’s resource management platform,
The suggestion/motivation for doing so would have been adapting to demand automatically, as explicitly stated by Saxena [Saxena, pg. 3 “This emphasizes the need for automatic resource management techniques that enable systems to auto-adapt according to the dynamic resource demands by using the existing resources more efficiently”].
Per claim 15, Desai-Joshi-Saxena disclose claim 12.
Desai does not fully disclose, but with Saxena does teach: automatically integrating the one or more features into the information processing system in response to the approval, wherein the one or more features are integrated via at least one of the existing automated resource and the new automated resource [Saxena, pg. 4 "Resource Management System (RMS) is deployed at the data center to receive application requests from cloud clients and generate satisfactory responses for them, by allocating required capacity of resources (as per application demand) in the form of VMs." (note: this shows integrating via the existing or new automated resource since allocating an application request's required capacity in the form of VMs is the act of integrating that request into a resource.)]; and
and computing a utilization score for at least one of the existing automated resource and the new automated resource following the integrating of the one or more features. [Saxena, pg. 4 "The workload execution information is collected as a historical workload database which is used to train the workload predictor for estimation of future workload and resource utilization information. This predicted information is utilized for decision making of energy-efficient resource distribution and optimized load balancing." (note: the workload execution information which is collected requires the inclusion of utilization information, because that is then used to predict future utilization information))]
Desai and Saxena are analogous art because they are from the same field of endeavor of resource management in a cloud computing environment. They are further reasonable pertinent to the same problem of efficiently managing automated computing resources in response to user demand.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate Saxena's user-facing request submission workflow into Desai and Joshi’s resource management platform,
The suggestion/motivation for doing so would have been adapting to demand automatically, as explicitly stated by Saxena [Saxena, pg. 3 “This emphasizes the need for automatic resource management techniques that enable systems to auto-adapt according to the dynamic resource demands by using the existing resources more efficiently”].
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Desai in view of Joshi, further in view of Saxena and Pandey.
Per claim 14, Desai-Joshi-Saxena disclose claim 12.
Desai does not fully disclose, but with Pandey does teach: identifying, in response to the rejection of the one or more features, one or more reasons for the rejection [Pandey, pg. 4 "If a deviation from normal local profile is detected, packets are marked as attack packet. ... Each incoming packet is marked as attack packet or non-attack packet by measuring the deviation from normal profile. Higher rating of a packet shows that it is more legitimate. Lower rating of a packet means it might be an attack packet." (note: Pandey's filter computes a numeric rating (deviation score) and rejects a packet when that rating falls outside the accepted range, which is the reason.)]; and
automatically generating an electronic communication indicating the rejection and the one or more reasons for the rejection, wherein the electronic communication is transmitted to a user device associated with a user that submitted the one or more features. [Pandey, pg. 4 "It monitors incoming packets in all individual nodes and generates alerts or logs. If a deviation from normal local profile is detected, packets are marked as attack packet." (note: this provides the reason for the rejection, showing the notification generation limitation as the causative event for communication.);
pg. 4 "If the coordinator fails, it detects the failure and sends an alert to administrator to instantiate a new coordinator node." (note: an alert to administrator is a user device notification.)
pg. 4 "It registers the VMs, when a profile is shared or requested by them. It monitors the activity of packet filters in registered VMs and alerts the administrator on the failure of any filter." (note: the alert to administrator is a user device notification, and the coordinator's node-monitoring alert function is the specific user that submitted the features)]
Desai, Joshi, Saxena, and Pandey are analogous art because they are from the same field of endeavor of automated decision making in a cloud computing environment based on analysis of collected data. They are further reasonably pertinent to the same problem of making a decision based on a deviation score from historical data.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Pandey's deviation based rejection to the feature approval workflow of Desai, Joshi, and Saxena, to provide a quantified basis for a rejection.
The suggestion/motivation for doing so would have been improving the clarity of a rejection decision. [Pandey, pg. 5 "Higher rating of a packet shows that it is more legitimate. Lower rating of a packet means it might be an attack packet."]
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Sayed M Shah whose telephone number is (571)272-9406. The examiner can normally be reached Monday-Friday 7:30 am - 5:00 pm, alternate Fridays off.
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/SAYED MUNEER SHAH/Examiner, Art Unit 2124
/ALAN CHEN/Primary Examiner, Art Unit 2125
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