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 .
DETAILED ACTION
The instant application having Application No. 18/488,982 filed on 10/17/2023 is presented for examination.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax, Regular postal mail, or EFS Web.
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submissions of the Information Disclosure Statement dated 10/17/2023 and 3/16/2024 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract ide without significantly more.
Step 1: Claim 1 is a processor claim as such it is a method.
Step 2:
2A Prong 1:
The claim recites
performing a first evaluation of a first configuration in a first cloud service of the plurality of cloud services;
performing a second evaluation of a second configuration in a second cloud service of the plurality of cloud services; and
using a first result of the first evaluation and a second result of the second evaluation to select an unevaluated configuration in one of the first and second cloud services for performing another evaluation
under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
2A Prong 2:
This judicial exception is not integrated into a practical application because the
cloud service
are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)).
2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the “cloud services” are merely a generic computer or generic computer components to apply the judicial exception which cannot provide an inventive concept. Accordingly, the claim does not appear to be patent eligible under 35 USC 101.
As such, claim 1 is rejected under 35 U.S.C. 101.
Claims 2-12 depend from claim 1 and do not add additional elements that would overcome the rejection of claim 1 and are thus rejected for at least the same reason.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Adams (US 2014/0358831) in view of Breternitz (US 8,887,056).
As per claim 1, Adams discloses a Bayesian optimization which performs evaluations of candidate configurations and uses the results to select the next unevaluated configurations (Paragraph 63 “The probabilistic model, together with a so-called acquisition utility function (examples of which are described in more detail below), is used to make informed decisions about where to evaluate the objective function next, and the new evaluations may be used to update the probabilistic model of the objective function.” See also, paragraphs 27-31.). As such, Adams discloses performing a first evaluation of a first configuration
performing a second evaluation of a second configuration; and
using a first result of the first evaluation and a second result of the second evaluation to select an unevaluated configuration for performing another evaluation.
Adams does not expressly disclose that the Bayesian optimization technique is performed in a cloud environment. As such, Adams is silent with regard to performing the above steps as claimed below:
a computer-implemented method for serving a cloud workload across a plurality of cloud services, comprising:
in a first cloud service of the plurality of cloud services;
in a second cloud service of the plurality of cloud services
in one of the first and second cloud services.
However, Breternitz teaches “configuring a computing system, such as a cloud computing system,… based on user selections received via a user interface, configuring a cluster of nodes by selecting the cluster of nodes from a plurality of available nodes, selecting a workload container module from a plurality of available workload container modules for operation on each node of the selected cluster of nodes, and selecting a workload for execution with the workload container on the cluster of nodes.” (Abstract)
As such, Breternitz discloses the limitations claimed below:
a computer-implemented method for serving a cloud workload across a plurality of cloud services, comprising:
in a first cloud service of the plurality of cloud services;
in a second cloud service of the plurality of cloud services
in one of the first and second cloud services
Therefore it would have been obvious to one of ordinary skill in the art at before the effective filing date of the claimed invention to modify the method of Adams to include the teachings of Breternitz because it provides for the purpose of optimizing cloud configurations in order to reduce the resources required to run the cloud. In this way, the combination is cheaper for the cloud customer.
As per claim 2, Adams in view of Breternitz further discloses further comprising:
performing a third evaluation of a third configuration in a third cloud service of the plurality of cloud services (Adams, paragraph 63 and Breternitz, Abstract); and
using the first result and the second result and a third result of the third evaluation to select the unevaluated configuration in one of the first and second and third cloud services for performing the another evaluation (Adams, paragraph .63 and Breternitz, Abstract)
As per claim 3, Adams further discloses further comprising performing a plurality of different evaluations in at least one of the performing the first evaluation and the performing the second evaluation (Paragraph 68 “In these embodiments, the next point at which to evaluate the objective function may be selected prior to completion of one or more previously-initiated evaluations of the objective function, but the selection may be done based on respective likelihoods of potential outcomes of pending evaluations of the objective function so that some information about the pending evaluations (e.g., the particular points at which the evaluation is being performed) is taken into account when selecting the next point at which to evaluate the objective function. Parallelizing evaluations of the objective function may be useful when evaluation of the objective function is computationally expensive, for example, as the case may be when identifying hyper-parameter values for machine learning systems that take a long time (e.g., days) to train.”).
As per claim 4, Adams further discloses further comprising performing a plurality of different evaluations in each of the performing the first evaluation and the performing the second evaluation (Paragraph 68 “In these embodiments, the next point at which to evaluate the objective function may be selected prior to completion of one or more previously-initiated evaluations of the objective function, but the selection may be done based on respective likelihoods of potential outcomes of pending evaluations of the objective function so that some information about the pending evaluations (e.g., the particular points at which the evaluation is being performed) is taken into account when selecting the next point at which to evaluate the objective function. Parallelizing evaluations of the objective function may be useful when evaluation of the objective function is computationally expensive, for example, as the case may be when identifying hyper-parameter values for machine learning systems that take a long time (e.g., days) to train.”).
As per claim 5, Adams further discloses further comprising:
training a probabilistic model with training data that includes first performance data obtained from the performing the first evaluation and second performance data obtained from the performing the second evaluation (Abstract “using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain”); and
using performance predictions from the trained probabilistic model as inputs to an acquisition function to select the unevaluated configuration (Abstract, paragraphs 4-6).
As per claim 6, Adams further discloses further comprising maximizing the acquisition function using the input performance predictions to select the unevaluated configuration (Paragraph 13).
As per claim 7, Adams further discloses further comprising:
evaluating the selected unevaluated configuration (Paragraph 4); and
retraining the trained probabilistic model with a result of the evaluating the selected unevaluated configuration (Paragraph 4 “updating the probabilistic model of the objective function using results of the evaluating to obtain an updated probabilistic model of the objective function.).
As per claim 8, Adams further discloses further comprising:
defining two or more subsets of configurations in at least one of the cloud services (Paragraph 182 “T subsets”); and
maximizing the acquisition function using the input performance predictions to select one of the plurality of subsets from which to select the unevaluated configuration (Paragraph 183 “Accordingly, it should be appreciated that multi-task optimization techniques described herein may be used to maximize a single objective function that may be specified as a function of multiple other objective functions (e.g., which may be termed "sub-objective" functions).”).
As per claim 9, Adams further discloses wherein the subsets are based on hardware characteristics (Paragraph 77).
As per claim 10, Adams does not expressly disclose but Breternitz discloses wherein the hardware characteristics are selected from a group consisting of virtual machine type, processor arrangement, and data storage memory arrangement (Column 35, line 63 – column 36, line 26 “Referring to FIG. 29, the I/O Time tab 506 provides user access to configure additional monitoring tools, including virtual memory statistics (VMStat) and input/output statistics (IOStat) that are loaded on one or more nodes 16. VMStat collects data associated with availability and utilization of system memory and block I/O controlled with the operating system, the performance of processes, interrupts, paging, etc., for example. For example, VMStat collects data associated with a utilization of system memory such as the amount or percent of time that system memory and/or the memory controller is busy performing read/write operations or is waiting. IOStat collects data associated with statistics (e.g., utilization, availability, etc.) of storage I/O controlled with the operating system, for example. For example, IOStat collects data associated with the percentage of time that processing cores of the processor 40 of the corresponding node 16 is busy executing instructions or waiting to execute instructions. VMStat and IOStat are enabled/disabled by data monitor configurator 82 based on corresponding user selection of respective inputs 546, 548, and the sampling rate (i.e., refresh interval) are selected by data monitor configurator 82 based on values (illustratively in seconds) entered into fields 550, 552. Based on user selection of corresponding "enabled" inputs 546, 548 and values input into fields 550, 552 of tab 506, data monitor configurator 82 configures the VMStat and IOStat monitoring tools, and configurator 22 loads the tools onto each node 16 upon user selection of the corresponding "enabled" inputs 546, 548.).
As per claim 11, Adams in view of Breternitz further discloses wherein at least one of the cloud services has a first subset based on virtual machine type and a second subset based on processor arrangement (Adams, paragraph 182 “T subsets” and Breternitz column 35, line 63 – column 36, line 26.).
As per claim 12, Adams further discloses wherein the maximizing the acquisition function further comprises selecting an unevaluated configuration in the selected subset having a lowest user cost (Paragraph 125).
As per claims 13-19, they are product claims having similar limitations as cited in claims 1-12 and are rejected under the same rationale.
As per claim 20, it is a system claims having similar limitations as cited in claims 1-12 and are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Juels (US 9,128,739) disclose running a set of instances on at least one cloud for a first time interval, each of the instances comprising a bundle of virtualized resources. The method also includes the step of evaluating one or more performance characteristics of each of the instances in the set of instances over the first time interval. The method further includes the step of determining a first subset of the set of instances to maintain for a second time interval and a second subset of the set of instances to terminate for the second time interval responsive to the evaluating step. The steps are performed by at least one processing device comprising a processor coupled to a memory.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY A MUDRICK whose telephone number is (571)270-3374. The examiner can normally be reached 9am-5pm Central Time.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at (571)272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIMOTHY A MUDRICK/Primary Examiner, Art Unit 2198 2/29/2026