DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the application filed 05/15/2024.
Claims 1-20 are presented for examination.
Information Disclosure Statement
2. The Applicants’ Information Disclosure Statement (filed 06/04/2025) has been received, entered into the record, and considered.
Drawings
3. The drawings filed 05/15/2024 are acceptable for examination purposes.
Specification
4. The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant's cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
5. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1- 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mahadik et al. (US 20210357255).
It is noted that any citations to specific, pages, columns, paragraphs, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
As to claim 1:
Mahadik teaches a system (Fig.6: 600) comprising:
a memory (Fig.6: memory 612) that stores instructions; and
one or more processors (Fig.6: processor(s) 614) coupled to the memory and configured to execute the instructions to perform operations comprising:
providing resource usage data for software to a trained machine learning model as input ([0006]: a resource management system employs reinforcement learning for resource allocation for a service. The resource management service receives resource usage data of the service and applies the resource usage data to a linear quadratic regulator (LQR) to find an optimal stationary policy, wherein the resource usage data is treated as states and at least one of the following are treated as actions: (1) a threshold at which to scale up or scale down the computing resources provisioned for the service, and (2) an amount or percentage by which to scale up or scale down the computing resources provisioned for the service; [0024]: The forecaster 12 is configured to generate predictive resource usage of a service applying a predictive model to historical usage data from the historical usage data source 18…the predictive model used is a time series …other machine-learning models can be used…the predictive model is a predictive time series based model that generates predictive resource usage for one time interval in advance, and the time interval used is adjustable based on specifications of the services);
receiving, from the trained machine learning model, a forecast resource usage for the software ([0024]: The forecaster 12 is configured to generate predictive resource usage of a service applying a predictive model to historical usage data; [0025]: Historical usage data may be fed to the forecaster 12 and the forecaster 12 may fit the predictive model onto the historical usage data; [0026]: Seasonal variations may be considered by the forecaster 12, such as a 6-hour seasonality, a 24-hour seasonality, a seven day seasonality, or even no seasonality. In some embodiments, the forecaster 12 is configured to estimate the predictive resource usage one time interval in advance, with the time interval based on the selected seasonality. However, the time interval may be adjusted based on the specific service, as determined using trial and error or other. The forecaster 12 may be configured to predict predictive resource usage with low error using the predictive model and historical service usage data; see also, [0034]); and
based on the forecast resource usage and a predetermined threshold, sending a notification to an administrator ([0023]: The resource manager 20 includes a forecaster 12, a simulator 14, and a controller 16. Furthermore, the resource manager 20 is communicably coupled with a historical usage data source 18 for providing historical usage data to the forecaster 12. The resource manager 20 is also communicably coupled with the cluster manager 30 for receiving instructions from the controller 16. These instructions include scale up and scale down thresholds (e.g., high and low watermarks) and/or information regarding an amount of scaling up or scaling down for the cluster manager 30 to executer…the historical usage data source 18 may receive ongoing usage data from the cluster manager 30 and/or the service to which the cluster manager 30 is provisioning resources and may store this as additional historical usage data for use by the forecaster 12; [0032]: The controller 16 operates by sending a search space of configuration parameters to the simulator 14 to estimate system utilization and overheads associated with each configuration. The controller 16 employs a cost function, as discussed above, to determine a cost value for each configuration. A cost-value list is returned to the controller 16 from the simulator 14, and the controller 16 may pick (based on results of the cost function described above) the configuration tuple that minimizes waste and usage overheads as the output value to be used to configure the service. The output value is provided to the cluster manager 30, which may launch the service with the specified service specifications using the recommended configuration values. The historical usage data source 18 and/or the forecaster 12 may then start receiving workload usage values for this service, creating a closed-loop solution; see also, [0035] and [0039]).
As to claim 2:
Mahadik teaches the operations further comprise: generating the trained machine learning model by providing a training set comprising historical resource usage data for a plurality of applications and databases ([0005-0006], [0017], and [0023-0025]).
As to claim 3:
Mahadik teaches the operations further comprise: automatically storing the resource usage data for the software in a spreadsheet; and accessing the resource usage data from the spreadsheet ([0017], [0023], and [0027]).
As to claim 4:
Mahadik teaches the software comprises a database ([0017], [0021-0023], and [0027]).
As to claim 5:
Mahadik teaches the resource usage data comprises memory usage data, memory suspension time data, garbage collection count data, and instance busy thread data ([0022], [0027-0030]).
As to claim 6:
Mahadik teaches the resource usage data comprises central processing unit (CPU) usage data, network usage data, disk usage data, and input/output operations per second (IOPS) data ([0022], [0027], and [0040]).
As to claim 7:
Mahadik teaches the forecast of resource usage for the software comprises a three-day forecast data ([0017], [0024], and [0026-0027]).
As to claim 8:
Mahadik teaches the forecast of resource usage for the software comprises a seven-day forecast ([0017], [0024], and [0026-0027]).
As to claims 9-16:
Refer to the discussion of claims 1-8 above, respectively, for rejections. Claims 9-16 are the same as claims 1-8, except claims 9-16 are non-transitory computer-readable medium claims and claims 1-8 are system claims.
As to claims 17-20:
Refer to the discussion of claims 1-4 above, respectively, for rejections. Claims 17-20 are the same as claims 1-4, except claims 17-20 are non-transitory computer-readable medium claims and claims 1-4 are method claims.
Conclusion
6. The prior art made of record, listed on PTO 892 provided to Applicant is considered to have relevancy to the claimed invention. Applicant should review each identified reference carefully before responding to this office action to properly advance the case in light of the prior art.
US 8180604 teaches “Optimizing A Prediction Of Resource Usage Of Multiple Applications In A Virtual Environment.”
US 9871741 teaches “Historical data relating to resource usage by the application is utilized to predict a resource usage amount for the application which is then stored.”
US 10904109 teaches “First resource utilization information for a first customer of a cloud platform and second resource utilization information for a second customer of the cloud platform are accessed. A first prediction regarding future resource utilization by the first customer and a second prediction regarding future resource utilization by the second customer are determined. A resource reallocation recommendation that recommends reallocating one or more resources between the first customer and the second customer is determined, based on the first prediction and the second prediction. The resource reallocation recommendation is provided.”
US 11392843 teaches “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). For example, the cloud resource prediction platform may receive historical cloud data associated with resources of a cloud computing environment, and may receive historical customer data associated with requested resource usage by customers of the cloud computing environment. The cloud resource prediction platform may determine a usage growth profile based on the historical cloud data and the historical customer data, and 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. 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, and may receive a request for new resource usage by a customer associated with the cloud computing environment. The cloud resource prediction platform may process the request for the new resource usage, with the trained machine learning model, to generate projected resource usage data, wherein the projected resource usage data identifies a projected resource usage of the cloud computing environment and by the customer, and may perform one or more actions based on the projected resource usage data.”
US 2018009774 teaches “A method implemented in a cloud-based data system includes a central controller receiving time-stamped reports from a plurality of agents including a server status and a server resource usage, calculating a number of active servers and a sum of resource usage on each server per interval based on each time-stamped report, generating a prediction model based on data results generated from calculating the number of active servers and the sum of resource usage per interval, predicting a number of servers needed in the cloud-based system based on the prediction model, generating a forecasting model to forecast an amount of resource usage at a future date, based on time series data associated with calculating the sum of resource usage over multiple intervals, and using the prediction model to predict whether a different number of servers is needed at the future date based on the forecasted amount of resource usage.”
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VAN H. NGUYEN whose telephone number is (571) 272-3765. The examiner can normally be reached on Monday- Friday from 9:00AM to 5:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, LEWIS BULLOCK, can be reached at telephone number (571) 272-3759. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VAN H NGUYEN/
Primary Examiner, Art Unit 2199
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