DETAILED ACTION
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 in response to the communication filed on 12/29/2025. Claims 1-20 are pending in this application.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/29/2025 has been entered.
Response to Amendment
Applicant’s arguments with respect to claims 1-20 have been considered but are moot based on the new grounds of rejection necessitated by Applicant’s amendments. Specifically, the arguments present that Gabrielson fails to provide for the amended language, where the rejection below now relies on Dou to teach this subject matter.
Claim Objections
Claims 1, 8 and 15 are objected to because of the following informalities:
In Claim 1, line 7, the limitation “one or more online resources” should be removed.
In Claim 8, line 7, the limitation “one or more online resources” should be removed.
In Claim 15, line 8, the limitation “one or more online resources” should be removed.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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, 3-8, 10-11 and 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bregman et al. (US 20200342416 A1, published 10/29/2020; hereinafter Bregman), in view of Vance et al. (US 6411605 B1, published 06/25/2002; hereinafter Vance), in view of Dou et al. (US 20200285503 A1, published 09/10/2020; hereinafter Dou), and in further view of Panuganty (US 20130263209 A1, published 10/03/2013; hereinafter Panuganty).
For Claim 1, Bregman teaches a non-transitory computer-readable media storing computer-executable instructions that, when executed by a processor, perform operations comprising (Bregman, FIG. 4, ¶ 0039 “… The computing device 12 may further include or be coupled to a non-transitory computer-readable storage medium such as a storage device 86 … any such media may contain computer-executable instructions …”): …
receiving, a request from a first user (Bregman exemplifies user 14 in FIGS 1A-1D) to access a multi-cloud resource scheduler, wherein the multi-cloud resource scheduler is connected to the plurality of different online resources (Bregman exemplifies a schedule compute instance window 58 in FIG. 1B to accept user request; FIG. 1A, FIG. 1B; ¶ 0017 “… a compute instance can be scheduled to be provisioned in a desired manner at a predetermined time … The term ‘compute instance’ as used herein, refers to an addressable computing environment, such as a bare metal machine, a virtual machine, or a container …”; ¶ 0019 “… The environment 10 includes a computing device 12 associated with a user 14 …”; ¶ 0021 “… The environment 10 also includes a compute instance pool 24 that contains a plurality of resources suitable for implementing a plurality of compute instances 26-1-26-N (generally, compute instances 26) …”; ¶ 0023 “… the environment 10 may include a scheduler 30 that may receive a request from the meeting task 20 to schedule the provisioning of a compute instance 26 at a predetermined time, …”); …
receiving instructions from the first user to schedule a start time and a shutdown time of at least one online cloud resource associated with the multi-cloud resource scheduler, wherein the shutdown time is a time when the at least one online cloud resource is completely shut down and made unavailable to any user (Bregman, FIG. 1A, FIG. 1B, ¶ 0027 “… The schedule compute instance window 58 includes a provision time identifier 60 that identifies a predetermined time prior to the meeting start time identified in the start time and date control 50 (FIG. 1A) when the compute instance 26 is to be provisioned. The provision time identifier 60 may identify an actual time, or may identify a time that is relative to the time in the start time and date control 50. …”; ¶ 0028 “… The schedule compute instance window 58 includes a deletion time identifier 64 that identifies the predetermined time after the meeting end time identified in the end time and date control 52 (FIG. 1A) when the compute instance 26 is to be deleted. The deletion time identifier 64 may identify an actual time, or may identify a time that is relative to the time in the end time and date control 52 …”); …
and based on the instructions, establishing an availability of the at least one online cloud resource for access by a second user (Bregman teaches making the compute instance 26 available to users 34 and 36; FIG. 1D, ¶ 0034 “… The scheduler 30, prior to the meeting, sends a compute instance update message 74 to the meeting task 20 that contains the IP address of the compute instance 26-1 and the authentication credentials. The meeting task 20 may automatically update the calendar 22 of the user 14 with the relevant information. The meeting task 20 may also automatically generate, based on the compute instance update message 74, a meeting update message 76 with the information identified in the compute instance update message 74, and send the meeting update message 76 to the meeting tasks 40, 44 so that the users 34 and 36 can be authenticated and access and utilize the compute instance 26-1 along with the user 14 …”).
Bregman does not explicitly teach, but Vance teaches responsive to the request from the first user, validating credentials of the first user to access the multi-cloud resource scheduler (Vance, FIG. 1, FIG. 2, col. 5, ll. 39-51 “… Once the scheduler is activated, the scheduler receives (56) access requests from network users who desire to schedule a conference call. For example, the user may initiate the process by opening a home page of the scheduler's server node. At the home page, the user may be prompted to enter identification information such as a password or numeric code. In this manner, the user can be authenticated (58) as an authorized user of the scheduler …”),
based upon the validating of the credentials of the first user, providing, to the first user, access to the multi-cloud resource scheduler (Vance, FIG. 1, FIG. 2, col. 5, ll. 52-60 “… If the access request is authenticated, either by identifying the user node or by comparing a password or other identification information to a user database, then the user is allowed (60) to access the scheduling functions of the scheduler …”).
Bregman and Vance are analogous art because they are both related to network resource schedulers.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the authentication techniques of Vance with the system of Bregman to provide secured utilization of network resources (Vance, col. 2, ll. 16-29).
Bregman-Vance does not explicitly teach, but Dou teaches monitoring access and use of instances of a plurality of different online resources (Dou, FIG. 1; ¶ 0024 “… During a training period, training usage data 138 and training metric data 140 are generated from the operation of a virtual machine 118 and sent to the VM monitor engine 122 (block 128) …”);
training a machine learning model to adjust start times and shutdown times of the one or more online resources based on the monitored access and use (Dou teaches training multiple machine learning (time series forecasting) models based on collected historical usage and metric data, the trained ML models predict when a virtual machine will be idle and could be shut down and when to be restarted; FIG. 1, FIG. 3; ¶ 0027 “… The machine learning engine 124 uses ensemble learning to train multiple models on the training usage and metric data of a virtual machine during a training period (block 130) … The best model to represent the time series of a virtual machine will be selected based on training performance metrics for further prediction …”; ¶ 0038 “… The selected model is used by the forecast engine 126 with production usage data 142 and production metric data 144 to forecast the time when the CPU usage will be below the idle threshold (block 132). The forecast engine 126 may utilize the usage data to produce cost estimates of the savings in shutting down a virtual machine during a forecasted idle time (block 132) …”; ¶ 0049 “… When an idle time is forecasted, the cloud resource management system 104 may take one of several actions (block 306). If the user of the virtual machine has configured the virtual machine for an automatic shutdown, the system may initiate actions to automatically shut down the virtual machine for a predetermined length of time. The virtual machine may be restarted after the forecasted idle time …”); …
using the machine learning model to adjust the start time and/or shutdown time of the at least one online cloud resource (Dou teaches that a selected forecasting model is used to predict future idle periods during regular VM operation periods and determine when to initiate actions such as automatically shutting down a virtual machine when idle; FIG. 1, FIG. 3; ¶ 0039 “… This forecast may be used to automatically shutdown the virtual machine at the forecasted time and to tum on the virtual machine thereafter (block 134). Alternatively, the forecast may be provided to the user of the virtual machine along with the estimated savings in order for the user to decide whether or not to shutdown the virtual machine (block 134). The user may direct the cloud resource management system 104 to take an appropriate action, such as, shutdown the virtual machine for a limited time span, increase usage of the virtual machine, ignore the forecast, and/or reduce the amount of resources consumed by the virtual machine (block 134) …”; ¶ 0051 “… In a production run, when the virtual machine is operational by 8 AM on one day and operational during the previous 24 hours, the time series model may be used predict when in the next 24 hours the CPU usage, disk I/O usage, and/or network usage may fall below an idle threshold. This forecasted time may be used to shutdown the virtual machine for the forecasted idle time and restarted thereafter …”).
Dou and Bregman-Vance are analogous art because they are both related to network resource management.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the training machine learning models to predict the idle time of VMs techniques of Dou with the system of Bregman-Vance to facilitate the customer of the cloud computing system saving on the cost of operating the virtual machine during the idle time (Dou, ¶ 0018).
Bregman-Vance-Dou does not explicitly teach, but Panuganty teaches the plurality of different online resources comprising at least two cloud resources operated by different entities (Panuganty teaches a services console including a scheduler to manage the application/service deployment on multiple clouds that might be owned by different providers; FIG. 1, FIG. 2; ¶ 0038 “… The services console hosted on servers 125 may be configured to receive multi-cloud management policies from the management console hosted on servers 115 and to actuate those policies on multiple clouds, such as clouds 130a and 130b, any of which may be owned by different providers and/or be deployed as private clouds …”; ¶ 0092 “… service console 230 also includes scheduler 254. Scheduler 254 may be user-configurable to execute batch jobs according to a static schedule. For example, an administrator may configure scheduler 254 to provision a given application during certain hours of the day and/or days of the week and to de-provision the application at other times. For example, an organization that has deployed an internal social networking application on one or more clouds may not want to waste money executing the application outside of business hours. Accordingly, an administrator may set a schedule that provisions and starts the application each weekday at the start of the workday and de-provisions it after working hours …”).
Panuganty and Bregman-Vance-Dou are analogous art because they are both related to network resource schedulers.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the multi-cloud application deployments and management techniques of Panuganty with the system of Bregman-Vance-Dou to facilitate an organization deploying applications across multiple clouds (Panuganty, ¶ 0026).
For Claim 3, Bregman-Vance-Dou-Panuganty teaches the non-transitory computer-readable media of claim 1. Dou also teaches the operations further comprising: prompting the first user to approve the adjusting of the start time and/or shutdown time prior to adjusting the start time and/or shutdown time (Dou, FIG. 1; ¶ 0039 “… Alternatively, the forecast may be provided to the user of the virtual machine along with the estimated savings in order for the user to decide whether or not to shutdown the virtual machine (block 134). The user may direct the cloud resource management system 104 to take an appropriate action, such as, shutdown the virtual machine for a limited time span, increase usage of the virtual machine, ignore the forecast, and/or reduce the amount of resources consumed by the virtual machine (block 134) …”). See motivation to combine for claim 1.
For Claim 4, Bregman-Vance-Dou-Panuganty teaches the non-transitory computer-readable media of claim 1. Dou also teaches the operations further comprising: receiving resource utilization metrics regarding utilization by the second user of the at least one online cloud resource (Dou teaches the collected metric data of the virtual machine and the virtual machine may be shared by a group of users; FIG. 1; ¶ 0001 “… A cloud computing service provides shared computing resources to users or customers …”; ¶ 0013 “… A customer (i.e., user, developer, client) of a cloud computing service may configure the virtual machine to utilize a certain amount and type of computing resources …”; ¶ 0014 “… Metric data representing the resource consumption of a virtual machine at equally-spaced time intervals is collected over the course of a training period …”). See motivation to combine for claim 1.
For Claim 5, Bregman-Vance-Dou-Panuganty teaches the non-transitory computer-readable media of claim 4. Dou also teaches the operations further comprising: adjusting at least one of the start time or the shutdown time of the at least one online cloud resource based at least in part on the resource utilization metrics (Dou teaches training multiple machine learning (time series forecasting) models based on collected historical usage and metric data and selecting a forecasting model to predict future idle periods during regular VM operation periods and initiate actions such as shutting down a virtual machine when idle; FIG. 1; ¶ 0027 “… The machine learning engine 124 uses ensemble learning to train multiple models on the training usage and metric data of a virtual machine during a training period (block 130) … The best model to represent the time series of a virtual machine will be selected based on training performance metrics for further prediction …”; ¶ 0038 “… The selected model is used by the forecast engine 126 with production usage data 142 and production metric data 144 to forecast the time when the CPU usage will be below the idle threshold (block 132). The forecast engine 126 may utilize the usage data to produce cost estimates of the savings in shutting down a virtual machine during a forecasted idle time (block 132) …”; ¶ 0039 “… This forecast may be used to automatically shutdown the virtual machine at the forecasted time and to tum on the virtual machine thereafter (block 134). Alternatively, the forecast may be provided to the user of the virtual machine along with the estimated savings in order for the user to decide whether or not to shutdown the virtual machine (block 134); ¶ 0051 “… In a production run, when the virtual machine is operational by 8 AM on one day and operational during the previous 24 hours, the time series model may be used predict when in the next 24 hours the CPU usage, disk I/O usage, and/or network usage may fall below an idle threshold. This forecasted time may be used to shutdown the virtual machine for the forecasted idle time and restarted thereafter …”). See motivation to combine for claim 1.
For Claim 6, Bregman-Vance-Dou-Panuganty teaches the non-transitory computer-readable media of claim 1. Bregman further teaches the operations further comprising: scheduling a synchronized start time and a synchronized shutdown time of a plurality of online cloud resources, wherein each online cloud resource in the plurality of online cloud resources has a start time and a shutdown time that are mutually synchronized (Bregman, FIG. 1A, ¶ 0023 “… the scheduler 30 may be configured to limit a maximum number of compute instances 26 that may be provisioned at the same time, and communicate with the meeting task 20 to advise the meeting task 20 whether or not a compute instance 26 can be scheduled to be provisioned at a particular time …”).
For Claim 7, Bregman-Vance-Dou-Panuganty teaches the non-transitory computer-readable media of claim 1. Bregman further teaches the operations further comprising: scheduling a start time and a shutdown time of a plurality of online cloud resources, wherein at least one online cloud resource within the plurality of online cloud resources comprises a different start time or a different shutdown time than at least one other online cloud resource within the plurality of online cloud resources (Bregman, FIG. 1A, ¶ 0018 “… a predetermined maximum number of compute instances may be scheduled for use at one time, and thus, at the time of the meeting, the meeting organizer can ensure either that a compute instance will be available for use during the meeting, or that the meeting must be scheduled for a different time when a compute instance can be available for use …”).
For Claim 8, the claim is substantially similar to claim 1 and therefore is rejected for the same reasoning set forth above.
For Claim 10, the claim is substantially similar to claim 3 and therefore is rejected for the same reasoning set forth above.
For Claim 11, Bregman-Vance-Dou-Panuganty teaches the method of claim 8. Bregman further teaches wherein the multi-cloud resource scheduler is configured to access a client device (Bregman FIG.1D exemplifies computing devices 38, 42 associated with users 34 and 36) of the second user (Bregman, FIG. 1D, ¶ 0034 “… The scheduler 30, prior to the meeting, sends a compute instance update message 74 to the meeting task 20 that contains the IP address of the compute instance 26-1 and the authentication credentials. The meeting task 20 may automatically update the calendar 22 of the user 14 with the relevant information. The meeting task 20 may also automatically generate, based on the compute instance update message 74, a meeting update message 76 with the information identified in the compute instance update message 74, and send the meeting update message 76 to the meeting tasks 40, 44 so that the users 34 and 36 can be authenticated and access and utilize the compute instance 26-1 along with the user 14 …”).
For Claim 14, Bregman-Vance-Dou-Panuganty teaches the method of claim 11. Bregman further teaches further comprising: reviewing credentials of the second user to determine if the second user is authorized to access the online cloud resource (Bregman, FIG. 1D, ¶ 0034 “… The scheduler 30, prior to the meeting, sends a compute instance update message 74 to the meeting task 20 that contains the IP address of the compute instance 26-1 and the authentication credentials. The meeting task 20 may automatically update the calendar 22 of the user 14 with the relevant information. The meeting task 20 may also automatically generate, based on the compute instance update message 74, a meeting update message 76 with the information identified in the compute instance update message 74, and send the meeting update message 76 to the meeting tasks 40, 44 so that the users 34 and 36 can be authenticated and access and utilize the compute instance 26-1 along with the user 14 …”).
For Claim 15, the claim is substantially similar to claim 1 and therefore is rejected for the same reasoning set forth above.
For Claim 16, Bregman-Vance-Dou-Panuganty teaches the system of claim 15. Panuganty also teaches wherein additional online cloud resources may be added to the multi-cloud resource scheduler and online cloud resources may be removed from the multi-cloud resource scheduler (Panuganty teaches the deployed and undeployed applications/services for a scheduler to manage; FIG. 1, FIG. 2; ¶ 0092 “… service console 230 also includes scheduler 254. Scheduler 254 may be user-configurable to execute batch jobs according to a static schedule. For example, an administrator may configure scheduler 254 to provision a given application during certain hours of the day and/or days of the week and to de-provision the application at other times. For example, an organization that has deployed an internal social networking application on one or more clouds may not want to waste money executing the application outside of business hours. Accordingly, an administrator may set a schedule that provisions and starts the application each weekday at the start of the workday and de-provisions it after working hours …”). See motivation to combine for claim 1.
For Claim 17, Bregman-Vance-Dou-Panuganty teaches the system of claim 15. Dou also teaches further wherein the operations further comprise: monitoring use of the at least one online cloud resource by the second user (Dou teaches the collected metric data of the virtual machine and the virtual machine may be shared by a group of users; FIG. 1; ¶ 0001 “… A cloud computing service provides shared computing resources to users or customers …”; ¶ 0013 “… A customer (i.e., user, developer, client) of a cloud computing service may configure the virtual machine to utilize a certain amount and type of computing resources …”; ¶ 0014 “… Metric data representing the resource consumption of a virtual machine at equally-spaced time intervals is collected over the course of a training period …”). See motivation to combine for claim 1.
For Claim 18, Bregman-Vance-Dou-Panuganty teaches the system of claim 15. Bregman further teaches wherein the at least one online cloud resource is a hyperscaler instance (Bregman teaches resource instances provided by major cloud service providers such as Amazon or Microsoft; FIG. 1B; ¶ 0021 “… The environment 10 also includes a compute instance pool 24 that contains a plurality of resources suitable for implementing a plurality of compute instances 26-1-26-N (generally, compute instances 26) … The environment 10, in some examples, may comprise a cloud computing environment, such as an Amazon Amazon Web Services (AWS) cloud computing environment, a Microsoft® Azure™ cloud computing environment, or a private cloud computing environment …”).
Claim Rejections - 35 USC § 103
Claim 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bregman et al. (US 20200342416 A1, published 10/29/2020; hereinafter Bregman), in view of Vance et al. (US 6411605 B1, published 06/25/2002; hereinafter Vance), in view of Dou et al. (US 20200285503 A1, published 09/10/2020; hereinafter Dou), in view of Panuganty (US 20130263209 A1, published 10/03/2013; hereinafter Panuganty), and in further view of Srinivasan et al. (US 20200382437 A1, published 12/03/2020; hereinafter Srinivasan).
For Claim 2, Bregman-Vance-Dou-Panuganty teaches the non-transitory computer-readable media of claim 1. Bregman-Vance-Dou-Panuganty does not explicitly teach, but Srinivasan teaches the operations further comprising: receiving, from the first user, a configuration parameter indicating that the start time and the shutdown time are selectively disabled (Srinivasan teaches that the operator may deactivate the control of the scheduling of the computing resources, FIG. 1, FIG. 2, ¶ 0039 “… Following operator review, adjustment and approval of the uniform activation determinations … the RASL 200 (i.e. resource aggregation stack logic) may generate, at the activation execution layer 170 an activation timetable 172 that may be sent to a host interface for control of the scheduling of the computing resources (e.g., activation/deactivation for the timeslots). The activation timetable 172 may specify the activation states for computing resources for the timeslots within a given period …”).
Srinivasan and Bregman-Vance-Dou-Panuganty are analogous art because they are both related to scheduling computing resources.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the activation/deactivation of the computing resources scheduling techniques of Srinivasan with the system of Bregman-Vance-Dou-Panuganty to provide increased scheduling accuracy to increase the utilization of cloud computing resources (Srinivasan ¶ 0010).
Claim Rejections - 35 USC § 103
Claim 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bregman et al. (US 20200342416 A1, published 10/29/2020; hereinafter Bregman), in view of Vance et al. (US 6411605 B1, published 06/25/2002; hereinafter Vance), in view of Dou et al. (US 20200285503 A1, published 09/10/2020; hereinafter Dou), in view of Panuganty (US 20130263209 A1, published 10/03/2013; hereinafter Panuganty), and in further view of Meck et al. (US 20210021538 A1, published 01/21//2021; hereinafter Meck).
For Claim 9, Bregman-Vance-Dou-Panuganty teaches the method of claim 8. Bregman-Vance-Dou-Panuganty does not explicitly teach, but Meck teaches further comprising receiving instructions from the first user to allow the second user to access the online cloud resource after a scheduled shutdown time (Meck, FIGS 1-3, ¶ 0020 “… For example, the cloud-based resource 108-2 can include a group tag of ‘test group A’ such that a search for ‘test group A’ in field 304 will return the cloud-based resource 108-2. …”; ¶ 0024 “… When the user 102 selects a listed cloud-based resource 108 to keep awake, the web-based interface can operate to cause an operational schedule (e.g., an original operational schedule) of the selected cloud-based resource 108 to be copied and stored-for example, from a first tag into a second tag. The web-based interface can then cause the operational schedule for the selected cloud-based resource 108 to be modified - for example, to be modified to keep awake. In various embodiments, the operational schedule can be modified to keep awake the selected cloud-based resource 108 indefinitely, for a first desired period of time specified by the user, or for a period of time not to exceed a predetermined time interval (e.g., 12 hours or 24 hours) …”; ¶ 0026 “… the web-based interface can automatically notify other authorized users of the change to the original operating schedule …”; ¶ 0027 “… Further, the web-based interface enables the user to efficiently start up shutdown resources 108 that the user may want online (e.g., to work on a project or maintenance issue related to the shutdown resource 108) …”).
Meck and Bregman-Vance-Dou-Panuganty are analogous art because they are both related to scheduling computing resources.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the network resource management features of Meck with the system of Bregman-Vance-Dou-Panuganty to provide “a more efficient management of cloud-based resources to enable a user to more effectively perform maintenance on the cloud-based resources (Meck ¶ 0003).”
Claim Rejections - 35 USC § 103
Claims 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bregman et al. (US 20200342416 A1, published 10/29/2020; hereinafter Bregman), in view of Vance et al. (US 6411605 B1, published 06/25/2002; hereinafter Vance), in view of Dou et al. (US 20200285503 A1, published 09/10/2020; hereinafter Dou), in view of Panuganty (US 20130263209 A1, published 10/03/2013; hereinafter Panuganty), and in further view of Guan et al. (US 20120054623 A1, published 03/01/2012; hereinafter Guan).
For Claim 12, Bregman-Vance-Dou-Panuganty teaches the method of claim 11. Bregman-Vance-Dou-Panuganty does not explicitly teach, but Guan teaches wherein the start time and the shutdown time of the online cloud resource are automatically adjusted for time zones (Guan ¶ 0011 “… Instead of storing and using statically stored timezone information to display a calendar item to the end user, … the calendar system may dynamically calculate an intended local time based on the statically stored timezone information. Up-to-date timezone information available on a computer system may be used to apply this intended local time to the timezone information to display the meeting to the end user in the correct time …”).
Guan and Bregman-Vance-Dou-Panuganty are analogous art because they are both related to computer networking.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the timezone calculation features of Guan with the system of Bregman-Vance-Dou-Panuganty to show meetings at the correct time and save costs for maintenance and support of the calendar system (Guan ¶ 0001).
For Claim 13, Bregman-Vance-Dou-Panuganty teaches the method of claim 11. Bregman-Vance-Dou-Panuganty does not explicitly teach, but Guan teaches further comprising: reviewing a calendar or meeting schedule stored in a memory of the client device and adjusting either of a scheduled start time or a scheduled shutdown time based at least in part on the reviewing (Guan ¶ 0022 “… The processing unit may be operative to receive a request to process a scheduled event, such as a request to view a calendar item or to determine whether a task associated with the scheduled event should be executed. The processing unit may be further operative to retrieve a base time associated with the calendar item, identify a local bias associated with the request to view the calendar item, convert the base time to a local time according to the local bias, and display the local time and the calendar item …”). See motivation to combine for claim 12.
Claim Rejections - 35 USC § 103
Claims 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bregman et al. (US 20200342416 A1, published 10/29/2020; hereinafter Bregman), in view of Vance et al. (US 6411605 B1, published 06/25/2002; hereinafter Vance), in view of Dou et al. (US 20200285503 A1, published 09/10/2020; hereinafter Dou), in view of Panuganty (US 20130263209 A1, published 10/03/2013; hereinafter Panuganty), in view of Gabrielson et al. (US 20200301741 A1, published 09/24/2020; hereinafter Gabrielson), and in further view of Chen et al. (US 20160308786 A1, published 10/20/2016; hereinafter Chen).
For Claim 19, Bregman-Vance-Dou-Panuganty teaches the system of claim 15. Dou also teaches the operations further comprising: receiving resource utilization metrics regarding utilization by the second user of the at least one online cloud resource (Dou teaches the collected metric data of the virtual machine and the virtual machine may be shared by a group of users; FIG. 1; ¶ 0001 “… A cloud computing service provides shared computing resources to users or customers …”; ¶ 0013 “… A customer (i.e., user, developer, client) of a cloud computing service may configure the virtual machine to utilize a certain amount and type of computing resources …”; ¶ 0014 “… Metric data representing the resource consumption of a virtual machine at equally-spaced time intervals is collected over the course of a training period …”); and … See motivation to combine for Claim 1.
Bregman-Vance-Dou-Panuganty does not teach, but Gabrielson teaches adjusting a … start time based at least in part on the utilization metrics (Gabrielson teaches using a trained machine learning model to help the users to schedule a future interval of time that can satisfy the requirements for executing a workload (e.g. the second computing workload); FIG. 1, FIG. 6; ¶ 0078 “… The operations 600 further include, at block 604, generating, based on the historical data, a prediction regarding available computing resources of the computing resource pool that will be unused by the one or more first computing workloads during a future interval of time. In some embodiments, the predicted amount of computing resources to be used to execute the first computing workload at the one or more future points in time is generated using a RNN trained based on historical data related to the compute instance pool …”; ¶ 0080 “… The operations 600 further include, at block 608, determining that the prediction regarding available computing resources and the future interval of time can satisfy the requirements for executing the second computing workload …”; ¶ 0081 “… The operations 600 further include, at block 610, scheduling execution of the second computing workload during the future interval of time, including scheduling use of the amount of computing resources of the computing resource pool by the second computing workload …”).
Gabrielson and Bregman-Vance-Dou-Panuganty are analogous art because they are both related to network resource schedulers.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the machine learning techniques of Gabrielson with the system of Bregman-Vance-Dou-Panuganty to “enable more efficient use of computing resources available to a user or group of users and improve organizations' ability to manage the execution of any number of separate workloads, thereby reducing computing time (and computing resource usage generally), power usage, and possibly expense” (Gabrielson, ¶ 0022).
Bregman-Vance-Dou-Panuganty-Gabrielson does not explicitly teach, but Chen teaches the start time is a recurring start time (Chen, ¶ 0005 “… the disclosures includes an ingress node in a network, comprising a receiver configured to receive a first request for a temporal label switched path (LSP) in the network, wherein the first request indicates a network constraint and a scheduled time interval having a predetermined start time and a predetermined end time for the temporal LSP to carry traffic, a processor coupled to the receiver and configured to compute a path in the network for the temporal LSP, wherein the path satisfies the network constraint in the scheduled time interval, and reserve a network resource for use during the scheduled time interval for the temporal LSP in advance of the predetermined start time … wherein the scheduled time interval is a recurrent time interval, and wherein the path request message further indicates a repeat period that the scheduled time interval repeats and a number of repeats for the scheduled time interval, and/or wherein the first request further indicates a desired start time and an elastic time range for the scheduled time interval …”).
Chen and Bregman-Vance-Dou-Panuganty-Gabrielson are analogous art because they are both related to scheduling computing resources.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the recurrent computing resources scheduling techniques of Chen with the system of Bregman-Vance-Dou-Panuganty-Gabrielson to provide efficient usage of network resources (Chen ¶ 0081).
Claim Rejections - 35 USC § 103
Claims 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bregman et al. (US 20200342416 A1, published 10/29/2020; hereinafter Bregman), in view of Vance et al. (US 6411605 B1, published 06/25/2002; hereinafter Vance), in view of Dou et al. (US 20200285503 A1, published 09/10/2020; hereinafter Dou), in view of Panuganty (US 20130263209 A1, published 10/03/2013; hereinafter Panuganty), and in further view of Chen et al. (US 20160308786 A1, published 10/20/2016; hereinafter Chen).
For Claim 20, Bregman-Vance-Dou-Panuganty teaches the system of claim 15. Dou also teaches the operations further comprising: receiving resource utilization metrics regarding utilization by the second user of the at least one online cloud resource (Dou teaches the collected metric data of the virtual machine and the virtual machine may be shared by a group of users; FIG. 1; ¶ 0001 “… A cloud computing service provides shared computing resources to users or customers …”; ¶ 0013 “… A customer (i.e., user, developer, client) of a cloud computing service may configure the virtual machine to utilize a certain amount and type of computing resources …”; ¶ 0014 “… Metric data representing the resource consumption of a virtual machine at equally-spaced time intervals is collected over the course of a training period …”); and
adjusting a … shutdown based at least in part on the utilization metrics (Dou teaches training multiple machine learning (time series forecasting) models based on collected historical usage and metric data and selecting a forecasting model to predict future idle periods and initiate actions such as shutting down a virtual machine when idle; FIG. 1; ¶ 0027 “… The machine learning engine 124 uses ensemble learning to train multiple models on the training usage and metric data of a virtual machine during a training period (block 130) … The best model to represent the time series of a virtual machine will be selected based on training performance metrics for further prediction …”; ¶ 0038 “… The selected model is used by the forecast engine 126 with production usage data 142 and production metric data 144 to forecast the time when the CPU usage will be below the idle threshold (block 132). The forecast engine 126 may utilize the usage data to produce cost estimates of the savings in shutting down a virtual machine during a forecasted idle time (block 132) …”; ¶ 0039 “… This forecast may be used to automatically shutdown the virtual machine at the forecasted time and to tum on the virtual machine thereafter (block 134). Alternatively, the forecast may be provided to the user of the virtual machine along with the estimated savings in order for the user to decide whether or not to shutdown the virtual machine (block 134)). See motivation to combine for claim 1.
Bregman-Vance-Dou-Panuganty does not explicitly teach, but Chen teaches the shutdown time is a recurring shutdown (Chen, ¶ 0005 “… the disclosures includes an ingress node in a network, comprising a receiver configured to receive a first request for a temporal label switched path (LSP) in the network, wherein the first request indicates a network constraint and a scheduled time interval having a predetermined start time and a predetermined end time for the temporal LSP to carry traffic, a processor coupled to the receiver and configured to compute a path in the network for the temporal LSP, wherein the path satisfies the network constraint in the scheduled time interval, and reserve a network resource for use during the scheduled time interval for the temporal LSP in advance of the predetermined start time … wherein the scheduled time interval is a recurrent time interval, and wherein the path request message further indicates a repeat period that the scheduled time interval repeats and a number of repeats for the scheduled time interval, and/or wherein the first request further indicates a desired start time and an elastic time range for the scheduled time interval …”)
Chen and Bregman-Vance-Dou-Panuganty are analogous art because they are both related to scheduling computing resources.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the recurrent computing resources scheduling techniques of Chen with the system of Bregman-Vance-Dou-Panuganty to provide efficient usage of network resources (Chen ¶ 0081).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed below, thank you:
i. Hammond (US 20190026148 A1) teaches a deployment scheduler may dynamically create rules or train rules via a machine learning system with inputs of the user and/or group calendars, and indicators of access to or manual deployment or undeployment of virtual machines or computing environments. For example, responsive to determining that users access the virtual computing environment during a repeated weekly meeting, the deployment scheduler 100 may create a rule specifying that the repeated weekly meeting is associated with user presence. Conversely, responsive to determining that users do not access the virtual computing environment during a different repeated weekly meeting, the deployment scheduler 100 may create a different rule specifying that the different repeated weekly meeting is associated with user absence. Thus, deployment scheduler 100 may dynamically determine whether users or groups are likely to access a virtual computing environment, and direct a deployment engine 102 to deploy or undeploy the virtual computing environment accordingly (Hammond, ¶ 0014).
ii. Chen et al. (US 20210255899 A1) teaches that a method for establishing system resource prediction and resource management model through multi-layer correlations is provided. The method builds an estimation model by analyzing the relationship between a main application workload, resource usage of the main application, and resource usage of sub-application resources and prepares in advance the specific resources to meet future requirements. This multi-layer analysis, prediction, and management method is different from the prior arts, which only focus on single-level estimation and resource deployment. The present invention can utilize more interactive relationships at different layers to effectively perform predictions, thereby achieving the advantage of reducing hidden resource management costs when operating application services (Chen, Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZONGHUA DU whose telephone number is (408)918-7596. The examiner can normally be reached Monday - Friday 8 AM - 5 PM PST.
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, John Follansbee can be reached on (571) 272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Z.D./Examiner, Art Unit 2444
/SCOTT B CHRISTENSEN/Primary Examiner, Art Unit 2444