DETAILED ACTION
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 Amendment
Applicant’s Remarks/Arguments filed on June 6th, 2026, have been carefully considered.
Claims 1 and 7-10 have been amended.
No claims have been canceled.
Claims 11-20 have been added.
Claims 1-20 are currently pending in the instant application.
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claims 1-6, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Matosevich et al. [US2022/0004320] in view of Yemini et al. [US8,396,807] further in view of Ho et al. [US2023/0110722]. Matosevich teaches sharing memory resources between asynchronous replication workloads. Yemini teaches managing resources in virtualization systems. Ho teaches integrated development environment for development and continuous delivery of cloud-based applications.
Regarding claims 1, 9-10, Matosevich teaches a storage system [Matosevich abstract “…data replication system…”], comprising:
one storage device that executes input and output processing of data in response to a request from a host device [Matosevich figure 1B, feature 180 “Data Storage Devices” and 150 “Storage Control System”];
another storage device [Matosevich figure 1B, feature 182-D “Replica Volume”] that includes a virtual computer [Matosevich paragraph 0020, middle lines “…the storage nodes 140 comprise storage server nodes (e.g., server node 600, shown in FIG. 6) having one or more processing devices each having a processor and a memory, possibly implementing virtual machines and/or containers…”] that executes asynchronous copy processing of asynchronously copying the data with the one storage device [Matosevich paragraph 0015, last lines “…a data replication management module 117 which implements methods to perform various management functions to control, e.g., asynchronous data replication operations that are performed by the storage nodes 140…”],
Matosevich fails to explicitly teach the processing performance of the virtual computer being able to be changed; and a processing performance adjustment unit that changes in advance processing performance required at a second time point temporally later than a first time point by the virtual computer of the other storage device before the second time point when the asynchronous copy processing is executed at the second time point according to a state of the input and output processing at the first time point in the one storage device.
However, Yemini does teach the processing performance of the virtual computer being able to be changed [Yemini column 2, lines 55-64 “…an application element manager running on a data processor in the virtualization system, the value of a service level agreement parameter for the application based on the allocated computer resource bundle; comparing the determined service level agreement parameter level for the application to a threshold service level agreement parameter level; automatically modifying the allocation of computer resources to the application depending on whether the identified service level agreement parameter level for the application is below or above the threshold service level agreement parameter level…” and column 7, lines 41-49 “…The software system 200 may be used to allocate server and I/O resources (such as CPU, memory and I/O bandwidth) to virtual machines. The software system 200 may also be used, for example, to monitor, detect and handle congestion conditions along I/O pathways, and to move virtual machines among available servers to optimize or improve application performance and utilization…”].
a processing performance adjustment unit [Yemini column 3, lines 40-41 “…by a virtual machine element manager running on a data processor in a virtualization system…”(Where the virtual machine element manager reads on the performance adjustment unit.)] that changes in advance processing performance required at a second time point [Yemini column 3, lines 55-56 “…the amount of storage I/O bandwidth to the virtual machine for a second period of time…”] temporally later than a first time point [Yemini column 3, lines 45-47 “…the amount of I/O bandwidth to the virtual machine for a first period of time; after the first period of time has elapsed…”(Where the examiner has determined it to be obvious that second comes later than first by the very nature of number ordering.)] by the virtual computer of the other storage device [Yemini column 3, line 49, “…a second computer server…”] before the second time point when the asynchronous copy processing is executed at the second time point according to a state of the input and output processing at the first time point in the one storage device [Yemini column 3, lines 47-58 “…determining that the I/O bandwidth utilization of the first computer server is greater than a threshold limit; automatically identifying at least a second computer server in the virtualization system offering at least the determined amount of storage I/O bandwidth in response to the determined I/O bandwidth utilization of the first computer server; moving the virtual machine from the first computer server to the identified second computer server; and allocating, from the second computer server, the amount of storage I/O bandwidth to the virtual machine for a second period of time. Other embodiments of this aspect include corresponding systems, apparatus, and computer program products…”].
Matosevich and Yemini are analogous arts in that they both deal with improving virtual storage.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Matosevich’s asynchronous copy operations on virtual computer with Yemini’s teachings of reconfiguring the virtual computers for the benefit of optimizing and improving application performance and utilization [Yemini column 7, lines 45-49 “…The software system 200 may also be used, for example, to monitor, detect and handle congestion conditions along I/O pathways, and to move virtual machines among available servers to optimize or improve application performance and utilization…”].
Matosevich and Yemini fail to explicitly teach wherein the processing performance adjustment unit predicts a load on the other storage device at the second time point from a performance value of the one storage device at the first time point, and increases or decreases processing performance of the other storage device in advance based on the prediction.
However, Ho does teach wherein the processing performance adjustment unit predicts a load [Ho paragraph 0045, middle lines “…The machine learning model is trained to predict workloads, for example, to detect a potential spike in workload or a spike in requests received by the system based on these features…”(The machine learning model reads on the processing performance adjustment unit.)] on the other storage device [Ho paragraph 0045, middle lines “…to request additional computing resources from the cloud platform to handle the increase in workload…”] at the second time point from a performance value of the one storage device at the first time point [Ho paragraph 0045, middle lines “…the machine learning based model predicts a score indicating an expected amount of increase or decrease in the workload…”(The expected amount reads on a second time point in the future that is after the first time point.)], and increases or decreases processing performance of the other storage device in advance based on the prediction [Ho paragraph 0045, last lines “…the service automatically reconfigures the system to add or remove computing resources based on the predicted workload as determined by the machine learning based model. According to an embodiment, the system sends instructions to the cloud platform to increase computing resources associated with the system responsive to predicting an increase in load on the system exceeding a threshold value; alternatively, the system sends instructions to the cloud platform to decrease computing resources associated with the system responsive to predicting a decrease in load on the system exceeding a threshold value. The service adjusts the computing resources by invoking cloud platform APIs that allow computing resources to be configured. These include computing resources such as servers, memory resources, storage resources (such as databases or storage devices), and so on…].
Matosevich, Yemini, and Ho are analogous arts in that they both deal with improving storage performance.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Matosevich and Yemini with Ho’s teachings of a machine learning adjustment unit that predicts workload needs for the benefit of using AI insight feedback to automate self-services and thus reduce the on-boarding times for the platform [Ho paragraph 0007, all lines “…he benefits of the system include auto provisioning, computation orchestration, storage requests, and AI (artificial intelligence) insight feedback, and automated self-services to navigation of complex systems, thereby reducing on-boarding times of the platform…”].
Regarding claim 10, the examiner acknowledges the inclusion of additional limitations that are addressed further in claim 6 of this rejection.
Regarding claim 2, as per claim 1, Yemini teaches the processing performance adjustment unit changes processing performance required at the second time point [Yemini column 3, lines 55-56 “…the amount of storage I/O bandwidth to the virtual machine for a second period of time…”] by the virtual computer of the other storage device to be high in a case where a processing load is equal to or more than a threshold as a state of the input and output processing at the first time point in the one storage device [Yemini column 3, lines 45-48 “…the amount of I/O bandwidth to the virtual machine for a first period of time; after the first period of time has elapsed, determining that the I/O bandwidth utilization of the first computer server is greater than a threshold limit..”].
Regarding claim 3, as per claim 1, Yemini teaches the processing performance adjustment unit changes processing performance required at the second time point by the virtual computer of the other storage device to be low in a case where a processing load is equal to or less than a threshold as a state of the input and output processing at the first time point in the one storage device [Yemini column 3, lines 45-48 “…the amount of I/O bandwidth to the virtual machine for a first period of time; after the first period of time has elapsed, determining that the I/O bandwidth utilization of the first computer server is greater than a threshold limit..”(Where it would be obvious that the opposite could also be true and thus lower than the threshold limit.) and column 17, lines 34-42 “…The virtual machine element manager determines whether the SLA parameter of interest is below a desired target (606), in which case, for example, the application's payments to the virtual machine (e.g., of virtual currency units) are increased such that the virtual machine's budget is increased, and it is able to purchase more resources to increase the SLA parameter of the application (608). After such an increase, the virtual machine's budget use is again monitored and optimized or improved as described above…”].
Regarding claim 4, as per claim 1, Matosevich teaches the one storage device includes:
one volume to which the data is written [Matosevich figure 1B, feature 182 “Primary data volume”]; and
a master journal that stores a base journal corresponding to data written in the one volume and an update journal corresponding to a difference between data written thereafter and data already written to the one volume [Matosevich figure 1B, feature 184 “Replication Journal Volume”(Where the journal volume reads on difference between new data and old data.)], and
the other storage device [Matosevich figure 1B, feature 140-D “Destination Storage Node”] includes:
a restore journal to which the base journal and the update journal stored in the master journal are copied [Matosevich figure 1B, feature 192-D “Replication Journal”]; and
another volume in which the data is restored based on the restore journal [Matosevich figure 1B, feature 182-D “Replica Volume”].
Regarding claim 5, as per claim 1, Yemini teaches the processing performance adjustment unit changes processing performance of at least one of a memory and a processor included in the other storage device as processing performance of the other storage device [Yemini column 2, lines 55-64 “…an application element manager running on a data processor in the virtualization system, the value of a service level agreement parameter for the application based on the allocated computer resource bundle; comparing the determined service level agreement parameter level for the application to a threshold service level agreement parameter level; automatically modifying the allocation of computer resources to the application depending on whether the identified service level agreement parameter level for the application is below or above the threshold service level agreement parameter level…” and column 7, lines 41-49 “…The software system 200 may be used to allocate server and I/O resources (such as CPU, memory and I/O bandwidth) to virtual machines. The software system 200 may also be used, for example, to monitor, detect and handle congestion conditions along I/O pathways, and to move virtual machines among available servers to optimize or improve application performance and utilization…”].
Regarding claims 6 and 10, as per claim 1, Yemini teaches the processing performance adjustment unit displays a processing performance change screen including: the processing performance before change; and the processing performance after change [Yemini column 8, lines 40-50 “…The software system 200 shown in FIG. 2 also includes a functional management layer 252, which includes user interface (UI) software 260 for use by administrators or other users to monitor and control a virtualization system (such as the example virtualization environment 100 shown in FIG. 1). For example, an administrator may use UI software 260 to set proactive automation policies to optimize or improve performance and resource utilization, detect and resolve operational problems and performance bottlenecks, allocate priorities and usage charges to different applications, and plan capacity expansions…”(The examiner has determined giving the BRI to a visual indication of before change and after change would include the use of UI software that controls policies and usage changes so that a user would see a visual indication of before change and after changes.)].
Regarding claim 11, as per claim 1, Ho teaches wherein the performance value of the one storage device at the first time point comprises a processing load of the input and output processing [Ho paragraph 0045, middle lines “…The machine learning model is trained to predict workloads, for example, to detect a potential spike in workload or a spike in requests received by the system based on these features…”].
Regarding claim 12, as per claim 1, Ho teaches the processing performance adjustment unit changes the processing performance of the other storage device to be high when the predicted load on the other storage device at the second time point is equal to or more than a threshold [Ho paragraph 0045, last lines “…the service automatically reconfigures the system to add or remove computing resources based on the predicted workload as determined by the machine learning based model. According to an embodiment, the system sends instructions to the cloud platform to increase computing resources associated with the system responsive to predicting an increase in load on the system exceeding a threshold value…”].
Regarding claim 13, as per claim 1, Ho teaches the processing performance adjustment unit changes the processing performance of the other storage device to be low when the predicted load on the other storage device at the second time point is equal to or less than a threshold [Ho paragraph 0045, last lines “…alternatively, the system sends instructions to the cloud platform to decrease computing resources associated with the system responsive to predicting a decrease in load on the system exceeding a threshold value. The service adjusts the computing resources by invoking cloud platform APIs that allow computing resources to be configured. These include computing resources such as servers, memory resources, storage resources (such as databases or storage devices), and so on…].
Regarding claim 14, as per claim 1, Ho teaches the processing performance adjustment unit prepares the processing performance required at the second time point in advance to continuously maintain performance higher than a certain level regarding the asynchronous copy processing [Ho paragraph 0045, middle lines “…the service automatically reconfigures the system to add or remove computing resources based on the predicted workload as determined by the machine learning based model…”(The examiner has determined the purpose of the automatic adjustments is to continuously maintain performance high enough to perform the processing.)].
Regarding claim 15, as per claim 1, Ho teaches the one storage device and the other storage device are located at different sites [Ho paragraph 0004, “…A system maintains services on a cloud platform…”(Where the cloud is on a different platform.)], and the asynchronous copy processing is performed between plurality of sites [Ho paragraph 0046, middle lines “…the system performs asynchronous processing…”].
Regarding claim 16, as per claim 1, Ho teaches the processing performance adjustment unit changes processing performance of at least one of a memory and a processor included in the other storage device based on the prediction [Ho paragraph 0045, last lines “…automatically reconfigures the system to add or remove computing resources…”(Where computing resources reads on memory and processor.)].
Regarding claim 17, as per claim 1, Ho teaches the one storage device includes one volume to which the data is written, and the other storage device includes another volume in which the data is restored based on a journal [Ho paragraph 0042, most lines “…The trained machine learning model is executed 460 to generate derived data. The derived data is incorporated 470 in a data source and provided as input to the service mesh. The target application is incorporated as a service of the service mesh. Accordingly, the target application adds the generated data to the traffic of the service mesh. The data generated by the target application may be used by other services. As a result, the system supports an extensible service mesh. The target application may be an analytics application that monitors various services and generates analytical reports based on the interactions associated with the services…”].
Regarding claim 18, as per claim 10, Ho teaches wherein the processing load comprises an amount of data written to the one storage device at the first time point [Ho paragraph 0045, first lines “…The ability to monitor the traffic associated with the service mesh allows the system to extract features that describe an individual service as well as features that describe a group of services…”].
Regarding claim 20, as per claim 1, Ho teaches the second time point is a time point at which the asynchronous copy processing of data written at the first time point is executed by the other storage device [Ho paragraph 0045, middle lines “…e machine learning based model predicts a score indicating an expected amount of increase or decrease in the workload. The system administrator can determine the amount of computing resources to be added or removed from the system based on the score. In an embodiment, the service automatically reconfigures the system to add or remove computing resources based on the predicted workload as determined by the machine learning based model. According to an embodiment, the system sends instructions to the cloud platform to increase computing resources associated with the system responsive to predicting an increase in load on the system exceeding a threshold value…”(The second time point is the predicted future workload.)].
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable over Matosevich et al. [US2022/0004320] in view of Yemini et al. [US8,396,807] in view of Ho et al. [US2023/0110722] further in view of Elshafey et al. [US2024/0362051]. Matosevich teaches sharing memory resources between asynchronous replication workloads. Yemini teaches managing resources in virtualization systems. Ho teaches integrated development environment for development and continuous delivery of cloud-based applications. Elshafey teaches endpoint machine software change automation pipeline.
Regarding claims 7, as per claim 1, Matosevich and Yemini fail to explicitly teach the processing performance change screen includes: a graph of transition of the processing performance before change according to lapse of time including the first time point and the second time point; and a transition graph of the processing performance after change according to lapse of time including the first time point and the second time point.
However, Elshafey does teach the processing performance change screen includes: a graph of transition of the processing performance before change according to lapse of time including the first time point and the second time point; and a transition graph of the processing performance after change according to lapse of time including the first time point and the second time point [Elshafey paragraph 0050, most lines “…graphs of the resource utilization (CPU, memory, network bandwidth, and storage, for example) that represent the aggregate utilization over all testing VMs may be provided through UEM clients installed on the VMs. These graphs may indicate the utilization of the resources before, and after, the change has been installed on an endpoint device, and may enable an admin to easily see if there are increases in resource utilization after the change, offering some insight into the impact of the change on endpoint devices where the change has been installed…”].
Matosevich, Yemini, and Elshafey are analogous arts in that they all deal with improving virtual storage.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Matosevich and Yemini with Elshafey’s before and after graphs for the benefit of helping save on time and resources by making it easier to visualize the changes [Elshafey paragraph 0020, last lines “…This approach may also improve external services and IT operations by making it easier for IT admins and internal services business units to test updates pushed internally. This can be a time and resource save for the organization as a whole and would reduce sources of frustration for users and IT admins alike…”]
Ho teaches wherein the processing performance after change is based on the prediction of the load on the other storage device at the second time point [Ho paragraph 0045, middle lines “…The machine learning model is trained to predict workloads, for example, to detect a potential spike in workload or a spike in requests received by the system based on these features…”…”(The expected amount reads on a second time point in the future that is after the first time point.)].
Claims 8 and 19 is rejected under 35 U.S.C. 103 as being unpatentable over Matosevich et al. [US2022/0004320] in view of Yemini et al. [US8,396,807] in view of Ho et al. [US2023/0110722] in view of Elshafey et al. [US2024/0362051] further in view of Arifin [US2010/0138621]. Matosevich teaches sharing memory resources between asynchronous replication workloads. Yemini teaches managing resources in virtualization systems. Ho teaches integrated development environment for development and continuous delivery of cloud-based applications. Elshafey teaches endpoint machine software change automation pipeline. Arifin teaches information processing system controlling method in information processing system and managing apparatus.
Regarding claim 8, as per claim 1, Matosevich, Yemini, and Elshafey fail to explicitly teach a main screen including a progress status of the asynchronous copy processing is displayed on a background of the processing performance change screen.
However, Arifin does teach a main screen including a progress status of the asynchronous copy processing is displayed on a background of the processing performance change screen [Arifin claim 4, most lines “…the management apparatus screen-displays a copy progress status and a power on/off status of each volume for a copy pair which is a pair of the volume in the asynchronous remote copy, and a consistency group which is a group for maintaining the consistency of time sequence for the copy pair…”(The examiner had determined it would have been obvious to include such important data on the background of the processing performance change screen so as to maximize the valuable display real estate.)]
Matosevich, Yemini, Elshafey, and Arifin are analogous arts in that they all deal with improving data storage.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Matosevich, Yemini, and Elshafey with Arifin’s teachings of displaying a copy progress status for the benefit of knowing when copies are complete and thus lowering the use of resources and saving on power costs [Arifin paragraph 0090, most lines “…for efficiently realizing the power off under the environment of the remote copy by utilizing such characteristics of the remote copy. To realize such a function, the volume, which needs to constantly operate, and the volume, which does not need to constantly operate, are separated to specific array groups when the copy is created, or when the command device is created. Therefore, it is possible to realize a function for efficiently power off each array group…”].
and wherein the asynchronous copy processing is executed by the virtual computer of the other storage device with the processing performance changed in advance based on the prediction [Ho paragraph 0045, last lines “…the service automatically reconfigures the system to add or remove computing resources based on the predicted workload as determined by the machine learning based model. According to an embodiment, the system sends instructions to the cloud platform to increase computing resources associated with the system responsive to predicting an increase in load on the system exceeding a threshold value; alternatively, the system sends instructions to the cloud platform to decrease computing resources associated with the system responsive to predicting a decrease in load on the system exceeding a threshold value. The service adjusts the computing resources by invoking cloud platform APIs that allow computing resources to be configured. These include computing resources such as servers, memory resources, storage resources (such as databases or storage devices), and so on…”(Cloud resources implies virtual computer resources. Predicted would read on in advance.)].
Response to Arguments
Applicant’s arguments with respect to claims 1, 9, and 10 have been considered but are moot in view of new grounds of rejection.
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 ERIC CARDWELL whose telephone number is (571)270-1379. The examiner can normally be reached on Monday - Friday 10-6pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Reginald Bragdon can be reached on (571) 272-4204. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC CARDWELL/Primary Examiner, Art Unit 2139
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