DETAILED ACTION
This communication is in response to the amendments filed July 26, 2026, in which claims 1-20 are pending in the application. Claims 1, 8, and 17 were amended. Claims 1, 8, and 17 are in independent form.
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
This Final Office Action is in response to the applicant’s remarks and arguments filed on July 26, 2026.
Claims 1, 8, and 17 were amended. No claims were canceled or added.
Claims 1-20 remain pending in the application. Claims 1-20 are being considered on the merits.
The rejection of claims 1-20 under 35 U.S.C 101 under Abstract Idea Groupings has been withdrawn due to the amendment to the claims filed on July 26, 2026.
Response to Arguments
The applicant’s remarks and/or arguments, filed July 26, 2026, have been fully considered with the following result(s).
Applicant’s arguments, see pages 8- 12, filed July 26, 2026, with respect to 35 U.S.C 101 rejection under Abstract Idea Groupings have been fully considered and are persuasive.
Applicant asserts on page 8-9; the claimed inventions imposes a meaningful limitations on the judicial exception because it go beyond mere abstract ideas.
Examiner respectfully agrees, with respect to the newly amended claims. The amended claims provide an improvement through a meaningful limitation that integrates the judicial exception into a practical application rather than an abstract idea. Therefore, the rejection under 35 U.S.C 101 has been withdrawn.
Applicant’s arguments, see pages 12-16 , filed July 26, 2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C 103 have been fully considered and are persuasive.
Applicant assert the prior art references Tino and Tsirkin does not fully disclose the newly amended claims.
Examiner respectfully agrees, based on the reasoning the Applicant set forth. Neither Tino nor Tsirskin disclose the newly amended claims. Therefore, the rejection under 35 U.S.C 103 has been withdrawn.
However, upon further consideration, a new ground(s) of rejection is made in view of Tsirkin (US 20140245293 A1), Tino (US 20170295224 A1), in view of Cropper (US 9983908 B1) which Cropper discloses the newly amended claims , and in view of the previously cited prior art(s).
Applicant further argues that the claims 2-7, 9-16 and 18-20 are patentable over the previously cited prior art because of their dependency from independent claims 1, 8, and 17. However, for the rationale stated above, the rejection in view of the previously cited arts is maintained and reiterated below.
Examiner Note
Examiner cites particular paragraphs or columns and lines in the references as applied to Applicant’s claims 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. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06.
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.
Claims 1, 8, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tsirkin (US 20140245293 A1), Tino (US 20170295224 A1), in view of Cropper (US 9983908 B1).
As per claim 1, Tsirkin discloses
A computer-implemented method comprising:
by a virtual machine resource management module in communication with a hypervisor of a source host environment and a hypervisor of a target host environment via an application programming interface: [0046] “Migration manager 130 may perform actions to assist in migrating one or more VMs from the source host machine to the target host machine. In an embodiment, migration manager 130 is an external application that has access to host machines 110 and 120.”
assessing compute resources available on the target host environment; [0049] “In an embodiment, target module 134 identifies a host machine as a target for migration of a VM of the plurality of VMs. A host machine may be identified as the target host machine for various reasons. In an example, the source host machine is resource constrained and the target host machine has a lot of available resources to run the VM.”
and migrating the set of virtual machines from the source host environment to the target host environment, [0042] “Hypervisor 116, 126 includes a migration module 118, 128. Migration modules 118 and 128 may migrate a VM from the source host machine to the target host machine.”
wherein migrating comprises transferring virtual machine memory and state from the source host environment to the target host environment, [0044] “An example of the VM state that may be transferred to the target host machine may be a memory of the VM. Further, after the VM stops, registers of the CPU in the source host machine or configuration information in the source host machine of the virtual devices assigned to the VM may be transferred to the target host machine.”
switching execution of each migrated virtual machine to the target host environment, [0048] “Migration manager 130 includes an identification module 132, target module 134, and allocation module 136.” [0049] “target module 134 identifies a host machine as a target for migration of a VM of the plurality of VMs. A host machine may be identified as the target host machine for various reasons.” [0050] “allocation module 136 allocates a memory region coupled to the target host machine for the selected VM to be migrated. VM 113 may have access to a memory region that is coupled to the source host machine and allocated to VM 113.”
and releasing compute resources allocated to each migrated virtual machine on the source host environment. [0043] “Migration modules 118 and 128 may also communicate with each other to migrate one or more VMs from the source host machine to the target host machine. In an example, migration of a VM from the source host machine to the target host machine may include stopping execution of the VM on the source host machine, copying at least a portion of the state of the VM from the source host machine to the target host machine, resuming execution of the VM on the target host machine, and enabling the VM to access the state on the target host machine. [0054] “Allocation module 136 may allocate for VM 113 a second private memory region coupled to host machine 120. Migration modules 118 and 128 may copy content from the first private memory region to the second private memory region. In an example, migration module 118 may retrieve the content from the first private memory region and send the copy of the content to host machine 120.”
Tsirkin does not explicitly disclose
assessing, for each virtual machine of a set of virtual machines hosted on the source host environment, a current compute resource consumption and a minimum compute resource requirement;
generating a compute resource allocation limit parameter for controlling a total amount of compute resources consumed by a set of virtual machines over a combination of a source host environment and a target host environment, wherein the compute resource allocation limit parameter defines a maximum aggregate amount of at least one type of compute resource permitted to be consumed by the set of virtual machines across any distribution of the set of virtual machines between the source host environment and the target host environment during migration;
adjusting an allocation of compute resources for the set of virtual machines according to the compute resource allocation limit parameter, wherein adjusting the allocation comprises transmitting, to at least one of the hypervisor of the source host environment or the hypervisor of the target host environment, an instruction that reconfigures a hypervisor-enforced resource allocation for at least one virtual machine of the set of virtual machines such that an aggregate allocation of the at least one type of compute resource across the source host environment and the target host environment does not exceed the compute resource allocation limit parameter while satisfying the minimum compute resource requirement for each virtual machine of the set of virtual machines;
However, Tino discloses
assessing, for each virtual machine of a set of virtual machines hosted on the source host environment, a current compute resource consumption and a minimum compute resource requirement; [0015] “Each forecast may include an upper and lower limit on the expected capacity consumption for the workload at a given probabilistic confidence.” [0022] “Virtualization hypervisors 120 may manage virtualized resources in aggregate by defining entitlements to and limits on the consumption of virtualized infrastructure by virtual machines” [0028] “FIG. 5 is a monitoring process (e.g., monitoring agent routine 410) according to an embodiment of the invention. An embodiment of the monitoring agent 310 may gather time-coded virtualized computer infrastructure resource consumption metrics from the virtualization hypervisors 120 accessible via the connected computer network 510…These collected measurements may be correlated to individual virtual machines 140 or software processes and ultimately the workload 130 to which the individual virtual machine 140 or software process belongs.”
Tsirkin and Tino are in similar field of endeavor, as they are both in resource management within virtual machines and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin to include assessing, for each virtual machine of a set of virtual machines hosted on the source host environment, a current compute resource consumption and a minimum compute resource requirement as disclosed by Tino to provide a projection agent to generate workload demands.
Modification would optimize the migration of VMs by handling resource that are limited based on the predicted demands. (Tsirkin [0032], [0045])
Tsirkin and Tino do not explicitly disclose
generating a compute resource allocation limit parameter for controlling a total amount of compute resources consumed by a set of virtual machines over a combination of a source host environment and a target host environment, wherein the compute resource allocation limit parameter defines a maximum aggregate amount of at least one type of compute resource permitted to be consumed by the set of virtual machines across any distribution of the set of virtual machines between the source host environment and the target host environment during migration;
adjusting an allocation of compute resources for the set of virtual machines according to the compute resource allocation limit parameter, wherein adjusting the allocation comprises transmitting, to at least one of the hypervisor of the source host environment or the hypervisor of the target host environment, an instruction that reconfigures a hypervisor-enforced resource allocation for at least one virtual machine of the set of virtual machines such that an aggregate allocation of the at least one type of compute resource across the source host environment and the target host environment does not exceed the compute resource allocation limit parameter while satisfying the minimum compute resource requirement for each virtual machine of the set of virtual machines
However, Cropper discloses
generating a compute resource allocation limit parameter for controlling a total amount of compute resources consumed by a set of virtual machines over a combination of a source host environment and a target host environment, [col 5:26-30, 35-37] “ Calculating (240) resource entitlements may include calculating the new resource entitlements that are to be allocated to VMs in violation of their thresholds. When there are multiple VMs in violation, their new resource entitlements may be calculated according to the priority of the VMs… Resource entitlement calculations can be performed using rules or historical data to determine an optimal resource entitlement for a specific situation.”
wherein the compute resource allocation limit parameter defines a maximum aggregate amount of at least one type of compute resource permitted to be consumed by the set of virtual machines across any distribution of the set of virtual machines between the source host environment and the target host environment during migration; [col 3 : 21-28] “Provisioning module 160 can be used to provision VMs 190A-190C, which are then managed by hypervisor 150 and/or associated modules. Provisioning module 160 can be used to define the parameters of each VM 190 such as, but not limited to, CPU entitlements, memory entitlements, storage entitlements, and the like. Once a VM 190 has been launched, monitoring module 170 can manage and oversee resource usage by each VM 190. [Col 4 29-34] “ Each VM 190 may be either a capped VM or an uncapped VM. While both capped and uncapped VMs may vary in the amount of resources currently allocated to them, a capped VM can only be allocated a predetermined maximum amount of resources”
adjusting an allocation of compute resources for the set of virtual machines according to the compute resource allocation limit parameter, [col 4: 1-11] “Reallocation resource 180 can adjust resource reallocations. Reallocation resource 180 can calculate new thresholds for each VM 190 based on the detected violation by monitoring module 170. Reallocation resource 180 can receive a list from monitoring module 170 of which VMs 190 to adjust. The adjustment of each VM 190 can be done in order of priority of each given VM 190. Reallocation resource 180 can make these adjustments by changing the entitlements to both the capped and uncapped VMs.”
wherein adjusting the allocation comprises transmitting, to at least one of the hypervisor of the source host environment or the hypervisor of the target host environment, an instruction that reconfigures a hypervisor-enforced resource allocation for at least one virtual machine of the set of virtual machines [col 3 ; 32-44] “Monitoring module 170 can be programed to screen for specific instances where a VM 190 shows characteristics of overutilization or underutilization. The user can define the specific parameters for both capped and uncapped VMs that monitoring module 170 can use to monitor the VMs 190. These specific parameters are typically thresholds which may vary depending on whether a VM 190 is capped or uncapped. As an example, a VM may be configured such that when its resource utilization surpasses 75% for more than 10 minutes, monitoring module 170 may note that a violation has occurred, and reallocation module 180 will correct the violation by allocating additional resources to VM 190.” such that an aggregate allocation of the at least one type of compute resource across the source host environment and the target host environment does not exceed the compute resource allocation limit parameter while satisfying the minimum compute resource requirement for each virtual machine of the set of virtual machines; [col 4 : 29-33, 34-37] “Each VM 190 may be either a capped VM or an uncapped VM. While both capped and uncapped VMs may vary in the amount of resources currently allocated to them, a capped VM can only be allocated a predetermined maximum amount of resources…. For example, if a capped VM is allocated a certain amount of a given resource when it is created, the capped VM can never exceed more than 100% utilization of that resource.(See fig 3) [col 4: 63-67, col 5:1-2] “Provisioning (210) VMs can occur sequentially or several VMs may be created at the same time. Each VM provisioned can have different entitlements than the VM before and/or after. During the provisioning operation 210 a user can set the at least one or more thresholds for each capped and uncapped VM 190.”
Tsirkin, Tino, and Cropper are in similar field of endeavor, as they are all in resource management within virtual machines and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin and Tino to include adjusting the allocation of resource hypervisor host to enforced the allocation of resources by still satisfying the requirement for each virtual machines and migrating the set of virtual machines, by transferring their memory state to the target host environment by switching execution and releasing resource to a virtual machine disclosed by Cropper in order to allocated resource based on the virtual machine metrics.
Modification would establish a proper resource management for the virtual machine based on the requirement needed so the virtual machine can be fully operational. (Cropper [Abstract] [col 1: 57-66])
As per claim 8, it has similar limitation as claim 1, and therefore it is rejected under the same rationale.
As per claim 17, it has similar limitation as claim 1, and therefore it is rejected under the same rationale.
Claims 2, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Tsirkin (US 20140245293 A1), Tino (US 20170295224 A1), in view of Cropper (US 9983908 B1, in view Segmuller (US 20110296429 A1).
As per claim 2, Tsirkin, Tino, and Cropper disclose a method of claim 1 detailed above.
Tsirkin, Tino, and Cropper do not explicitly disclose "wherein generating the compute resource
allocation limit parameter is based at least in part on a set of service license agreement details
corresponding to a licensed amount of compute resources."
However, Segmuller discloses “wherein generating the compute resource allocation limit parameter is based at least in part on a set of service license agreement details corresponding to a licensed amount of compute resources." [0036] "These license entitlements and license rules are taken
into account, for gathering sufficient information about the infrastructure being managed so as to be
able to compute the current license usage." [0053] "The resource manager 110 (and or the licensing
constraint management module 112) accounts for the license entitlements and license rules and
gathers sufficient information about the infrastructure being managed so as to be able to compute
the current license usage."
Tsirkin, Tino, Cropper, and Segmuller are in similar field of endeavor, as they are all in managing virtual machines resources and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin, Tino, and Cropper to include generate a compute resource allocation limit parameter based on a set of service license agreement details corresponding to a licensed amount of compute resources as disclosed by Segmuller in order to increase the number of PVU for the system.
Modification would be to properly manage resource in the virtualized environment based on the constraints and by doing so would reduce neglection of service license agreements for a virtual machine. (Segmuller [0034])
As per claim 11, it has similar limitation as claim 2, therefore it is rejected using the same rationale.
As per claim 18, it has similar limitation as claim 2, therefore it is rejected using the same rationale.
Claims 3, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Tsirkin (US 20140245293 A1), Tino (US 20170295224 A1), in view of Cropper (US 9983908 B1, in view Segmuller (US 20110296429 A1), in view of Dave (US 20180331928).
As per claim 3, Tsirkin, Tino, Cropper, and Segmuller disclose the method of claim 2 detailed above.
Tsirkin, Tino, Cropper, and Segmuller do not explicitly disclose "further comprising parsing a service license agreement to extract the set of service license agreement details from the service license
agreement via an NLP algorithm."
However, Dave discloses “further comprising parsing a service license agreement to extract the set of service license agreement details from the service license agreement via an NLP algorithm." [0068] "Additionally, or alternatively, resource analysis platform 220 may receive text or a document (e.g., a contract agreement, a license agreement, etc.), and may process the text or the document using natural language processing, text analysis, computational linguistics,"
Tsirkin, Tino, Cropper, Segmuller, and Dave are in similar field of endeavor, as they are all in managing virtual machines resources and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin, Tino, Cropper, and Segmuller to include parsing a service license agreement to extract the set of service license agreement details from the service agreement via an NLP algorithm as disclosed by Dave to provide recommendation of resource utilization.
Modification would reduce overconsumption of computing resource by intelligently analyzing the document to determine the threshold resource utilization. ( Dave [0013], [0068])
As per claim 12, it has similar limitation as claim 3, therefore it is rejected under the same rationale.
As per claim 19, it has similar limitation as claim 3, therefore it is rejected under the same rationale.
Claims 4, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tsirkin (US 20140245293 A1) (previously presented) , Tino (US 20170295224 A1) (previously presented), in view of Cropper (US 9983908 B1) , in view of Beveridge (US 20180260251 A1) (previously presented).
As per claim 4, Tsirkin, Tino, and Cropper discloses the method of claim 1 detailed above.
Tsirkin, Tino, and Cropper do not explicitly disclose
“generating a set of options for adjusting the allocation of compute resources for the set of virtual machines.”
However, Beveridge discloses “ generating a set of options for adjusting the allocation of compute resources for the set of virtual machines." [0162] "FIG. 35 illustrates 4 different choices that a resource consumer may select from when faced with the need for allocating computational resources to run additional virtual machines."
Tsirkin, Tino, Cropper, and Beveridge are in similar field of endeavor, as they are all in managing computational resource for virtual machine and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin, Tino, and Cropper to include generating a set of options for adjusting the allocation of compute resources for the set of virtual machines as disclosed by Beveridge in order to provide a resource-exchange system.
Modification would improve the system efficiency and performance by expanding the computing resource based on demands (requirement ) of the computer system without causing delays. (Beveridge [0041])
As per claim 13, it has similar limitation as claim 4, therefore it is rejected using the same rationale.
As per claim 20, it has similar limitation as claim 4, therefore it is rejected using the same rationale.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Tsirkin (US 20140245293 A1) (previously presented) , Tino (US 20170295224 A1) (previously presented), in view of Cropper (US 9983908 B1) , in view of Shakari (US 20140082201 A1)(previously presented).
As per claim 5, Tsirkin, Tino, and Cropper discloses the method of claim 1 detailed above.
Tsirkin, Tino, and Cropper do not explicitly disclose
“ wherein generating the compute resource allocation limit parameter is based at least in part on manually defining a limit for compute resource consumption for a subset of the set of virtual machines.”
However, Shakari disclose “wherein generating the compute resource allocation limit parameter is based at least in part on manually defining a limit for compute resource consumption for a subset of the set of virtual machines” [0003] "...resource allocation in a distributed computer system by
manually adjusting low-level controls, such as shares, limits, and reservations, to manage the
resources allocated to VMs..." [0031] “The management computer 106 operates to monitor and manage the host computers H-1, H-2….The client information may also include resource settings, such as limit, reservation, entitlement and share values for various resources, e.g., CPU, memory, network bandwidth and storage,”[0042] “Resources allocated to VMs or RPs can be controlled by adjusting resource control settings, which may include reservation, limit and shares parameters…”
Tsirkin, Tino, Cropper, and Shakari are in similar field of endeavor, as they are all in managing computational resource for virtual machine and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin, Tino, and Cropper to include wherein generating the compute resource allocation limit parameter is based at least in part on manually defining a limit for compute resource consumption for a subset of the set of virtual machines as disclosed by Shakari to perform a resource allocation on distributed computer system.
Modification would establishing a proper recommendation based on the current demand of virtual machines by reconfiguring or expanding resource. (Shakari [0007])
As per claim 14, it has similar limitation as claim 5, therefore it is rejected using the same rationale.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tsirkin (US 20140245293 A1) (previously presented) , Tino (US 20170295224 A1) (previously presented), in view of Cropper (US 9983908 B1) ,in view of Greene (US20100100877 A1)(previously presented)
As per claim 6 Tsirkin, Tino, and Cropper disclose the method of claim 1 detailed above.
Tsirkin, Tino, and Cropper do not explicitly disclose
"wherein generating the compute resource allocation limit parameter is based at least in part on minimum requirements for each virtual machine of the set of virtual machines."
However, Greene discloses "wherein generating the compute resource allocation limit parameter is based at least in part on minimum requirements for each virtual machine of the set of virtual machines." [0035] "The resource specification can, for example, include minimum and maximum resource requirements for the virtual machines. The resource specification can thus result in a reduction of the total resources reserved by the virtual machines in a cluster."
Tsirkin, Tino, Cropper, and Greene are in similar field of endeavor, as they are all in managing computational resource for virtual machine and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin, Tino, and Cropper to include wherein generating the compute resource allocation limit parameter is based at least in part on minimum requirements for each virtual machine of the set of virtual machines as disclosed by Greene to provide a provisioning succuss for each virtual machine.
Modification would improve the predictability of resource consumption within the system in order to reduce disaster or failures. (Greene [0007], [0008], [0025])
As per claim 15, it has similar limitation as claim 6, therefore it is rejected using the same rationale.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tsirkin (US 20140245293 A1) (previously presented) , Tino (US 20170295224 A1) (previously presented), in view of Cropper (US 9983908 B1), in view in view of Srinivasan (US 20220405137 A1) (previously presented)
As per claim 7 Tsirkin, Tino, and Cropper disclose the method of claim 1 detailed above.
Tsirkin, Tino, and Cropper do not explicitly disclose
"wherein generating the compute resource allocation limit parameter is based at least in part on a proportional reduction of allocation of compute resources for each virtual machine of the set of virtual machines."
However, Srinivasan discloses "wherein generating the compute resource allocation limit parameter is based at least in part on a proportional reduction of allocation of compute resources for each virtual machine of the set of virtual machines." [0027] “In some cases, the computational-performance score may include an efficiency-type score, in which the score is assigned favoring shorter execution duration and fewer compute resources. In some cases, the computational-performance score may be based weights determined using the consumption metric data 114. Accordingly, the iterative process may search for the computer buffer size that balances execution duration with compute resource allocation to reduce consumption metrics (e.g., achieve efficiency).”
Tsirkin, Tino, Cropper, and Srinivasan are in similar field of endeavor, as they are all in managing computational resource for virtual machine and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin, Tino, and Cropper to include wherein generating the compute resource allocation limit parameter is based at least in part on a proportional reduction of allocation of compute resources for each virtual machine of the set of virtual machines as disclosed Srinivasan in order to determine computing resource allocation for virtual machines.
Modification would further increase the capabilities of the system by providing an increase for resource allocation and consumption prediction. (Srinivasan [0002])
As per claim 16, it has similar limitation as claim 7, therefore it is rejected using the same rationale.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Tsirkin (US 20140245293 A1) (previously presented) , Tino (US 20170295224 A1) (previously presented), in view of Cropper (US 9983908 B1), in view of in view of Madduri (WO 2012000850 A1) (previously presented).
As per claim 9 , Tsirkin, Tino, and Cropper disclose the method of claim 8 detailed above.
Tsirkin, Tino, and Cropper do not explicitly disclose
"wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system."
However, Madduri discloses “ wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system.” "the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer"
Tsirkin, Tino, Cropper, and Madduri are in similar field of endeavor, as they are all in managing computational resource for virtual machine and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin, Tino, and Cropper to include wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system as disclosed by Madduri in order to allocate resource in a network data processing system.
Modification would improve the system flexibility by allocating computer resource to meet various goals. (Madduri [ p.9] )
As per claim 10, Tsirkin, Tino, and Cropper disclose the method of claim 8 detailed above.
Tsirkin, Tino, and Cropper do not explicitly disclose
“wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising:
program instructions to meter use of the program instructions associated with the request; and
program instructions to generate an invoice based on the metered use.”
However, Madduri discloses
“wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising: [p.9] “present invention provides a computer program product wherein the program code is stored on the computer recordable storage medium in a data processing system, and wherein the program code is downloaded over a network from a remote data processing system to the data processing system.”
“program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use. “ [p.12] “Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.” [p.17 ] “Resource provisioning provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and pricing provide cost tracking as resources and are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. [p.20] “A request for resources in a network data processing system is received. The request comprises requested values for a plurality of attributes for resources in the network data processing system. In response to receiving the request, potential resources in the network data processing system are identified. The request is evaluated using a function configured to generate identified values for the plurality of attributes from the requested values in the request. These identified values are generated based on the cost for the plurality of attributes in the potential resources relative to an ideal allocation of the potential resources.”
Tsirkin, Tino, Cropper, and Madduri are in similar field of endeavor, as they are all in managing computational resource for virtual machine and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed inventions to modify the system of Tsirkin, Tino, and Cropper to include wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising a program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use as disclosed by Madduri in order to allocate resource in a network data processing system.
Modification would improve the system’s flexibility by allocating computer resource to meet various goals which would further reduce cost . (Madduri [ p.9] )
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Govindan; Sunil (US 20240378088 A1) dynamically allocate compute resources to workloads to accommodate resource requirements of a tenant in a more efficient and cost-effective manner, especially in scenarios where compute resource availability is elastic in nature.
SASTRY; Ann Marie (US 20230388248 A1) disclose a method for dynamically allocating server resources includes receiving a request from a client system.
RHIM; Young-Yik (US 20220277140 A1) disclose an invention that provide relevant laws and detailed explanation by analyzing the legal risk in a legal document having a structure such as legal clauses, terms and conditions and contracts Longo; Austino (US 20220276907 A1) disclose an instruction that cause a processor to use the real-time performance metrics and the inertial parameter to determine whether to transfer responsibility for a compute process on a first storage server node to a second storage server node.
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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.
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/A.A./Examiner, Art Unit 2198
/PIERRE VITAL/Supervisory Patent Examiner, Art Unit 2198
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