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 .
Claims 1-20 are pending in this application.
Response to Arguments
Applicant’s arguments regarding the rejections of claims 1-20 under 35 U.S.C. 112b have been fully considered and are persuasive. The rejections have been withdrawn. However, new 35 U.S.C. 112b rejections are applied to claims 1-20 based on the amendments.
Applicant's arguments regarding the 35 U.S.C. 101 rejections of claims 18-20 have been fully considered and are persuasive.
Applicant's arguments regarding the 35 U.S.C. 103 rejections of claims 1-20 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 103 rejection, the applicant argues the following in the remarks:
Cited paragraph [0058] from Smith discloses each edge node 422, 424 may implement the use of containers, such as with the use of a container "pod" 426, 428 providing a group of one or more containers. Notably, the containers of Smith are not "simulated containers" wherein "each simulated container of the simulated job flow simulates a corresponding actual container relating to the actual job flow such that the simulated job flow simulates the series of jobs to be executed as part of executing the actual job flow" as recited in amended claim 1. In fact, edge nodes 422, 424 of Smith do not run simulations at all. While paragraph [0130] from Smith states "[f]or each possible action, the DT may launch parallel simulations to identify the consequences of the attack and recommend some conservative responses to the real-world entities as a way of minimizing the possible negative consequences from the attack (e.g., avoidance of killing a pedestrian, etc.)", DTs (digital twins) of Smith do not use containers for their simulation. Further, the DTs of Smith do not simulate "an actual job flow to be executed in the computing infrastructure" as recited in amended claim 1. Amaro, JR fails to remedy these deficiencies in the teachings of Smith.
As described above, while paragraph [0130] from Smith states "[f]or each possible action, the DT may launch parallel simulations to identify the consequences of the attack and recommend some conservative responses to the real-world entities as a way of minimizing the possible negative consequences from the attack (e.g., avoidance of killing a pedestrian, etc.)", the DTs (digital twins) of Smith do not use containers for their simulation, much less "deploying, using a simulated load balancer of the digital twin, the simulated containers relating to the simulated job flow on a first plurality of simulated hardware components of the digital twin that represent a first plurality of actual hardware components of the computing infrastructure" as recited in amended claim 1. As described above, DTs of Smith do not simulate "an actual job flow to be executed in the computing infrastructure" as recited in amended claim 1. Amaro, JR fails to remedy these deficiencies in the teachings of Smith.
Cited paragraph [0186] from Smith states "In an example, upon detection of the error, attestation of the physical node or the digital twin for the physical node is performed using a root-of-trust. Read and write latches may be set in memory during performance of the attestation. Execution of a workload may be transferred to another physical node or another digital twin." However, Smith fails to teach, suggest, or disclose "reallocating at least a portion of the simulated containers to a second plurality of simulated hardware components of the digital twin that represent a second plurality of actual hardware components of the computing infrastructure" as recited in amended claim 1. While Smith discloses transferring a workload to another digital twin, Smith does not transfer the workload to another portion of the same digital twin. Amaro, JR fails to remedy these deficiencies in the teachings of Smith.
Claims 3, 12, and 20 are allowable based on their dependency upon the allowable independent claims.
Examiner has thoroughly considered Applicant’s arguments, but respectfully finds them unpersuasive for at least the following reasons:
As to point (a), the examiner respectfully disagrees. The digital twins recited in Smith model (simulate) containers that run functions on physical infrastructure. See rejection below.
As to point (b), the examiner respectfully disagrees. The digital twins in Smith model (simulate) containers that run functions on physical infrastructure. See rejection below. Additionally, Smith recites in the abstract “a digital twin model may be generated for physical nodes of an edge network”. Therefore, the digital twin models (simulates) actual hardware components.
As to point (c), the examiner respectfully disagrees. Smith recites in [0186] “In an example, upon detection of the error, attestation of the physical node or the digital twin for the physical node is performed using a root-of-trust. Read and write latches may be set in memory during performance of the attestation. Execution of a workload may be transferred to another physical node or another digital twin. The digital twin model may be updated with another physical node”. A digital twin of a physical node can be updated with another physical node and a workload can be transferred to the other physical node. Therefore, the workload is transferred to another portion of the same digital twin.
As to point (d), the examiner respectfully disagrees. Applicant's arguments regarding dependent claims fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the dependent claims define a patentable invention without specifically pointing out how the language of the dependent claims patentably distinguishes them from the references.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 1, 10, and 18 (line numbers refer to claim 1):
Lines 15-17 recite “the simulated job flow simulates the series of jobs to be executed as part of executing the actual job flow” and lines 11-12 recite “the actual job flow comprises a series of jobs divided into a plurality of actual containers executed in the computing infrastructure”. Therefore, it is unclear if the series of jobs is to be executed or was executed.
Claims 2-9, 11-17, and 19-20 are dependent claims of claims 1, 10, and 18, and fail to resolve the deficiencies of claims 1, 10, and 18, so they are rejected for the same reasons.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4-11, and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (US 20220191648 A1 hereinafter Smith) in view of Amaro, JR et al. (US 20220404811 A1 hereinafter Amaro).
Smith and Amaro were cited in a prior office action.
As per claim 1, Smith teaches the invention substantially as claimed including a system comprising: a computing infrastructure comprising a plurality of computing nodes ([0182] A digital twin model may be generated for physical nodes in an edge network; [0037] Compute, memory, and storage resources which are offered at the edges in the edge cloud 110);
a memory that stores a digital twin of at least a portion of the computing infrastructure, wherein the digital twin is a software representation of at least the portion of the computing infrastructure ([0182] A digital twin model may be generated for physical nodes in an edge network; claim 15 At least one non-transitory machine-readable memory including instructions for a digital twin framework for an edge network that, when executed by at least one processor, cause the at least one processor to perform operations to: generate a digital twin model for physical nodes in the edge network, wherein the digital twin model includes a digital twin for a physical node of the physical nodes, the digital twin replicating the physical node; [0117] The digital representation may contain a single digital twin that models the entire physical world (e.g., digital twin of the physical world 1005) or multiple digital twin objects that model individual objects/sensors); and
at least one processor communicatively coupled to the computing infrastructure and the memory, wherein the at least one processor is configured to (claim 15. At least one non-transitory machine-readable memory including instructions for a digital twin framework for an edge network that, when executed by at least one processor, cause the at least one processor to perform operations; [0106] In an example, the instructions 782 provided via the memory 754, the storage 758, or the processor 752 may be embodied as a non-transitory, machine-readable medium 760 including code to direct the processor 752 to perform electronic operations in the edge computing node 750):
generate a plurality of simulated containers for a simulated job flow that corresponds to an actual job flow to be executed in the computing infrastructure, wherein: the actual job flow comprises a series of jobs divided into a plurality of actual containers executed in the computing infrastructure; each simulated container of the simulated job flow simulates a corresponding actual container relating to the actual job flow, such that the simulated job flow simulates the series of jobs to be executed as part of executing the actual job flow; and each container is a self-contained virtual environment that comprises software components to process at least a portion of a job relating to the job flow (Fig. 13; [0130] the DT may launch parallel simulations; [0138] Consistency and synchronization may be challenges in a digital twin infrastructure. Execution checkpointing is used to synchronize a DT while the primary environments (e.g., VNF, container, etc.) performs the workload, function, or operation. Multiple local DTs may be synchronized to a primary; [0139] An orchestrator node 1335 provides the workload 1330 (e.g., VNF, container, etc.) to participating DTs (e.g., the local digital twin 1305 and the remote digital twin 1310) and to the primary 1340; [0072] a container is used to provide an environment in which function code (e.g., an application which may be provided by a third party) is executed; [0117] The digital representation may contain a single digital twin that models the entire physical world (e.g., digital twin of the physical world 1005) or multiple digital twin objects that model individual objects/sensors (e.g., local digital twin of the VNF 1010, remote digital twin of the VNF 1015, remote digital twin of the physical world 1020, etc.); [0059] The pod controller also manages container lifecycle operations such as: creating the container, provisioning it with resources and applications, coordinating intermediate results between multiple containers working on a distributed application together; [0073] distribution of functions between containers; [0137] a primary edge computing node; [0058] For instance, each edge node 422, 424 may implement the use of containers, such as with the use of a container “pod” 426, 428 providing a group of one or more containers; [0072] In an example of FaaS, a container is used to provide an environment in which function code (e.g., an application which may be provided by a third party) is executed. The container may be any isolated-execution entity such as a process, a Docker or Kubernetes container… The function code gets executed on the physical infrastructure (e.g., edge computing node) device and underlying virtualized containers; [0059] container requires which resources and for how long in order to complete the workload; Abstract A digital twin model may be generated for physical nodes of an edge network; Since the digital twin is a simulation, and containers are provided to a digital twin, containers are simulated.);
receive configuration parameters relating to the digital twin, wherein the configuration parameters, when applied to the digital twin, configure the digital twin to mimic a particular state of the computing infrastructure; configure the digital twin based on the received configuration parameters ([0182] The digital twin may replicate the physical node. In an example, the physical nodes may be discovered in the edge network…In an example, the digital twin model may include synchronization paths between the physical node and the digital twin and between a first digital twin of the physical node and a second digital twin of the physical node. In an example, the digital twin model may be periodically (or dynamically) updated based on position, features, or trajectory of the physical node or the digital twin of the physical node.);
run a simulation of the job flow by executing the simulated job flow on the digital twin that is configured based on the configuration parameters, the simulated containers relating to the simulated job flow on a first plurality of simulated hardware components of the digital twin that represent a first plurality of actual hardware components of the computing infrastructure ([0029] digital twin model-based computation and analytics services at the edge/cloud; [0130] the DT may launch parallel simulations; [0182] the digital twin model may be periodically (or dynamically) updated based on position, features, or trajectory of the physical node; [0139] FIG. 13 illustrates an example of a local digital twin 1305 and remote digital twin 1310 architecture 1300 with execution logs 1315, 1320, and 1325 and workload 1330 provisioning for a digital twin framework for next generation networks, according to an embodiment. An orchestrator node 1335 provides the workload 1330 (e.g., VNF, container, etc.) to participating DTs (e.g., the local digital twin 1305 and the remote digital twin 1310); [0058] For instance, each edge node 422, 424 may implement the use of containers, such as with the use of a container “pod” 426, 428 providing a group of one or more containers; [0182] The digital twin may replicate the physical node. In an example, the physical nodes may be discovered in the edge network; [0138] Consistency and synchronization may be challenges in a digital twin infrastructure. Execution checkpointing is used to synchronize a DT while the primary environments (e.g., VNF, container, etc.) performs the workload, function, or operation. Multiple local DTs may be synchronized to a primary; [0117] The digital representation may contain a single digital twin that models the entire physical world (e.g., digital twin of the physical world 1005) or multiple digital twin objects that model individual objects/sensors (e.g., local digital twin of the VNF 1010, remote digital twin of the VNF 1015, remote digital twin of the physical world 1020, etc.); Abstract A digital twin model may be generated for physical nodes of an edge network;);
record one or more performance parameter as a result of the simulation, wherein the one or more performance parameter is indicative of a performance of a simulated hardware component of the computing infrastructure ([0029] digital twin model-based computation and analytics services at the edge/cloud; [0185] An error may be identified of the physical node or the digital twin for the physical node (e.g., at operation 2310)…the error may be identified based on receipt of degraded data from the physical node or the digital twin for the physical node, failure to receive data from the physical node or the digital twin for the physical node, or receipt of erroneous data from the physical node or the digital twin for the physical node; [0186] In an example, upon detection of the error, attestation of the physical node or the digital twin for the physical node is performed using a root-of-trust. Read and write latches may be set in memory during performance of the attestation. Execution of a workload may be transferred to another physical node or another digital twin; [0139] FIG. 13 illustrates an example of a local digital twin 1305 and remote digital twin 1310 architecture 1300 with execution logs 1315, 1320, and 1325 and workload 1330 provisioning for a digital twin framework for next generation networks; [0130] assuming an attack (e.g., a ghost vehicle on the road, etc.) is detected in the physical world via a dedicated process integrated into the digital twin, the DT may analyze all possible actions from the attacker. For each possible action, the DT may launch parallel simulations to identify the consequences of the attack); and
if the one or more of the performance parameters do not satisfy respective thresholds, run an iteration comprising: reallocating at least a portion of the simulated containers to a second plurality of simulated hardware components of the digital twin that represent a second plurality of actual hardware components of the computing infrastructure (Fig. 23; [0139] An orchestrator node 1335 provides the workload 1330 (e.g., VNF, container, etc.) to participating DTs; [0130] In an example, trustworthiness of data is also assessed for the digital twins…. Data from a sensor may be filtered out if the reputation of that sensor goes below a certain threshold; [0186] In an example, upon detection of the error, attestation of the physical node or the digital twin for the physical node is performed using a root-of-trust. Read and write latches may be set in memory during performance of the attestation. Execution of a workload may be transferred to another physical node or another digital twin. The digital twin model may be updated with another physical node; [0185] the error may be identified based on receipt of degraded data from the physical node or the digital twin for the physical node, failure to receive data from the physical node or the digital twin for the physical node, or receipt of erroneous data from the physical node or the digital twin for the physical node; [0117] multiple digital twin objects that model individual objects/sensors…The process of building a reliable digital representation of the world involves communication/interaction with the sensors/physical entities, quality data collection, utilization of computing resources at an edge/cloud computing node; [0185] a digital twin of the second physical node); and
rerunning the simulation ([0142] In the case of sparcified DTs, the RCN 1500 reconstructs local and remote DTs, recovers execution logs, integrity checks workload/container images for primary and DTs, establishes a common execution synchronization point, and restarts execution; [0130] the DT may launch parallel simulations)
Smith fails to teach wherein the simulation comprises deploying, using a simulated load balancer of the digital twin; running the simulation after reallocating at least the portion of the simulated containers; and if the one or more performance parameters satisfy the respective thresholds, recording an allocation of the simulated containers that resulted in the one or more performance parameters satisfying the respective thresholds.
However, Amaro teaches wherein the simulation comprises deploying, using a simulated load balancer of the digital twin ([0131] the SD Orchestrator service 222 not only establishes the running container processes, but also manages the fault tolerance, load-balancing; [0135] Additionally, in some implementations, the SDCS 200 may implement digital twins of various SD application services 235, 240, 248, the entire SD application layer 212, various SD support services 215-225, 252-260, and/or the entire SD networking layer 210; [0136] Further, in some implementations, the SDCS 200 may implement simulations of or changes to various SD application services 235, 240, 248, to the entire SD application layer 212, to various SD support services 215-225, 252-260, and/or to the entire SD networking layer 210;);
running the simulation after reallocating at least the portion of the simulated containers ([0150] The newly-instantiated container may be instantiated on different hardware (different processor, different server, etc.) from the poorly performing container, in embodiments, depending, for example, on whether the QoS metrics indicate that the container was performing poorly as a result of the hardware on which the container was instantiated. At the same time, if the QoS metrics indicate that one hardware resource is underutilized, while another hardware resource is heavily utilized, the load balancing service 258 may cause the orchestrator 222 to move one or more containers to the underutilized resource from the heavily utilized resource; [0135] Additionally, in some implementations, the SDCS 200 may implement digital twins of various SD application services 235, 240, 248, the entire SD application layer 212, various SD support services 215-225, 252-260, and/or the entire SD networking layer 210; [0098] Within the SDCS 200, some configured containers may be allocated or assigned to respective SD compute nodes 208 and dynamically re-assigned to different SD compute nodes 208 by the SD compute service 215 based on dynamically changing configurations, performance, and needs of the logical process control system 245; [0136] Further, in some implementations, the SDCS 200 may implement simulations of or changes to various SD application services 235, 240, 248, to the entire SD application layer 212, to various SD support services 215-225, 252-260, and/or to the entire SD networking layer 210; [0133] re-assign containers which are presently assigned to execute on cluster C2 to other clusters in accordance with the present needs of the logical process control system 245 and the availability of hardware and/or software resources of the other clusters, and the SD HCI support services 215-225 may adjust routing tables utilized by the clusters 208 accordingly so that continuity of execution of said containers is maintained even when the cluster C2 is taken out of service. As such, the SDCS networking layer 210 automatically, dynamically, and responsively determines, initiates, and performs changes to the allocation of hardware and software resources of the nodes of the computing platform 208 to different SD application layer software components 212 based on detected conditions, such as improvement in performance of individual logical and/or physical components or groups thereof, degradation of performance of individual logical and/or physical components or groups thereof, fault occurrences, failures of logical and/or physical components); and
if the one or more performance parameters satisfy the respective thresholds, recording an allocation of the simulated containers that resulted in the one or more performance parameters satisfying the respective thresholds ([0144] As used herein, the phrase “load balancing” refers to the use of computing, memory, and/or communication resources for processes (e.g., containers) running on a system, such that the processor cores, processors, compute nodes, and or servers meet desired quality-of-service metrics. Load balancing, then, includes equalizing the use of computing, memory, and/or communication resources in some instances and, in some instances, ensuring minimum QoS parameters are met (i.e., ensuring maximum network latency for certain signals does not exceed a programmed value, ensuring maximum processing latency for certain processes does not exceed a programmed value, ensuring total latency for a particular value does not exceed a programmed value, ensuring sufficient memory resources exist for certain processes, etc.); [0304] The discovery service stores a record of the identity, capabilities, and/or location of each physical or logical asset in the process plant 10 which may be utilized during run-time of the process plant 10 to control at least a portion of the industrial process, such as field devices, controllers, process control devices, I/O devices, compute nodes, containers; [0388] In any event, as will be understood, the visualization service 3202 may create the hierarchy 3210 so that the hierarchy 3210 indicates (1) the permanently configured (non-changeable or pinned) relationships between physical and logical elements and between logical elements and other logical elements, (2) the temporarily configured (user assignable or dynamically assignable during run-time) relationships between physical and logical elements… the hierarchy 3210 can be used to indicate dynamically assignable containers and may even be used or manipulated by the user to perform reassign of dynamically assignable containers during runtime. In this case, the visualization service 3202, upon receiving an instruction to reassign a container to another logical and/or physical element, will instruct the orchestrator 222 of the reassignment and the orchestrator 222 will perform the reassign of the container).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined Smith with the teachings of Amaro to meet QoS requirements (see Amaro [0144] ensuring minimum QoS parameters are met).
As per claim 2, Smith and Amaro teach the system of Claim 1. Smith teaches wherein the at least one processor is further configured to assign actual containers relating to the job flow to the computing nodes in the computing infrastructure ([0058] For instance, each edge node 422, 424 may implement the use of containers; [0072] In an example of FaaS, a container is used to provide an environment in which function code (e.g., an application which may be provided by a third party) is executed.), wherein the actual containers correspond to the simulated containers ([0139] An orchestrator node 1335 provides the workload 1330 (e.g., VNF, container, etc.) to participating DTs (e.g., the local digital twin 1305 and the remote digital twin 1310) and to the primary 1340; [0137] primary edge computing node).
Additionally, Amaro teaches assign actual containers relating to the job flow to the computing nodes in the computing infrastructure according to the recorded allocation of the simulated containers that resulted in the one or more performance parameters satisfying the respective thresholds ([0135] Additionally, in some implementations, the SDCS 200 may implement digital twins of various SD application services 235, 240, 248, the entire SD application layer 212, various SD support services 215-225, 252-260, and/or the entire SD networking layer 210. That is, a digital twin of the target components/layers may execute in concert with the active target components/layers on top of the computing platform 208, and thereby receive run-time data from the field environment of the industrial process plant and operate accordingly, with the same logic, states, timing, etc. as the active target components/layers; [0388] In any event, as will be understood, the visualization service 3202 may create the hierarchy 3210 so that the hierarchy 3210 indicates (1) the permanently configured (non-changeable or pinned) relationships between physical and logical elements and between logical elements and other logical elements, (2) the temporarily configured (user assignable or dynamically assignable during run-time) relationships between physical and logical elements… the hierarchy 3210 can be used to indicate dynamically assignable containers and may even be used or manipulated by the user to perform reassign of dynamically assignable containers during runtime. In this case, the visualization service 3202, upon receiving an instruction to reassign a container to another logical and/or physical element, will instruct the orchestrator 222 of the reassignment and the orchestrator 222 will perform the reassign of the container; [0150] The newly-instantiated container may be instantiated on different hardware (different processor, different server, etc.) from the poorly performing container, in embodiments, depending, for example, on whether the QoS metrics indicate that the container was performing poorly as a result of the hardware on which the container was instantiated. At the same time, if the QoS metrics indicate that one hardware resource is underutilized, while another hardware resource is heavily utilized, the load balancing service 258 may cause the orchestrator 222 to move one or more containers to the underutilized resource from the heavily utilized resource;).
As per claim 4, Smith and Amaro teach the system of Claim 1. Smith teaches wherein each simulated hardware component comprises a simulation of a computing node in the computing infrastructure ([0182] A digital twin model may be generated for physical nodes in an edge network; [0117] The digital representation may contain a single digital twin that models the entire physical world (e.g., digital twin of the physical world 1005) or multiple digital twin objects that model individual objects/sensors; [0130] the DT may launch parallel simulations;).
As per claim 5, Smith and Amaro teach the system of Claim 1. Smith teaches wherein: the digital twin comprises a first digital twin and a second digital twin ([0139] FIG. 13 illustrates an example of a local digital twin 1305 and remote digital twin 1310);
the first digital twin is a software representation of a first portion of the computing infrastructure; the second digital twin is a software representation of a second portion of the computing infrastructure ([0117] multiple digital twin objects that model individual objects/sensors; [0115] there may be multiple DTs (e.g., local DT A 910, local DT B 915, remote DT A 920, and remote DT B 925) that represent a physical node; [0118] create a digital representation of the physical objects (e.g., digital objects 1135, 1140, 1145, and 1150) in an edge infrastructure 1120.); and
the at least one processor is configured to: run the simulation of the job flow on the digital twin by deploying the simulated containers on a first set of simulated hardware components of the first digital twin; and if the one or more performance parameters do not satisfy respective thresholds, run the iteration by reallocating at least the portion of the simulated containers to a second set of simulated hardware components of the second digital twin ([0139] An orchestrator node 1335 provides the workload 1330 (e.g., VNF, container, etc.) to participating DTs; [0130] In an example, trustworthiness of data is also assessed for the digital twins…. Data from a sensor may be filtered out if the reputation of that sensor goes below a certain threshold; [0186] In an example, upon detection of the error, attestation of the physical node or the digital twin for the physical node is performed using a root-of-trust. Read and write latches may be set in memory during performance of the attestation. Execution of a workload may be transferred to another physical node or another digital twin; [0185] the error may be identified based on receipt of degraded data from the physical node or the digital twin for the physical node, failure to receive data from the physical node or the digital twin for the physical node, or receipt of erroneous data from the physical node or the digital twin for the physical node; [0117] multiple digital twin objects that model individual objects/sensors).
As per claim 6, Smith and Amaro teach the system of Claim 5. Smith teaches wherein: the first portion of the computing infrastructure is located in a first geographical region; and the second portion of the computing infrastructure is located in a second geographical region ([0057] The virtual edge instances may also be spanned across systems of multiple owners at different geographic locations; [0111] identify the one or more devices (e.g., connected Edge devices) geographically and/or logically separated from each other).
As per claim 7, Smith and Amaro teach the system of Claim 1. Smith teaches wherein the configuration parameters comprise one or more of, a particular time of a particular day, a particular day of a particular week, a particular day of a particular month, a particular geographical region related to the computing infrastructure, one or more particular servers of the computing infrastructure, one or more particular software components of the computing infrastructure, a particular central processing unit (CPU) load, a particular amount of memory allocation, and an outage of one or more servers ([0182] A digital twin model may be generated for physical nodes in an edge network (e.g., at operation 2305). The digital twin model may include a digital twin for a physical node of the physical nodes…In an example, the digital twin model may be periodically (or dynamically) updated based on position, features, or trajectory of the physical node or the digital twin of the physical node; [0050] The edge cloud 110 may also include one or more servers; [0070] the edge node will move to other geographic locations).
As per claim 8, Smith and Amaro teach the system of Claim 1. Smith teaches wherein the particular state of the computing infrastructure comprises one or more of a state of the computing infrastructure on a particular day, a state of the computing infrastructure at a particular time, a state of the computing infrastructure when one or more servers are out of service, and a portion of the computing infrastructure in a particular geographical region ([0182] A digital twin model may be generated for physical nodes in an edge network (e.g., at operation 2305). The digital twin model may include a digital twin for a physical node of the physical nodes…In an example, the digital twin model may be periodically (or dynamically) updated based on position, features, or trajectory of the physical node or the digital twin of the physical node; [0050] The edge cloud 110 may also include one or more servers; [0070] the edge node will move to other geographic locations).
As per claim 9, Smith and Amaro teach the system of Claim 1. Smith teaches wherein the performance parameters include one or more of whether the job flow was successfully processed ([0186] In an example, upon detection of the error, attestation of the physical node or the digital twin for the physical node is performed using a root-of-trust. Read and write latches may be set in memory during performance of the attestation. Execution of a workload may be transferred to another physical node or another digital twin; [0185] the error may be identified based on receipt of degraded data from the physical node or the digital twin for the physical node, failure to receive data from the physical node or the digital twin for the physical node, or receipt of erroneous data from the physical node or the digital twin for the physical node).
Additionally, Amaro teaches a job latency of one or more jobs in the job flow, a CPU load at one or more servers of the computing infrastructure, a memory allocation at one or more servers of the computing infrastructure ([0144] Load balancing, then, includes equalizing the use of computing, memory, and/or communication resources in some instances and, in some instances, ensuring minimum QoS parameters are met (i.e., ensuring maximum network latency for certain signals does not exceed a programmed value, ensuring maximum processing latency for certain processes does not exceed a programmed value, ensuring total latency for a particular value does not exceed a programmed value, ensuring sufficient memory resources exist for certain processes, etc.); [0389] the visualization service 3202 may present, on the display 3300, a set of performance indictors 3340 for each of the servers, including, for example, current CPU loading, storage utilization, network bandwidth, and core temperature; [0133] assign the newly created containers to execute on corresponding nodes, may re-balance existing containers among nodes, may assign specific hardware memory resources to support the logical memory resource needs of the additional containers).
As per claim 10, it is a method claim of claim 1, so it is rejected for similar reasons.
As per claims 11, 13, 14, 15, 16, and 17, they are method claims of claims 2, 5, 6, 7, 8, and 9, so they are rejected for similar reasons.
As per claim 18, it is a non-transitory computer-readable medium claim of claim 1, so it is rejected for similar reasons.
As per claim 19, it is a non-transitory computer-readable medium claim of claim 2, so it is rejected for similar reasons.
Claims 3, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Smith and Amaro, as applied to claims 1, 10, and 18 above, in view of Bank et al. (US 20200030979 A1 hereinafter Bank).
Bank was cited in a prior office action.
As per claim 3, Smith and Amaro teach the system of Claim 1.
Smith and Amaro fail to teach wherein the processor is further configured to after rerunning the simulation, if the one or more performance parameters do not satisfy the respective thresholds, rerun the iteration.
However, Bank teaches wherein the processor is further configured to after rerunning the simulation, if the one or more performance parameters do not satisfy the respective thresholds, rerun the iteration ([0031] The MR simulation may be rerun to test the adjusted application, and with additional iterations as necessary, until operation of the simulated robotic unit 231 is successful according to constraints, such as a spatial tolerance threshold; [0022] MR environment are to be simulated as digital twins).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined Smith and Amaro with the teachings of Bank in order to meet a constraint (see Bank [0031] The MR simulation may be rerun to test the adjusted application, and with additional iterations as necessary, until operation of the simulated robotic unit 231 is successful according to constraints, such as a spatial tolerance threshold).
As per claims 12 and 20, they are method and non-transitory computer-readable medium claims of claim 3, so they are rejected for similar reasons.
Conclusion
THIS ACTION IS MADE FINAL. 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 HSING CHUN LIN whose telephone number is (571)272-8522. The examiner can normally be reached Mon - Fri 9AM-5PM.
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, Aimee Li can be reached at (571) 272-4169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/H.L./Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195