DETAILED ACTION
Claims 1-20 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Notes
Examiner has cited particular columns and line numbers, paragraph numbers, or figures in the references as applied to the claims below 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 from the applicant, in preparing the responses, to 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.
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.
Claim(s) 1, 4-7, 9, 12-15, 17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Radhakrishnan et al. (US-PGPUB-NO: 2014/0365626 A1) hereinafter Radhakrishnan, in further view of Bayani (US-PGPUB-NO: 2013/0007435 A1).
As per claim 1, Radhakrishnan teaches a computer-implemented method comprising: receiving, by one or more processors, pre-imaging data for a plurality of pre-imaged computing devices (see Radhakrishnan paragraph [0029], “It should be appreciated that although the embodiments disclosed herein are described primarily in the context of virtual machine instances, other types of instances can be utilized with the concepts and technologies disclosed herein. For instance, the technologies disclosed herein may be utilized with instances of storage resources, instances of data communications resources and with other types of resources, which may all be referred to as compute instances. The embodiments disclosed herein may also execute all or a portion of an application directly on a computer system without utilizing virtual machine instances”); generating, by the one or more processors and using the pre-imaging data, a plurality of data objects that respectively correspond to the plurality of pre-imaged computing devices (see Radhakrishnan paragraph [0039], “Generally, in response to receiving a request from customer computing system 104 to launch an instance, datacenter 102 (i.e., computing components in the datacenter as described below) determines if the request could be satisfied using a pre-warmed instance. If so, the datacenter performs the procedure described in FIG. 4; otherwise, it performs the procedure described in FIG. 3. As shown in FIG. 3, when the instance cannot be launched from a pre-warmed instance, datacenter 102 configures its computing resources in response to the request, launches the instance and provides information to allow the customer computing system to establish a connection to the instance. FIG. 3 is presented first to illustrate the general end-to-end process of preparing an instance for operation on a computing device. Thereafter, FIG. 4 is presented to contrast with FIG. 3 to demonstrate the reduction in launch time effectuated when pre-warmed instances are available”); receiving, by the one or more processors, a computing device provisioning request comprising one or more hardware specifications and application criteria (see Radhakrishnan paragraph [0040], “Referring to FIG. 3, operation 302 illustrates datacenter 102 receiving a request from the customer computing system to launch the instance. The request may be in the form of a call to a web API and typically includes information identifying attributes of the customer and attributes of the desired instance. For example, management component 210 can receive a username, password and credentials of the customer along with input parameters that define some configuration attributes of the instance. The input parameters can specify, for example, an identifier of a machine image from which the instance should be launched, a compute instance type (e.g., a type identifier that indicates the amount of hardware resources that will be allocated to the instance, such as a number of processors, an amount of memory, an amount of storage, an amount of network bandwidth, etc.), network security to be applied, storage volume to be associated with the instance and other information”); determining, by the one or more processors, a computing device from the plurality of pre-imaged computing devices based at least in part on the one or more hardware specifications and the plurality of data objects (see Radhakrishnan paragraph [0044], “Operation 308 illustrates datacenter 102 returning an instance identification to the customer computing system. This operation can include deployment component 214 further validating the request to determine, for example, whether sufficient capacity is available at the datacenter to host the instance. This determination considers various parameters from the request such as, for example, the instance type, the machine image, whether the instance can be hosted on a server computer 202 along with instances of other customers and a profile of the customer that includes its usage pattern. If validated, deployment component 214 generates the instance identification, updates an instance status to pending and returns the information to management component 210. In turn, the management component transmits the instance identification to the customer computing system with an indication of the instance's status and other-instance related information”); generating, by the one or more processors, a custom build instruction identifying the application criteria (see Radhakrishnan paragraph [0046], “Operation 310 illustrates datacenter 102 configuring various computing resources to launch the instance. In this operation, deployment component 214 can use the information received in the request to set up the various computing resources to allow a server computer 202 to launch the instance by booting up the machine image. For example, deployment component 214 determines a root storage device that contains the machine image, allocates a block-level persistent storage to the instance, assigns an internet protocol (IP) address to the instance, sets up a network interface, generates routing information, configure firewalls, and attaches other physical and virtual resources to the server computer associated with the instance”); and transmitting, by the one or more processors, the custom build instruction to the computing device (see Radhakrishnan paragraph [0051], “Once a server computer is selected, load balancer component 216 passes information about the configuration of the instance to the server computer and passes information about the server computer to deployment component 214. Operation 314 illustrates datacenter 102 calling the host to launch the instance. For example, deployment component 214 sends a command to the server computer to initiate the launch of the instance”).
Radhakrishnan does not explicitly teach wherein a first data object of the plurality of data objects comprises a data construct that describes a first computing device of the plurality of pre-imaged computing devices. However, Bayani teaches wherein a first data object of the plurality of data objects comprises a data construct that describes a first computing device of the plurality of pre-imaged computing devices (see Bayani paragraph [0027], “In the example of FIG. 1, a pre-boot image generator 130 represents the type of agents/modules which may be executing at the server 106, and which may be configured, based on instruction from the pre-boot image handler 116, to dynamically construct a pre-boot image 132 for storage at the designated server 106. Specifically, as described in detail herein, the pre-boot image generator 130 may include an analyzer 134 which may be configured to analyze, e.g., operations of a currently-active execution of the operating system 128, so as to thereby detect or otherwise determine various boot-critical drivers, configuration settings, network settings, or a number of other aspects of the executing operating system 128 which may be necessary or desirable for inclusion within the pre-boot image 132”).
Radhakrishnan and Bayani are analogous art because they are in the same field of endeavor of software development / deployment. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective filing date to modify Radhakrishnan’s teaching of pre-warming compute instances in datacenters by pre-configuring and pre-launching computer resources with Bayani’s teaching of server management with dynamic construction of pre-boot images to incorporate constructing deployment data for faster more reliable server management and data centers.
As per claim 4, Radhakrishnan modified with Bayani teaches wherein providing the custom build instruction further comprises: determining a network address that is associated with the computing device; and generating the custom build instruction that initiates installation of one or more applications that are associated with the application criteria to the computing device at the network address (see Radhakrishnan paragraph [0046], “Operation 310 illustrates datacenter 102 configuring various computing resources to launch the instance. In this operation, deployment component 214 can use the information received in the request to set up the various computing resources to allow a server computer 202 to launch the instance by booting up the machine image. For example, deployment component 214 determines a root storage device that contains the machine image, allocates a block-level persistent storage to the instance, assigns an internet protocol (IP) address to the instance, sets up a network interface, generates routing information, configure firewalls, and attaches other physical and virtual resources to the server computer associated with the instance”).
As per claim 5, Radhakrishnan modified with Bayani teaches further comprising: identifying a first subset of data objects from the plurality of data objects based at least in part on the one or more hardware specifications (see Radhakrishnan paragraph [0042], “Operation 304 illustrates datacenter 102 validating the request. In an example, management component 210 authenticates the customer based on a digital signature in the API request, the username and password, etc., determines whether the customer has permission to launch the instance based on the credentials of the customer received in the request. If the request is valid, management component 210 requests deployment component 214 to initiate the launch of the instance (shown in operations 308-318 of FIG. 3) or to satisfy the request from a pre-warmed instance (shown in the operations of FIG. 4)”), wherein determining the computing device from the plurality of pre-imaged computing devices comprises determining a data object from the first subset of data objects using the one or more hardware specifications and one or more object attributes of the data object (see Radhakrishnan paragraph [0046], “Operation 310 illustrates datacenter 102 configuring various computing resources to launch the instance. In this operation, deployment component 214 can use the information received in the request to set up the various computing resources to allow a server computer 202 to launch the instance by booting up the machine image. For example, deployment component 214 determines a root storage device that contains the machine image, allocates a block-level persistent storage to the instance, assigns an internet protocol (IP) address to the instance, sets up a network interface, generates routing information, configure firewalls, and attaches other physical and virtual resources to the server computer associated with the instance”).
As per claim 6, Radhakrishnan modified with Bayani teaches wherein determining the data object from the first subset of data objects comprises: determining, using the one or more hardware specifications and data indicated by the data object, a hardware similarity score; and determining that the hardware similarity score meets or exceeds a similarity threshold (see Radhakrishnan paragraph [0071], “Operation 508 illustrates the pre-warming component refining the target based on availabilities and constraints of the computing resources of datacenter 102. The pre-warming component may further revise the target to balance the expected demand for the instances and/or computing resources with expected availability of its computing resources. To determine the computing resources that are expected to be available, the pre-warming component may also apply a similar analysis as above to historical uses of its resources. For example, the pre-warming component can determine that over one week long periods of time, the particular datacenter in California runs on average at eighty percent capacity”).
As per claim 7, Radhakrishnan modified with Bayani teaches further comprising generating one or more logistical operation instructions for the computing device (see Radhakrishnan paragraph [0051], “Once a server computer is selected, load balancer component 216 passes information about the configuration of the instance to the server computer and passes information about the server computer to deployment component 214. Operation 314 illustrates datacenter 102 calling the host to launch the instance. For example, deployment component 214 sends a command to the server computer to initiate the launch of the instance”).
As per claims 9 and 12-15, these are the system claims comprising one or more processors; and at least one memory storing processor-executable instructions that, when collectively or independently executed by any one or more of the one or more processors, comprise causing the one or more processors (see Radhakrishnan paragraph [0016], “Those skilled in the art will also appreciate that the subject matter described herein may be practiced on or in conjunction with other computer system configurations beyond those described herein, including multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, handheld computers, personal digital assistants, e-readers, cellular telephone devices, special-purposed hardware devices, network appliances and the like. The embodiments described herein may also be practiced in distributed computing environments, where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices”) of method claims 1 and 4-7, respectively. Therefore, they are rejected for the same reasons as above.
As per claims 17 and 19-20, these are the non-transitory computer readable storage media (see Radhakrishnan paragraph [0099], “In addition to mass storage device 928 described above, computer 900 may have access to other computer-readable storage media to store and retrieve information, such as program modules, data structures or other data. It should be appreciated by those skilled in the art that computer-readable storage media can be any available media that provides for the storage of non-transitory data and that may be accessed by computer 900”) claims to method claims 1 and 4-5, respectively. Therefore, they are rejected for the same reasons as above.
Claim(s) 2, 3, 10, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Radhakrishnan (US-PGPUB-NO: 2014/0365626 A1) and Bayani (US-PGPUB-NO: 2013/0007435 A1), in further view of Abelha Ferreira et al. (US-PGPUB-NO: 2021/0334597 A1) hereinafter Ferreira.
As per claim 2, Radhakrishnan modified with Bayani teaches wherein the plurality of pre-imaged computing devices is pre-imaged based at least in part on a plurality of demand predictions respectively and the computer-implemented method further comprises: receiving at least one of: deployment data indicating at least one of a first set of devices that were provisioned with respective custom build instructions or respective times the first set of devices were provisioned (see Radhakrishnan paragraph [0065], “Turning to FIG. 5, that figure illustrates an example procedure for determining the expected demand. In the interest of clarity of explanation, the service for determining the expected demand is referred to herein as a pre-warming component that may be configured as a an application within or outside of datacenter 102 as described herein above. Operation 502 illustrates the pre-warming component considering a dataset representative of a history of requests for instances and analyzing the dataset to determine expected requests for instances”); replacement data indicating at least one of a second set of devices that were replaced or respective times the second set of devices were replaced (see Radhakrishnan paragraph [0062], “Operation 408 illustrates datacenter 102 (e.g., management component 210) notifying the customer that the instance is ready by providing information to the customer computing system to allow it to establish a connection to the instance. This operation may be similar to operation 318 of FIG. 3. More particularly, datacenter 102 (e.g., management component 210) updates the status of the instance to running and uses the credentials of the customer to provide information to the customer computing system to allow it to establish a secure connection to the instance”); capability requirement data indicating at least one hardware specifications associated with at least one the first set of devices, the second set of devices, or a set of requests for new or replacement computing devices (see Radhakrishnan paragraph [0067], “The pre-warming component may also apply a similar analysis based on either the instance types or the machine images or based on other configuration parameters of the instances. For example, when datacenter 102 is configured such that specific types of instances can only be hosted on specific computing resources, the pre-warming component may determine the mathematical equations that estimate the expected demand per instance type. These equations can be used to estimate the expected demand for the computing resources that can support the instance types. As such, in this example, the pre-warming component can estimate the needed computing capacity to pre-warm the instances”); or provisioning trend data indicating one or more metrics associated with at least one of the deployment data, the replacement data, or the capability requirement data (see Radhakrishnan paragraph [0044], “Operation 308 illustrates datacenter 102 returning an instance identification to the customer computing system. This operation can include deployment component 214 further validating the request to determine, for example, whether sufficient capacity is available at the datacenter to host the instance. This determination considers various parameters from the request such as, for example, the instance type, the machine image, whether the instance can be hosted on a server computer 202 along with instances of other customers and a profile of the customer that includes its usage pattern. If validated, deployment component 214 generates the instance identification, updates an instance status to pending and returns the information to management component 210. In turn, the management component transmits the instance identification to the customer computing system with an indication of the instance's status and other-instance related information”).
Radhakrishnan modified with Bayani do not explicitly teach generating, by a machine learning model using at least one of the deployment data, the replacement data, the capability requirement data, or the provisioning trend data, one or more demand predictions of the plurality of demand predictions indicating a target number or percentage of at least one of hardware configurations or software configurations with respect to the plurality of pre-imaged computing devices. However, Ferreira teaches generating, by a machine learning model using at least one of the deployment data, the replacement data, the capability requirement data, or the provisioning trend data, one or more demand predictions of the plurality of demand predictions indicating a target number or percentage of at least one of hardware configurations or software configurations with respect to the plurality of pre-imaged computing devices (see Ferreira paragraph [0129], “The predictor of the prediction manager may introduce the conditioned hypothetical data into the confidence prediction model. In other words, the confidence prediction model may use the conditioned hypothetical data as inputs and produce a confidence indicator prediction as an output. The confidence indicator prediction may be that the predicted response time is likely a non-peak response time, possibly a non-peak response time, possibly a peak response time, or likely a peak response time. The confidence indicator prediction may be associated with a probability. For example, the confidence indicator prediction may a numerical percentage as to whether the predicted response time is a peak response time. The confidence indicator prediction may be another type of prediction that indicates the likelihood the predicted response is a peak and/or a non-peak response time without departing from the invention. The confidence indicator prediction may be generated via other and/or additional methods without departing from the invention”).
Radhakrishnan, Bayani and Ferreira are analogous art because they are in the same field of endeavor of software development / deployment. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective filing date to modify Radhakrishnan’s teaching of pre-warming compute instances in datacenters by pre-configuring and pre-launching computer resources and Bayani’s teaching of server management with dynamic construction of pre-boot images with Ferreira’s teaching of confident peak-aware response time estimation by exploiting telemetry data from different system configuration to incorporate the use of machine learning to predict possible likelihood of deployment configuration in order to have a better understanding of the deployment plan and having better responsiveness.
As per claim 3, Radhakrishnan modified with Bayani and Ferreira teaches further comprising determining, using the one or more demand predictions, the pre-imaging data (see Ferreira paragraph [0131], “In one or more embodiments of the invention, the combined prediction is generated by reading both the response time prediction and the confidence indicator prediction. Using both the response time prediction and the confidence indicator prediction, the prediction manager may generate a combined prediction that indicates whether the predicted response time is a reliable prediction that may accurately reflect a potential measured response time of the hypothetical deployment configuration. The combined prediction may also indicate whether the predicted response time exceeds a responsiveness threshold. The combined prediction may be generated via other and/or additional methods without departing from the invention”).
As per claim 10, Radhakrishnan modified with Bayani do not explicitly teach wherein the pre-imaged computing device is pre-imaged based at least in part on a demand prediction and the one or more processors are further configured to generate, by a machine learning model using at least one of deployment data, replacement data, capability requirement data, or provisioning trend data, the demand prediction. However, Ferreira teaches wherein the pre-imaged computing device is pre-imaged based at least in part on a demand prediction and the one or more processors are further configured to generate, by a machine learning model using at least one of deployment data, replacement data, capability requirement data, or provisioning trend data, the demand prediction (see Ferreira paragraph [0129], “The predictor of the prediction manager may introduce the conditioned hypothetical data into the confidence prediction model. In other words, the confidence prediction model may use the conditioned hypothetical data as inputs and produce a confidence indicator prediction as an output. The confidence indicator prediction may be that the predicted response time is likely a non-peak response time, possibly a non-peak response time, possibly a peak response time, or likely a peak response time. The confidence indicator prediction may be associated with a probability. For example, the confidence indicator prediction may a numerical percentage as to whether the predicted response time is a peak response time. The confidence indicator prediction may be another type of prediction that indicates the likelihood the predicted response is a peak and/or a non-peak response time without departing from the invention. The confidence indicator prediction may be generated via other and/or additional methods without departing from the invention”).
Radhakrishnan, Bayani and Ferreira are analogous art because they are in the same field of endeavor of software development / deployment. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective filing date to modify Radhakrishnan’s teaching of pre-warming compute instances in datacenters by pre-configuring and pre-launching computer resources and Bayani’s teaching of server management with dynamic construction of pre-boot images with Ferreira’s teaching of confident peak-aware response time estimation by exploiting telemetry data from different system configuration to incorporate the use of machine learning to predict possible likelihood of deployment configuration in order to have a better understanding of the deployment plan and having better responsiveness.
As per claim 11, Radhakrishnan modified with Bayani and Ferreira teaches wherein the one or more processors are further configured to determine, using the demand prediction, the pre-imaging data (see Ferreira paragraph [0131], “In one or more embodiments of the invention, the combined prediction is generated by reading both the response time prediction and the confidence indicator prediction. Using both the response time prediction and the confidence indicator prediction, the prediction manager may generate a combined prediction that indicates whether the predicted response time is a reliable prediction that may accurately reflect a potential measured response time of the hypothetical deployment configuration. The combined prediction may also indicate whether the predicted response time exceeds a responsiveness threshold. The combined prediction may be generated via other and/or additional methods without departing from the invention”).
As per claim 18, Radhakrishnan modified with Bayani do not explicitly teach wherein the pre-imaged computing device is pre-imaged based at least in part on a demand prediction, and further including instructions that, when executed by the one or more processors, cause the one or more processors to: generate, by a machine learning model using at least one of deployment data, replacement data, capability requirement data, or provisioning trend data, the demand prediction; and determine, using the demand prediction, the pre-imaging data. However, Ferreira teaches wherein the pre-imaged computing device is pre-imaged based at least in part on a demand prediction, and further including instructions that, when executed by the one or more processors, cause the one or more processors to: generate, by a machine learning model using at least one of deployment data, replacement data, capability requirement data, or provisioning trend data, the demand prediction (see Ferreira paragraph [0129], “The predictor of the prediction manager may introduce the conditioned hypothetical data into the confidence prediction model. In other words, the confidence prediction model may use the conditioned hypothetical data as inputs and produce a confidence indicator prediction as an output. The confidence indicator prediction may be that the predicted response time is likely a non-peak response time, possibly a non-peak response time, possibly a peak response time, or likely a peak response time. The confidence indicator prediction may be associated with a probability. For example, the confidence indicator prediction may a numerical percentage as to whether the predicted response time is a peak response time. The confidence indicator prediction may be another type of prediction that indicates the likelihood the predicted response is a peak and/or a non-peak response time without departing from the invention. The confidence indicator prediction may be generated via other and/or additional methods without departing from the invention”) and determine, using the demand prediction, the pre-imaging data (see Ferreira paragraph [0131], “In one or more embodiments of the invention, the combined prediction is generated by reading both the response time prediction and the confidence indicator prediction. Using both the response time prediction and the confidence indicator prediction, the prediction manager may generate a combined prediction that indicates whether the predicted response time is a reliable prediction that may accurately reflect a potential measured response time of the hypothetical deployment configuration. The combined prediction may also indicate whether the predicted response time exceeds a responsiveness threshold. The combined prediction may be generated via other and/or additional methods without departing from the invention”).
Radhakrishnan, Bayani and Ferreira are analogous art because they are in the same field of endeavor of software development / deployment. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective filing date to modify Radhakrishnan’s teaching of pre-warming compute instances in datacenters by pre-configuring and pre-launching computer resources and Bayani’s teaching of server management with dynamic construction of pre-boot images with Ferreira’s teaching of confident peak-aware response time estimation by exploiting telemetry data from different system configuration to incorporate the use of machine learning to predict possible likelihood of deployment configuration in order to have a better understanding of the deployment plan and having better responsiveness.
Claim(s) 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Radhakrishnan (US-PGPUB-NO: 2014/0365626 A1) and Bayani (US-PGPUB-NO: 2013/0007435 A1), in further view of Wilson et al. (US-PGPUB-NO: 2023/0038226 A1) hereinafter Wilson.
As per claim 8, Radhakrishnan modified with Bayani do not explicitly teach wherein generating the one or more logistical operation instructions comprises at least one of: generating a shipment request based at least in part on a determined data object that is associated with the computing device from the plurality of data objects; or transmitting an instruction to one or more computing devices to cause a machine to at least one of remove the computing device from physical storage, store the computing device in a physical container, affix a shipping label to the physical container, or deliver the physical container to a location. However, Wilson teaches wherein generating the one or more logistical operation instructions comprises at least one of: generating a shipment request based at least in part on a determined data object that is associated with the computing device from the plurality of data objects; or transmitting an instruction to one or more computing devices to cause a machine to at least one of remove the computing device from physical storage, store the computing device in a physical container, affix a shipping label to the physical container, or deliver the physical container to a location (see Wilson paragraph [0038], “The tracking engine 140 may be configured to provide functionality for tracking information associated with deployments, such as by recording the tracked information in the one or more databases 118. For example, the tracking engine 140 may track transactions related to a deployment over time. The tracked transactions may include various types of milestones associated with the deployment, such as: a transaction in which the solution module is requested by the customer; a transaction indicating the solution module is ready for deployment (e.g., the blueprint, etc. are complete); a transaction indicating the deployment has been initiated; a transaction indicating the deployment has completed; transactions indicating changes (e.g., an update or upgrade) to the deployment; a transaction associated with decommissioning the deployment (i.e., removing the deployment from the target infrastructure); or other types of transactions. In an aspect, the one or more databases 118 may include a distributed ledger and the transactions may be recorded to the distributed ledger as events corresponding to the transactions are detected. Exemplary aspects of monitoring functionality for detecting events associated with transactions to be recorded to the distributed ledger are described in more detail below”).
Radhakrishnan, Bayani and Wilson are analogous art because they are in the same field of endeavor of software development / deployment. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective filing date to modify Radhakrishnan’s teaching of pre-warming compute instances in datacenters by pre-configuring and pre-launching computer resources and Bayani’s teaching of server management with dynamic construction of pre-boot images with Wilson’s teaching of network-based solution module deployment platform to incorporate removing deployed solution modules from infrastructure to keep track of the devices.
As per claim 16, this is the system claim to method claim 8. Therefore, it is rejected for the same reasons as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nair et al. (US-PGPUB-NO: 2025/0077216 A1) teaches cloud-based fleet and asset management for edge computing of machine learning and artificial intelligence workloads.
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