CTNF 18/638,395 CTNF 88803 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION Claims 1-20 are presented for examination. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1,7,10,16 and 19 are rejected under 35 U.S.C. 102(a)(1) as anticipated by Dutta (US 2015/0200867 A1) As per claim 1, Dutta teaches A computer-implemented method (CIM), comprising: maintaining an inventory of available data storage and/or data processing components in a distributed system; (Dutta [0031] Operationally, the resource allocation process 244, or, more generally, the device 200, e.g., computing device 120, first gathers information that represents the capability attributes or "resource properties" of resource devices within the network 100. This information may be represented, for example, by attribute information 170 sent from resource devices 130 in the network 100 and received by computing device 120, as depicted in FIG. 1. And [0032]… That is, each of these resources has a corresponding set of resource property values or attributes that are useful for purposes of management and provisioning service requests in a manner that is optimal to both the requestor, e.g., client device 110 (such as a customer), and a cloud service provider. As an example, these property values or capability attributes can include any one or more of the following: a central processing unit (CPU) count, a CPU speed, processor unit utilization, an available memory, hypervisor type, power consumption; amount of local storage availability, average load, a number of VM resources…) receiving a data request; (Dutta [0014] regarding a data set to be processed by a map-reduce process. The device generates a set of virtual clusters for the map-reduce process based on network bandwidths between nodes of the virtual clusters, each node of the virtual cluster corresponding to a resource device, and associates the data set with a map-reduce process task. The device then schedules the execution of the task by a node of the virtual clusters based on the network bandwidth between the node and a source node on which the data set resides). determining a combination of the available data storage and/or data processing components that is capable of satisfying the data request; (Dutta Fig 5 and [0047] In one embodiment, resource allocation process 244 includes a traditional, rack-aware scheduler 508 that assigns tasks to VMs within a map-reduce framework. Rack-aware scheduler 508 may be configured, in some cases, to base map reduce task assignments on the available computing resources at each resource device and on a naive relationship between the resource devices. For example, rack-aware scheduler 508 may coordinate the assignment of tasks to resource devices based in part on computing resources 512 received by resource allocation process 244. Computing resources 512 may include data such as the amount of memory available at a particular computing resource node and/or device, the amount of processing resources available at the computing resource node and/or device (e.g., the instructions per second available by the resource's processor, a measure of the processor's clock speed, etc) synthesizing the combination of available data storage and/or data processing components into a virtual cluster; and using the virtual cluster to satisfy the data request. (Dutta [0048] According to various embodiments, resource allocation process 244 also includes a cluster generator 504 configured to generate virtual clusters 506 based on the network resources 502 received by resource allocation process 244 and [0046] Referring now to FIG. 5, resource allocation process 244 receives network data 502 and uses network data 502 to organize computing resource nodes into virtual clusters, according to one embodiment. As shown, resource allocation process 244 may receive various data regarding the available computing resources 512 and network resources 502 of the cloud computing environment (e.g., as part of attribute information 170 received by computing device 120). Based on network resources 502 and computing resources 512, resource allocation process 244 generates resource allocations 510 and provides instructions to the corresponding resource devices regarding the allocations. In other words, resource allocations 510 may indicate the assignment of map-reduce tasks to resource devices. For example, resource allocations 510 may include an instruction sent by resource allocation process 244 to a particular resource device to spawn a VM/map-reduce task at a particular computing resource node, to process a data set from the previous phase of the computing job. [0061] At step 825, the task is scheduled for execution by one of the nodes of the virtual cluster ( e.g., by the corresponding resource node/device).). The “virtual cluster” is being interpreted as being a logical combination of storage and processing resources from one or several nodes. As per claim 7, Dutta teaches wherein the using of the virtual cluster to satisfy the data request includes: sending one or more instructions to cloud environments associated with the data storage and/or data processing components in the virtual cluster, the one or more instructions causing the cloud environments to use the data storage and/or data processing components to satisfy a respective portion of the data request. (Dutta [0001] The present disclosure relates generally to cloud computing systems, and, more particularly, to task scheduling using virtual clusters. [0024] A simple example of a multi-phase, distributed computing job is provided by the map-reduce framework. A map-reduce job has two phases of execution: a mapping phase and reducing phase. In each phase, one or more tasks are scheduled to run in parallel on different machines of the cloud computing environment. A job scheduler may be used to coordinate where and how the tasks in each phase are executed, to optimize the overall execution of the job. A specialized file system may also be used for query processing and temporary data storage (e.g., the Hadoop File System (HDFS) by the Apache Software Foundation, etc.). Such file systems are often deployed in large datacenters with powerful hardware, especially on bare metal under fast servers with high speed disks. [0037] As shown, resource allocation process 244 receives data regarding an input data set 302 for processing by computing job 300. In response, resource allocation process 244 divides the data set 302 into any number of data subsets to be processed in parallel. For example, resource allocation process 244 may divide data set 302 into n-number of data subsets to be processed by n-number of mapper tasks (e.g., a first mapper task 308 through an nth mapper task 310) executed by n-number of VMs (e.g., a first VM 304 through an nth VM 306). In one embodiment, the maximum number of tasks/VMs available to computing job 300 at any given phase of computing job 300 may be limited. For example, the number of tasks in any given phase of computing job 300 may be limited by a user's configuration, a policy of the cloud computing environment, etc and see Fig 5 and [0047] In one embodiment, resource allocation process 244 includes a traditional, rack-aware scheduler 508 that assigns tasks to VMs within a map-reduce framework. Rack-aware scheduler 508 may be configured, in some cases, to base map reduce task assignments on the available computing resources at each resource device and on a naive relationship between the resource devices. For example, rack-aware scheduler 508 may coordinate the assignment of tasks to resource devices based in part on computing resources 512 received by resource allocation process 244. Computing resources 512 may include data such as the amount of memory available at a particular computing resource node and/or device, the amount of processing resources available at the computing resource node and/or device (e.g., the instructions per second available by the resource's processor, a measure of the processor's clock speed, etc) As to claims 10 and 19, they are rejected based on the same reason as claim 1. As to claim 16, it is rejected based on the same reason as claim 7. Claim Rejections - 35 USC § 103 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim s 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 2015/0200867 A1) in view of Santos (US 2005/0228852 A1) . As per claim 2, Dutta do not teach comparing distances between each of the available data storage and/or data processing components; and selecting an arrangement of the available data storage and/or data processing components that: are capable of satisfying the data request, and have a lowest combined distance between the selected components. However, Santos teaches comparing distances between each of the available data storage and/or data processing components; and selecting an arrangement of the available data storage and/or data processing components that: are capable of satisfying the data request, and have a lowest combined distance between the selected components. (Santos [0115] The interpretation of the objective function follows. To reduce the traffic-weighted average inter-server distance, it may be equivalent to minimize the total amount of traffic flowing on all the Ethernet links. Because the total amount of traffic originating from and received by all the application components is a constant, the total amount of traffic flowing on all the server-to-switch links is a constant. Therefore, an equivalent objective function may be to minimize the total amount of inter-switch traffic, which is exactly what J3 is. The term "inter-switch traffic" refers to the traffic flowing on a link that connects two switches. [0117] FIG. 6 is a flowchart 600 illustrating steps in performing the resource assignment (block 601) in accordance with embodiments of the present invention. The "application design" block 602 may first be performed, which involves determining for each application a set of processing, communication, and storage resources required by the application. The system parameters are also determined (block 604), including available process resources, storage resources, and capacities of network data links. These resources may be considered constant or variable depending on the application (e.g., application deployment time versus automatic fail-over [0118] Once the application and network resources have been defined, the resource assignment problem can be solved (block 606). This typically involves determining an assigned subset of the available resources as a function of the application resource requirements and the available resources. The solution may involve minimizing communication delays between resources). It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Santos with the system of Dutta to compare distances between data storages. One having ordinary skill in the art would have been motivated to use Santos into the system of Dutta for the purpose of facilitating automatic allocation of resources to applications in a utility computing environment. (Santos paragraph 14) As to claim 11, it is rejected based on the same reason as claim 2 . 07-21-aia AIA Claim s 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 2015/0200867 A1) in view of Karp (US 2025/0045083 A1) . As per claim 3, Dutta do not teach wherein the synthesizing of the combination of available data storage and/or data processing components into a virtual cluster includes: using geo-distributed control plane constructs and a virtual data lake synthesizer to synthesize the combination of available data storage and/or data processing components. However, Karp teaches wherein the synthesizing of the combination of available data storage and/or data processing components into a virtual cluster includes: using geo-distributed control plane constructs and a virtual data lake synthesizer to synthesize the combination of available data storage and/or data processing components. (Karp [0034] Aspects of the present disclosure provide techniques for distributed control plane enablement in a development environment. The disclosed techniques deliver a realistic approach towards running a distributed control plane in the development environment, allowing control plane developers to run a complex distributed control plane and set up a minimal deployment locally on their development laptops for testing, validation, and experimentation. [0101] In some aspects, the computing environment 500 (e.g., as illustrated in FIG. 5 and FIG. 6) is configured as a unified architecture that allows integration and processing of data from a wide range of data sources and any type, and use it to power use case workloads (or jobs) across data lakes, data warehousing, unistore, data engineering, data science, and others. As discussed above, the computing environment 500 includes an intelligent control plane (e.g., CPS 502), an elastic data execution engine (e.g., execution platform 504), and an optimized storage layer (e.g., object storage 506). These layers are independent of each other. In some aspects, each of the cloud services layer (e.g., CPS 502) and the execution platform 504 (e.g., data plane) layer comprises a range of servers that can operate and scale independently) It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Karp with the system of Dutta to use a geo-distributed control plane. One having ordinary skill in the art would have been motivated to use Karp into the system of Dutta for the purpose of providing control plane configuration. (Karp paragraph 01) As to claim 12, it is rejected based on the same reason as claim 3 . 07-21-aia AIA Claim s 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 2015/0200867 A1) in view of Deviprasad (US 2023/0224257 A1) . As per claim 4, Dutta do not teach wherein the synthesizing of the combination of available data storage and/or data processing components into a virtual cluster includes: mapping a virtual cluster application programming interface (API) to a first controller configured to assign workloads to the data processing components in the virtual cluster. However, Deviprasad teaches wherein the synthesizing of the combination of available data storage and/or data processing components into a virtual cluster includes: mapping a virtual cluster application programming interface (API) to a first controller configured to assign workloads to the data processing components in the virtual cluster. (Deviprasad [0039] Cloud-specific orchestrator 205 may be configured with a multi-cloud adapter that stores the cloud-specific formatting, links and mappings between different cloud-specific artifacts, cloud-specific APIs, and/or other cloud-specific operations supported by each virtualized environment 103. Accordingly, cloud-specific orchestrator 205 may provide the one or more identifiers for a selected virtualized environment 103 and/or allocated set of resources to the multi-cloud adapter in order to select and/or receive the cloud-specific templates, artifacts, APIs, and/or other operations for adapting, deploying, and/or executing the virtualized instance on the set of resources allocated from the selected virtualized environment 103. [0059] Generating the cloud-agnostic virtualized instance may include converting from the first format of the standard template used to define the virtualized instance to a second format of a cloud-agnostic template that virtualized environments 103 support via intermediary layer 503. In some embodiments, virtualized environments 103 integrate intermediary layer 503 such that the cloud-agnostic format is parsed and/or processed directly within each virtualized environment 103. In some embodiments, intermediary layer 503 may be used by cloud-agnostic orchestrator 501 to provide an emulation, translation, or mapping between the cloud-agnostic formatting, artifacts, APIs, and/or other operations and corresponding cloud-specific formatting, artifacts, APIs, and/or other operations. For instance, virtualized environments 103 may directly support the vRealize Automation (“vRA”) cloud-agnostic templates and artifacts, and cloud-agnostic orchestrator 501 may convert the standard template definition of the virtualized instance to the vRA formatting, and may deploy the virtualized instance to each of the two or more virtualized environments 103 using vRA supported artifacts and vRA API calls. Accordingly, converting from the standard template definition to the cloud-agnostic definition may include issuing calls via a cloud-agnostic API to install cloud-agnostic artifacts on the allocated sets of resources). The examiner will take this to be cloud agnostic API. This is consistent with what is disclosed in the specification ([0084] Step 4 “4” further includes making the data mill available for use, e.g., such that it can be consumed by deploying target workload using resource consumption API. For example, a Kubernetes™ based API corresponding to the virtual cluster may be exposed via a KCP end-point.) It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Deviprasad with the system of Dutta to map a virtual cluster application programming interface (API). One having ordinary skill in the art would have been motivated to use Deviprasad into the system of Dutta for the purpose of dynamically generating a virtualized instance of a network function ("NF"), service (Deviprasad paragraph 12) As to claim 13, it is rejected based on the same reason as claim 4 . 07-21-aia AIA Claim s 5, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 2015/0200867 A1) in view of Deviprasad (US 2023/0224257 A1) in further view of Anjanappa (US 12,061,928 B1) . As per claim 5, Dutta and Deviprasad do not teach wherein the synthesizing of the combination of available data storage and/or data processing components into a virtual cluster includes: mapping a virtual data lake API to a second controller configured to assign workloads to the data storage components in the virtual cluster. However, Anjanappa teaches wherein the synthesizing of the combination of available data storage and/or data processing components into a virtual cluster includes: mapping a virtual data lake API to a second controller configured to assign workloads to the data storage components in the virtual cluster. (Anjanappa [col 5, lines 17-52] Referring to FIG. 1, the process 100 may include a user 102 of a device 104 that may execute cloud-based jobs for serverless applications in a cloud-based network 106. The user 102 may use the device 104 to make calls 107 (e.g., API calls) to one or more service-agnostic APIs 108 of the cloud-based network 106 (e.g., the one or more service-agnostic APIs 108 may integrate with multiple different types of services in the cloud-based network 106). The one or more service-agnostic APIs 108 may be exposed to the user 102 via the device 104. The one or more service-agnostic APIs 108 may call a job scheduling and management service 110 of the cloud-based network 106 based on the calls 107. The job scheduling and management service 110 may include serverless computing functions, and may facilitate orchestration of cloud-based jobs for serverless applications based on the calls 107. To orchestrate cloud-based jobs for serverless applications based on the calls 107, the job scheduling and management service 110 may execute one or more activities, flows, and jobs that may use one or multiple services 112 (e.g., serverless services) provided by the cloud-based network 106 (e.g., service 1, . . . , service N), and may use data stored in data storage 114 (e.g., data lakes or other data storage). The calls 107 may include any combination of data 122 (e.g., structured or unstructured), defined activities 124 (self-contained tasks and configurations defining what is to be done and how), flows 126 (e.g., a sequence of activities), and/or jobs 128 (e.g., defining which activities and/or flows to run, when, etc.). The activities 124, the flows 126, and the jobs 128 may use multiple of the services 112 (e.g., one activity may use service 1, another activity may use service 2). In this manner, the one or more service-agnostic APIs 108 may facilitate the orchestration of activities, flows, and jobs in the cloud-based network 106 regardless of which of the services 112 are used by the activities, flows, and jobs defined by the calls 107). It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Anjanappa with the system of Dutta and Deviprasad to map a virtual data lake. One having ordinary skill in the art would have been motivated to use Anjanappa into the system of Dutta and Deviprasad for the purpose of providing cloud based services (Anjanappa col 1, lines 5-10). As per claim 20, Dutta does not teach wherein the synthesizing of the combination of available data storage and/or data processing components into a virtual cluster includes: mapping a virtual cluster application programming interface (API) to a first controller configured to assign workloads to the data processing components in the virtual cluster; However, Deviprasad teaches wherein the synthesizing of the combination of available data storage and/or data processing components into a virtual cluster includes: mapping a virtual cluster application programming interface (API) to a first controller configured to assign workloads to the data processing components in the virtual cluster (Deviprasad [0039] Cloud-specific orchestrator 205 may be configured with a multi-cloud adapter that stores the cloud-specific formatting, links and mappings between different cloud-specific artifacts, cloud-specific APIs, and/or other cloud-specific operations supported by each virtualized environment 103. Accordingly, cloud-specific orchestrator 205 may provide the one or more identifiers for a selected virtualized environment 103 and/or allocated set of resources to the multi-cloud adapter in order to select and/or receive the cloud-specific templates, artifacts, APIs, and/or other operations for adapting, deploying, and/or executing the virtualized instance on the set of resources allocated from the selected virtualized environment 103. [0059] Generating the cloud-agnostic virtualized instance may include converting from the first format of the standard template used to define the virtualized instance to a second format of a cloud-agnostic template that virtualized environments 103 support via intermediary layer 503. In some embodiments, virtualized environments 103 integrate intermediary layer 503 such that the cloud-agnostic format is parsed and/or processed directly within each virtualized environment 103. In some embodiments, intermediary layer 503 may be used by cloud-agnostic orchestrator 501 to provide an emulation, translation, or mapping between the cloud-agnostic formatting, artifacts, APIs, and/or other operations and corresponding cloud-specific formatting, artifacts, APIs, and/or other operations. For instance, virtualized environments 103 may directly support the vRealize Automation (“vRA”) cloud-agnostic templates and artifacts, and cloud-agnostic orchestrator 501 may convert the standard template definition of the virtualized instance to the vRA formatting, and may deploy the virtualized instance to each of the two or more virtualized environments 103 using vRA supported artifacts and vRA API calls. Accordingly, converting from the standard template definition to the cloud-agnostic definition may include issuing calls via a cloud-agnostic API to install cloud-agnostic artifacts on the allocated sets of resources). It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Deviprasad with the system of Dutta to map a virtual cluster application programming interface (API). One having ordinary skill in the art would have been motivated to use Deviprasad into the system of Dutta for the purpose of dynamically generating a virtualized instance of a network function ("NF"), service (Deviprasad paragraph 12) Dutta and Deviprasad do not teach mapping a virtual data lake API to a second controller configured to assign workloads to the data storage components in the virtual cluster. However, Anjanappa teaches mapping a virtual data lake API to a second controller configured to assign workloads to the data storage components in the virtual cluster. (Anjanappa [col 5, lines 17-52] Referring to FIG. 1, the process 100 may include a user 102 of a device 104 that may execute cloud-based jobs for serverless applications in a cloud-based network 106. The user 102 may use the device 104 to make calls 107 (e.g., API calls) to one or more service-agnostic APIs 108 of the cloud-based network 106 (e.g., the one or more service-agnostic APIs 108 may integrate with multiple different types of services in the cloud-based network 106). The one or more service-agnostic APIs 108 may be exposed to the user 102 via the device 104. The one or more service-agnostic APIs 108 may call a job scheduling and management service 110 of the cloud-based network 106 based on the calls 107. The job scheduling and management service 110 may include serverless computing functions, and may facilitate orchestration of cloud-based jobs for serverless applications based on the calls 107. To orchestrate cloud-based jobs for serverless applications based on the calls 107, the job scheduling and management service 110 may execute one or more activities, flows, and jobs that may use one or multiple services 112 (e.g., serverless services) provided by the cloud-based network 106 (e.g., service 1, . . . , service N), and may use data stored in data storage 114 (e.g., data lakes or other data storage). The calls 107 may include any combination of data 122 (e.g., structured or unstructured), defined activities 124 (self-contained tasks and configurations defining what is to be done and how), flows 126 (e.g., a sequence of activities), and/or jobs 128 (e.g., defining which activities and/or flows to run, when, etc.). The activities 124, the flows 126, and the jobs 128 may use multiple of the services 112 (e.g., one activity may use service 1, another activity may use service 2). In this manner, the one or more service-agnostic APIs 108 may facilitate the orchestration of activities, flows, and jobs in the cloud-based network 106 regardless of which of the services 112 are used by the activities, flows, and jobs defined by the calls 107). It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Anjanappa with the system of Dutta and Deviprasad to map a virtual data lake. One having ordinary skill in the art would have been motivated to use Anjanappa into the system of Dutta and Deviprasad for the purpose of providing cloud based services (Anjanappa col 1, lines 5-10). As to claim 14, it is rejected based on the same reason as claim 5 . 07-21-aia AIA Claim s 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 2015/0200867 A1) in view of Dhakal (US 2024/0220832 A1) As per claim 6, Dutta does not teach wherein the using of the virtual cluster to satisfy the data request includes: deploying a target workload using a resource consumption application programming interface (API). However, Dhakal teaches wherein the using of the virtual cluster to satisfy the data request includes: deploying a target workload using a resource consumption application programming interface (API). (Dhakal [0046] The edge-based inference service system 78 may include an application programming interface (API) 86 that exposes the inference functionality of the edge computing resource nodes 80, regardless of the heterogeneous hardware devices 82 that enable the particular functionality of the edge computing resource nodes 80. The API 86 may be hosted on an API server configured to receive a request (e.g., API request) from a client device (e.g., application 60) and forward computing tasks to the edge computing resource nodes 80 based on the request. In certain embodiments, the application 60 may request (e.g., call) a machine learning output from the edge computing resources 74 via the API 86, without including code or software dependencies that are typically utilized to interface with the hardware devices 82. For example, the API 86 may map libraries and tools of each of the hardware devices 82 to a common set of calls (e.g., subroutines, methods, requests, endpoints, and the like). In this way, the API 86 may hide the heterogeneity of the edge computing resources 74 behind a layer of abstraction, such that the developer may utilize the hardware devices 82 via the API 86 without needing to know internal details of how the hardware devices 82 work. For example, the developer may request an inference by calling “request_inference(data[ ], model)” through the API 86. Then, the edge computing resources 74 may use the API 86 to interpret the request and distribute the workload associated with the request to one or more suitable edge computing resource nodes 80. In this way, the API 86 exposes the edge computing resources 74 to the application 60 as a service on demand). It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Dhakal with the system of Dutta to deploy a target workload. One having ordinary skill in the art would have been motivated to use Dhakal into the system of Dutta for the purpose of utilizing edge computing techniques. (Dhakal paragraph 02) As to claim 15, it is rejected based on the same reason as claim 6 . 07-21-aia AIA Claim s 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 2015/0200867 A1) in view of Thimmegowda (US 2025/0272387 A1) As per claim 8, Dutta does not teach in response to synthesizing the virtual cluster, updating the inventory to indicate the combination of data storage and/or data processing components in the virtual cluster are not available. However, Thimmegowda teaches in response to synthesizing the virtual cluster, updating the inventory to indicate the combination of data storage and/or data processing components in the virtual cluster are not available. (Thimmegowda [0220] A hosted computing environment may include one or more rapidly provisioned and released computing resources, which computing resources may include computing, networking and/or storage devices. A hosted computing environment may also be referred to as a cloud computing environment and [0321] In some cases, the resource catalog 420 includes one or more indexing node identifiers. As mentioned, the indexing system 212 can include a plurality of indexing nodes 404. In some cases, the resource catalog 420 can include a different indexing node identifier for each indexing node 404 of the indexing system 212. In some cases, for example if the resource monitor 418 or the indexing system manager 402 generates a new indexing node 404, the resource monitor 418 can update the resource catalog 420 to include an indexing node identifier associated with the new indexing node 404. In some cases, for example, if an indexing node 404 is removed from the indexing system 212 or the indexing node 404 becomes unresponsive or unavailable, the resource monitor 418 can update the resource catalog 420 to remove an indexing node identifier associated with that indexing node 404. In this way, the resource catalog 420 can include up-to-date information relating to which indexing nodes 404 are instantiated in the indexing system 212). It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Thimmegowda with the system of Dutta to update the inventory. One having ordinary skill in the art would have been motivated to use Thimmegowda into the system of Dutta for the purpose of expediting the processing of events by an information technology (IT) and security operations application on a time-limited basis. (Thimmegowda paragraph 02) As to claim 17, it is rejected based on the same reason as claim 8 . 07-21-aia AIA Claim s 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 2015/0200867 A1) in view of Sharma (US 2018/0191859 A1) As per claim 9, Dutta does not teach in response to the data request being satisfied, deallocating the combination of data storage and/or data processing components in the virtual cluster; and updating the inventory to indicate the deallocated data storage and/or data processing components are available. However, Sharma teaches in response to the data request being satisfied, deallocating the combination of data storage and/or data processing components in the virtual cluster; and updating the inventory to indicate the deallocated data storage and/or data processing components are available. (Sharma [0043] A compute resource may be characterized by the number of virtual CPUs (vCPUs), memory and storage and uses networking ports for communications. A compute resource may also characterized by other properties like availability zone, aggregate, etc. In some examples discussed herein, a host may also be referred to as a compute nod [0174] After deallocating or de-scheduling the virtual machine from the host 102a, at step S804 the Nova scheduler 1002 updates the database entry for the host 102a to reflect the resources released as a result of the deallocation/descheduling of the HA virtual machine. Additionally, the Nova scheduler 1002 updates the color value stored in the compute_nodes record for the host 102a such that, in this example, the bit value at the fourth position of the color value stored in the compute_nodes record is returned to a value of 1 while the remaining bit values are unchanged). It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Sharma with the system of Dutta to deallocate the combination of data storage and/or data processing components. One having ordinary skill in the art would have been motivated to use Sharma into the system of Dutta for the purpose of scheduling resources for cloud deployments. (Sharma paragraph 01) As to claim 18, it is rejected based on the same reason as claim 9 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 12314757 B1 – discloses using adaptive scheduling criteria to efficiently schedule resources based on dynamically changing data. In one aspect, a method includes obtaining, by the resource scheduler and from multiple resource consumers, resource requests for consuming a set of resources. For each of multiple resource usage time periods for the set of resources, a respective actual utilization rate for the set of resources during the resource usage time period is updated. For each resource usage time period, the respective actual utilization rate for the resource usage time period is compared to a target utilization rate. A determination is made, based on the comparing, that the respective actual utilization rate for a given resource usage time period is at least a threshold amount different from the target utilization rate. A scheduling criterion that conditions subsequent resource requests for the set of resources is adjusted. US 20240202221 A1 – discloses generating a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems. US 20220103518 A1 – discloses a method and scalable security service is implemented by a service provider in association with a set of cloud computing services. The method begins by the service provider provisioning a plurality of data lakes across one or more cloud computing services. A data lake is provisioned within a private data cloud of the one or more cloud computing services. To provide scalable security, the service provider configures a virtual firewall in each of two or more regions of the one or more cloud computing services. In particular, the firewall in a given region is associated with a subset of the plurality of data lakes, and wherein the subset comprises at least first and second data lakes associated to at least first and second distinct external enterprise networks. Using the virtual firewall, the service provider then enforces security requirements associated with the subset of the plurality of data lakes via the virtual firewall. US 11175944 B2 – discloses optimizing cluster-wide operations in a hyper-converged infrastructure (HCI) deployment are provided. In one set of embodiments, a computer system can receive a request to initiate a cluster-wide operation on a cluster of the HCI deployment, where the cluster includes a plurality of host systems, and where the cluster-wide operation involves a host-by-host evacuation of virtual machines (VMs) and storage components from the plurality of host systems. The computer system can further generate a set of recommendations for executing the host-by-host evacuation in a manner that minimizes the total amount of time needed to complete the cluster-wide operation. The computer system can then execute the host-by-host evacuation in accordance with the set of recommendations. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHRAN KAMRAN whose telephone number is (571)272-3401. The examiner can normally be reached on 9-5. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Blair can be reached on (571)270-1014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MEHRAN KAMRAN/ Primary Examiner, Art Unit 2196 Application/Control Number: 18/638,395 Page 2 Art Unit: 2196 Application/Control Number: 18/638,395 Page 3 Art Unit: 2196 Application/Control Number: 18/638,395 Page 4 Art Unit: 2196 Application/Control Number: 18/638,395 Page 5 Art Unit: 2196 Application/Control Number: 18/638,395 Page 6 Art Unit: 2196 Application/Control Number: 18/638,395 Page 7 Art Unit: 2196 Application/Control Number: 18/638,395 Page 8 Art Unit: 2196 Application/Control Number: 18/638,395 Page 9 Art Unit: 2196 Application/Control Number: 18/638,395 Page 10 Art Unit: 2196 Application/Control Number: 18/638,395 Page 11 Art Unit: 2196 Application/Control Number: 18/638,395 Page 12 Art Unit: 2196 Application/Control Number: 18/638,395 Page 13 Art Unit: 2196 Application/Control Number: 18/638,395 Page 14 Art Unit: 2196 Application/Control Number: 18/638,395 Page 15 Art Unit: 2196 Application/Control Number: 18/638,395 Page 16 Art Unit: 2196 Application/Control Number: 18/638,395 Page 17 Art Unit: 2196 Application/Control Number: 18/638,395 Page 18 Art Unit: 2196 Application/Control Number: 18/638,395 Page 19 Art Unit: 2196 Application/Control Number: 18/638,395 Page 20 Art Unit: 2196 Application/Control Number: 18/638,395 Page 21 Art Unit: 2196 Application/Control Number: 18/638,395 Page 22 Art Unit: 2196 Application/Control Number: 18/638,395 Page 23 Art Unit: 2196 Application/Control Number: 18/638,395 Page 24 Art Unit: 2196
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