DETAILED ACTION
Applicant’s response filed on 07/17/2026 has been entered and made of record.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claim 20 is amended.
No new claim is/are added.
Claims 1-20 are pending for examination.
Applicant’s Argument
Note: claim objection to claim 20 is withdrawn in view of the amendment of claim 20.
Applicant’s response has been fully considered. Below are applicant’s main arguments and examiner’s response to those arguments:
Applicant’s argument: (remark pages 10-14), filed on 07/17/2026, with respect to claim 1, ‘Claim 1 recites … As such, the Office Action does not sufficiently establish a motivation to combine the cited references. Based on the above remarks, claim 1 is allowable over the art of record’.
Examiner’s response: Examiner respectfully disagrees. Oliver teaches Network Provisioning Engine (Agent) to provision and allocate network resources based on a traffic forecasting model (a predictive model of traffic such as calls), resource usage forecasting model and resource allocation model (Oliver: [FIG.1], [FIG.2], [FIG.4B], [0006], [0009], [0013], [0015], [0046], [0048], [0083]). Kim teaches a server to scale resource allocation based on traffic prediction model to predict traffic demands and resource allocation adjustment model (Kim: [FIG.2], [FIG.3], [0009], [0077], [0093], [0126]). Therefore, combination of Oliver and Kim teaches a Network Provisioning Engine to provision and allocate the resource of the Network Provisioning Engine based on traffic forecasting model of the traffic demands of the Network Provisioning Engine, resource usage forecasting model and resource allocation model.
Palaniappan teaches Network Provisioning Engine Cluster (Palaniappan: [FIG.2]) and provisioning transaction request to the NPE cluster (Palaniappan: [FIG.3], [0016]). Calton teaches API transaction request rate (Calton: [0003], [0005]). Therefore, combination of Palaniappan and Calton teaches Network Provisioning Engine Cluster and the traffic demands of NPE cluster are measured by provisioning request rate.
Therefore, combination of Oliver, Kim, Palaniappan and Calton teaches the subjected matters as claimed. See the detailed Office Action bellow under 35 U.S.C. § 103 section.
Applicant’s arguments (remark pages 10-14), filed on 07/17/2026, with respect to claims 1-20 have been considered but are not convincing. The claim rejections under 35 USC § 103 are not withdrawn.
This Office Action is made Final.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6, 8-9, 10, 15 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Oliver et al. (US 20040010582 A1), hereinafter “Oliver”, in view of Kim et al. (US 20220329539 A1), hereinafter “Kim”, in view of Palaniappan et al. (US 20220201440 A1), hereinafter “Palaniappan”, and in view of Calton et al. (US 20230045994 A1), hereinafter “Calton”.
Per claim 1, 10 and 19:
Regarding claim 10, Oliver teaches ‘A wireless communication network’ (Oliver: [0054]: “The provisioning agent and/or managed agent may also be coupled to alternate network infrastructures, such as a wireless network”); ‘comprising: network provisioning circuitry to: host’ (Oliver: [0005]: “FIG. 1 is a block diagram illustrating a provisioning system”; [FIG.2]: “Processor”, “Memory”; [0006]: “FIG. 2 is an example of a typical computer system upon which components of one embodiment of the present invention can be implemented”; [0015]: “The steps of the present invention may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the steps. Alternatively, the steps may be performed by a combination of hardware and software”);
‘a network provisioning engine cluster’ (Oliver: [FIG.1]: “Provisioning Agent”, “Managed agent”). However, Oliver fails to expressly teach cluster;
‘transfer traffic data that characterizes a provisioning request rate in the network provisioning engine cluster’ (Oliver: [0013]: “performing predictive provisioning of functional packages based on offered traffic and a predictive model of the offered traffic”; [0038]: “The queuing model may be used to predict the number of calls active and/or queued at a telecommunications system”; [0046]: “the predictive models 115, 125 may consist of a queuing model”); However, Oliver fails to expressly teach provisioning request rate and cluster;
‘resource allocation circuitry to host’ (Oliver: [FIG.4B]; [0009]: “resource allocation model”; [FIG.2]: “Processor”, “Memory”; [0006]: “FIG. 2 is an example of a typical computer system upon which components of one embodiment of the present invention can be implemented”; [0015]: “The steps of the present invention may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the steps. Alternatively, the steps may be performed by a combination of hardware and software”);
‘a traffic forecasting machine learning model’ (Oliver: [0013]: “a predictive model of the offered traffic”; [0038]: “The queuing model may be used to predict the number of calls active and/or queued at a telecommunications system”);
‘a resource forecasting machine learning model trained’ (Oliver: [0046]: “a predictive model 115, 125 which models the usage of the resources provisioned in the management agents”; [0048]: “may be provided with static predictive models”, trained model; [0069]: “The expected number of resources needed by a managed agent”);
‘a resource allocation machine learning model trained’ (Oliver: [0009]: “Hidden Markov Model representation of the resource allocation model”; [0046]: “a resource allocation model that models the stream of resource allocation and deallocation”; [0083]: “the predictive model may be a static model”, trained model);
‘obtain the traffic data’ (Oliver: [FIG.1]: “Agent” obtains “Offered Traffic”);
‘provide the traffic data to the traffic forecasting machine learning model and obtain a first machine learning output that comprises a future traffic prediction for the network provisioning engine cluster’ (Oliver: [0013]: “performing predictive provisioning of functional packages based on offered traffic and a predictive model of the offered traffic. According to one embodiment of the present invention, a managed agent provides predictions regarding an anticipated need for functional package provisioning based on traffic offered to the managed agent and a predictive model of offered traffic”; [0038]: “The queuing model may be used to predict the number of calls active and/or queued at a telecommunications system”); However, Oliver fails to expressly teach traffic prediction for the network provisioning engine cluster.
However, Kim in the same field of endeavor teaches traffic prediction model to predict traffic expected to be generated in a server (Kim: [FIG.3]: “TRAFFIC PREDICTION MODEL”; [FIG.2]: “PREDICTED TRAFFIC”; [0077]: “an AI model for obtaining the predicted traffic 233 expected to be generated in the server”; [0009]: “provide a method and server capable of increasing prediction accuracy and improving or optimizing allocation of computing resources by predicting traffic to be generated in a server and adjusting allocation of the computing resources using one or more artificial intelligence (AI) model”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s teaching with that of Oliver to provide the traffic data to the traffic forecasting machine learning model and obtain a first machine learning output that comprises a future traffic prediction for the network provisioning engine in order to optimize resource allocation by predicting traffic (see reference quotes in element above).
Combination of Oliver and Kim teaches ‘provide the future traffic prediction to the resource forecasting machine learning model and obtain a second machine learning output that comprises a future hardware requirement prediction for the network provisioning engine cluster’ (Oliver: [FIG.1]; [0046]: “Each managed agent 110, 120 has access to a predictive model 115, 125 which models the usage of the resources provisioned in the management agents”; [0069]: “The expected number of resources needed by a managed agent”, resource requirement (expected number). Kim: [FIG.3]: “COMPUTING RESOURCES”; [0093]: “the computing resources 310 may be implemented by a multicore CPU”). However, combination of Oliver and Kim fails to express teach cluster;
‘provide the future hardware requirement prediction to the resource allocation machine learning model and obtain a third machine learning output that comprises a hardware allocation recommendation for the network provisioning engine cluster’ (Oliver: [0009]: “resource allocation model”; [0013]: “A provisioning agent receives the predictions and in response to the predictions instructs the managed agent to provision a new functional package”. Kim: [FIG.3]: “COMPUTING RESOURCE ALLOCAYION ADJUSTMENT MODEL”; [0126]: “a server may adjust allocation of computing resources therein using a second AI model trained to adjust the allocation of the computing resources”; [0075]: “prediction may, for example, refer to … recommendations”). However, combination of Oliver and Kim fails to express teach cluster;
‘direct the network provisioning circuitry to allocate hardware resources to the network provisioning engine cluster based on the hardware allocation recommendation’ (Oliver: [0013]: “A provisioning agent receives the predictions and in response to the predictions instructs the managed agent to provision a new functional package”. Kim: [FIG.2]: block 236: “AJUSTMENT OF ALLOCATION OF COMPUTING RESOURCES”). However, combination of Oliver and Kim fails to express teach cluster.
However, Palaniappan in the same field of endeavor teaches network provisioning engine cluster (Palaniappan: [FIG.2]: “NPE Cluster”; [0009]: “a network provisioning engine (NPE) cluster”) and provisioning request (Palaniappan: [FIG.3]: “NPE”: block 52: “receive transaction request”; [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests 18 from billing system computers 20 and translate the requests into provisioning instructions 22 that are sent to relevant network nodes”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Palaniappan’s teaching with that of combination of Oliver and Kim in order to increase reliability and scalability by clustering.
Combination of Oliver, Kim and Palaniappan does not expressly teach provisioning request rate.
However, Calton in the same field of endeavor teaches API transaction request per second (Calton: [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”; [0005]: “automatically scale and perform load balancing based on the current volume of API traffic”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Calton’s teaching with that of combination of Oliver, Kim and Palaniappan to consider provisioning request rate in order to automatically scale based on the current volume of API traffic (see reference quotes in element above).
Regarding claim 1, claim 1 recites the method implemented by the wireless communication network of claim 10 (see rejection of claim 10 above).
Regarding claim 19, claim 19 recites the memory and the method implemented by the wireless communication network of claim 10 (see rejection of claim 10 above).
Per claim 6 and 15:
Regarding claim 15, combination of Oliver, Kim, Palaniappan and Calton teaches the wireless communication network of claim 10 (discussed above).
Combination of Oliver, Kim, Palaniappan and Calton teaches ‘provide a predicted Transaction Per Second (TPS) rate generated by the traffic forecasting machine learning model to the resource forecasting machine learning model to predict a future Central Processing Unit (CPU) requirement, a future Random Access Memory (RAM) requirement, and a future disk memory requirement based on the predicted TPS rate’ (Oliver: [0013]: “performing predictive provisioning of functional packages based on offered traffic and a predictive model of the offered traffic”; [0046]: “Each managed agent 110, 120 has access to a predictive model 115, 125 which models the usage of the resources provisioned in the management agents”; [0069]: “The expected number of resources needed by a managed agent”, resources requirement (expected number). Kim: [FIG.3]: “TRAFFIC PREDICTION MODEL”; [0077]: “an AI model for obtaining the predicted traffic 233 expected to be generated in the server”; [FIG.3]: “COMPUTING RESOURCES”; [0071]: “computing resources (CPU)”; [0096]: “hard disk-type memory … random access memory (RAM)”. Palaniappan: [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”. Calton: [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”. Resource requirements (expected number) based on predicted traffic such as request TPS rate);
‘obtain a predicted CPU requirement, RAM requirement, and disk memory requirement to support the predicted TPS rate in the network provisioning engine cluster from the resource forecasting machine learning model’ (Oliver: [0009]: “resource allocation model”; [0013]: “A provisioning agent receives the predictions and in response to the predictions instructs the managed agent to provision a new functional package”. Kim: [FIG.3]: “COMPUTING RESOURCE ALLOCAYION ADJUSTMENT MODEL”; [0126]: “a server may adjust allocation of computing resources therein using a second AI model trained to adjust the allocation of the computing resources”; [FIG.1]: “VNF MANAGER”, [0005]: “NFV Management and Orchestration (MANO) (especially an orchestrator of MANO) may automatically manage allocation of computing resources depending on various factors such as network service requirements, maximum performance and capacity of computing resources, computing resource management policies of network operators, changes in the real-time status of network services and computing resources”, MANO would obtain resources for allocation. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s teaching of NFV, Palaniappan’s teaching of NPE cluster and Calton’s teaching of TPS with that of Oliver in order to shift from a hardware-centric network infrastructure to a general-purpose cloud infrastructure (Kim: [0055]: “NFV technology is a technology for implementing a software-centric network infrastructure by shifting from a hardware-centric network infrastructure to a general-purpose cloud infrastructure environment”), to increase reliability and scalability by clustering, and to automatically scale based on the current volume of API traffic (Calton: [0005]: “automatically scale and perform load balancing based on the current volume of API traffic”).
Regarding claim 6, claim 6 recites the method implemented by the wireless communication network of claim 15 (see rejection of claim 15 above).
Per claim 8 and 17:
Regarding claim 17, combination of Oliver, Kim, Palaniappan and Calton teaches the wireless communication network of claim 10 (discussed above).
Combination of Oliver, Kim and Palaniappan teaches ‘a virtualized infrastructure’ (Kim: [FIG.1]: “VNF”; [0001]: “allocation of computing resources to a plurality of virtualized network functions (VNFs) based on status information of computing resources within an associated server”; [0055]: “NFV technology is a technology for implementing a software-centric network infrastructure by shifting from a hardware-centric network infrastructure to a general-purpose cloud infrastructure environment”);
‘direct the virtualized infrastructure to assign an amount of Central Processing Units (CPUs), Random Access Memory (RAM), and disk memory to the network provisioning engine cluster based on the hardware allocation recommendation’ (Oliver: [0009]: “resource allocation model”; [0013]: “A provisioning agent receives the predictions and in response to the predictions instructs the managed agent to provision a new functional package”; [0069]: “The expected number of resources needed by a managed agent”. Kim: [FIG.3]: “COMPUTING RESOURCE ALLOCAYION ADJUSTMENT MODEL”; [0001]: “allocation of computing resources to a plurality of virtualized network functions (VNFs) based on status information of computing resources within an associated server”; [0070]: “Each of the servers 101, 102, and 103 may allocate computing resources (e.g., a CPU) required for calculation to the assigned VNFs”; [0096]: “a hard disk-type memory … random access memory (RAM)”; [0075]: “prediction may, for example, refer to … recommendations”; [0005]: “NFV Management and Orchestration (MANO) (especially an orchestrator of MANO) may automatically manage allocation of computing resources depending on various factors such as network service requirements, maximum performance and capacity of computing resources, computing resource management policies of network operators, changes in the real-time status of network services and computing resources”. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Palaniapan’s teaching of NPE cluster and Kim’s teaching of NFV with that of Oliver in order to increase reliability and scalability by clustering and to shift from a hardware-centric network infrastructure to a general-purpose cloud infrastructure (see reference quotes from Kim in element above).
Regarding claim 8, claim 8 recites the method implemented by the wireless communication network of claim 17 (see rejection of claim 17 above).
Per claim 9 and 18:
Regarding claim 18, combination of Oliver, Kim, Palaniappan and Calton teaches the wireless communication network of claim 10 (discussed above).
Combination of Oliver and Kim does not expressly teach, but Palaniappan teaches ‘wherein the wireless communication network comprises a Third Generation Partnership Project (3GPP) communication network’ (Palaniappan: [0015]: “a system 10 for provisioning new or updated features … The system 10 may include the wireless communication network 12 which may be a 4G long-term evolution (LTE) network”, 4G LTE is a 3GPP network).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Palaniappan’s teaching with that of combination of Oliver and Kim in order to provide provisioning system for LTE network (see reference quotes in element above).
Regarding claim 9, claim 9 recites the method implemented by the wireless communication network of claim 18 (see rejection of claim 18 above).
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Oliver, Kim, Palaniappan and Calton, in view of Sandgren et al. (US 20240356862 A1), hereinafter “Sandgren”.
Per claim 2 and 11:
Regarding claim 11, combination of Oliver, Kim, Palaniappan and Calton teaches the wireless communication network of claim 10 (discussed above).
Combination of Oliver and Kim teaches ‘generate first feature vectors that represent the traffic data and provide the first feature vectors to the traffic forecasting machine learning model’ (Oliver: [0029]: “feature vectors”; [0013]: “a predictive model of the offered traffic”. Kim: [FIG.3]: “TRAFFIC PREDICTION MODEL”; [0077]: “an AI model for obtaining the predicted traffic 233 expected to be generated in the server”). However, combination of Oliver, Kim, Palaniappan and Calton fails to teach feature vectors that represent the traffic data;
‘generate second feature vectors that represent the future traffic prediction and provide the second feature vectors to the resource forecasting machine learning model’ (Oliver: [0029]: “feature vectors”; [0046]: “a predictive model 115, 125 which models the usage of the resources provisioned in the management agents”; [0069]: “The expected number of resources needed by a managed agent”). However, combination of Oliver, Kim, Palaniappan and Calton fails to teach feature vectors that represent the future traffic prediction;
‘generate third feature vectors that represent the future hardware requirement prediction and provide the third feature vectors to the resource allocation machine learning model’ (Oliver: [0029]: “feature vectors”; [0009]: “resource allocation model”. Kim: [FIG.3]: “COMPUTING RESOURCE ALLOCAYION ADJUSTMENT MODEL”, “COMPUTING RESOURCES”). However, combination of Oliver, Kim, Palaniappan and Calton fails to teach feature vectors that represent the future hardware requirement prediction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s teaching of traffic prediction model with that of Oliver in order to optimize resource allocation by predicting traffic (Kim: [0009]: “optimizing allocation of computing resources by predicting traffic to be generated in a server”).
However, Sandgren in the same field of endeavor teaches feature vectors for AI model (Sandgren: [0037]: “feature extractor 236 to define feature vectors”; [0039]: “The feature vector may be passed as input into machine-learning models”; [0009]: “dynamic agent resource allocation system configured to dynamically scale access to agent resources for a communication network”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Sandgren’s teaching with that of combination of Oliver, Kim, Palaniappan and Calton to generate first feature vectors that represent the traffic data and provide the first feature vectors to the traffic forecasting machine learning model; generate second feature vectors that represent the future traffic prediction and provide the second feature vectors to the resource forecasting machine learning model; and generate third feature vectors that represent the future hardware requirement prediction and provide the third feature vectors to the resource allocation machine learning model in order to dynamically scale agent resources (see reference quotes in element above).
Regarding claim 2, claim 2 recites the method implemented by the wireless communication network of claim 11 (see rejection of claim 11 above).
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Oliver, Kim, Palaniappan and Calton, in view of Basak et al. (US 20250337808 A1), hereinafter “Basak”.
Per claim 3 and 12:
Regarding claim 12, combination of Oliver, Kim, Palaniappan and Calton teaches the wireless communication network of claim 10 (discussed above).
Combination of Oliver, Palaniapan and Calton teaches ‘wherein the traffic data indicates a number of Application Programming Interface (API) requests received by the network provisioning engine cluster over a time period’ (Oliver: [0013]: “performing predictive provisioning of functional packages based on offered traffic and a predictive model of the offered traffic”. Palaniapan: [FIG.2]: “NPE Cluster”; [0009]: “a network provisioning engine (NPE) cluster”; [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests 18 from billing system computers 20 and translate the requests into provisioning instructions 22 that are sent to relevant network nodes”. Calton: [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”; [0027]: “track the total number of transactions that have occurred during a 1 second period”);
‘an API request success rate’ (Palaniapan: [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”. Calton: [0008]: “if the “hole in the bucket” is configured to allow 100 TPS, it will allow one API request to be sent to the API service every 10 milliseconds”). However, combination of Oliver, Kim, Palaniappan and Calton fails to expressly teach success rate;
‘an API request failure rate’ (Palaniapan: [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”. Calton: [0008]: “if the “hole in the bucket” is configured to allow 100 TPS, it will allow one API request to be sent to the API service every 10 milliseconds”). However, combination of Oliver, Kim, Palaniappan and Calton fails to expressly teach failure rate;
‘a total Transaction Per Second (TPS) rate for the network provisioning engine cluster’ (Palaniapan: [0009]: “a network provisioning engine (NPE) cluster”. Calton: [0003]: “a total number of transaction requests per second that the API operation is capable of handling”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Palaniapan’s teaching of NPE cluster and Calton’s teaching of TPS with that of combination of Oliver and Kim in order to increase reliability and scalability by clustering and to automatically scale based on the current volume of API traffic (Calton: [0005]: “automatically scale and perform load balancing based on the current volume of API traffic”).
However, Basak in the same field of endeavor teaches API request success rate and failure rate (Basak: [0024]: “each of the nodes (152a-n) may include a collection of application program interfaces (APIs) that allow the user to execute requests”; [0036]: “the process calculates the rate of completion and the error rate. The rate of completion might comprise the success rate (e.g., the number of requests that were completed successfully) … If no failures occurred the error rate is zero”; [0038]: “monitor success and failure rates for another time period”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Basak’s teaching with that of combination of Oliver, Kim, Palaniappan and Calton in order to determine available capacity based on success or failure of requests (Basak: [Abstract]: “determining, at the computing cluster, an available capacity of the storage service based on success or failure of requests”).
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Oliver, Kim, Palaniappan and Calton, in view of Young et al. (US 20210160897 A1), hereinafter “Young”, and in view of Vohra et al. (US 8522241 B1), hereinafter “Vohra”.
Per claim 4 and 13:
Regarding claim 13, combination of Oliver, Kim, Palaniappan and Calton teaches the wireless communication network of claim 10 (discussed above).
Combination of Oliver, Kim, Palaniappan and Calton teaches ‘train the traffic forecasting machine learning model to predict the future traffic conditions’ (Oliver: [0048]: “train the models”; [0064]: “During the training phase, this data is collected and periodically an HMM training algorithm is performed on the data”; [0013]: “a predictive model of the offered traffic”. Kim: [FIG.3]: “TRAFFIC PREDICTION MODEL”; [FIG.2]: “PREDICTED TRAFFIC”; [0077]: “an AI model for obtaining the predicted traffic 233 expected to be generated in the server”; [0030]: “the first AI model includes a deep neural network (DNN) with a long short-term memory (LSTM) structure and is trained to obtain the predicted traffic”);
‘based on first training data that comprises Transaction Per Second (TPS) rates associated with times of day, days of week, and dates’ (Calton: [0003]: “Transactions Per Second (TPS) … number of transaction requests per second that the API operation is capable of handling”). However, combination of Oliver, Kim, Palaniappan and Calton fails to expressly teach associated with times of day, days of week, and dates;
‘train the resource forecasting machine learning model to predict the future hardware requirements’ (Oliver: [0048]: “train the models”; [0064]: “During the training phase, this data is collected and periodically an HMM training algorithm is performed on the data”; [0046]: “Each managed agent 110, 120 has access to a predictive model 115, 125 which models the usage of the resources provisioned in the management agents”; [0069]: “The expected number of resources needed by a managed agent”. Kim: [FIG.3]: “COMPUTING RESOURCES”; [0093]: “the computing resources 310 may be implemented by a multicore CPU”);
‘based on second training data that comprises correlations between TPS rates in the network provisioning engine cluster and hardware requirements to support the TPS rates’ (Kim: [FIG.3]: “COMPUTING RESOURCES”; [0093]: “the computing resources 310 may be implemented by a multicore CPU”. Palaniappan: [FIG.2]: “NPE Cluster”; [0009]: “a network provisioning engine (NPE) cluster”; [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”. Calton: [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”; [0005]: “automatically scale and perform load balancing based on the current volume of API traffic”);
‘train the resource allocation machine learning model to allocate the hardware resources’ (Oliver: [0048]: “train the models”; [0064]: “During the training phase, this data is collected and periodically an HMM training algorithm is performed on the data”; [0009]: “resource allocation model”. Kim: [FIG.3]: “COMPUTING RESOURCE ALLOCAYION ADJUSTMENT MODEL”, “COMPUTING RESOURCES”; [0093]: “the computing resources 310 may be implemented by a multicore CPU”; [0041]: “the second AI model includes a multi-agent deep reinforcement learning model and is trained to adjust the allocation of the computing resources in the server”);
‘based on third training data that comprises Central Processing Units (CPU) availability, Random Access Memory (RAM) availability, and disk memory availability’ (Oliver: [0069]: “The expected number of resources needed by a managed agent”. Kim: [0008]: “adjusting allocation of the computing resources to a plurality of virtualized network functions (VNFs) based on the predicted traffic and status information of computing resources in an associated server”; [FIG.3]: “COMPUTING RESOURCES”; [0026]: “the computing resources to a plurality of VNFs … information about an occupancy rate of the computing resources in the at least one associated server, information related to whether central processing unit (CPU) cores in the at least one associated server are turned on or off”; [0071]: “computing resources (CPU)”; [0096]: “hard disk-type memory … random access memory (RAM)”);
‘the Transaction Per Second (TPS) rates in the network provisioning engine cluster’ (Palaniappan: [FIG.2]: “NPE Cluster”; [0009]: “a network provisioning engine (NPE) cluster”; [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”. Calton: [0003]: “Transactions Per Second (TPS) … number of transaction requests per second that the API operation is capable of handling”);
‘a maximum hardware utilization’ (Oliver: [0085]: “the expected resource utilization of a media gateway exceeds the number of actual resources available”, maximum hardware utilization).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s teaching of traffic prediction model, Palaniappan’s teaching of NPE cluster and Calton’s teaching of TPS with that of Oliver in order to optimize resource allocation by predicting traffic (Kim: [0009]: “optimizing allocation of computing resources by predicting traffic to be generated in a server”), to increase reliability and scalability by clustering, and to automatically scale based on the current volume of API traffic (Calton: [0005]: “automatically scale and perform load balancing based on the current volume of API traffic”).
However, Young in the same field of endeavor teaches train model using training data associated with time of day, day of week and date (Young: [0028]: “model may be trained using, as training data, historical weight values assigned to the plurality of network slices in the plurality of coverage areas, parameters associated with the historical weight values (e.g., traffic on the plurality of network slices, a time of day, a day of the week, a date”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Young’s teaching with that of combination of Oliver, Kim, Palaniappan and Calton to train model with training data that comprises Transaction Per Second (TPS) rates associated with times of day, days of week, and dates in order to enable efficient prediction with consideration of time dependent traffic request.
Combination of Oliver, Kim, Palaniappan, Calton and Young does not expressly teach, but Vohra in the same field of endeavor teach ‘a minimum hardware utilization threshold’ (Vohra: [Col 13]: “hardware resource utilization of the particular processing computer 105a-n is above a first minimum system threshold (e.g., 25% total system resource utilization)”; [Col 12]: “resource allocation module 141 may identify the processing stages to be considered for rebalancing of processing power or allocated hardware resources”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vohra’s teaching with that of combination of Oliver, Kim, Palaniappan, Calton and Young in order to rebalance hardware resources (see reference quotes in element above).
Regarding claim 4, claim 4 recites the method implemented by the wireless communication network of claim 13 (see rejection of claim 13 above).
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Oliver, Kim, Palaniappan and Calton, in view of Kaluri et al. (US 20250191086 A1), hereinafter “Kaluri”.
Per claim 5 and 14:
Regarding claim 14, combination of Oliver, Kim, Palaniappan and Calton teaches the wireless communication network of claim 10 (discussed above).
Combination of Oliver, Kim, Palaniappan and Calton teaches ‘provide the traffic data to the traffic forecasting machine learning model to predict a future Transaction Per Second (TPS) rate for the network provisioning engine cluster based on a current TPS rate, current time, current day of week, and current date’ (Oliver: [FIG.1]: provide “Offered Traffic” to “Agent” (“Predictive Model 115”); [0013]: “performing predictive provisioning of functional packages based on offered traffic and a predictive model of the offered traffic”. Kim: [FIG.3]: “TRAFFIC PREDICTION MODEL”; [0077]: “an AI model for obtaining the predicted traffic 233 expected to be generated in the server”; [0079]: “the predicted traffic 233 may be obtained based on … the amount of work currently waiting in a buffer to be processed”, based on current traffic. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”; [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”. Calton: [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”; [0005]: “based on the current volume of API traffic”; [0010]; “takes current API traffic into consideration”; [0030]: “the current TPS”; based on current TPS rate). However, combination of Oliver, Kim, Palaniappan and Calton fails to expressly teach based on current time, current day of week, and current date;
‘obtain a TPS rate prediction in the network provisioning engine cluster from the traffic forecasting machine learning model’ (Oliver: [0013]: “a predictive model of the offered traffic”. Kim: [FIG.2]: “PREDICTED TRAFFIC”; [0077]: “an AI model for obtaining the predicted traffic 233 expected to be generated in the server”. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”; [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”; [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”; obtain predicted traffic such as TPS rate).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s teaching of traffic prediction model, Palaniappan’s teaching of NPE cluster and Calton’s teaching of TPS with that of Oliver in order to optimize resource allocation by predicting traffic (Kim: [0009]: “optimizing allocation of computing resources by predicting traffic to be generated in a server”), to increase reliability and scalability by clustering, and to automatically scale based on the current volume of API traffic (Calton: [0005]: “automatically scale and perform load balancing based on the current volume of API traffic”).
However, Kaluri in the same field of endeavor teaches forecast based on current time, current day of week, and current date (Kaluri: [Claim 20]: “the forecast generated by the machine learning model is further based on … a current day of a week; a current date; a current time”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kaluri’s teaching with that of combination of Oliver, Kim, Palaniappan and Calton in order to enable efficient prediction with consideration of time dependent traffic request.
Regarding claim 5, claim 5 recites the method implemented by the wireless communication network of claim 14 (see rejection of claim 14 above).
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Oliver, Kim, Palaniappan and Calton, in view of Vohra.
Per claim 7 and 16:
Regarding claim 16, combination of Oliver, Kim, Palaniappan and Calton teaches the wireless communication network of claim 10 (discussed above).
Combination of Oliver, Kim, Palaniappan and Calton teaches ‘provide a predicted Central Processing Unit (CPU) requirement, Random Access Memory (RAM) requirement, and disk memory requirement generated by the resource forecasting machine learning model to the resource allocation machine learning model to select a CPU allocation, a RAM allocation, and a disk memory allocation for the network provisioning engine cluster based on the predicted CPU, RAM, and disk memory requirements’ (Oliver: [0009]: “resource allocation model”; [0013]: “A provisioning agent receives the predictions and in response to the predictions instructs the managed agent to provision a new functional package”; [0069]: “The expected number of resources needed by a managed agent”, resources requirement (expected number). Kim: [FIG.3]: “COMPUTING RESOURCE ALLOCAYION ADJUSTMENT MODEL”; [0126]: “a server may adjust allocation of computing resources therein using a second AI model trained to adjust the allocation of the computing resources”; [0071]: “computing resources (CPU)”; [0096]: “hard disk-type memory … random access memory (RAM)”;. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”);
‘current CPU, RAM, and disk memory availabilities’ (Kim: [0008]: “adjusting allocation of the computing resources to a plurality of virtualized network functions (VNFs) based on the predicted traffic and status information of computing resources in an associated server”; [0026]: “the computing resources to a plurality of VNFs … information about an occupancy rate of the computing resources in the at least one associated server, information related to whether central processing unit (CPU) cores in the at least one associated server are turned on or off”; [0071]: “computing resources (CPU)”; [0096]: “hard disk-type memory … random access memory (RAM)”)
‘current Transaction Per Second (TPS) rate’ (Kim: [0079]: “the predicted traffic 233 may be obtained based on … the amount of work currently waiting in a buffer to be processed”, based on current traffic. Calton: [0003]: “Transactions Per Second (TPS) … number of transaction requests per second that the API operation is capable of handling”; [0030]: “the current TPS”);
‘a maximum hardware utilization’ (Oliver: [0085]: “the expected resource utilization of a media gateway exceeds the number of actual resources available”, maximum hardware utilization);
‘obtain the CPU allocation, the RAM allocation, and the disk memory allocation for the network provisioning engine cluster from the resource allocation machine learning model’ (Kim: [FIG.1]: “VNF MANAGER”, [0005]: “NFV Management and Orchestration (MANO) (especially an orchestrator of MANO) may automatically manage allocation of computing resources depending on various factors such as network service requirements, maximum performance and capacity of computing resources, computing resource management policies of network operators, changes in the real-time status of network services and computing resources”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s teaching of NFV, Palaniappan’s teaching of NPE cluster and Calton’s teaching of TPS with that of Oliver in order to shift from a hardware-centric network infrastructure to a general-purpose cloud infrastructure (Kim: [0055]: “NFV technology is a technology for implementing a software-centric network infrastructure by shifting from a hardware-centric network infrastructure to a general-purpose cloud infrastructure environment”), to increase reliability and scalability by clustering, and to automatically scale based on the current volume of API traffic (Calton: [0005]: “automatically scale and perform load balancing based on the current volume of API traffic”).
Combination of Oliver, Kim, Palaniappan and Calton does not expressly teach, but Vohra in the same field of endeavor teach ‘a minimum hardware utilization threshold’ (Vohra: [Col 13]: “hardware resource utilization of the particular processing computer 105a-n is above a first minimum system threshold (e.g., 25% total system resource utilization)”; [Col 12]: “resource allocation module 141 may identify the processing stages to be considered for rebalancing of processing power or allocated hardware resources”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vohra’s teaching with that of combination of Oliver, Kim, Palaniappan, Calton and Young in order to rebalance hardware resources (see reference quotes in element above).
Regarding claim 7, claim 7 recites the method implemented by the wireless communication network of claim 16 (see rejection of claim 16 above).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over combination of Oliver, Kim, Palaniappan and Calton as applied to claim 19 above, in view of Kaluri, and in view of Vohra.
Regarding claim 20, combination of Oliver, Kim, Palaniappan and Calton teaches the computer readable storage media of claim 19 (discussed above).
Combination of Oliver, Kim, Palaniappan and Calton teaches ‘the traffic data comprises a current Transaction Per Second (TPS) rate in the network provisioning engine cluster’ (Kim: [0079]: “the predicted traffic 233 may be obtained based on … the amount of work currently waiting in a buffer to be processed”, based on current traffic. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”; [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”. Calton: [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”; [0005]: “automatically scale and perform load balancing based on the current volume of API traffic”; [0030]: “the current TPS”; current traffic such as API request TPS rate);
‘providing the traffic data to the traffic forecasting machine learning model comprises providing the current TPS rate to the traffic forecasting machine learning model to predict a future Transaction Per Second (TPS) rate for the network provisioning engine cluster based on the current TPS rate, current time, current day of week, and current date’ (Oliver: [FIG.1]: provide “Offered Traffic” to “Agent” (“Predictive Model 115”); [0013]: “performing predictive provisioning of functional packages based on offered traffic and a predictive model of the offered traffic”. Kim: [FIG.3]: “TRAFFIC PREDICTION MODEL”; [0077]: “an AI model for obtaining the predicted traffic 233 expected to be generated in the server”; [0079]: “the predicted traffic 233 may be obtained based on … the amount of work currently waiting in a buffer to be processed”, based on current traffic. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”; [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”. Calton: [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”; [0005]: “based on the current volume of API traffic”; [0010]; “takes current API traffic into consideration”; [0030]: “the current TPS”; based on current TPS rate). However, combination of Oliver, Kim, Palaniappan and Calton fails to expressly teach based on current time, current day of week, and current date;
‘obtaining the first machine learning output comprises obtaining a predicted TPS rate in the network provisioning engine cluster’ (Oliver: [0013]: “a predictive model of the offered traffic”. Kim: [FIG.2]: “PREDICTED TRAFFIC”; [0077]: “an AI model for obtaining the predicted traffic 233 expected to be generated in the server”. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”; [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”; [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”; obtain predicted traffic such as TPS rate);
‘providing the future traffic prediction to the resource forecasting machine learning model comprises providing the predicted TPS rate to the resource forecasting machine learning model to predict a future Central Processing Unit (CPU) requirement, a future Random Access Memory (RAM) requirement, and a future disk memory requirement based on the predicted TPS rate’ (Oliver: [0013]: “performing predictive provisioning of functional packages based on offered traffic and a predictive model of the offered traffic”; [0046]: “Each managed agent 110, 120 has access to a predictive model 115, 125 which models the usage of the resources provisioned in the management agents”; [0069]: “The expected number of resources needed by a managed agent”, resources requirement (expected number). Kim: [FIG.3]: “TRAFFIC PREDICTION MODEL”; [0077]: “an AI model for obtaining the predicted traffic 233 expected to be generated in the server”; [FIG.3]: “COMPUTING RESOURCES”; [0071]: “computing resources (CPU)”; [0096]: “hard disk-type memory … random access memory (RAM)”. Palaniappan: [0016]: “network provisioning engines (NPEs) 16 that receive transaction requests”. Calton: [0003]: “APIs are limited by a total number of transaction requests per second that the API operation is capable of handling”. Resource requirements (expected) based on predicted traffic such as request TPS rate);
‘obtaining the second machine learning output comprises obtaining a predicted CPU requirement, RAM requirement, and disk memory requirement to support the predicted TPS rate’ (Oliver: [0009]: “resource allocation model”; [0013]: “A provisioning agent receives the predictions and in response to the predictions instructs the managed agent to provision a new functional package”. Kim: [FIG.3]: “COMPUTING RESOURCE ALLOCAYION ADJUSTMENT MODEL”; [0126]: “a server may adjust allocation of computing resources therein using a second AI model trained to adjust the allocation of the computing resources”; [FIG.1]: “VNF MANAGER”, [0005]: “NFV Management and Orchestration (MANO) (especially an orchestrator of MANO) may automatically manage allocation of computing resources depending on various factors such as network service requirements, maximum performance and capacity of computing resources, computing resource management policies of network operators, changes in the real-time status of network services and computing resources”, MANO would obtain resources for allocation. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”);
‘providing the future hardware requirement prediction to the resource allocation machine learning model comprises providing the predicted CPU, RAM, and disk memory requirements to the resource allocation machine learning model to select a CPU allocation, a RAM allocation, and a disk memory allocation for the network provisioning engine cluster based on the predicted CPU, RAM, and disk memory requirements’ (Oliver: [0009]: “resource allocation model”; [0013]: “A provisioning agent receives the predictions and in response to the predictions instructs the managed agent to provision a new functional package”; [0069]: “The expected number of resources needed by a managed agent”, resources requirement (expected number). Kim: [FIG.3]: “COMPUTING RESOURCE ALLOCAYION ADJUSTMENT MODEL”; [0126]: “a server may adjust allocation of computing resources therein using a second AI model trained to adjust the allocation of the computing resources”; [0071]: “computing resources (CPU)”; [0096]: “hard disk-type memory … random access memory (RAM)”;. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”);
‘current CPU, RAM, and disk memory availabilities’ (Kim: [0008]: “adjusting allocation of the computing resources to a plurality of virtualized network functions (VNFs) based on the predicted traffic and status information of computing resources in an associated server”; [0026]: “the computing resources to a plurality of VNFs … information about an occupancy rate of the computing resources in the at least one associated server, information related to whether central processing unit (CPU) cores in the at least one associated server are turned on or off”; [0071]: “computing resources (CPU)”; [0096]: “hard disk-type memory … random access memory (RAM)”));
‘the current TPS rate’ (Kim: [0079]: “the predicted traffic 233 may be obtained based on … the amount of work currently waiting in a buffer to be processed”, based on current traffic. Calton: [0003]: “Transactions Per Second (TPS) … number of transaction requests per second that the API operation is capable of handling”; [0030]: “the current TPS”);
‘a maximum hardware utilization’ (Oliver: [0085]: “the expected resource utilization of a media gateway exceeds the number of actual resources available”, maximum hardware utilization);
‘obtaining the third machine learning output comprises obtaining the CPU allocation, the RAM allocation, and the disk memory allocation for the network provisioning engine cluster; and allocating the hardware resources to the network provisioning engine cluster comprises directing the network provisioning engine cluster to utilize the CPU allocation, the RAM allocation, and the disk memory allocation ’ (Oliver: [0009]: “resource allocation model”; [0013]: “A provisioning agent receives the predictions and in response to the predictions instructs the managed agent to provision a new functional package”. Kim: [FIG.3]: “COMPUTING RESOURCE ALLOCAYION ADJUSTMENT MODEL”; [0001]: “allocation of computing resources to a plurality of virtualized network functions (VNFs) based on status information of computing resources within an associated server”; [0070]: “Each of the servers 101, 102, and 103 may allocate computing resources (e.g., a CPU) required for calculation to the assigned VNFs”; [0096]: “a hard disk-type memory … random access memory (RAM)”; [FIG.1]: “VNF MANAGER”, [0005]: “NFV Management and Orchestration (MANO) (especially an orchestrator of MANO) may automatically manage allocation of computing resources depending on various factors such as network service requirements, maximum performance and capacity of computing resources, computing resource management policies of network operators, changes in the real-time status of network services and computing resources”. Palaniappan: [0009]: “a network provisioning engine (NPE) cluster”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s teaching of NFV, Palaniappan’s teaching of NPE cluster and Calton’s teaching of TPS with that of Oliver in order to shift from a hardware-centric network infrastructure to a general-purpose cloud infrastructure (Kim: [0055]: “NFV technology is a technology for implementing a software-centric network infrastructure by shifting from a hardware-centric network infrastructure to a general-purpose cloud infrastructure environment”), to increase reliability and scalability by clustering, and to automatically scale based on the current volume of API traffic (Calton: [0005]: “automatically scale and perform load balancing based on the current volume of API traffic”).
However, Kaluri in the same field of endeavor teaches forecast based on current time, current day of week, and current date (Kaluri: [Claim 20]: “the forecast generated by the machine learning model is further based on … a current day of a week; a current date; a current time”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kaluri’s teaching with that of combination of Oliver, Kim, Palaniappan and Calton in order to enable efficient prediction with consideration of time dependent traffic request.
Combination of Oliver, Kim, Palaniappan, Calton and Young does not expressly teach, but Vohra in the same field of endeavor teach ‘a minimum hardware utilization threshold’ (Vohra: [Col 13]: “hardware resource utilization of the particular processing computer 105a-n is above a first minimum system threshold (e.g., 25% total system resource utilization)”; [Col 12]: “resource allocation module 141 may identify the processing stages to be considered for rebalancing of processing power or allocated hardware resources”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vohra’s teaching with that of combination of Oliver, Kim, Palaniappan, Calton and Young in order to rebalance hardware resources (see reference quotes in element above).
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.
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/G.F./Examiner, Art Unit 2462
/YEMANE MESFIN/Supervisory Patent Examiner, Art Unit 2462
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