CTNF 18/571,092 CTNF 82189 DETAILED ACTION Statement of claims The present application includes: Claims 20-30 are cancelled. Claims 1-20 are pending in the application. Claims 1-20are being considered on the merits. Information Disclosure Statement 06-52 The information disclosure statement (IDS) submitted on 08/15/2024 and 12/15/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Priority Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed on 12/23/2021. 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. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. Claims 16 and 19, the limitations “means for interfacing paylods to …” , “means for accelerating to…” , “means for queueing payloads to …” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses “means” coupled with functional languages “adapted to” without reciting sufficient structure to achieve the function. Note that “technical” does not modify “means” with sufficient structure. A review of the specification does not disclose equivalent structure to perform recited functions for the 35 U.S.C. 112(f) or pre- AIA 35 U.S.C. 112, sixth paragraph limitation. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 AIA Claim 16 and 19 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. 07-34-23 Claim elements “means for interfacing paylods to …” , “means for accelerating to…” , “means for queueing payloads to …” are limitations that invokes 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Thus, the metes and bounds of claims are vague and indefinite. Applicant may: (a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph; (b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)). If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either: (a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181. Dependent claims 17-20 are rejected inherently have the same deficiency as stated in claim 16 above. Appropriate correction is required. 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, 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) 1-2, 8-10 and 16-17 are r ejected under 35 U.S.C. 103 as being unpatentable over G uim Bernat et al. (US 2021/0328886, Guim hereinafter) in view of Jaafar Bendriss, “Forecasting and Anticipating SLO Breaches in Programmable Networks”, 2017, Jaafar hereinafter) . A s to claim 1, Guim an apparatus to select workload resources (e.g., see FIG. 13, para 28, “ the amount of resources needed for network services, through the distribution of more resources “, “edge computing attempts to bring the compute resources to the workload data where appropriate, or, bring the workload data to the compute resources ”) , the apparatus comprising: interface circuitry to communicate with an edge device ( see FIG. 13); and processor circuitry (see FIG. 13) including one or more of: at least one of : a central processing unit, a graphic processing unit, or a digital signal processor, the at least one of the central processing unit, the graphic processing unit, or the digital signal processor having control circuitry to control data movement within the processor circuitry, arithmetic and logic circuitry to perform one or more first operations corresponding to instructions, and one or more registers to store a result of the one or more first operations ( e.g., see FIG. 13, par 26, “ IPU sidecar circuitry “, see FIG. 7, para 72, “ IPU sidecar circuitry 714 transmits data corresponding to the service and/or instance properties at different nodes (e.g., edge appliance) of the corresponding platform (e.g., to discover and/or register the service and/or instance) ” ) , the instructions in the apparatus; a Field Programmable Gate Array (FPGA), the FPGA including logic gate circuitry, a plurality of configurable interconnections, and storage circuitry, the logic gate circuitry and interconnections to perform one or more second operations, the storage circuitry to store a result of the one or more second operations (para 81, “Field Programmable Gate Array (FPGA)”) ; or Application Specific Integrate Circuitry (ASIC) including logic gate circuitry to perform one or more third operations (e.g., para 81, “ an FPGA, an Application Specific Integrated Circuit (ASIC)”) ; the processor circuitry to perform at least one of the first operations, the second operations, or the third operations to instantiate payload interface circuitry to extract workload objective information and service level agreement (SLA) criteria corresponding to a workload (e.g., para 25, “T he service level agreement (SLA) requirements and/or service level objectives (SLO) are requirements for performing the task . “an SLA/SLO requirement(s) of a service request from an application may correspond to bandwidth requirements, latency requirements, jitter requirements, number/percentage of packet drops, number of images processed per second, etc.” and “ receiving key performance indicator (KPI) targets based on SLA contracts”, “resources and for how long in order to complete th e workload and satisfy the SLA .”, “infrastructure sidecar circuitry 720 can make load balancing decisions, scaling decisions, etc., based on the up-to-date (e.g., live) telemetry data corresponding to the infrastructure”, “ the services are being executed according to service objectives” In para 49 and 72 ) ; and acceleration circuitry (e.g., see FIG. 7, para 71, “the IPUs 710a-710d integrate with accelerators” and see FIG. 13, “1364” ) to: select a processing model based on (a) the workload objective information and (b) feedback corresponding to workload performance metrics (e.g., para 186, “ compare first telemetry data corresponding to the active service instance to a service quality metric, select a second node to service the service request based on the comparison and further telemetry data, and cause transmission of the service request to the second node” and “the IPUs 710a-710d integrate with accelerators”, “ assist with telemetry processing”, “adding programmable acceleration in hardware”, “ provide real-time telemetry data of the capacity of the service 706 to absorb new requests.” , “ IPU sidecar circuitry 714 transmits intercepted service requests “ , “he real-time telemetry data includes utilization and response time metrics from the infrastructure telemetry indicative of the processing capacity, response time, and network congestions “ in see para 71 and 72, 75 ). Guim does not teach a pre-processing model, the workload performance metrics of at least one prior workload execution iteration, execute the pre-processing model to calculate a complexity metric corresponding to the workload; and select candidate resources based on the complexity metric. Jaafar teaches pre-processing mode based on (a) the workload objective information and (b) feedback corresponding to workload performance metrics of at least one prior workload execution iteration, execute the pre-processing model to calculate a complexity metric corresponding to the workload (e.g., see abstract, “ deployment of networked resources and services.”, “ efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become essential to fulfill established service contracts with clients page 129 and 130, “ Data Preparation and Pre-processing”, “ an SLO as a range of values (i.e.lower or upper thresholds) that guarantee a certain level of quality with respect to a specific service and to a specific set of variables (or aggregates, i.e. mean value or percentiles). “, “After preparing the data we receive 10 native metrics”, “CSE/Forecasting” , , “SLO repositories contain the low-level/metriclevel definition of SLOs targeted “, “The ANNs are generally referred to as black boxes. They take inputs, perform complex calculations , and output the desired results” for “ 𝑆𝐿𝑂 2 is the combination of response time with respect to the workload.” , “For each workload interval, a specific threshold is set on the response time: If the workload is between 0 and 20% the minimum response time is 20 𝑚𝑠 . If the workload is between 20% and 70% then the threshold is set at 50 𝑚𝑠 .” In page 128); and select candidate resources based on the complexity metric (e.g., para 130, “Artificial Neural Network”, “The ANNs are generally referred to as black boxes. They take inputs, perform c omplex calculations, and output the desired results .” And “The evaluation of the proposed Framework is performed in two distinct phases. (1) Evaluation of the forecasting accuracy, and (2) Evaluation of SLOV identification. Each phase is mapped to an evaluation metric. In (1) the evaluation is based on the difference between the predicted value 𝑣 in a time 𝑡 and its expected value at the same time. The more this value is low the better is the result. “ in page 132. Thus, the “output the desired results” include select candidate resources based on the complexity metric ). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Guim with those of Jaafar because both references are directed to related systems addressing similar technical problems within the same field and seek to improve system performance, reliability, and efficiency. Guim et al. disclose payload interface circuitry to extract workload objective information and service level agreement (SLA) criteria corresponding to a workload; and acceleration circuitry to: select a processing model based on (a) the workload objective information and (b) feedback while Jaafar et al. teaches a pre-processing model execute the pre-processing model to calculate a complexity metric corresponding to the workload; and select candidate resources based on the complexity metric. Incorporating the teachings of Jaafar et al. into the system of Guim et al. would have been a predictable and logical modification, yielding improved operational robustness and efficiency without requiring undue experimentation. Such a combination would merely involve the substitution or integration of known elements performing their established functions, as taught by Jaafar et al., into the system of Guim et al., consistent with design incentives and market demands for improved performance and scalability. Moreover, Jaafar et al. explicitly recognize benefits to “complete automation of SLA management that combines all the necessary blocks of cognitive management and proactive provisioning” (see Jaafar, page 128) and to provide “ a sophisticated policy engine (CSE SLA enforcer) that pushes proactive management actions according to different policies, avoiding SLO breaches before they occur” (see Jaafar, conclusion) . —that would naturally be desirable in the system of Guim et al. Accordingly, to one of ordinary skill in the art would have had a reasonable expectation of success in combining Guim et al. with Jaafar et al., and the combination represents no more than the predictable use of prior art elements according to their known functions. As to claim 2, Guim teaches wherein the processor circuitry is to perform at least one of the first operations, the second operations, or the third operations to instantiate the payload interface circuitry to identify the extracted workload information as at least one of image processing, facial identification or object detection (e.g., para 25, “ number of images processed per second”) . As to claim 8, Guim teaches wherein at least one of the first operations, the second operations, or the third operations are instantiated on at least one of a switching device (e.g., para 25, “Auto-scaling includes activating new devices (e.g., …hardware switches, etc.)”) , a smart network interface card, or an infrastructure processing unit. As to claim 9, see rejection of claim 1 above. Guim teaches further at least one non-transitory computer readable medium comprising instructions that, when executed, cause processor circuitry to at least (e.g., para 89, “ a non-transitory computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. including the software and/or firmware.”) : parse workload objective information and service level agreement (SLA) criteria corresponding to a workload (see rejection of claim 1 above, see FIG. 13, para 186, wherein “ to process service requests, the apparatus including interface circuitry to access a service request intercepted by from an infrastructure processing unit, the service request corresponding to a first node, instructions in the apparatus, and infrastructure sidecar circuitry to execute the instructions to identify an active service instance corresponding to the service request, compare first telemetry data corresponding to the active service instance to a service quality metric, select a second node to service the service request based on the comparison and further telemetry data, and cause transmission of the service request to the second node. Thus , parse workload objective information and service level agreement (SLA) criteria corresponding to a workload) . As to claim 10 see rejection of claim 2 above. As to claim 16, see rejection of claims 1and 9 above . Guim teaches further an apparatus to invoke workload resources, the apparatus comprising: means for interfacing paylods to extract workload objective information and service level agreement (SLA) criteria corresponding to a workload; and means for accelerating ( see rejection of claim 1 above). As to claim 17, see rejection of claim 2 above . 07-22-aia AIA Claim (s) 3-7, 11-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Guim Bernat et al. (US 2021/0328886, Guim hereinafter) in view of Jaafar Bendriss, et al. “Forecasting and Anticipating SLO Breaches in Programmable Networks”, 2017, Jaafar hereinafter) , as applied to claim s 1, 9 and 16 above, and further in view of László Toka et al. “ Machine Learning-Based Scaling Management for Kubernetes Edge Clusters”, March 2021, László hereinafter) . As to claim 3, Guim and Jaafar do not teach wherein the processor circuitry is to perform at least one of the first operations, the second operations, or the third operations to instantiate the acceleration circuitry to apply a Markovian model to generate the feedback corresponding to the workload performance metrics. However, László teaches to apply a Markovian model to generate the feedback corresponding to the workload performance metrics (e.g., page 960, 961, wherein “ statistical learning algorithms to achieve online predictions of dynamic application resource requirements. Their forecast solution was based either on signatures of historic resource usage, or on a discrete-time Markov chain with a finite number of states”, “HPA models with Markovian assumption regarding the incoming request rate”, therefore to apply a Markovian model to generate the feedback corresponding to the workload performance metrics would have been inherent). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Guim and Jaafar by adopting the teachings of László to provide “ quality of fitting provisioned resources to service demand.” (see László, Abstract) . As top claim 4., Guim and Jaafar do not teach wherein the processor circuitry is to perform at least one of the first operations, the second operations, or the third operations to instantiate payload queue circuitry to add a request for execution of the workload to a switch queue. However, László teaches to instantiate payload queue circuitry to add a request for execution of the workload to a switch queue (e.g., see page 963, “model is similar to the one presented in [13], where the scaling decision was based on the ratio of customers waiting and on the number of servers. Although instead of the number of queued requests, we use the number of served requests in the scaling decision, because the number of served requests and the CPU usage are tightly coupled, and the latter is the basis of scaling decisions of HPA.”) . Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Guim and Jaafar by adopting the teachings of László to provide “ quality of fitting provisioned resources to service demand.” (see László, Abstract) . As to claim 5, Guim teaches wherein the payload queue circuitry is to cause an execution trigger based on the SLA criteria (e.g., para 49, “receiving key performance indicator (KPI) targets based on SLA contracts. The pod controller determines which container requires which resources and for how long in order to complete the workload and satisfy the SLA.”) . As to claim 6, Guim and Jaafar do not teach wherein the processor circuitry is to perform at least one of the first operations, the second operations, or the third operations to instantiate telemetry analyzation circuitry to invoke a handshake with first resources in response to the execution trigger. However, László teaches to invoke a handshake with first resources in response to the execution trigger (e.g., page 962, “A. HPA Operation in Details”, “HPA is influenced by several parameters” , “Pod synchronization period and scaling tolerance” . Thus, the “synchronization” must include a handshake, therefore to invoke a handshake with first resources in response to the execution trigger would have been inherent). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Guim and Jaafar by adopting the teachings of László to provide “ quality of fitting provisioned resources to service demand.” (see László, Abstract) . As to claim 7, Guim and Jaafar do not teach wherein the telemetry analyzation circuitry is to invoke second resources when the handshake with the first resources is unsuccessful. However, László teaches to invoke second resources when the handshake with the first resources is unsuccessful (e.g., age 962, “A. HPA Operation in Details”, “HPA is influenced by several parameters” , “Pod synchronization period and scaling tolerance” . Thus, the “synchronization” must include a handshake coupled with “scaling tolerance”, therefore to invoke second resources when the handshake with the first resources is unsuccessful would have been inherent). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Guim and Jaafar by adopting the teachings of László to provide “ quality of fitting provisioned resources to service demand.” (see László, Abstract) . As to claim 11-15 and 18-20, see rejection of claim 3-7 above . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Signh et al. (US 2021/0224178) discloses information (workload, performance, and configuration) is obtained about identified sub-systems (a target component plus other components that influence its performance). The identified sub-systems are clustered into workload clusters and also into performance clusters, where identified sub-systems of particular workload clusters have similar workload measurements, and identified sub-systems of particular performance clusters have similar performance metrics. The techniques herein then determine a given mapped performance cluster for a given workload cluster that corresponds to a best set of performance metrics from among all performance clusters mapped to the given workload cluster. A configuration change recommendation is then generated for a given identified sub-system of the given workload cluster that is not within the given mapped performance cluster corresponding to the best set of performance metrics based on configuration information about each identified sub-system within the given mapped performance cluster that corresponds to the best set of performance metrics. Abbas (US 2021/0160162) discloses receiving resource usage data for an instance of an application. The instance is executed on a cloud computing infrastructure, and the resource usage relates to one or more resources of the cloud computing infrastructure. The approach also involves processing the resource usage data to calculate average and maximum usage values of the resources of the instance. The approach further involves calculating an upper usage bound and a lower usage bound between which the instance is estimated to operate based on the average and maximum usage values of the resources. The approach further involves determining a recommended instance type for instantiating the application in the cloud computing infrastructure based on the upper usage bound and the lower usage bound. The approach further involves providing the recommended instance type as an output. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDOU K SEYE whose telephone number is (571)270-1062. The examiner can normally be reached M-F 9-5:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at 5712724215. 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. /ABDOU K SEYE/Examiner, Art Unit 2198 /PIERRE VITAL/Supervisory Patent Examiner, Art Unit 2198 Application/Control Number: 18/571,092 Page 2 Art Unit: 2198 Application/Control Number: 18/571,092 Page 3 Art Unit: 2198 Application/Control Number: 18/571,092 Page 4 Art Unit: 2198 Application/Control Number: 18/571,092 Page 5 Art Unit: 2198 Application/Control Number: 18/571,092 Page 6 Art Unit: 2198 Application/Control Number: 18/571,092 Page 7 Art Unit: 2198 Application/Control Number: 18/571,092 Page 8 Art Unit: 2198 Application/Control Number: 18/571,092 Page 9 Art Unit: 2198 Application/Control Number: 18/571,092 Page 11 Art Unit: 2198 Application/Control Number: 18/571,092 Page 12 Art Unit: 2198 Application/Control Number: 18/571,092 Page 13 Art Unit: 2198 Application/Control Number: 18/571,092 Page 14 Art Unit: 2198 Application/Control Number: 18/571,092 Page 15 Art Unit: 2198
