DETAILED ACTION
The communication is in response to the application filed on 10/02/2023 in which claims 1-20 are pending in the application. Claims 1,12 and 17 are independent form.
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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claim is directed to an abstract idea without significantly more.
Step 1: Claims 1-11 are directed to a method (series of step) and therefore is a process which is one of the statuary categories of inventions. Claims 12-16 are directed to a non-transitory processor-readable storage medium and therefore is a manufacture which is one of the statuary categories of inventions. Claims 17-20 are directed to an apparatus and therefore is a machine which is one of the statuary categories of inventions.
Step 2A, Prong 1:
Claims 1,12, and 17 recite the limitations “generating, for each of the plurality of microservices, at least one corresponding forecast by processing at least a portion of the utilization information using a machine learning model, wherein the at least one forecast corresponding to a given one of the microservices predicts a utilization of the computing resources….” , “combining the forecasts generated for the plurality of microservices to generate at least one combined forecast for the second time period;” and “determining a second container configuration for the plurality of microservices by evaluating the at least one combined forecast against at least one resource threshold value;” These limitations is a process that, under the broadest reasonable interpretation, cover the performance of the mind, with the use of generic computer components. In within the claims “….one corresponding forecast by processing at least a portion of the utilization information…. forecast corresponding to a given one of the microservices predicts a utilization of the computing resources…”, “….one combined the forecasts…” and “determining a second container configuration for the plurality of microservices by evaluating the at least one combined forecast against at least one resource threshold value;” encompasses a user mentally, based on observation, evaluation, judgement or opinion.
If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind, then it falls within the “Mental Process” grouping of abstract ideas concepts performed in the minds include an observation, evaluation, judgement, and opinion.
Step 2A, Prong 2:
The judicial exception is not integrated into practical application. The claims recite the additional limitations “collecting utilization information for a plurality of microservices that are implemented using a first container configuration in a computing environment, wherein the utilization information corresponds to computing resources utilized by the plurality of microservices over a first time period; “ This limitation amounts to data gathering which is considered to be insignificant solution activity(See MPEP 2106.05(g)); “initiating a deployment of the second container configuration of the plurality of microservices in the computing environment;” This limitation does not integrate the judicial exception into a practical application because they merely recite the words “apply it”(or an equivalent) with the judicial exception, or merely including instruction to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
The “first/second container configuration”, “computing environment”, “first/second time period “, “plurality of microservices”, “machine learning model”, “processing device” and “processor” and “memory” are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that is amounts no more than mere instruction to apply the exception using a generic computer component. Accordingly, this additional element doesn't integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (See MPEP 2106.059f)). The claim is directed to an abstract idea.
Step 2B:
The claim does not include additional element that sufficient to amount to significantly more than the judicial exception. The limitation of “collecting utilization information…” is considered mere data gathering which the court have identified as well-understood, routine, and conventional. (See MPEP 2106.05(d)). And the limitations of “generating…”, “initiating….” is consider “apply it” which the court have identified as merely including instruction to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As discussed above the with respect to integration of the abstract idea into a practical application, the additional element of the “first/second container”, “computing environment”, “first/second time period “, “plurality of microservices”, “machine learning model”, “processing device” and “processor” and “memory” are merely a generic computer or generic computer components to apply the judicial exception which cannot provide an inventive concept.
Dependent claim 2 recite the limitation “The computer-implemented method of claim 1, wherein the computing resources utilized by the plurality of microservices comprise processing resources and memory resources.” This additional element does not integrate the abstract idea into a practical application because they amount to no more than mere instructions to apply the abstract idea using generic computer components. (see MPEP 2106.05(f))
Dependent claim 3 recite the limitation “The computer-implemented method of claim 2, wherein, for the given microservice, the machine learning model generates (This does not integrate the abstract idea into a practical application because they amount to no more than mere instructions to apply the abstract idea using generic computer components. (see MPEP 2106.05(f))) a first forecast corresponding to the processing resources and a second forecast corresponding to the memory resources. (This is a mental process and amounts to an abstract idea under the mental processes grouping (observation, evaluation, judgement, and opinion). (See MPEP 2106.04(a)(2)))”
Dependent claim 4 recite the limitation “The computer-implemented method of claim 2, wherein the machine learning model comprises a multivariate long short-term memory model that is trained to identify dependencies corresponding to the processing resources and the memory resources.” This additional element does not integrate the abstract idea into a practical application because they amount to no more than mere instructions to apply the abstract idea using generic computer components. (see MPEP 2106.05(f))
Dependent claim 5 recite the limitation “The computer-implemented method of claim 2, wherein the collecting comprises (This amounts to data gathering which is considered to be insignificant extra solution activity (See MPEP 2106.05(g)) filtering the utilization information based on at least one of: an amount of time the processing resources of the given microservice exceeded a first filtering threshold value in the first time period; and whether the memory resources of the given microservice exceeded a second filtering threshold value in the first time period.(These additional elements does not integrate the abstract idea into a practical application because they amount to no more than insignificant extra solution activity. (See MPEP 2106.05(g))”
Dependent claim 6 recite the limitation “The computer-implemented method of claim 1, wherein the utilization information collected (This amounts to data gathering which is considered to be insignificant extra solution activity (See MPEP 2106.05(g)) for the given microservice comprises at least one of: one or more container identifiers; one or more process identifiers; processing resource utilization information for the given microservice at a plurality of time points; and memory resource utilization information for the given microservice at a plurality of time points.(These additional elements does not integrate the abstract idea into a practical application because they amount to no more than insignificant extra solution activity. (See MPEP 2106.05(g))”
Dependent claim 7 recite the limitation “The computer-implemented method of claim 1, wherein the collected utilization information is stored according to a designated database schema. (This amounts to data gathering which is considered to be insignificant extra solution activity (See MPEP 2106.05(g))”
Dependent claim 8 recite the limitation “The computer-implemented method of claim 1, further comprising: adjusting the forecasts (This is a mental process and amounts to an abstract idea under the mental processes grouping (observation, evaluation, judgement, and opinion). (See MPEP 2106.04(a)(2))) generated for the plurality of microservices to add a bias towards overutilization of the computing resources. (This additional element does not integrate the abstract idea into a practical application because they amount to no more than mere instructions to apply the abstract idea using generic computer components. (see MPEP 2106.05(f)))”
Dependent claim 9 recite the limitation “ The computer-implemented method of claim 1, wherein initiating the second container configuration comprises at least one of ( This amount to “apply it” as merely including instruction to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f)) : moving at least one of the plurality of microservices to a different container selected from among a plurality of existing containers associated with the computing environment; and moving at least one of the plurality of microservices to a new container.(These additional elements does not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f))).”
Dependent claim 10 recite the limitation “The computer-implemented method of claim 1, wherein initiating the second container configuration comprises( This amount to “apply it” as merely including instruction to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f)): providing an indication of the second container configuration to at least one user.(This additional elements does not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)))”
Dependent claim 11 recite the limitation “The computer-implemented method of claim 1, wherein the computing environment comprises a containerized embedded system. (This additional element does not integrate the abstract idea into a practical application because they amount to no more than mere instructions to apply the abstract idea using generic computer components. (see MPEP 2106.05(f))”
As per claim 13, it has similar limitation as claim 2, and is therefore rejected using the same rationale.
As per claim 14, it has similar limitation as claim 3, and is therefore rejected using the same rationale.
As per claim 15, it has similar limitation as claim 4, and is therefore rejected using the same rationale.
As per claim 16, it has similar limitation as claim 5, and is therefore rejected using the same rationale.
As per claim 18, it has similar limitation as claim 2, and is therefore rejected using that same rationale.
As per claim 19, it has similar limitation as claim 3, and is therefore rejected using the same rationale.
As per claim 20, it has similar limitation as claim 4, and is therefore rejected using the same rationale.
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.
Claims 1,2, 6-7, 9-10, 12-13, and 17- 18 are rejected under 35 U.S.C. 103 as being unpatentable over Vijendra et al. (US 20190391897 A1), in view of Antoun et al (US 20240152515 A1) in view of Jammalamadaka et al. (US20200311615 A1), in view of Caldato et al. (US20190102226 A1).
As per claim 1 Vijendra discloses:
collecting utilization information for a plurality of microservices that are implemented using a first container configuration in a computing environment, wherein the utilization information corresponds to computing resources utilized by the plurality of microservices over a first time period; [0021] “FIG. 1 shows an information processing system 100 including a container infrastructure or environment 102 that includes multiple containers 120-1, 120-2, …..“ [0022] “The container management system 104 includes container monitoring tools 140 configured to monitor various metrics for the containers 120 running in the container environment 102” [0023] “The container configuration controller 142 includes an adaptive threshold generation module 144, configured to utilize various AI-driven or machine learning algorithms for predicting expected levels or thresholds for various container metrics for a given time period.….where a pod is a group of containers deployed together on the same container host device….pod may contain a group of containers that represent multiple cooperating processes that form a cohesive service which simplifies application deployment and management” [0024] “adaptive threshold generation module 144 is configured to run periodically (e.g., every minute, every hour, every 12 hours, every day, every week, etc.), to set new adaptive or dynamic thresholds for each of a set of container metrics for an upcoming time period (e.g., for a next hour, a next 12 hours, a next day, a next week, etc.). It should be appreciated that the adaptive or dynamic thresholds may be generated or updated at different time intervals or time periods for different types or ones of the container metrics.” [0035] “…. The identified set of container metrics may include state metrics (e.g., a number of running containers, a number of available containers, a number of unavailable containers, etc.), resource utilization metrics (e.g., central processing unit (CPU) usage, CPU capacity, memory usage, memory capacity, file system usage, storage input/output (I/O), etc.) ….”
Under BRI container 120-1 is consider the first container configuration in a computing environment. Within BRI a “plurality of microservices” reads as any collection of tasks, processes or workloads running within a containerized environment.
Examiner note: Vijendra does not explicitly disclose a first or second time period however in para [0023-0024] Vijendra state “different time intervals” which encapsule a first and second time periods.
….wherein the at least one forecast corresponding to a given one of the microservices predicts a utilization of the computing resources by the given microservice over a second time period; (See Fig 1)([0024] “adaptive threshold generation module”] [0023] “The container configuration controller 142 includes an adaptive threshold generation module 144, configured to utilize various AI-driven or machine learning algorithms for predicting expected levels or thresholds for various container metrics for a given time period.”
initiating a deployment of the second container configuration of the plurality of microservices in the computing environment; [0041] “…alerts generated in step 208 trigger remedial action. Such remedial action may include, by way of example, spawning or creating additional containers on a same or additional container host device …….”
wherein the method is performed by at least one processing device comprising a processor coupled to a memory. [0026] “container environment 102 and container management system 104 in the FIG. 1 embodiments are assumed to be implemented using at least one processing platform each comprising one or more processing devices each having a processor coupled to a memory.”
Vijendra does not explicitly disclose:
generating, for each of the plurality of microservices, at least one corresponding forecast by processing at least a portion of the utilization information using a machine learning model,
combining the forecasts generated for the plurality of microservices to generate at least one combined forecast for the second time period;
determining a second container configuration for the plurality of microservices by evaluating the at least one combined forecast against at least one resource threshold value;
However, Antoun does disclose “at least one corresponding forecast by processing at least a portion of the utilization information using a machine learning model” [0008] “…. Analysis tasks can also include other types of tasks, such as clustering tasks to cluster data into multiple clusters.” [0014] “A “workload” can refer to any collection of one or more activities performed in response to a database….” [0009] “Analysis tasks such as machine learning tasks and/or clustering tasks can be based on feature vectors containing features derived from representations of database queries to be executed on database system(s).” [0039] “The feature vectors 130 produced by the embedding machine learning model 126 are provided to one or more analysis engines 106…” [0040] “……For example, the prediction machine learning model can predict a usage of a resource of a DBMS, such as a processing resource (e.g., a central processing unit (CPU) or a portion of a CPU) ….” machine learning model can then be used in various analysis tasks, which can include clustering tasks as well as tasks performed by machine learning models that perform machine learning tasks, including predictions,”
Both Vijendra and Antoun are combinable because they are both concern with generating prediction using a machine learning model.
Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Vijendra, and the teaching of Antoun to process a portion of utilization information from the plurality of microservice. Motivation to combine would be properly partition the collected utilization for each of the microservice so that the machine learning model of only focus on the relevant portion of the utilization information which would improve the accuracy of the forecasts generated.
Vijendra in view of Antoun does not explicitly disclose:
combining the forecasts generated for the plurality of microservices to generate at least one combined forecast for the second time period;
determining a second container configuration for the plurality of microservices by evaluating the at least one combined forecast against at least one resource threshold value;
However, Jammalamadaka discloses “combining the forecasts generated for the plurality of microservices to generate at least one combined forecast for the second time period;” [0071] “The models are then used to compute 170 a plurality of forecasts (or predictions) which are aggregated 172 to compute a single forecast (or single prediction))”
Both Vijendra and Jammalamadaka are in the similar field of endeavor as they are both in processing environment and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Vijendra, with the teaching of Antoun and Jammalamadaka to combine the forecast for plurality of microservices into one combined forecast for the second time period. Motivation to combine would be to properly train the model to compute a plurality of forecast/prediction for the plurality of microservices and within the model to condensed the predictions for the plurality of microservice into a single forecast during the second time period which can simplify the need of managing the plurality different forecasts.
Vijendra, in view of Antoun, in view of Jammalamadaka does not explicitly disclose:
determining a second container configuration for the plurality of microservices by evaluating the at least one combined forecast against at least one resource threshold value;
However, Caldato disclose “determining a second container configuration for the plurality of microservices by evaluating the at least one combined forecast against at least one resource threshold value;”
[0129] “After monitoring the usage information, the scheduler 2202 and/or the API registry 404 can identify when the actual usage characteristics of any pods in the container platform deviate beyond a threshold amount from their initial estimated usage.” [0130] “When sufficient deviation from a current estimate of an actual usage is detected, the scheduler 2202 and/or the API registry 404 can reassign pods across the plurality of nodes…. if all the deviating pods are able to be redeployed using this method, then that may represent the most efficient reallocation method available.” [0136] “the CPU usage can be tracked by the scheduler 2202 and/or the API registry 404 in real time as processing is requested by the service of pod…. When the rate of increase of the CPU usage exceeds a first threshold 2704, these systems can “warm-up” a new instance of the service of pod 2224 in another part of node 2206 or in a different node, such as node 2204. Warming up a new instance of a service may include instantiating a new pod and loading an instance of the service into the new pod.”
Both Vijendra and Caldato are in the similar field of endeavor as they are both in containerized environment and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Vijendra, with the teaching of Antoun, Jammalamadaka, and Caldato to determining a second container configuration for the plurality of microservices by evaluating the at least one combined forecast against at least one resource threshold value. Motivation to combine would be properly examine the need of a second container, based on the combine forecast and the threshold value, which will would prevent the overutilization of resource.
As per claim 2 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 1 detailed above.
Vijendra further discloses “wherein the computing resources utilized by the plurality of microservices comprise processing resources and memory resources.” [0043] “includes various metrics that relate to resource utilization per pod, such as CPU usage, node CPU capacity, memory usage, node memory capacity, file system usage, disk I/O, etc.
As per claim 6 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 1 detailed above.
Vijendra further discloses “, wherein the utilization information collected for the given microservice comprises at least one of:
one or more container identifiers;
one or more process identifiers;
processing resource utilization information for the given microservice at a plurality of time points; and
memory resource utilization information for the given microservice at a plurality of time points.
[0035] “the process begins with step 200, identifying a set of container metrics for containers 120 running in a container environment 102. The identified set of container metrics may include state metrics (e.g., a number of running containers, a number of available containers, a number of unavailable containers, etc.), resource utilization metrics (e.g., central processing unit (CPU) usage, CPU capacity, memory usage, memory capacity, file system usage, storage input/output (I/O), etc.) ….”
As per claim 7 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 1 detailed above.
Vijendra further discloses “wherein the collected utilization information is stored according to a designated database schema.”
[0036] “container data for the containers 120 is collected, such as using container monitoring tools 140 of container management system 104. The container data may be obtained from a metric repository or data store where the container monitoring tools 140 or other tools store information regarding containers 120 in container environment 102, or may be provided as telemetry data directly from container monitoring tools” (See Fig 3)
As per claim 9 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 1 detailed above.
Vijendra further discloses “wherein initiating the second container configuration comprises at least one of:
moving at least one of the plurality of microservices to a different container selected from among a plurality of existing containers associated with the computing environment; and
moving at least one of the plurality of microservices to a new container.”
[0041] “…Such remedial action may include, by way of example, spawning or creating additional containers on a same or additional container host device to handle increased load, taking down or stopping running containers if utilization is low, etc. The remedial action may also or alternatively include migrating containers from one container host device to another (e.g., to a container host device with more available resources) ….”
As per claim 10 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 1 detailed above.
Vijendra further discloses “wherein initiating the second container configuration comprises: providing an indication of the second container configuration to at least one user.” [0040] “Alerts are generated in step 208 responsive to the monitoring in step 206. For example, on detecting that the monitored behavior of one or more containers is outside the range of accepted container behavior as specified by the adaptive thresholds for the container metrics, an alert may be provided to a specified user or authorized personnel (e.g., an IT administrator….”
As per claim 12, it has similar limitation as claim 1 and is therefore rejected using the same rationale.
As per claim 13, it has similar limitation as claim 2 and is therefore rejected using the same rationale.
As per claim 17, it has similar limitation as claim 1 and is therefore rejected using the same rationale.
As per claim 18, it has similar limitation as claim 2 and is therefore rejected using the same rationale.
Claims 3, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Vijendra et al. (US 20190391897 A1), in view of Antoun et al (US 20240152515 A1) in view of Jammalamadaka et al. (US20200311615 A1), in view of Caldato et al. (US20190102226 A1), in view of Kita (US 20240248763 A1)
As per claim 3 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 2 detailed above.
Vijendra, Antoun, Jammalamadaka and Caldato do not explicitly disclose “wherein, for the given microservice, the machine learning model generates a first forecast corresponding to the processing resources and a second forecast corresponding to the memory resources.”
However, Kita a first and second forecast corresponding with processing resource and memory resource, [0205] “……five separate pieces of predicted-value data (CPU predicted-value data, memory predicted-value data, storage predicted-value data, network predicted-value data, and power consumption predicted-value data).”
Both Vijendra and Kita are in the same field of endeavor as they are both in management of containers and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Vijendra, with the teaching of Antoun, Jammalamadaka, Caldato, and Kita to have the machine learning model to generate a first and second forecast for the processing and memory resources. Motivation to combine would be to distinctly separate the forecasts for the microservices between the processing resource and the memory resource.
As per claim 14, it has similar limitation as claim 3 and is therefore rejected using the same rationale.
As per claim 19, it has similar limitation as claim 3 and is therefore rejected using the same rationale.
Claims 4,15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Vijendra et al. (US 20190391897 A1), in view of Antoun et al (US 20240152515 A1) in view of Jammalamadaka et al. (US20200311615 A1), in view of Caldato et al. (US20190102226 A1), in view of Shah et al. (US 20180300621 A1).
As per claim 4 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 2 detailed above.
Vijendra, Antoun, Jammalamadaka and Caldato do not explicitly disclose “wherein the machine learning model comprises a multivariate long short-term memory model that is trained to identify dependencies corresponding to the processing resources and the memory resources.”
However, Shah discloses the use of “machine learning model comprises a multivariate long short-term memory model” [0029] “In one or more embodiments, a neural network application utilizes a recurrent neural network (RNN) to process the performance metrics and discover dependencies between the performance metrics. In conventional use of a neural network, such as an RNN, the neural network is trained over input data. “[0081] LSTM-based multivariate prediction model 504 uses multiple variables to predict an output using an LSTM-based model.
Both Vijendra and Shah are in the similar field of endeavor as they are both in processing systems and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Vijendra, with the teaching of Antoun, Jammalamadaka, Caldato, and Shah to have a machine learning model comprising a multivariate long short-term memory model to be able to be trained to identify dependencies of the processing and memory resources. Motivation to combine would be capture the utilization of the processing and memory resource to specifically train the model using a multivariate long short -term which would cause a more precise prediction.
As per claim 15, it has similar limitation as claim 4 and is therefore rejected using the same rationale.
As per claim 20, it has similar limitation as claim 4 and is therefore rejected using the same rationale.
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Vijendra et al. (US 20190391897 A1), in view of Antoun et al (US 20240152515 A1) in view of Jammalamadaka et al. (US20200311615 A1), in view of Caldato et al. (US20190102226 A1), in view of Manes et al (US 20130047039 A1)
As per claim 5 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 2 detailed above.
Vijendra discloses filtering metrics information “[0055] The parse and filter module 618 in some embodiments decodes, filters, and transforms incoming container metric data from various data sources (e.g., historical container metric data from metric repository 602, telemetry data from container monitoring tools, etc.)”
Vijendra, Antoun, Jammalamadaka and Caldato do not explicitly disclose “
an amount of time the processing resources of the given microservice exceeded a first filtering threshold value in the first time period; and
whether the memory resources of the given microservice exceeded a second filtering threshold value in the first time period. “
However, Manes disclose [0017] “…. The collection module 120 tracks system information on system resource usage (such as CPU, disk, memory, and network utilization, for example) and tracks information on process activity….” [0033] “…the filter 140 is configured to check for at least one or more items responsible for causing a resource pool to be near maximum usage, at least one spike in resource usage, at least one spike in a number or processes started or active, whether a computer swaps memory more than a predetermined number of times in a given time period.,… filter 140 is configured to check for at least one item selected from the group consisting of CPU memory usage exceeding a predetermined threshold…] [0034] “Filter 140 looks for at least one triggering event. In certain examples, the at least one triggering event is selected from the group consisting of a process exceeding a threshold of resource usage for a predetermined amount of time”
Both Vijendra and Manes are in the similar field of endeavor as they are both in containerized environment and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Vijendra, with the teaching of Antoun, Jammalamadaka, Caldato, and Manes to count the amount of the time the processing resource and memory resource that exceed the threshold value of the first time period. Motivation to combine would be to determine which of the resources has exceed the filtering threshold value and how many times it has exceed the threshold value in the first time period based on their utilization which would eliminate unnecessary and irrelevant data that are being collected and processed the machine learning model and focus on the performance.
As per claim 16, it has similar limitation as claim 5 and is therefore rejected using the same rationale.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Vijendra et al. (US 20190391897 A1), in view of Antoun et al (US 20240152515 A1) in view of Jammalamadaka et al. (US20200311615 A1), in view of Caldato et al. (US20190102226 A1), in view of Dasari et al. (US 20200210226 A1)
As per claim 8 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 1 detailed above.
Vijendra discloses “adjusting the forecast generated for the plurality of microservices” [0039] “The change in adaptive threshold may be based on new container data that is collected, as step 202 in some embodiments is performed continuously or periodically (e.g., the collected container data may be updated before computing the adaptive threshold for a subsequent time period).”
Vijendra, Antoun, Jammalamadaka and Caldato do not disclose “…. to add a bias towards overutilization of the computing resources.”
However, Dasari does disclose “…. to add a bias towards overutilization of the computing resources.” [0022] “…A server that under utilizes its resources, for instance, may not be favored for migration to a container, because the server is not sufficiently utilizing its resources, such that there may be fewer tangible benefits to migrating the server to a container. By comparison, a server that over utilizes its resources may not be favored for container migration, because such migration may overly tax the underlying hardware infrastructure of the container platform.”
Both Vijendra and Dasari are in the similar field of endeavor as they are both in containerized environment and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Vijendra, with the teachings of Antoun, Jammalamadaka, Caldato, and Dasari to adjust to the forecast of the plurality of the microservices then to add bias towards overutilization of the computing resource. Motivation to combine would be favor overutilization of computing resources such that the adjusted forecasts reflect the peak of overutilization of resource which can reduce the demands resource for the plurality of microservice, while actively reducing system failure or yet resource errors.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Vijendra et al. (US 20190391897 A1), in view of Antoun et al (US 20240152515 A1) in view of Jammalamadaka et al. (US20200311615 A1), in view of Caldato et al. (US20190102226 A1) in view of Vokaliga et al. (US 20220103627 A1).
As per claim 11 Vijendra, Antoun, Jammalamadaka and Caldato disclose a method of claim 1 detailed above.
Vijendra, Antoun, Jammalamadaka and Caldato do not disclose “wherein the computing environment comprises a containerized embedded system.”
However, Vokaliga disclose wherein the computing environment comprises a containerized embedded system. [0001] “…. to a fully orchestrated setup of a containerized cloud communication system within an embedded operating system.”
Both Vijendra and Vokaliga are in the similar field of endeavor as they are both in the containerized environment and, therefore, are combinable/modifiable.
Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Vijendra, with the teaching of Antoun, Jammalamadaka, Caldato, and Vokaliga to have the computing environment that contain a containerized embedded system. Motivation to combine would be to enhance the system.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sreenivasa Rao Pamidala et al. (US20220164186A1) disclose a number of containers required at a future point in time for each respective microservice of the plurality of different microservices is predicted using a trained forecasting model and the first set of features extracted from each respective microservice.
Miles Mulholland et al. (US20210149571A1) disclose a method and system are provided for storage allocation enhancement of microservices.
Apparsamy Perumal et al. (US 11314630 B1) disclose a processor may determine recommended changes to system environments for one or more microservice containers of the set of microservice containers.
Yifan Yu (US 20220236978 A1) disclose a system may include a microservice deployment device, a plurality of computing resource pools, and a target service chain.
Giuseppe Coviello et al. (US 20240394110 A1) discloses adjusting computing resources allocated to tasks within a stream processing application, including initiating monitoring of application-specific characteristics for each task.
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/A.A./Examiner, Art Unit 2198
/PIERRE VITAL/Supervisory Patent Examiner, Art Unit 2198
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