DETAILED ACTION
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 .
Claims 1 – 22 are pending for examination.
Examiner’s Note
The prior art rejection below cites particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
Claim Objections
Claims 4 and 17 are objected to because of the following informalities:
As to claims 4 and 17: limitation “YAML” is required to be spelled out and can be subsequent refers to.
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 - 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1, the claim recites
A method for scaling services at a Platform as a Service (PaaS) layer of a server, comprising:
at the PaaS layer of the server:
executing an application as an application proxy;
exposing the application proxy to a network;
receiving a service request from a user of the network who accessed the application proxy;
detecting an intent of the user;
predicting services that are appropriate in response to the service request of the user based on the detected intent of the user;
scaling the predicted services; and
scheduling the application and any other supporting applications corresponding to the scaled predicted services for execution.
Step 2A:
Prong 1: the limitations of scaling services, detecting an intent of the user;
predicting services that are appropriate in response to the service request of the user based on the detected intent of the user;
scaling the predicted services; and
scheduling the application and any other supporting applications corresponding to the scaled predicted services for execution are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, opinion, thus it is reasonable to identify these limitation as reciting a mental process.
Prong 2:
The additional element of receiving a service request from a user of the network who accessed the application proxy merely recite insignificant extra solution activity such as gathering, displaying, updating, transmitting and storing data which does not integrate the judicial exception into a practical application. See MPEP 2106.05(d).
The additional elements in executing an application as an application proxy;
exposing the application proxy to a network merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea.
The additional element in at a Platform as a Service (PaaS) layer of a server merely link the use of the judicial exception to a particular technological environment or field of use, thus does not integrate the judicial exception into a practical application. MPEP 2106.05(h).
Thus, these additional elements do not integrate the judicial exception into a practical application.
Step 2B:
The additional element of receiving a service request from a user of the network who accessed the application proxy merely recite insignificant extra solution activity such as gathering, displaying, updating, transmitting and storing data which does not integrate the judicial exception into a practical application. See MPEP 2106.05(d).
The additional elements in executing an application as an application proxy;
exposing the application proxy to a network merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea.
The additional element in at a Platform as a Service (PaaS) layer of a server merely link the use of the judicial exception to a particular technological environment or field of use, thus does not integrate the judicial exception into a practical application. MPEP 2106.05(h).
Accordingly, the additional elements do not amount to significantly more than the abstract idea.
As to claim 2. The method of claim 1, wherein the executing of the application as the application proxy and the exposing of the application proxy to the network is performed by an orchestrator merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea running on the PaaS layer of the server merely link the use of the judicial exception to a particular technological environment or field of use, thus does not integrate the judicial exception into a practical application. MPEP 2106.05(h).
As to claim 3. The method of claim 1, wherein the application proxy executes on a fraction of a virtual computer processing unit merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea.
As to claim 4. The method of any of claim 1, further comprising registering the application to a load balancer using a YAML file prior to executing the application as the application proxy merely recite insignificant extra solution activity such as gathering, displaying, updating, transmitting and storing data which does not integrate the judicial exception into a practical application. See MPEP 2106.05(d).
As to claim 5. The method of claim 4, wherein the PaaS layer merely link the use of the judicial exception to a particular technological environment or field of use, thus does not integrate the judicial exception into a practical application. MPEP 2106.05(h) includes the load balancer merely recite insignificant extra solution activity such as gathering, displaying, updating, transmitting and storing data which does not integrate the judicial exception into a practical application. See MPEP 2106.05(d).
As to claim 6. The method of any of claim 1, wherein the load balancer receives the service request from the user merely recite insignificant extra solution activity such as gathering, displaying, updating, transmitting and storing data which does not integrate the judicial exception into a practical application. See MPEP 2106.05(d).
As to claim 7. The method of any of claim 1, wherein the PaaS layer merely link the use of the judicial exception to a particular technological environment or field of use, thus does not integrate the judicial exception into a practical application. MPEP 2106.05(h) includes an intent engine merely link the use of the judicial exception to a particular technological environment or field of use, thus does not integrate the judicial exception into a practical application. MPEP 2106.05(h) for detecting the intent of the user and predicting of the services that are appropriate in response to the service request of the user based on the intent of the user are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, opinion, thus it is reasonable to identify these limitation as reciting a mental process.
As to claim 8. The method of claim 7, wherein the PaaS layer merely link the use of the judicial exception to a particular technological environment or field of use, thus does not integrate the judicial exception into a practical application. MPEP 2106.05(h) further includes an orchestrator and wherein the intent engine is a component of the orchestrator merely recite insignificant extra solution activity such as gathering, displaying, updating, transmitting and storing data which does not integrate the judicial exception into a practical application. See MPEP 2106.05(d).
As to claim 9. The method of claim 7, wherein the PaaS layer merely link the use of the judicial exception to a particular technological environment or field of use, thus does not integrate the judicial exception into a practical application. MPEP 2106.05(h) includes a load balancer that executes the intent engine merely recite insignificant extra solution activity such as gathering, displaying, updating, transmitting and storing data which does not integrate the judicial exception into a practical application. See MPEP 2106.05(d).
As to claim 10. The method of claim 7, wherein the intent engine comprises a neural network merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea for detecting the intent of the user and predicting of the services that are appropriate in response to the service request of the user based on the intent of the user are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, opinion, thus it is reasonable to identify these limitation as reciting a mental process.
As to claim 11. The method of claim 10, wherein the neural network includes an input layer, neural network layers, and an output layer, wherein the input layer receives input parameters contained in the service request merely recite insignificant extra solution activity such as gathering, displaying, updating, transmitting and storing data which does not integrate the judicial exception into a practical application. See MPEP 2106.05(d), wherein the neural network layers filter the input parameters to create a feature map that summarizes the presence of detected intent features in the input, and wherein the detected intent features are presented at the output layer as predicted services are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, opinion, thus it is reasonable to identify these limitation as reciting a mental process.
.
As to claim 12. The method of claim 11, wherein the PaaS layer merely link the use of the judicial exception to a particular technological environment or field of use, thus does not integrate the judicial exception into a practical application. MPEP 2106.05(h) includes a delivery controller for scheduling the executing of the application and any other supporting applications corresponding to the scaled predicted services is scheduled on an associated second server or on an associated computing device are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, opinion, thus it is reasonable to identify these limitation as reciting a mental process.
As to claim 13. The method of any of claim 1, wherein the server comprises an edge server of a cloud service provider merely recite insignificant extra solution activity such as gathering, displaying, updating, transmitting and storing data which does not integrate the judicial exception into a practical application. See MPEP 2106.05(d).
As to claim 14, this is a system claim of claim 1. See rejection for claim 1 above. Further,
The additional elements of a server including a processor and a memory accessible by the processor; a set of processor readable instructions stored in the memory that are executable by the processor of the server merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea.
As to claims 15 - 20, see rejection for claims 2 – 7 above.
As to claims 21 - 22, see rejection for claims 10 - 11 above.
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, 5 – 15, and 18 - 22 are rejected under 35 U.S.C. 103 as being unpatentable over Calder et al., (US PUB 2013/0179895 hereinafter Calder) in view of Zhou et al., (US PUB 2019/0347134 hereinafter Zhou) in view of Hermoni et al., (US PUB 2019/0280918 hereinafter Hermoni).
Calder was cited in IDS filed 11/06/2025.
As to claim 1, Calder teaches method for scaling services at a Platform as a Service (PaaS) layer of a server (“Job Scheduling with a Platform as a Service (PAAS)” title and para. 0128) and (“.pool server...” figure 10 and associated text, especially para. 0023 and 0104), comprising:
at the PaaS layer of the server
executing an application as an application proxy (“...the task location services 512 can also provide application programming interfaces (APIs) for allowing users to interact with the distributed computing environment. Such APIs can include handling APIs associated with pools of virtual machines, pool management logic, and coordination of pool management logic across task tenants within a given geographic region. The APIs can also include APIs for handling tasks submitted by a user, as well as maintaining, scheduling, and terminating work items or jobs associated with the user tasks. The APIs can further include APIs for statistics collection, aggregation, and reporting for all work items, jobs, tasks, and pools in a geographic region. Additionally, the APIs can include APIs for allowing auction of available virtual machines as preemptible VMs to users on a short term basis based on a spot market for virtual machines. The APIs can also include APIs for metering usage and providing billing support.” Para. 0067);
exposing the application proxy to a network (“The task location services 512 can be linked together by a global location service 502 ("XLS"). The global location service 502 can be responsible for account creation and management of accounts, including managing task accounts in conjunction with the task location service 512. For example, the global location service may be responsible for disaster recovery and availability of work items and jobs in the event of a data center disaster. This may include running a work item or job in a different location due to the data center not being available and allowing customers to migrate their work items, jobs, and pools from one data center to another data center...” para. 0068);
receiving a service request from a user of the network who accessed the application proxy (“...When a user makes a request for additional dedicated virtual machines, the excess virtual machines can be used to fulfill the user's request...” para. 0049) and (“..The user request to create a task account may optionally specify a geographic region that the account needs to be created in. In this example, Sally requests an account associated with the Geo Region 1, which has a failover region of Geo Region 2. In response, the global location service 502 contacts the task location service 512 that corresponds to the requested geographic region...” para. 0069) and (“..At a block 1202, a request is received to create a task account from a user. For example, a user may request, by way of a client portal (including a service management API), that the service generates an account that is useable by the user to perform computations. The request may be received at a high-level location service (e.g., XLS) of the system.....” para. 0128);
[detecting an intent of the user];
predicting services that are appropriate in response to the service request of the user based on the detected intent of the user (“...For example, it is contemplated that if additional tasks are added to the job or the time for processing the job is exceeding initial predictions...” para. 0148);
scaling the predicted services (“..the auto-scaling rules may be employed to determine how many additional resources will be required to complete the job. Similarly, it is contemplated that the invocation of the auto-scaling rules may result in a determination that the number of VMs in the pool may be excessive and that some of those resources may be converted or dropped...” para. 0148); and
scheduling the application and any other supporting applications corresponding to the scaled predicted services for execution (“...For example, in an account, a customer may pay to reserve a number of standby VMs and that standby VM reservation may be used for either time-based criteria or dynamic threshold auto-scaling criteria. Further, it is contemplated that a standby VM reservation may be converted at any point, regardless of reservation time or other scheduling reservation...” title and para. 0053) and (“...The WIJ schedulers 618 may also use generic partitioning mechanisms for scaling within a task location service. In an embodiment, there are multiple WIJ schedulers 618 in each task location service...” para. 0075).
While Calder can track activity and behavior to collect and aggregate to detailed statistics for tasks to automatically determine a desired number of dedicated, standby, and/or preemptible virtual machines for a pool (paras. 0078 para. 0104, Calder does not but Hermoni teaches detecting an intent of the user (“...Fast and automatic reconfiguration also enable customers to place a demand for a service...” para. 0029) and (“...In one embodiment, the AI-based network management system, and particularly the AI-analysis engine, may detect and/or predict load-changes ...” para. 0033) and (“In another embodiment, the AI-analysis engine may be used to detect such development of a load-change and/or consumption situation early enough to determine and effect the network configuration change before any service is adversely affected. It should be noted that such a sequence and/or cluster of events may be a classifier which may predict an event...” para. 0196).
It 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 was made to modify Calder by applying the teachings of Hermoni because Hermoni would provide a technique to detect user demand changes to prepare and provide service more efficiently (para. 0028 - 0030, and 0196).
As to claim 2, Calder modified by Hermoni teaches the method of claim 1, Calder teaches wherein the executing of the application as the application proxy and the exposing of the application proxy to the network is performed by an orchestrator running on the PaaS layer of the server (“.pool server...” figure 10 and associated text, especially para. 0023 and 0104).
As to claim 5, Calder modified by Hermoni teaches The method of claim 4, Calder teaches wherein the PaaS layer includes the load balancer (“...load balancing work across different computational resources...” para. 0150).
As to claim 6, Calder modified by Hermoni teaches The method of claim 1, Calder teaches wherein the load balancer receives the service request from the user (“The pool servers 614 and WIJ schedulers 618 receive requests from users via task location service front ends ...” para. 0076) and (“...The WIJ schedulers 618 may also use generic partitioning mechanisms for scaling within a task location service. In an embodiment, there are multiple WIJ schedulers 618 in each task location service, and each of the WIJ schedulers handles a range of work items.” Para. 0075).
As to claim 7, Calder modified by Hermoni teaches The method of any of claim 1, Calder teaches wherein the PaaS layer (title, abstract).
Calder does not but Hermoni teaches an intent engine for detecting the intent of the user (“In order to track the activity and behavior of the computing environment, a task location service master 620 can communicate with one or more statistics aggregation servers 622. The statistics aggregation servers are responsible for collecting and aggregating detailed statistics for tasks, jobs, work items and pools.....” paras. 0078) and (“...As previously discussed with respect to the auto-scaling component 616 of FIG. 6, the auto-scaling module 1008 is responsible for running user provided auto scaling rules (e.g., formulas) that automatically determine a desired number of dedicated, standby, and/or preemptible virtual machines for a pool” para. 0104. Note: determine a desire number is detecting an intent) and predicting of the services that are appropriate in response to the service request of the user based on the intent of the user (“...For example, it is contemplated that if additional tasks are added to the job or the time for processing the job is exceeding initial predictions...” para. 0148). See motivation for claim 1 above.
As to claim 8, Calder modified by Hermoni teaches The method of claim 7, Calder teaches wherein the PaaS layer (title, abstract).
Calder does not but Hermoni teaches an orchestrator and wherein the intent engine is a component of the orchestrator (“In one embodiment, the AI-based network management system, and particularly the AI-analysis engine, may detect and/or predict load-changes to change a network configuration...” para. 0033).
See motivation for claim 1 above.
As to claim 9, Calder modified by Hermoni teaches The method of claim 7, Calder teaches wherein the PaaS layer includes a load balancer (“...This spanning of multiple pools may be achieved by load balancing...” para. 0044).
Calder does not but Hermoni teaches that executes the intent engine (“...the AI-analysis engine, may detect and/or predict load-changes...” para. 0033).
See motivation for claim 1 above.
As to claim 10, Calder modified by Hermoni teaches The method of claim 7, Calder does not but Hermoni teaches wherein the intent engine comprises a neural network for detecting the intent of the user (“...Further, the classifier may be a pattern of data elements, and the AI-model is a piece of software (e.g., a neural network) that detects the particular pattern in a stream of log data,” para. 0174) and predicting of the services that are appropriate in response to the service request of the user based on the intent of the user (“In another embodiment, the AI-analysis engine may be used to detect such development of a load-change and/or consumption situation early enough to determine and effect the network configuration change before any service is adversely affected. It should be noted that such a sequence and/or cluster of events may be a classifier which may predict an event...” para. 0196).
See motivation for claim 1 above.
As to claim 11, Calder modified by Hermoni teaches The method of claim 10, Calder does not but Hermoni teaches wherein the neural network includes an input layer, neural network layers, and an output layer, wherein the input layer receives input parameters contained in the service request (“...In addition, the ability to reconfigure the network quickly (and cost effectively) may enable network operators to introduce new services faster, reduce time-to-market, and reduce onboarding costs. Fast and automatic reconfiguration also enable customers to place a demand for a service...” para. 0029), wherein the neural network layers filter the input parameters to create a feature map that summarizes the presence of detected intent features in the input, and wherein the detected intent features are presented at the output layer as predicted services (“...particularly the AI-analysis engine, may detect and/or predict load-changes...” para. 0033).
See motivation for claim 1 above.
As to claim 12, Calder teaches modified by Hermoni The method of any of claim 1, Calder teaches wherein the PaaS layer includes a delivery controller for scheduling the executing of the application and any other supporting applications corresponding to the scaled predicted services is scheduled on an associated second server or on an associated computing device (“...For example, in an account, a customer may pay to reserve a number of standby VMs and that standby VM reservation may be used for either time-based criteria or dynamic threshold auto-scaling criteria. Further, it is contemplated that a standby VM reservation may be converted at any point, regardless of reservation time or other scheduling reservation...” title and para. 0053) and (“...The WIJ schedulers 618 may also use generic partitioning mechanisms for scaling within a task location service. In an embodiment, there are multiple WIJ schedulers 618 in each task location service...” para. 0075).
As to claim 13, Calder modified by Hermoni teaches The method of claim 1, Calder does not but Hermoni teaches wherein the server comprises an edge server of a cloud service provider (“In one embodiment, network configuration may refer to a configuration of any part of a network, or a combination of network, including network slicing, self-organizing networks (SON), edge computing, etc...” para. 0056).
It 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 was made to modify Calder by applying the teachings of Hermoni because Hermoni would provide edge computing network to support edge devices which is popular used (para. 0056).
As to claim 14, this is a system claim of claim 1. See rejection for claim 1 above. Further, Calder teaches processor and a memory (“..processors and memory....” para. 0021);
a set of processor readable instructions stored in the memory that are executable by the processor of the server to (“..computer-storage media storing computer-useable instructions that, when executed by a computing device having a processor and memory.” para. 0023)
As to claims 15, and 18 - 20, these claims recite similar scope of claims 2, 5 - 7 . See rejection for claims 2, and 5 – 7 above.
As to claims 21 - 22, these claims recite similar scope of claims 10 - 11. See rejection for claims 10 - 11 above.
Claims 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Calderin view of Hermoni, as applied to claim 1, and further in view of Fontoura et al., (US PUB 2019/0163517 hereinafter Fontoura).
As to claim 3, Calder modified by Hermoni teaches The method of claim 1, Calder and Hermoni do not but Fontura teaches wherein the application proxy executes on a fraction of a virtual computer processing unit (“.It may be possible that only a small percentage of virtual machines remain alive for the duration of the defined period for monitoring the VM to determine the recommended rightsized VM. It is common, for an exemplary cloud computing system, to have a significant number (e.g., 90%) of VMs last just 1 day, while a small percent (e.g., 4%) last 8 days and even smaller percent (e.g. 3%) last 14 days or more...” para. 0025).
It 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 was made to modify Calder and Hermoni by applying the teachings of Fontura because Fontura would provide details with percentage of virtual machine resources to give prediction rightsizing deployment of needed resources (abstract).
As to claim 16, this claim recites similar scope of claim 3. See rejection for claim 3 above.
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Calderin view of Hermoni, as applied to claim 1, and further in view of Bispo et al., (US PUB 2021/0311859 hereinafter Bispo).
As to claim 4, Calder modified by Hermoni teaches The method of any of claim 1, Calder and Hermoni do not but Bispo teaches further comprising registering the application to a load balancer using a YAML file prior to executing the application as the application proxy (“...For example, the load generator API 335 may transmit an indication of a load (e.g., a request load) to the load transformer 340, and the load transformer 340 may configure a load client (e.g., JMeter or some other load client) based on the indication of the load (e.g., as indicated in a .json file, as indicated in a .yaml file, etc.)...” para. 0054).
It 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 was made to modify Calder and Hermoni by applying the teachings of Bispo because Bispo would provide document in YAML, a type of markup language easy to understand and commonly used.
As to claim 17, this claim recites similar scope of claim 4. See rejection for claim 4 above.
Conclusion
The prior art made of record but not relied upon request is considered to be pertinent to applicant’s disclosure.
Pietro, (US PUB 2021/0306224), discloses a method for AI engine that is cloud-based to manage to monitor load on the network and provide load management decisions (title, abstract and figures 1 – 4).
Dizengof, (US PAT 12,489,845), discloses an AI system utilizing Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) for detecting issues and loading a respective response protocol (title, abstract and figures 1 – 4).
Kim, (US PUB 2020/0029172), discloses a method for monitoring and controlling device in edge computing system (title, abstract and figures 1 – 8).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUONG N HOANG whose telephone number is (571)272-3763. The examiner can normally be reached 9:5-30.
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/PHUONG N HOANG/Examiner, Art Unit 2194 /KEVIN L YOUNG/Supervisory Patent Examiner, Art Unit 2194
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