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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 6-11, 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Files et al. 20230022222 herein Files and Ding et al. 9065783 herein Ding in view of Khorsrowpour 20220224759 herein Khorsrowpour.
Per claim 1, Files discloses: at least one processing platform comprising at least one processor coupled to at least one memory, wherein the at least one processing platform is configured to: (fig. 6, ¶0078; The processing device 602-1 in the processing platform 600 comprises a processor 610 coupled to a memory 612) utilize a machine learning algorithm comprising one or more first decision trees generated based on device data representing a set of one or more physical devices in an information processing system (fig. 2, ¶0037; using a decision tree algorithm to identify the problem(s), using a separate machine learning algorithm to recommend one or more configuration updates, and sending an updated version of the machine learning algorithm to peer systems to detect and/or recommend similar configuration adjustments in one or more of the peer systems) to determine a recommendation for deploying one or more additional physical devices… to the information processing system based on a given system goal; (fig. 2, ¶0042-43; it is determined whether the critical alert is related to one or more hardware changes. If no, then data pertaining to that determination is incorporated into and/or added to data 233. If yes, then step 227 includes determining whether user system 202-2 and/or user system 202-3 have similar hardware changes (to user system 202-1) and/or whether the hardware is similarly configured (to that of user system 202-1). If yes, then data pertaining to that (positive) determination is incorporated into and/or added to data 233.… step 231 includes determining whether user system 202-2 and/or user system 202-3 have similar system usage configurations (to user system 202-1). If yes, then data pertaining to that (positive) determination is incorporated into and/or added to data 233. If no, then (different) data pertaining to that (negative) determination is incorporated into and/or added to data 233…..In conjunction with the above-noted descriptions, at least one recommendation 237 pertaining to the alert received by user system 202-1 is generated by the trained machine learning model 235 and provided to user system 202-1, as well as to user system 202-2 and user system 202-3 for implementation (e.g., the recommendation 237 can include an instruction to adjust one or more system configurations)) in response to a recommendation for adding one or more additional physical devices, utilize the machine learning algorithm comprising one or more second decision trees (fig. 2, ¶0037; using a decision tree algorithm to identify the problem(s), using a separate machine learning algorithm to recommend one or more configuration updates, and sending an updated version of the machine learning algorithm to peer systems to detect and/or recommend similar configuration adjustments in one or more of the peer systems.;) generated based on information processing system data to determine one or more hardware profiles for the one or more additional physical devices in accordance with the given system goal; (fig. 2, ¶0042-43; it is determined whether the critical alert is related to one or more hardware changes. If no, then data pertaining to that determination is incorporated into and/or added to data 233. If yes, then step 227 includes determining whether user system 202-2 and/or user system 202-3 have similar hardware changes (to user system 202-1) and/or whether the hardware is similarly configured (to that of user system 202-1). If yes, then data pertaining to that (positive) determination is incorporated into and/or added to data 233.… step 231 includes determining whether user system 202-2 and/or user system 202-3 have similar system usage configurations (to user system 202-1). If yes, then data pertaining to that (positive) determination is incorporated into and/or added to data 233. If no, then (different) data pertaining to that (negative) determination is incorporated into and/or added to data 233…..In conjunction with the above-noted descriptions, at least one recommendation 237 pertaining to the alert received by user system 202-1 is generated by the trained machine learning model 235 and provided to user system 202-1, as well as to user system 202-2 and user system 202-3 for implementation (e.g., the recommendation 237 can include an instruction to adjust one or more system configurations) and deploy the one or more hardware profiles to the one or more additional physical devices to enable the one or more additional physical devices to operate in the information processing system with the set of one or more physical devices (¶0043; .In conjunction with the above-noted descriptions, at least one recommendation 237 pertaining to the alert received by user system 202-1 is generated by the trained machine learning model 235 and provided to user system 202-1, as well as to user system 202-2 and user system 202-3 for implementation (e.g., the recommendation 237 can include an instruction to adjust one or more system configurations; ¶0055; processing data related to hardware profiles, device health metrics, and performance metrics for similarly configured hardware devices before accepting and/or implementing a trained machine learning model recommendation).
Files does a decision tree recommending configurations but does not specifically disclose physical devices based on measurements of physical variables of components: wherein the one or more first decision trees determine the recommendation for adding the one or more additional physical devices based on one or more device states of the one or more physical devices, wherein the one or more device states are respectively computed for the one or more physical devices based on measurements of physical variables of components internal to the one or more physical devices; deploying one or more additional physical devices including one or more bare metal servers, and subsequent to deployment of the one or more additional physical devices including one or more bare metal servers to the information processing system,.
However, Ding discloses: wherein the one or more first decision trees determine the recommendation for adding the one or more additional physical devices based on one or more device states of the one or more physical devices, wherein the one or more device states are respectively computed for the one or more physical devices based on measurements of physical variables of components internal to the one or more physical devices (claim 7; the recommendation tool configured to generate at least one provisioning policy based on an analysis of the operational profile in conjunction with the set of attributes, the at least one provisioning policy specifying a condition for re-allocating resources associated with at least one processing node in a manner that satisfies the performance goals of the set of attributes, the at least one provisioning policy indicating to allocate at least one additional server when a response time of the one or more processing nodes is greater than the response time defined by the attribute; and a provisional tool configured to re-allocate, during runtime of the computer system,).
It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to combine the teachings of Files and Deng’s automated capacity provisioning to be proactive instead of reactive. Ding ensures overall system performance (col. 5 lines 20-30).
The combined teachings of Files and Ding do not specifically disclose a bare metal server added: deploying one or more additional physical devices including one or more bare metal servers, and subsequent to deployment of the one or more additional physical devices including one or more bare metal servers to the information processing system, execute the machine learning algorithm comprising one or more second decision trees generated based on information processing system data to determine one or more hardware profiles for the one or more additional physical devices in accordance with the given system goal.
However, Khorsrowpour disclose: deploying one or more additional physical devices including one or more bare metal servers, (¶0044; Although application profile manager 304 is shown and described herein to provision a container 308 when requested by client IHS 102, it should be appreciated that application profile manager 304 may be configured to provision other interfaces (e.g., NFs) to communication network, such as physical machines (bare metal machines), virtual machines (VMs), and the like when requested by application 108) and subsequent to deployment of the one or more additional physical devices including one or more bare metal servers to the information processing system, execute the machine learning algorithm…..(¶0006; an IHS may include computer-executable instructions for determining one or more application performance features of a target application using an application machine learning (ML) engine, and generating one or more application profile recommendations for the target application according to the determined application performance features. Using the profile recommendations, the instructions adjust one or more settings of the IHS to optimize a performance of the target application, and transmit the application profile recommendations to a server that is configured to provide a service for the target application. The server then uses the one or more application profile recommendations to provision the service for use by the target application).
It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to combine the teachings of Files, Deng and Khosrowpour’s optimization of services toa adjust one or more service profile recommendations. Khorsrowpour optimizes performance through profile recommendations. (¶0008).
Per claim 6, Files discloses: wherein the device data for each of the set of physical devices comprises data indicative of at least one of: one or more memory features; one or more processor features; one or more network interface features; one or more remote access features; one or more storage component features; one or more peripheral interface features; and one or more device environmental features (¶0058; Step 400 includes obtaining system configuration data from at least a portion of multiple user systems within a network. In at least one embodiment, obtaining the system configuration data includes obtaining, from the at least a portion of the multiple user systems within the network, data pertaining to one or more of thermal characteristics of one or more hardware components, one or more usage metrics, user system information, battery information, disk information, memory information, application information, driver information, power history information, and one or more alert logs).
Per claim 7, Files discloses: wherein the information processing system comprises data indicative of at least one of: one or more system configuration features; one or more driver features; one or more system environmental features; one or more system bandwidth features; and one or more hardware profile features (¶0058; Step 400 includes obtaining system configuration data from at least a portion of multiple user systems within a network. In at least one embodiment, obtaining the system configuration data includes obtaining, from the at least a portion of the multiple user systems within the network, data pertaining to one or more of thermal characteristics of one or more hardware components, one or more usage metrics, user system information, battery information, disk information, memory information, application information, driver information, power history information, and one or more alert logs).
Per claim 8, Files discloses: wherein the given system goal comprises at least one of an efficiency goal, a security goal, a performance goal, a reliability goal, a scalability goal, and an agility goal (¶0063; For example, some embodiments are configured to automatically detect and remediate issues across multiple user systems using healing-as-a-service techniques. These and other embodiments can effectively overcome problems associated with latency and data security).
Per claim 9, Files discloses: wherein the set of one or more physical devices comprises at least one of one or more bare metal servers, one or more components of a bare metal server, and combinations thereof (¶0074; , the VMs/container sets 502 comprise respective containers implemented using virtualization infrastructure 504 that provides operating system level virtualization functionality, such as support for Docker containers running on bare metal hosts, or Docker containers running on VMs).
Per claim 10, Files discloses: wherein the information processing system comprises a communication service provider network (fig. 1, ¶0012; FIG. 1 shows a computer network (also referred to herein as an information processing system) 100 configured in accordance with an illustrative embodiment. The computer network 100 comprises a plurality of user systems 102-1, 102-2, 102-3, 102-4, 102-5, 102-6, . . . 102-M, collectively referred to herein as user systems 102.).
Claims 11 and 15-16 are the computer product claims corresponding to the apparatus claims 1 and 9-10 and are rejected under the same reasons set forth in connection with the rejection of claims 1 and 9-10.
Claims 17 is the method claim corresponding to the apparatus claims 1 and is rejected under the same reasons set forth in connection with the rejection of claim 1.
Claim(s) 2-5, 12-14 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over File, Ding and Khosrowspour in view of Gao et al. 20220101438 herein Gao.
Per claim 2, Files and Ding do not specifically disclose: wherein the machine learning algorithm comprises a gradient boosting algorithm.
However, Gao discloses: wherein the machine learning algorithm comprises a gradient boosting algorithm (¶0220; The decision tree ensembles may be trained on the selected training data point using gradient boosting at 553. In one implementation, the XGBoost library may be used to train the decision tree ensembles using the training data data-structure).
It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to combine the teachings of Files, Ding and Khosrowspour with the XGBoost library of Gao to train the decision tree. Gao optimizes the training.
Per claim 3, Gao discloses: wherein, in the utilization of the machine learning algorithm comprising the one or more first decision trees, the gradient boosting algorithm utilizes one or more machine learning models trained on at least a portion of the device data (¶0220; The decision tree ensembles may be trained on the selected training data point using gradient boosting at 553. In one implementation, the XGBoost library may be used to train the decision tree ensembles using the training data data-structure).
Per claim 4, Gao discloses: wherein, in the utilization of the machine learning algorithm comprising the one or more second decision trees, the gradient boosting algorithm utilizes one or more machine learning models trained on at least a portion of the information processing system data (¶0220; The decision tree ensembles may be trained on the selected training data point using gradient boosting at 553. In one implementation, the XGBoost library may be used to train the decision tree ensembles using the training data data-structure).
Per claim 5, Gao discloses: wherein the device data is collected via a physical device orchestration platform operatively coupled between the set of physical devices and the apparatus (¶0832; taking advantages of compute power provided by cloud platform and applies AWS Elastic Container Service (ECS) which is a fully managed container orchestration service to handle requests in a parallel way. Multiple user requests are loaded into different containers that are hosted on a cluster of EC2 machines.).
Claims 12-14 are the computer product claims corresponding to the apparatus claims 2-5 and are rejected under the same reasons set forth in connection with the rejection of claims 2-5.
Claims 18-20 are the method claim corresponding to the apparatus claims 2-5 and are rejected under the same reasons set forth in connection with the rejection of claims 2-5.
Response to Arguments
Applicant’s arguments, filed 9/18/26, with respect to the rejection(s) of claim(s) 1, 11 and 17 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Khosrowspour.
Remark
Examiner respectfully requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
Conclusion
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BABOUCARR . FAAL
Primary Examiner
Art Unit 2138
/BABOUCARR FAAL/Primary Examiner, Art Unit 2138
