DETAILED ACTION
1. Claims 1-20 are pending.
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 .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 3/8/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
3. The information disclosure statement (IDS) submitted on 3/11/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
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.
Claim 1-20 are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The following claim language is unclear:
Claim 1 includes the limitations of “wherein the relative ratios indicate relative processing performances that are relative to the processing performance.” It is unclear from the context of the claim how the relative processing performances are relative to the processing performance. Is the processing performance a static number? Is the processing performance related to a baseline processing performance mentioned earlier? Is the processing performance the same as the relative processing performances? For examination purposes, Examiner interprets the limitation as the relative ratios are relative to the baseline performance scores.
Additionally, claim 1 includes the limitations of “based on efficiency scores for respective virtualized nodes of the one or more recommended virtualized nodes and the virtualized node.” It is unclear from the context of the claim what these efficiency scores are. For examination purposes, Examiner interprets the limitation as a score/ranking that indicates that the virtual node is the target/recommended node for the action.
Regarding claims 12-17, they are dependent on claim 11 and fail to cure the deficiencies set forth above for claim 1. Therefore, they are rejected under the same rationale.
Regarding claims 19-20, they are dependent on claim 18 and fail to cure the deficiencies set forth above for claim 1. Therefore, they are rejected under the same rationale.
Claim Rejections - 35 USC § 102
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
4. Claims 1-16 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nie US 20250284532 A1.
5. With regard to claim 1, Nie teaches:
A system for optimizing virtualized node selection in a cloud computing environment, the system comprising:
one or more memories ([0012] According to a third aspect of an embodiment of the present specification, a computing device is provided, including: [0013] a memory and a processor; [0014] the memory is configured to store computer-executable instructions; and the processor is configured to execute the executable the computer-executable instructions, when the computer-executable instructions are executed by the processor, the steps of the task processing method described are implemented.); and
one or more processors, communicatively coupled to the one or more memories ([0012] According to a third aspect of an embodiment of the present specification, a computing device is provided, including: [0013] a memory and a processor; [0014] the memory is configured to store computer-executable instructions; and the processor is configured to execute the executable the computer-executable instructions, when the computer-executable instructions are executed by the processor, the steps of the task processing method described are implemented.), configured to:
determine a baseline processing performance score using an origin virtualized node deployed in the cloud computing environment, wherein the baseline processing performance score is based on a processing performance of a physical processing system supporting the origin virtualized node ([0070] In actual applications, in a case where the initial virtual nodes are GPU instances and the physical computing unit is a GPU, the current state information of the initial virtual nodes being determined based on the physical computing unit corresponding to the initial virtual nodes can be understood as determining a utilization rate index of each hardware component inside a GPU hardware as a current utilization rate of the GPU instance corresponding to each hardware component, so as to facilitate subsequent task scheduling based on the utilization rate. A specific implementation method is as follows; [0123] In a case where there is one minimum utilization rate, the GPU instance corresponding to the minimum utilization is determined as a GPU instance for running the audio and video production task; Examiner’s Note: The current utilization rate and current state information of the initial virtual nodes is being identified as the baseline processing performance score.);
determine relative ratios for respective virtualized nodes of one or more virtualized nodes deployed in the cloud computing environment, wherein the relative ratios indicate relative processing performances that are relative to the processing performance ([0118] Further, in embodiments provided in the present specification, the based on the current state information of the candidate virtual nodes, selecting the target virtual node corresponding to the target task from the candidate virtual nodes includes: [0119] determining a target computing ratio of the candidate virtual nodes based on the current state information of the candidate virtual nodes; [0120] in a case that the target calculation ratio is less than a first ratio threshold, determining a minimum target calculation ratio from the target calculation ratio; and [0121] based on the candidate virtual nodes corresponding to the minimum target calculation ratio, determining the target virtual node corresponding to the target task; [0122] Continuing with the above example, the scheduling system determines the utilization of one or more hardware decoding units corresponding to the GPU instance. In a case that the utilization rate is determined less than or equal to the first ratio threshold of 70%, it is determined that there is a GPU instance in the current GPU instances that can run the audio and video production task. The scheduling system then determines a minimum utilization rate from the utilization rate that is less than or equal to the first ratio threshold of 70%.);
determine processing performance scores for the respective virtualized nodes based on the relative ratios and the baseline processing performance score ([0122] Continuing with the above example, the scheduling system determines the utilization of one or more hardware decoding units corresponding to the GPU instance. In a case that the utilization rate is determined less than or equal to the first ratio threshold of 70%, it is determined that there is a GPU instance in the current GPU instances that can run the audio and video production task. The scheduling system then determines a minimum utilization rate from the utilization rate that is less than or equal to the first ratio threshold of 70%; [0123] In a case where there is one minimum utilization rate, the GPU instance corresponding to the minimum utilization is determined as a GPU instance for running the audio and video production task; Examiner’s Note: The minimum utilization rate that determines the instance is being viewed as the processing performance score.);
determine, for an application deployed in the cloud computing environment via a virtualized node of the one or more virtualized nodes, a processing capacity utilization score based on a processing performance score of the virtualized node and a utilization score of the application that is based on telemetry data associated with the application and one or more processing characteristics of the virtualized node ([0090] Continuing with the above example, the GPU monitor can obtain the utilization rate index of each hardware component inside the GPU corresponding to each GPU instance, and periodically synchronize the utilization rate index to the scheduling system. After that, in a case that the scheduling system of the Serverless platform receives the target task, it determines the current utilization rate of each hardware unit in the GPU, and determines the hardware units corresponding to the GPU instance from multiple hardware units, and uses the current utilization rate of each hardware unit in the GPU as the utilization rate of the corresponding GPU instance. This makes it easier to determine the corresponding GPU instance for the target task based on the utilization rate; [0128] Continuing with the above example, in a case that it is determined that the utilization rate is less than or equal to the first ratio threshold of 70%, the scheduling system determines the GPU instances whose utilization rate is less than or equal to the first ratio threshold of 70%, and sorts the GPU instances in a descending order based on the utilization rate to obtain the sorting result of the GPU instances, where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.);
determine, based on the processing capacity utilization score and one or more processing parameters of the application, one or more recommended virtualized nodes from the one or more virtualized nodes ([0094] Specifically, after determining the current state information of the initial virtual nodes, the scheduling system can determine the task type information of the target task, and based on the task type information, determine the candidate virtual nodes corresponding to the task type information from the initial virtual nodes, that is, all virtual nodes in the initial virtual nodes that can process the target task; [0128] [...] where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.); and
perform, based on efficiency scores for respective virtualized nodes of the one or more recommended virtualized nodes and the virtualized node, an action associated with a selection of an operating virtualized node for the application from the virtualized node and the one or more recommended virtualized nodes ([0095] Step 306: determining a corresponding target virtual node for the target task based on the current state information of the candidate virtual nodes, and executing the target task through the target virtual node; [0129] Further, in embodiments provided in the present specification, the physical computing unit is a GPU. In this case, after the executing the target task through the target virtual node [...]).
6. With regard to claim 2, Nie further teaches:
wherein the one or more processing parameters of the physical processing system supporting the origin virtualized node include at least one of: a clock speed, a quantity of cores, a cache size, or an instruction set architecture ([0066] The initial virtual nodes can be understood as nodes that can run the heterogeneous computing task. For example, the initial virtual nodes can be general computing nodes, GPU instances, virtual machines, containers, etc; [0071] The determining the current state information of the initial virtual nodes based on the target task received includes: [0072] based on the target task received, determining physical computing subunits in the physical computing unit and current operation information of the physical computing subunits; [0073] determining a target physical computing subunit corresponding to the initial virtual nodes from the physical computing subunits; [0074] using the current operation information of the target physical computing subunit as the current state information of the initial virtual nodes; Examiner’s Note: The initial virtual nodes are being viewed as a quantity of cores.).
7. With regard to claim 3, Nie further teaches:
wherein the relative ratios are based on respective processing characteristics of physical processing systems supporting the one or more virtualized nodes ([0107] Continuing with the above example, the target task may be an audio and video production task, the current state information is the utilization rate of the hardware encoding unit, and the first ratio threshold may be 70%. Based on this, the scheduling system determines a utilization rate of a hardware decoding unit corresponding to a GPU instance, and uses the utilization rate of the hardware decoding unit as a utilization rate of the GPU instance corresponding to the hardware decoding unit, where the utilization rate may be 80%. ).
8. With regard to claim 4, Nie further teaches:
wherein the one or more processing parameters include at least one of: a memory utilization metric, or a disk utilization metric ([0170] The GPU monitor component (GPU Monitor) in the system framework is configured to obtain an internal hardware component utilization rate index of each GPU instance. It should be noted that the hardware components are different according to the different scenarios in which the task processing method provided in the present specification is applied. [...] Accordingly, the hardware component utilization rate index include but are not limited to [...], a video memory utilization rate of all scenarios, etc.).
9. With regard to claim 5, Nie further teaches:
wherein the one or more processors are further configured to:
determine, for the application, one or more performance thresholds based on the processing capacity utilization score and the one or more processing parameters of the application ([[0070] In actual applications, in a case where the initial virtual nodes are GPU instances [...]; [0128] Continuing with the above example, in a case that it is determined that the utilization rate is less than or equal to the first ratio threshold of 70%, the scheduling system determines the GPU instances whose utilization rate is less than or equal to the first ratio threshold of 70%, and sorts the GPU instances in a descending order based on the utilization rate to obtain the sorting result of the GPU instances, where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.).
10. With regard to claim 6, Nie further teaches:
wherein the one or more processors, to determine the one or more recommended virtualized nodes, are configured to:
determine, for each recommended virtualized node of the one or more recommended virtualized nodes, that values of respective processing characteristics of that recommended virtualized node satisfy the one or more performance thresholds ([0070] In actual applications, in a case where the initial virtual nodes are GPU instances [...]; [0128] Continuing with the above example, in a case that it is determined that the utilization rate is less than or equal to the first ratio threshold of 70%, the scheduling system determines the GPU instances whose utilization rate is less than or equal to the first ratio threshold of 70%, and sorts the GPU instances in a descending order based on the utilization rate to obtain the sorting result of the GPU instances, where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.).
11. With regard to claim 7, Nie further teaches:
wherein the processing capacity utilization score indicates a processing performance score threshold associated with supporting a deployment of the application ([00128] [...] sorts the GPU instances in a descending order based on the utilization rate to obtain the sorting result of the GPU instances, where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.).
12. With regard to claim 8, Nie further teaches:
wherein the one or more recommended virtualized nodes are associated with respective values, of one or more values, of a metric, and wherein the efficiency scores indicate ratios of the respective values to a lowest value or a highest value of the one or more values ([0125] In practical applications, in a case that it is determined that the utilization rate is less than or equal to the first ratio threshold of 70%, determining the GPU instance with better performance further includes: [0126] sorting the candidate virtual nodes based on the target computing ratio to obtain a sorting result of the candidate virtual nodes, where the candidate virtual nodes include at least two; [0127] determining a corresponding target virtual node for the target task from the candidate virtual nodes based on the sorting result; [0128] Continuing with the above example, in a case that it is determined that the utilization rate is less than or equal to the first ratio threshold of 70%, the scheduling system determines the GPU instances whose utilization rate is less than or equal to the first ratio threshold of 70%, and sorts the GPU instances in a descending order based on the utilization rate to obtain the sorting result of the GPU instances, where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.).
13. With regard to claim 9, Nie further teaches:
wherein the metric is a cost metric ([0137] The task processing method provided in the present specification can sample the utilization rate of the various components inside the GPU hardware and allow the user to set different elastic scaling indexes according to the heterogeneous computing tasks in different scenarios, thereby periodically elastically scaling a GPU instance based on the hardware utilization rate of the GPU instance, solving the cost waste caused by over-expansion and the performance loss caused by premature narrowing.).
14. With regard to claim 10, Nie further teaches:
wherein the one or more processors, to perform the action, are configured to:
determine, based on the efficiency scores, the operating virtualized node from the virtualized node and the one or more recommended virtualized nodes ([0128] Continuing with the above example, in a case that it is determined that the utilization rate is less than or equal to the first ratio threshold of 70%, the scheduling system determines the GPU instances whose utilization rate is less than or equal to the first ratio threshold of 70%, and sorts the GPU instances in a descending order based on the utilization rate to obtain the sorting result of the GPU instances, where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.); and
cause the application to be deployed via the operating virtualized node ([0129] Further, in embodiments provided in the present specification, the physical computing unit is a GPU. In this case, after the executing the target task through the target virtual node [...]).
15. With regard to claim 11, Nie teaches:
A method for optimizing virtualized node selection in a cloud computing environment comprising:
obtaining, by a device, telemetry data associated with the cloud computing environment, wherein the telemetry data indicates one or more processing parameters of an application deployed in the cloud computing environment via a current virtualized node ([0017] The present specification provides a task processing method including: determining current state information of initial virtual nodes based on a target task received, where the current state information is determined based on a physical computing unit corresponding to the initial virtual nodes; based on task type information of the target task, determining candidate virtual nodes corresponding to the task type information from the initial virtual nodes; and determining a corresponding target virtual node for the target task based on the current state information of the candidate virtual nodes, and executing the target task through the target virtual node.);
determining, by the device and for the application, a processing capacity utilization score based on a processing performance score of the current virtualized node and a utilization score of the application that is based on the one or more processing parameters and one or more processing characteristics of the current virtualized node ([0090] Continuing with the above example, the GPU monitor can obtain the utilization rate index of each hardware component inside the GPU corresponding to each GPU instance, and periodically synchronize the utilization rate index to the scheduling system. After that, in a case that the scheduling system of the Serverless platform receives the target task, it determines the current utilization rate of each hardware unit in the GPU, and determines the hardware units corresponding to the GPU instance from multiple hardware units, and uses the current utilization rate of each hardware unit in the GPU as the utilization rate of the corresponding GPU instance. This makes it easier to determine the corresponding GPU instance for the target task based on the utilization rate; [0128] Continuing with the above example, in a case that it is determined that the utilization rate is less than or equal to the first ratio threshold of 70%, the scheduling system determines the GPU instances whose utilization rate is less than or equal to the first ratio threshold of 70%, and sorts the GPU instances in a descending order based on the utilization rate to obtain the sorting result of the GPU instances, where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.), wherein the processing performance score is based on a baseline processing performance score of an origin virtualized node ([0070] In actual applications, in a case where the initial virtual nodes are GPU instances and the physical computing unit is a GPU, the current state information of the initial virtual nodes being determined based on the physical computing unit corresponding to the initial virtual nodes can be understood as determining a utilization rate index of each hardware component inside a GPU hardware as a current utilization rate of the GPU instance corresponding to each hardware component, so as to facilitate subsequent task scheduling based on the utilization rate. A specific implementation method is as follows; [0123] In a case where there is one minimum utilization rate, the GPU instance corresponding to the minimum utilization is determined as a GPU instance for running the audio and video production task; Examiner’s Note: The current utilization rate and current state information of the initial virtual nodes is being identified as the baseline processing performance score.) and a relative ratio indicating a first processing performance of the current virtualized node relative to a second processing performance associated with the origin virtualized node ([0118] Further, in embodiments provided in the present specification, the based on the current state information of the candidate virtual nodes, selecting the target virtual node corresponding to the target task from the candidate virtual nodes includes: [0119] determining a target computing ratio of the candidate virtual nodes based on the current state information of the candidate virtual nodes; [0120] in a case that the target calculation ratio is less than a first ratio threshold, determining a minimum target calculation ratio from the target calculation ratio; and [0121] based on the candidate virtual nodes corresponding to the minimum target calculation ratio, determining the target virtual node corresponding to the target task; [0122] Continuing with the above example, the scheduling system determines the utilization of one or more hardware decoding units corresponding to the GPU instance. In a case that the utilization rate is determined less than or equal to the first ratio threshold of 70%, it is determined that there is a GPU instance in the current GPU instances that can run the audio and video production task. The scheduling system then determines a minimum utilization rate from the utilization rate that is less than or equal to the first ratio threshold of 70%; [0075] The current operation information of the physical computing subunits can be understood as a utilization rate index of each hardware component. Based on this, the scheduling system determines the current operation information of each physical computing subunit in the physical computing unit, and can use the current operation information as the current state information of the initial virtual nodes. For example, the scheduling system can obtain the utilization rate index of each component inside the GPU hardware, and determine the utilization rate index as a utilization rate corresponding to the GPU instances; Examiner’s Note: The utilization rate corresponding to the GPU instances is being viewed as the processing performance scores for the respective virtualized nodes.);
determining, by the device and based on the processing capacity utilization score and the one or more processing parameters ([0090] Continuing with the above example, the GPU monitor can obtain the utilization rate index of each hardware component inside the GPU corresponding to each GPU instance, and periodically synchronize the utilization rate index to the scheduling system. After that, in a case that the scheduling system of the Serverless platform receives the target task, it determines the current utilization rate of each hardware unit in the GPU, and determines the hardware units corresponding to the GPU instance from multiple hardware units, and uses the current utilization rate of each hardware unit in the GPU as the utilization rate of the corresponding GPU instance. This makes it easier to determine the corresponding GPU instance for the target task based on the utilization rate; [0128] Continuing with the above example, in a case that it is determined that the utilization rate is less than or equal to the first ratio threshold of 70%, the scheduling system determines the GPU instances whose utilization rate is less than or equal to the first ratio threshold of 70%, and sorts the GPU instances in a descending order based on the utilization rate to obtain the sorting result of the GPU instances, where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.), one or more recommended virtualized nodes from one or more virtualized nodes associated with the cloud computing environment, wherein the one or more recommended virtualized nodes satisfy one or more processing performance criteria for the application indicated by the processing capacity utilization score and the one or more processing parameters ([0094] Specifically, after determining the current state information of the initial virtual nodes, the scheduling system can determine the task type information of the target task, and based on the task type information, determine the candidate virtual nodes corresponding to the task type information from the initial virtual nodes, that is, all virtual nodes in the initial virtual nodes that can process the target task; [0128] [...] where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.); and
performing, by the device and based on efficiency scores for respective virtualized nodes of the one or more recommended virtualized nodes and the current virtualized node, an action associated with a selection of an operating virtualized node for the application from the current virtualized node and the one or more recommended virtualized nodes ([0095] Step 306: determining a corresponding target virtual node for the target task based on the current state information of the candidate virtual nodes, and executing the target task through the target virtual node; [0129] Further, in embodiments provided in the present specification, the physical computing unit is a GPU. In this case, after the executing the target task through the target virtual node [...].).
16. With regard to claim 12, Nie further teaches:
wherein the telemetry data includes at least one of: a resource quantity, a resource performance setting, a quantity of communicated packets, a first size of communicated data, a quantity of read or write requests, a second size of the read or write requests, or one or more library architecture parameters ([0079] In embodiments of the present specification, in a case that the target task is received, the current state information of the initial virtual nodes is determined based on the current operation information of the physical computing subunits in the physical computing unit, so as to facilitate a subsequent determination of the corresponding target virtual node for the target task based on the utilization rate; [0080] Further, in an embodiment provided in the present specification, before determining the current state information of the initial virtual nodes based on the target task received, the method further includes: [0081] receiving the current operation information of the physical computing subunits in the physical computing unit sent by an information collection module, where the information collection module is a module configured to monitor the current operation information of the physical computing subunits in the physical computing unit; [0082] The information collection module may be understood as any module that realizes a function of collecting the current operation information of the physical computing unit, such as a GPU monitor; [0083] Specifically, the information collection module can monitor the current operation information of each physical computing subunit in the physical computing unit in real time, and send the current operation information to the scheduling system. Therefore, the scheduling system can receive the current operation information of each physical computing subunit in the physical computing unit sent by the information collection module. For example, the GPU monitor can obtain the utilization rate index of each hardware component inside the GPU corresponding to each GPU instance, and periodically synchronize the utilization rate index to the scheduling system; [0084] In embodiments of the present specification, that is to say, the current operation information of the physical computing unit sent by the information collection module can be received, so that the current state information of the initial virtual nodes can be determined based on the current operation information, where the current operation information of the physical computing unit may be current operation information of each physical computing subunit in the physical computing unit; [0105] In a case that the current state information is a utilization rate of a hardware encoding unit, the target calculation ratio can be understood as a utilization rate of a subsequent virtual node. The utilization rate can be set according to an actual application scenario, for example, the utilization rate can be any value in a range of 0% to 100%, or any value in a range of [0,1], etc.).
17. Regarding claim 13, it is rejected under the same reasoning as claim 2 above. Therefore, it is rejected under the same rationale.
18. Regarding claim 14, it is rejected under the same reasoning as claim 4 above. Therefore, it is rejected under the same rationale.
19. With regard to claim 15, Nie further teaches:
wherein the processing capacity utilization score indicates a processing performance score threshold associated with supporting a deployment of the application, and wherein the processing performance score threshold is included in the one or more processing performance criteria ([0090] Continuing with the above example, the GPU monitor can obtain the utilization rate index of each hardware component inside the GPU corresponding to each GPU instance, and periodically synchronize the utilization rate index to the scheduling system. After that, in a case that the scheduling system of the Serverless platform receives the target task, it determines the current utilization rate of each hardware unit in the GPU, and determines the hardware units corresponding to the GPU instance from multiple hardware units, and uses the current utilization rate of each hardware unit in the GPU as the utilization rate of the corresponding GPU instance. This makes it easier to determine the corresponding GPU instance for the target task based on the utilization rate; [0128] Continuing with the above example, in a case that it is determined that the utilization rate is less than or equal to the first ratio threshold of 70%, the scheduling system determines the GPU instances whose utilization rate is less than or equal to the first ratio threshold of 70%, and sorts the GPU instances in a descending order based on the utilization rate to obtain the sorting result of the GPU instances, where the closer the GPU instance is to the front in the sorting result, the lower the utilization rate, that is, the better the performance of the GPU instance. Based on this, the scheduling system schedules the audio and video production task to the first place in the sorting result, or the first specific number of GPU instances in the sorting result, where the specific number can be set according to an actual application scenario, such as the top three or the top ten.).
20. Regarding claim 16, it is rejected under the same reasoning as claim 8 above. Therefore, it is rejected under the same rationale.
21. Regarding claim 18, it is rejected under the same reasoning as claim 11 above. Therefore, it is rejected under the same rationale.
22. Regarding claim 19, it is rejected under the same reasoning as claim 2 above. Therefore, it is rejected under the same rationale.
23. Regarding claim 20, it is rejected under the same reasoning as claim 10 above. Therefore, it is rejected under the same rationale.
Claim Rejections - 35 USC § 103
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.
24. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Nie US 20250284532 A1, as applied in claim 11, in view of Wang US 20200059798 A1.
25. With regard to claim 17, Nie teaches the method of claim 11 but fails to explicitly teach wherein performing the action comprises: providing, for display, display information indicating virtualized nodes, from the current virtualized node and the one or more recommended virtualized nodes, and corresponding efficiency scores from the efficiency scores.
However, in analogous art, Wang teaches:
wherein performing the action comprises:
providing, for display, display information indicating virtualized nodes, from the current virtualized node and the one or more recommended virtualized nodes, and corresponding efficiency scores from the efficiency scores ([0045] Furthermore, in order to be able to display the respective virtual nodes and the parameter information of the respective virtual nodes more intuitively and clearly, the example of the present disclosure can generate a display interface including the respective virtual nodes in the virtual network system, according to the configuration file of the virtual network system, for displaying to a user, so that in a case where a trigger operation of the user for respective virtual nodes in the display interface is received, the network configuration parameters of the respective virtual nodes are extracted from the configuration file of the virtual network system.).
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 Nie with the teachings of Wang wherein performing the action comprises: providing, for display, display information indicating virtualized nodes, from the current virtualized node and the one or more recommended virtualized nodes, and corresponding efficiency scores from the efficiency scores. Wang teaches of displaying information related to the virtual nodes. Displaying the information to a user allows the installation personnel can quickly acquire the virtual nodes corresponding to the respective equipment nodes on site and write the network configuration parameters of the virtual nodes into the equipment nodes, thereby completing the network configuration of the entire wireless network system and further improving the efficiency of configuring the node on site, as discussed in Wang ([0045]).
Conclusion
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, AIMEE LI can be reached at (571) 272-4169. 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.
/AN-AN NGOC NGUYEN/Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195
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