DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 7/25/23. Claims 1-20 are pending in this Office 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8 and 16:
Step 1: Statutory Category ?: Yes. claim 1 recites a method (i.e., a “process”), claim 8 recites a non-transitory computer readable medium (i.e., an article of manufacture) and claim 16 recites a system (i.e., a “machine”) ,which are statutory categories.
Claim 1:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
The limitation “causing a machine learning model to determine an epoch training time and a processor utilization for computing instances of the set of computing instances based on the set of workload features of the workload , a set of computing instance features of the set of computing instances , and a set of performance features; ranking the set of computing instances in accordance with the metric based on the epoch training time and the processor utilization associated with the computing instances of the set of computing instances ” is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion . Except for the “cause a machine learning model” language, there is nothing in the claim that prevents the determining and ranking steps from being performed in the human mind.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 1 recites additional element of “ a machine learning model” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model.
The additional elements of “obtaining an indication of a metric to rank a set of computing instances and a set of workload features of a workload and causing the ranking of the set of computing instances in a user interface”
is simply data gathering step and therefore are insignificant extra-solution activities. (See MPEP 2106.05(g)).
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of using machine learning model is at best equivalent of adding the words “apply it” to the judicial exception. The additional element of “obtaining an indication of a metric to rank a set of computing instances and a set of workload features of a workload and causing the ranking of the set of computing instances in a user interface” is data gathering which is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 1 therefore is ineligible.
Claim 2 recites the additional element of “ causing a second machine learning model to determine the set of performance features based on the set of workload features and the set of computing instance features” which is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion . Except for the “cause a machine learning model” language, there is nothing in the claim that prevents the determining step from being performed in the human mind. The additional element of “ a second machine learning model” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model and is at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 2 therefore is ineligible.
Claim 3 recites the additional element of “wherein causing the second machine learning model to determine the set of performance features is in response to the workload or the set of computing instances having not been previously recorded” which is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion . Except for the “cause a machine learning model” language, there is nothing in the claim that prevents the determining step from being performed in the human mind. The additional element of “ a second machine learning model” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model and is at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 3 therefore is ineligible.
Claim 4 recites the additional element of “training the machine learning model and the second machine learning model using a training dataset including a set of metrics obtained by at least causing the set of computing instances to execute a set of workloads” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model and is at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 4 therefore is ineligible.
Claim 5 recites the additional element of “wherein the set of computing instance features includes at least one of: a number of Graphic Processing Units (GPUs), GPU memory, GPU memory type, GPU type, number of Central Processing Units (CPUs), number of virtual CPUs, CPU type, CPU memory, and CPU memory type” . The instance features are part of the determining step which is a mental process. Claim 5 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 5 is not patent eligible.
Claim 6 recites the additional element of “wherein the set of performance features includes at least one of: average Graphic Processing Unit (GPU) utilization, minimum GPU utilization, maximum GPU utilization, average Central Processing Unit (CPU) utilization, minimum CPU utilization, maximum CPU utilization, average memory utilization, minimum memory utilization, maximum memory utilization, core temperature, memory bandwidth, cache usage, and power usage” . The performance features are part of the determining step which is a mental process. Claim 6 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 6 is not patent eligible.
Claim 7 recites the additional element of wherein the set of workload features includes at least one of: a number of floating point operations (FLOPs), number of layers, number of activations, number of parameters and batch size”. The set of workload features are part of the obtaining step which is merely data gathering and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional element does not provide an inventive concept, claim 7 therefore is ineligible.
Claim 8:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
The limitation “causing a machine learning model to determine a metric associated with a computing instance when executing a workload, the machine learning model taking as inputs a set of workload features associated with the workload, a set of computing instance features associated with a plurality of computing instances, and a set of metrics obtained from the plurality of computing instances during execution of a plurality of workloads;
generating a ranking of a set of computing instances, including the computing instance, based on the metric” is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion . Except for the “cause a machine learning model” language, there is nothing in the claim that prevents the determining and ranking steps from being performed in the human mind.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 8 recites additional element of “non-transitory computer readable medium and processing device” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The “a machine learning model” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model.
The additional elements of “updating a display to include the ranking of the set of computing instances” is pre/post solution activity which is insignificant extra-solution activities. (See MPEP 2106.05(g)).
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 8 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of “machine learning model and non-transitory computer readable medium and processing device” is at best equivalent of adding the words “apply it” to the judicial exception. The additional element of “updating a display to include the ranking of the set of computing instances” is insignificant extra-solution activities which is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 8 therefore is ineligible.
Claim 9 recites the additional element of “causing a second machine learning model to determine at least a portion of the set of metrics” which is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion . Except for the “cause a machine learning model” language, there is nothing in the claim that prevents the determining step from being performed in the human mind. The additional element of “ a second machine learning model” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model and is at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 9 therefore is ineligible.
Claim 10 recites the additional element of “ wherein the set of metrics include benchmarks obtained from the plurality of computing instances during execution of the plurality of workloads” which is simply data gathering step and therefore are insignificant extra-solution activities. (See MPEP 2106.05(g)). Data gathering which is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional element does not provide an inventive concept, claim 10 therefore is ineligible.
Claim 11 recites the additional element of “wherein the machine learning model is trained using the set of metrics” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model and at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional element does not provide an inventive concept, claim 11 therefore is ineligible.
Claim 12 recites the additional element of “wherein the ranking of the set of computing instances further comprises an ordering of the set of computing instances from a lowest epoch training time to a highest epoch training time” which is a mental process. Claim 12 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 12 is not patent eligible.
Claim 13 recites the additional element of “wherein the ranking of the set of computing instances further comprises an ordering of the set of computing instances from a highest processor utilization to a lowest processor utilization” which is a mental process. Claim 13 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 13 is not patent eligible.
Claim 14 recites the additional element of wherein the machine learning model is a regression model” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine regression model and at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional element does not provide an inventive concept, claim 14 therefore is ineligible.
Claim 15 recites the additional element of wherein the processing device further performs operations comprising: obtaining an indication of the metric to optimize and the workload from a user interface; and determining the set of workload features based on the workload” . The determining step is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion . The additional element of “obtaining an indication of the metric to optimize and the workload from a user interface” is data gathering which is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional element does not provide an inventive concept, claim 15 therefore is ineligible.
Claim 16:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
The limitation “training a machine learning model to determine system performance features for a set of computing instances based on a set of workload features and the set of computing instances, the machine learning model trained using the training dataset including a set of computing instance features and a set of machine learning model features extracted from the training dataset; providing the machine learning model to an instance recommendation tool to rank computing instances based on the system performance features; causing the instance recommendation tool to rank computing instances based on the system performance features.” is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion . Except for the “train a machine learning model and an instance recommendation tool” language and , there is nothing in the claim that prevents the determining and ranking steps from being performed in the human mind.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 16 recites additional element of “memory and processing device” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The “a machine learning model and instance recommendation tool” which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model and generic recommendation tool.
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 16 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of “machine learning model, instance recommendation tool, memory and processing device” is at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 16 therefore is ineligible.
Claim 17 recites the additional element of “wherein the system performance features include at least one of: average Graphic Processing Unit (GPU) utilization, minimum GPU utilization, maximum GPU utilization, average Central Processing Unit (CPU) utilization, minimum CPU utilization, maximum CPU utilization, average memory utilization, minimum memory utilization, maximum memory utilization, core temperature, memory bandwidth, cache usage, and power usage”. The performance features are part of the determining step which is a mental process. Claim 17 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 17 is not patent eligible.
Claim 18 recites the additional element of “ wherein the set of computing instance features includes at least one of: a number of Graphic Processing Units (GPUs), GPU memory, GPU memory type, GPU type, number of Central Processing Units (CPUs), number of virtual CPUs, CPU type, CPU memory, and CPU memory type”. The computing instance features are part of the determining step which is a mental process. Claim 18 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 18 is not patent eligible.
Claim 19 recites the additional element of “wherein the set of machine learning model features includes at least one of: a number of floating point operations (FLOPs), number of layers, number of activations, number of parameters, and batch size”. The model features are part of the determining step which is a mental process. Claim 19 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 19 is not patent eligible.
Claim 20 recites the additional element of “wherein the training dataset is generated by at least causing a set of machine learning models corresponding to the set of machine learning model features to executed the plurality of workloads using a plurality of computing instances”. which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model and at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional element does not provide an inventive concept, claim 20 therefore is ineligible.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Eicher et al.( US Patent Application Publication 2016/0070590 A1, hereinafter “ Eicher”) and further in view of Volodarskiy et al.(US Patent Application Publication 2020/0175354 A1, hereinafter “Volodarskiy”)
As to claim 1, Eicher teaches a method comprising: obtaining an indication of a metric to rank a set of computing instances and a set of workload features of a workload (Eicher par [0048] teaches a computing instance request to launch may be received); causing a machine learning model to determine an epoch training time and a processor utilization for computing instances of the set of computing instances based on the set of workload features of the workload , a set of computing instance features of the set of computing instances (Eicher par [0023] teaches physical host features 120, and a set of performance features; (Eicher par [0023] teaches instance features 114 associated with the computing instance 112 and physical host features 120 may be provided to a machine learning model 130. The examiner interprets physical hosts are the claimed invention “computing instances” )
ranking the set of computing instances in accordance with the metric based on the epoch training time and the processor utilization associated with the computing instances of the set of computing instances ( Eicher par [0023] teaches the machine learning model 130 may provide the estimated launch times calculated by the model to a placement module 140 which determines which physical host is to place the computing instance using the estimated launch times. Eicher par [0029] teaches estimated launch times for the three hosts may be 10, 50 or two minutes respectively);
Eicher fails to expressly teach causing a machine learning model to determine an epoch training time and causing the ranking of the set of computing instances in a user interface.
However, Volodarskiy teaches causing a machine learning model to determine an epoch training time and causing the ranking of the set of computing instances in a user interface. (Volodarskiy par [008] teaches during execution of the batch of trials, provide intermediate evaluation results by, in each of one or more iterations, estimating a training time and model accuracy for two or more models. Volodarskiy par [0057] teaches providing the leader board of at least a topmost subset of the evaluated models in the graphical user interface)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Eicher and Volodarskiy to achieve the claimed invention. One would have been motivated to make such combination to optimally select which machine learning models to train to provide the best accuracy within a given time frame.( Volodarskiy par [003])
As to claim 2, Eicher and Volodarskiy teach the method of claim 1, wherein the method further comprises causing a second machine learning model to determine the set of performance features based on the set of workload features and the set of computing instance features. (Eicher par [0029] teaches estimated launch times for the three hosts may be 10, 50 or two minutes respectively. The examiner interprets time is the performance feature)
As to claim 3, Eicher and teach the method of claim 2, wherein causing the second machine learning model to determine the set of performance features is in response to the workload or the set of computing instances having not been previously recorded. ( Eicher par [0029] teaches estimated launch times for the three hosts may be 10, 50 or two minutes respectively. The examiner interprets time is the performance feature)
As to claim 4, Eicher and teach the method of claim 2, wherein the method further comprises training the machine learning model and the second machine learning model using a training dataset including a set of metrics obtained by at least causing the set of computing instances to execute a set of workloads. (Eicher par [0062] teaches the machine learning model may be created using the actual launch time prediction data that may include information for a plurality of computing instances that have been previously launched)
As to claim 5, Eicher and teach the method of claim 1, wherein the set of computing instance features includes at least one of: a number of Graphic Processing Units (GPUs), GPU memory, GPU memory type, GPU type, number of Central Processing Units (CPUs), number of virtual CPUs, CPU type, CPU memory, and CPU memory type. (Eicher par [0021] teaches host utilization. Eicher par [0022] teaches physical host features may include max number of computing instances, hardware type, vendor percentage of occupancy, and others)
As to claim 6, Eicher and teach the method of claim 1, wherein the set of performance features includes at least one of: average Graphic Processing Unit (GPU) utilization, minimum GPU utilization, maximum GPU utilization, average Central Processing Unit (CPU) utilization, minimum CPU utilization, maximum CPU utilization, average memory utilization, minimum memory utilization, maximum memory utilization, core temperature, memory bandwidth, cache usage, and power usage. (Eicher par [0021] teaches host utilization. Eicher par [0021] teaches host utilization. Eicher par [0022] teaches physical host features may include max number of computing instances, hardware type, vendor percentage of occupancy, and others)
As to claim 7, Eicher and teach the method of claim 1, wherein the set of workload features includes at least one of: a number of floating point operations (FLOPs), number of layers, number of activations, number of parameters and batch size.(Eicher par [0022] teaches the instance features 114 may include the size of the computing instance 112)
As to Claim 8, Eicher teaches a non-transitory computer-readable medium storing executable instructions embodied thereon, which, when executed by a processing device, cause the processing device to perform operations comprising:
causing a machine learning model to determine a metric associated with a computing instance when executing a workload, the machine learning model taking as inputs a set of workload features associated with the workload, a set of computing instance features associated with a plurality of computing instances, and a set of metrics obtained from the plurality of computing instances during execution of a plurality of workloads; (Eicher par [0023] teaches instance features 114 associated with the computing instance 112 and physical host features 120 may be provided to a machine learning model 130. The machine learning model 130 may provide the estimated launch times calculated by the model to a placement module 140 which determines which physical host is to place the computing instance using the estimated launch times).
generating a ranking of a set of computing instances, including the computing instance, based on the metric; and updating a display to include the ranking of the set of computing instances. ( Eicher par [0029] teaches estimated launch times for the three hosts may be 10, 50 or two minutes respectively)
Eicher fails to expressly teach updating a display to include the ranking of the set of computing instances.
However, Volodarskiy teaches updating a display to include the ranking of the set of computing instances. (Volodarskiy par [0057] teaches providing the leader board of at least a topmost subset of the evaluated models in the graphical user interface)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Eicher and Volodarskiy to achieve the claimed invention. One would have been motivated to make such combination to optimally select which machine learning models to train to provide the best accuracy within a given time frame.( Volodarskiy par [003])
As to claim 9, Eicher and Volodarskiy teach the medium of claim 8, wherein the processing device further performs operations comprising causing a second machine learning model to determine at least a portion of the set of metrics. ( Eicher par [0029] teaches estimated launch times for the three hosts may be 10, 50 or two minutes respectively. The examiner interprets time is the performance feature)
As to claim 10, Eicher and Volodarskiy teach the medium of claim 8, wherein the set of metrics include benchmarks obtained from the plurality of computing instances during execution of the plurality of workloads. (Eicher par [0062] teaches the machine learning model may be created using the actual launch time prediction data that may include information for a plurality of computing instances that have been previously launched)
As to claim 11, Eicher and Volodarskiy teach the medium of claim 8, wherein the machine learning model is trained using the set of metrics. (Eicher par [0062] teaches the machine learning model may be created using the actual launch time prediction data that may include information for a plurality of computing instances that have been previously launched)
As to claim 12, Eicher and Volodarskiy teach the medium of claim 8, wherein the ranking of the set of computing instances further comprises an ordering of the set of computing instances from a lowest epoch training time to a highest epoch training time. ( Eicher par [0029] teaches estimated launch times for the three hosts may be 10, 50 or two minutes respectively)
As to claim 13, Eicher and Volodarskiy teach the medium of claim 8, wherein the ranking of the set of computing instances further comprises an ordering of the set of computing instances from a highest processor utilization to a lowest processor utilization. ( Eicher par [0029] teaches estimated launch times for the three hosts may be 10, 50 or two minutes respectively. The ranking by processor utilization is well known in the art)
As to claim 14, Eicher and Volodarskiy teach the medium of claim 8, wherein the machine learning model is a regression model.(Eicher par [0023] teaches the learning model may be a regression model)
As to claim 15, Eicher and Volodarskiy teach the medium of claim 8, wherein the processing device further performs operations comprising: obtaining an indication of the metric to optimize and the workload from a user interface; and determining the set of workload features based on the workload. (Eicher par [0048] teaches customer may perform the computing instance request)
As to claim 16, Eicher teaches the system comprising: a memory component; and a processing device coupled to the memory component, the processing device to perform operations comprising: obtaining a training dataset including benchmark data captured from a plurality of computing instance configurations executing a plurality of workloads; (Eicher par [0062] teaches the machine learning model may be created using the actual launch time prediction data that may include information for a plurality of computing instances that have been previously launched)
training a machine learning model to determine system performance features for a set of computing instances based on a set of workload features and the set of computing instances, the machine learning model trained using the training dataset including a set of computing instance features and a set of machine learning model features extracted from the training dataset; Eicher par [0062] teaches the machine learning model may be created using the actual launch time prediction data that may include information for a plurality of computing instances that have been previously launched)
providing the machine learning model to an instance recommendation tool to rank computing instances based on the system performance features; and causing the instance recommendation tool to rank computing instances based on the system performance features. ( Eicher par [0029] teaches estimated launch times for the three hosts may be 10, 50 or two minutes respectively)
Eicher fails to expressly teach causing the instance recommendation tool to rank computing instances based on the system performance features.
However, Volodarskiy teaches causing the instance recommendation tool to rank computing instances based on the system performance features. (Volodarskiy par [0057] teaches providing the leader board of at least a topmost subset of the evaluated models in the graphical user interface)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Eicher and Volodarskiy to achieve the claimed invention. One would have been motivated to make such combination to optimally select which machine learning models to train to provide the best accuracy within a given time frame.( Volodarskiy par [003])
As to claim 17, Eicher and Volodarskiy teach the system of claim 16, wherein the system performance features include at least one of: average Graphic Processing Unit (GPU) utilization, minimum GPU utilization, maximum GPU utilization, average Central Processing Unit (CPU) utilization, minimum CPU utilization, maximum CPU utilization, average memory utilization, minimum memory utilization, maximum memory utilization, core temperature, memory bandwidth, cache usage, and power usage. (Eicher par [0021] teaches host utilization)
As to claim 18, Eicher and Volodarskiy teach the system of claim 16, wherein the set of computing instance features includes at least one of: a number of Graphic Processing Units (GPUs), GPU memory, GPU memory type, GPU type, number of Central Processing Units (CPUs), number of virtual CPUs, CPU type, CPU memory, and CPU memory type. (Eicher par [0021] teaches host utilization)
As to claim 19, Eicher and Volodarskiy teach the system of claim 16, wherein the set of machine learning model features includes at least one of: a number of floating point operations (FLOPs), number of layers, number of activations, number of parameters, and batch size. (Eicher par [0022] teaches the instance features 114 may include the size of the computing instance 112)
As to claim 20, Eicher and Volodarskiy teach the system of claim 16, wherein the training dataset is generated by at least causing a set of machine learning models corresponding to the set of machine learning model features to executed the plurality of workloads using a plurality of computing instances. (Eicher par [0062] teaches the machine learning model may be created using the actual launch time prediction data that may include information for a plurality of computing instances that have been previously launched)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147