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 .
DETAILED ACTION
Claims 1-8 and 12-23 are pending. Claims 9-11 are canceled and claims 21-23 are added by Applicant.
Examiner Notes
Examiner cites particular paragraphs and/or columns and lines in the references as applied to Applicant’s claims for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06.
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 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.
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 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter.
As per claim 18, it is directed to a signal directly or indirectly by claiming a medium and the Specification recites evidence where the computer readable medium can be defined as a signal or wave (see Specification [0088] reproduced below with emphasis added).
[0088] In some instances, software of the present embodiments may be available via a non-transitory computer useable medium (e.g., magnetic or optical mediums, magneto-optic mediums, CD-ROM, DVD, memory devices, etc.) of a stationary or portable program product apparatus, downloadable file(s), file wrapper(s), object(s), package(s), container(s), and/or the like. In some instances, non-transitory computer readable storage media may also be removable. For example, a removable hard drive may be used for memory/storage in some implementations. Other examples may include optical and magnetic disks, thumb drives, and smart cards that can be inserted and/or otherwise connected to a computing device for transfer onto another computer readable storage medium.
As highlighted above, [0088] of the instant specification includes numerous open-ended language phrases/terms that can support an interpretation of the claimed non-transitory computer readable storage media to actually include transitory embodiments. In other words, the instant specification does not specifically/explicitly limit non-transitory computer readable storage media to only non-transitory embodiments. A transitory signal, while physical and real, does not possess concrete structure that would qualify as a device or part under the definition of a machine, is not a tangible article or commodity under the definition of a manufacture (even though it is man-made and physical in that it exists in the real world and has tangible causes and effects), and is not composed of matter such that it would qualify as a composition of matter (see Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03). As such, a transitory, propagating signal does not fall within any statutory category (see Mentor Graphics Corp. v. EVE-USA, Inc., 851 F.3d 1275, 1294, 112 USPQ2d 1120, 1133 (Fed. Cir. 2017); Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03). The BRI of machine readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se (see In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007)). In that event, the claim is directed to a form of energy which does not fall into a category of invention.
To overcome the outstanding rejection, Applicant is advised to amend the claim to specifically disavow the recited “non-transitory computer-readable storage medium” from including transitory embodiments, or to limit the recited “non-transitory computer-readable storage medium” to include only specific hardware embodiments such as thumb drives, disks, memory cards etc. (i.e., see [0080], [0086], and [0088] of the instant specification).
As per claims 19-20, they are dependent on claim 18 and do not overcome the 35 U.S.C. 101 deficiency of claim 18. Therefore, they are rejected using 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.
Claims 1, 4, 7, 13, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Patel et al. (US 2024/0118738) (hereinafter Patel as previously cited) in view of
Kang et al. (US 2020/0077023) (hereinafter Kang as previously cited) in view of
Wesolowski et al. (US 2019/0114537) (hereinafter Wesolowski).
As per claim 1, Patel primarily teaches the invention as claimed including a computer-implemented method comprising:
obtaining power supply information about at least two computing resource groups, wherein the power supply information relates to one or more power sources that supply power to the at least two computing resource groups ([0004] receive power control information from the remote data center control system via the API, schedule the local energy sources to provide power to the loads based on the received power control information, and send control information to each a local controller of each local energy source; [0037] obtain the power level for each distributed energy resource from the configuration data, and determine the control information for each distributed energy resource based on the power levels, time periods for each power level etc.; [0075] determine power control information for the data center based on status information received from the wide-area energy control system and/or software loads for processing by the data center, wherein the power control information describes desired power loads and schedules for the data center and determines reactive power configurations; [0078] wide-area energy control system constantly receives power control information from the remote data center control system for data center operations during critical grid operations);
one or more renewable energy sources ([0028] and [0052] renewable energy sources);
migrating workloads using a different computing resource group than the current computing resource group, based on determining a lack of the availability of power provided to the current computing resource group from the one or more renewable energy sources ([0040] if there is insufficient power at a data center to power the equipment to process the currently assigned workloads, the remote data center control system can transfer some of the workloads to another data center that has capacity for processing the workload and sufficient available power to power the equipment to process the additional workloads, and the remote data center control system can transfer loads from the data center to another data center that has carbon-free energy capacity).
Patel does not explicitly teach:
determining, while training an artificial intelligence or machine learning model using a current computing resource group of the at least two computing resource groups, an availability of power provided to the current computing resource group from one or more energy sources, based on the power supply information; and
migrating the artificial intelligence or machine learning model for training using a different computing resource group than the current computing resource group, wherein the migration includes: checkpointing training results of the training of the artificial intelligence or machine learning model using the current computing resource group to generate checkpointed training results; causing the checkpointed training results to be transferred from checkpoint storage associated with the current computing resource group to checkpoint storage associated with the different computing resource group; and instructing a training service of the different computing resource group to continue training the artificial intelligence or machine learning model using the checkpointed training results.
However, Kang teaches:
determining, while training an artificial intelligence or machine learning model using a current computing resource group of the at least two computing resource groups, an availability of power provided to the current computing resource group from one or more energy sources, based on the power supply information ([0066] perform learning/training based on a power consumption threshold; [0077] deep learning network in the machine learning electronic image stabilization process can conduct online learning/training, where the deep learning network performs learning/training while also performing machine learning electronic image stabilization process operations. The online learning/training can be activated or deferred based on one or more thresholds, such as an available power threshold etc. The deep learning network can be configured to perform online learning/training only when specific power parameters are below a threshold. When such parameters exceed the threshold, the online learning/training can be stopped or deferred; [0130] and [0150] switch to online learning/training if the device's available power is below a threshold, and stop or defer online learning/training if one or more of such thresholds are exceeded).
Kang and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang because it would provide a way to consider power consumption and power thresholds when training models to optimize the learning/training of the machine learning.
Patel in view of Kang do not explicitly teach:
migrating the artificial intelligence or machine learning model for training using a different computing resource group than the current computing resource group, wherein the migration includes: checkpointing training results of the training of the artificial intelligence or machine learning model using the current computing resource group to generate checkpointed training results; causing the checkpointed training results to be transferred from checkpoint storage associated with the current computing resource group to checkpoint storage associated with the different computing resource group; and instructing a training service of the different computing resource group to continue training the artificial intelligence or machine learning model using the checkpointed training results.
However, Wesolowski teaches:
migrating the artificial intelligence or machine learning model for training using a different computing resource group than the current computing resource group, wherein the migration includes: checkpointing training results of the training of the artificial intelligence or machine learning model using the current computing resource group to generate checkpointed training results; causing the checkpointed training results to be transferred from checkpoint storage associated with the current computing resource group to checkpoint storage associated with the different computing resource group; and instructing a training service of the different computing resource group to continue training the artificial intelligence or machine learning model using the checkpointed training results ([0022] check-points may be determined during the training of a machine learning model and irrespective of the type of machine(s) on which the machine learning model is being trained. A check-point may record sufficient information to resume the training process at a later time or on a different machine continuing from the check-point, as if training were temporarily halted at a point in time following the recording of the check-point. Additionally, the check-point may include information regarding the architectural characteristics of the computing system which may include one or more computing machines on which the machine learning model is being trained. For example, if training of the ML model were halted on a first computing system having a first computer architecture or a first number of computing machines, then training of the same machine learning model may be transferred to a second computing system having a second computer architecture or second number of computing machines different than the first computer architecture or first number of computing machines, and resumed at a specified check-point e.g., at the most current check-point).
Wesolowski and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski because it would provide a way to transfer execution of a portion of the machine learning model from one machine to a faster or slower machine, as necessary, to maintain optimal timing between the transferring of processing data between the machines e.g., to minimize wait time by one machine waiting for another machine to reach a point where a check-point may be created or to complete transferring of processing data.
As per claim 4, Patel further teaches wherein the at least two computing resource groups are a plurality of data centers that host network and computing equipment for performing hosting and computing functions ([0002] many data centers can host mission critical applications and [0023] the data center can be configured to support, e.g., host, software workloads using data, software applications, and/or servers).
As per claim 7, Patel further teaches wherein obtaining the power supply information about the at least two computing resource groups includes: performing an application programming interface (API) call to an external entity to obtain data about a portion of a total power supplied by each of a plurality of power supply sources that power a respective computing resource group ([0038] API interface can include hardware and/or software that is capable of generating API calls to the energy services API and receive information from the remote data center control system through API calls initiated by the remote data center control system and [0049] provide the status information to the remote data center control system via the energy services API. The status of the distributed energy resources can include whether the distributed energy resources are actively producing power, how much power is being produced, the characteristics of the output power, the current capacity to produce power etc.).
As per claim 13, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
As per claim 16, it has similar limitations as claim 4 and is therefore rejected using the same rationale.
As per claim 18, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
Claims 2, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Kang in view of Wesolowski in view of Chakraborty et al. (US 2023/0196378) (hereinafter Chakraborty as previously cited).
As per claim 2, Kang teaches wherein the artificial intelligence or machine learning model is one of a neural network, a generative pre-trained transformer (GPT) model, a deep learning model, or a large language model (LLM) ([0159] any suitable neural network can be used).
Patel in view of Kang in view of Wesolowski do not explicitly teach training the artificial intelligence or machine learning model using the current computing resource group while determining that power supplied to the current computing resource group is from the one or more renewable energy sources.
However, Chakraborty teaches training the artificial intelligence or machine learning model using the current computing resource group while determining that power supplied to the current computing resource group is from the one or more renewable energy sources ([0023] arrange the training of the machine learning model in a region that relies solely on renewable resources).
Chakraborty and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski in view of Chakraborty because it would provide a way for model training to be prevented in an area with inefficient computing/cooling and reassigning the training to a region where temperatures are lower and where more power efficient computing resources may be utilized.
As per claim 14, it has similar limitations as claim 2 and is therefore rejected using the same rationale.
As per claim 19, it has similar limitations as claim 2 and is therefore rejected using the same rationale.
Claims 3, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Kang in view of Wesolowski in view of in view of Tasinga et al. (US 2022/0180178) (hereinafter Tasinga as previously cited).
As per claim 3, Patel further teaches wherein the at least two computing resource groups are a plurality of geographically remote enterprise sites of a distributed data center ([0039] data centers may be located in different geographic regions).
Patel in view of Kang in view of Wesolowski do not explicitly teach each of the plurality of geographically remote enterprise sites including at least one of a plurality of graphics processing units or a plurality of tensor processing units for training one or more learning models.
However, Tasinga teaches each of the plurality of geographically remote enterprise sites including at least one of a plurality of graphics processing units or a plurality of tensor processing units for training one or more learning models ([0140] data center may use CPUs, ASICs, GPUs, FPGAs, or other hardware to perform training and/or inferencing using the above-described resources).
Tasinga and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski in view of Tasinga because it would provide for hardware which implements an enhanced algorithm for managing power states, where allowed power states and expected wakeup times are specified, and hardware/microcode determines which best power state to enter for cores/clusters which may support simplified power state entry sequences in software with work offloaded to microcode. One or more of GPU(s) may also be power-optimized for best performance.
As per claim 15, it has similar limitations as claim 3 and is therefore rejected using the same rationale.
As per claim 20, it has similar limitations as claim 3 and is therefore rejected using the same rationale.
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Kang in view of Wesolowski in view of Dom (US 2015/0015404) (as previously cited).
As per claim 5, Patel in view of Kang in view of Wesolowski do not explicitly teach obtaining the power supply information about the at least two computing resource groups includes: obtaining time-series data about the one or more power sources that supply the power to a respective computing resource group of the at least two computing resource groups; and generating a time-series energy baseline for the respective computing resource group, wherein the time-series energy baseline indicates a first portion of power supplied by the one or more renewable energy sources and a second portion of power supplied by one or more non-renewable energy sources of a total power supplied to the respective computing resource group at a particular point in time.
However, Dom teaches obtaining the power supply information about the at least two computing resource groups includes: obtaining time-series data about the one or more power sources that supply the power to a respective computing resource group of the at least two computing resource groups; and generating a time-series energy baseline for the respective computing resource group, wherein the time-series energy baseline indicates a first portion of power supplied by the one or more renewable energy sources and a second portion of power supplied by one or more non-renewable energy sources of a total power supplied to the respective computing resource group at a particular point in time ([0003]-[0004] provide power from an energy provider to an electronic device, the power being sourced from at least one of a renewable energy source and a non-renewable energy source. Display time-series forecast information forecasting a property of the power provided from the energy provider over a predefined period of time which can describe the mixture of the renewable energy source and the non-renewable energy source that is being provided at a particular point in time. A network interface is configured to receive data from the energy provider to generate the time-series forecast information. Receiving, from an energy provider, data forecasting a property associated with power available from the energy provider, the power being sourced from at least one of a renewable energy source and a non-renewable energy source, generating, by the processor and from the received data, time-series forecast display information describing the forecasted property over a predefined period of time, and presenting, by the processor, the time-series forecast display information on a visual indicator. The property describes the mixture of the renewable energy source and the non-renewable energy source).
Dom and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski in view of Dom because it would provide a way for energy providers to supply consumers a combined solution of both traditional and renewable energy sources so that energy consumers can make informed decisions of when is the optimal time to use power.
As per claim 17, it has similar limitations as claim 5 and is therefore rejected using the same rationale.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Kang in view of Wesolowski in view of Dom in view of Mehta et al. (US 2024/0143854) (hereinafter Mehta as previously cited).
As per claim 6, Kang teaches wherein migrating is based on determining that the first portion is below a predetermined threshold ([0066] perform learning/training based on a power consumption threshold and [0150] start online learning/training when the system's power is below a threshold, and stop or defer online learning when the threshold is exceeded).
Patel in view of Kang in view of Wesolowski in view of Dom do not explicitly teach wherein determining the availability of the power from the one or more renewable energy sources provided to the current computing resource group is based on the time-series energy baseline.
However, Mehta teaches wherein determining the availability of the power from the one or more renewable energy sources provided to the current computing resource group is based on the time-series energy baseline ([0321] time-series data represents the available power from renewable sources as a function of time).
Mehta and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski in view of Dom in view of Mehta because it would provide for an optimization process that can utilize data relating to a tailored selection of wind and solar resources. Solar power may provide more consistent power during daylight hours, but wind power may provide more flexibility and power generation during hours of darkness. Thus, a specific mix of these power profiles can be used as part of the configuration selection to identify a maximized power profile for use with a particular configuration of the system.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Kang in view of Wesolowski in view of McGuire et al. (US 2022/0398515) (hereinafter McGuire as previously cited).
As per claim 8, Patel in view of Kang in view of Wesolowski do not explicitly teach wherein determining the availability of power from the one or more renewable energy sources provided to the current computing resource group includes: determining a first portion of power from the one or more renewable energy sources supplied to the current computing resource group; and determining a second portion of power from the one or more renewable energy sources supplied to the different computing resource group, wherein migrating is based on the first portion being lower by a predetermined value than the second portion.
However, McGuire teaches wherein determining the availability of power from the one or more renewable energy sources provided to the current computing resource group includes: determining a first portion of power from the one or more renewable energy sources supplied to the current computing resource group; and determining a second portion of power from the one or more renewable energy sources supplied to the different computing resource group, wherein migrating is based on the first portion being lower by a predetermined value than the second portion (abstract migrate workloads between data centers automatically to maximize a usage of renewable energy based on a predetermined threshold score of input power and a combination of renewal energy sources and [0023] workloads can start migrating to datacenters with higher renewable energy capacity. Energy sources would be priorities based on minimizing environmental impact).
McGuire and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski in view of McGuire because it would provide for executing an efficient carbon footprint awareness mechanism to create, place, and schedule workloads in compute clusters to reduce power usage and increase the proportion of power generated by renewable energy sources.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Kang in view of Wesolowski in view of da Silva et al. (US 2024/0078409) (hereinafter da Silva as previously cited).
As per claim 12, Patel further teaches wherein the at least two computing resource groups includes a new computing resource group (abstract multiple data centers) and obtaining additional power supply information for the new computing resource group ([0005] power control information includes expected power demands of computing tasks to be performed by the data center computers; [0078] constantly receive power control information for data center operations during critical grid operations; and [0085] receive and retransmit new status/control information).
Patel in view of Kang in view of Wesolowski do not explicitly teach obtaining a request for registering the new computing resource group; and copying a dataset for training the artificial intelligence or machine learning model to a storage associated with the new computing resource group
However, da Silva teaches obtaining a request for registering the new computing resource group ([0031] customer initiates registration process with a service provider); and copying a dataset for training the artificial intelligence or machine learning model to a storage associated with the new computing resource group ([0032] deploy a copy of internal data, which may comprise training data that was used by the service provider to train the deployed model, to a datacenter associated with the customer).
da Silva and Patel are both concerned with data centers and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski in view of da Silva because it would provide a way to select and use more efficient and/or more environmentally friendly energy sources available at different data centers. The wide-area energy control system can also utilize electric grid status from the API to ramp-up backup energy sources during routine maintenance of the grid and ensures resilient operation of the data center during grid outages. An additional advantage of the wide-area energy control system is the ability to aggregate energy and load resources to help provide services to an electric grid or utility, without dispatching and independently coordinating each diverse energy source locally.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Kang in view of Wesolowski in view of Bhattacharjee et al. (US 2020/0334567) (hereinafter Bhattacharjee).
As per claim 21, Patel in view of Kang in view of Wesolowski do not explicitly teach wherein instructing the training service of the different computing resource group to continue training includes providing, by a sustainability service, the training service of the different computing resource group with a new deadline to finish the training.
However, Bhattacharjee teaches wherein instructing the training service of the different computing resource group to continue training includes providing, by a sustainability service, the training service of the different computing resource group with a new deadline to finish the training ([0045] training manager may set a deadline for receiving the results of the training from the computing device).
Bhattacharjee and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski in view of Bhattacharjee because it would provide a way to determine whether results were received from a computing device by a completion deadline set for the computing device to train a portion of a machine learning model using a chunk of data. The system can determine whether the results were received by the completion deadline based on a receipt timestamp at the system such that a consistent timing source is used to determine whether the results were received by the completion deadline and avoid erroneous determinations that may be caused by clock skew between a clock maintained at the system and a clock maintained at a client device.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Kang in view of Wesolowski in view of Thyagaturu et al. (US 2022/0149625) (hereinafter Thyagaturu).
As per claim 22, Patel in view of Kang in view of Wesolowski do not explicitly teach before selecting the different computing resource group for the migrating, determining, by a sustainability service, that power from renewable energy sources is not available at the current computing resource group and at other registered computing resource groups; and responsive to the determining, instructing, by the sustainability service, a training service of the current computing resource group to stop training the artificial intelligence or machine learning model until power from renewable energy sources becomes available at the current computing resource group or at least one of the other registered computing resource groups.
However, Thyagaturu teaches before selecting the different computing resource group for the migrating, determining, by a sustainability service, that power from renewable energy sources is not available at the current computing resource group and at other registered computing resource groups; and responsive to the determining, instructing, by the sustainability service, a training service of the current computing resource group to stop training the artificial intelligence or machine learning model until power from renewable energy sources becomes available at the current computing resource group or at least one of the other registered computing resource groups ([0018] task scheduler in OS or hypervisor can defer execution of a workload based on non-availability of power to perform the workload. Applications may opt to defer jobs that are not time-sensitive until more renewable energy is available).
Thyagaturu and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski in view of Thyagaturu because it would provide a way for controlling power available to processes and hardware devices to control a monetary cost of utilized electricity and/or amount of energy utilized from non-renewable energy sources. The system can modify operating configurations of processes and/or hardware based on the available power. The system can control total power drawn to control a monetary cost of power and/or avoid drawing power from non-renewable sources.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Kang in view of Wesolowski in view of Vega et al. (US 2021/0123771) (hereinafter Vega).
As per claim 23, Patel in view of Kang in view of Wesolowski do not explicitly teach before generating the checkpointed training results, generating time-series energy baselines for the current computing resource group and the different computing resource group, and selecting the different computing resource group for the migrating using the time-series energy baselines based on predicted renewable-energy availability.
However, Vega teaches before generating the checkpointed training results, generating time-series energy baselines for the current computing resource group and the different computing resource group, and selecting the different computing resource group for the migrating using the time-series energy baselines based on predicted renewable-energy availability ([0235] benchmarking of energy consumption between comparable premises based on each of the given premises baseline model which integrates multiple variables impacting electricity consumption and their changes over time, whether uniform or non-uniform time series and provides a more precise point of reference to provide insights about consumption patterns, to determine energy optimization opportunities, and to forecast future energy consumption; [0288]-[0289] time series monitoring of actual versus forecasted energy usage with trending capabilities and the quantified changes in key energy indicators compares actual consumption usage vs. projected predictions and may be used to determine variation and predictive, diagnostic, and prescriptive analytics are performed to forecast usage, detect anomalies, provide alerts, diagnose actual against forecasted, etc. Deviations in energy consumption patterns are detected and prompt interactive capabilities to query the customer for changes in consumption behaviors and building attributes. Customer feedback is integrated to train and update the customer's personalized energy advisor forecasting, prescriptive, diagnostics and optimization capabilities; and [0291] energy advisor recommendations are acted upon and are recorded in the system and integrated to update and train the customer's personalized energy fingerprint and energy advisor personalized forecasting, prescriptive, diagnostics and optimization capabilities).
Vega and Patel are both concerned with power/energy management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patel in view of Kang in view of Wesolowski in view of Vega because it would provide a way for statistically analyzing and optimizing power consumption by a customer or user e.g., both consumers and businesses by obtaining and analyzing power consumption, and using the results of that analysis for reducing and optimizing energy use and its associated carbon footprint and to obtaining and analyzing power consumption to establish a baseline for energy consumption and monitoring actual consumption for variances from the baseline and the determination of the cause for and correction of a variance.
Response to Arguments
All of Applicant's arguments have been considered.
Applicant’s arguments with respect to the 35 U.S.C. 103 prior art rejections have been considered but are moot because the new grounds of rejection necessitated by Applicant’s amendments does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments regarding the 35 U.S.C. 101 rejections are not persuasive. In the Remarks on pg. 14, Applicant argues that the recited “one or more non-transitory computer readable storage media” does not include transitory embodiments. The examiner respectfully traverses using the rationale provided in the rejection above. To overcome the outstanding rejection, Applicant is advised to amend the claim to specifically disavow the recited “non-transitory computer-readable storage medium” from including transitory embodiments, or to limit the recited “non-transitory computer-readable storage medium” to include only specific hardware embodiments such as thumb drives, disks, memory cards etc. (i.e., see [0080], [0086], and [0088] of the instant specification). Thus, for at least the reasons provided above, Applicant’s arguments are unpersuasive and the rejections are sustained.
Citation of Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Singh et al. (US 2012/0131137) disclose energy efficient task execution offloading.
Naidu et al. (US 2021/0342185) disclose relocation of workloads across data centers.
Dunne et al. (US 2020/0272899) disclose deploying and updating neural networks at the edge of a network.
Das et al. (US 2023/0418663) disclose dynamic workload migration based on multiple constraints.
Barton et al. (US 2023/0236899) disclose dynamic workload placement using cloud service energy impacts.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Adam Lee whose telephone number is (571) 270-3369. The examiner can normally be reached on M-TH 8AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached on 571-272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated-interview-request-air-form.
/Adam Lee/Primary Examiner, Art Unit 2198 July 27, 2026
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