DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the
first inventor to file provisions of the AIA .
This action is a Non-Final Rejection after the action of the instant petition and reopens
prosecution of the application.
Response to Arguments
The Amendment filed on January 5, 2026, has been entered. The examiner
notes claim 17 previously presented and claims 1-16, 18-20 as original.
Rejections under 35 U.S.C. § 101: Applicant argues that the claims have been mischaracterized as nothing more than observation, evaluation, judgment or opinion. The Examiner will point out that the abstract ideas behind the claims fundamentally rely on those mental process and some underlying economic principles and practices as well as organizing human activity to specifically include following rules or instructions that underly the programmed processes and steps. It is evident that the claims describe much more, involving predicting renewable energy supplies and moving and rehosting prediction applications, as argued. Applicant argues that since the operations cannot be reasonably performed in the human mind, those processes are not abstract or mental processes. If that were true, potentially any rendition of a computer performing operations that cannot reasonably be performed in the human mind could become subject matter eligible, and this is certainly not the case.
Applicant also argues that the additional elements should not be characterized as tools or conventional, citing the use of a renewable-attributable supply forecast across data center sites to drive workflow, etc. First, the Examiner notes that the various computer environments listed as additional elements are both tools and conventional, supporting the processor and invention in conventional ways, processing circuits, processors, a system comprising a memory, natural language processing, and hashing techniques are all tools and conventional. The Examiner notes that a renewable-attributable supply forecast is not cited as an additional element.
Applicant also argues the improvement of the functioning of distributed computing systems. Examiner note the absence of any “improvements” stated in the claims, nor “enhancements” to any system, and references to performance refer to performance of a first job or a second job. It is evident that software run on computers predicts future energy supplies, scheduling a plurality of jobs defining workflow, and deploys jobs according to scheduling. Claim 5 cites changing the hosting computer environment of the one or more jobs is changed wherein the dynamically rescheduling is performed in dependence on a predicted change in an energy supply profile of the computer environment. If this is an enhancement or improvement, it is not stated, but if it is intended to be an improvement, additional information on how the performance of a computer or processor has been improved, it must be disclosed. If there is integration of an additional element integral to the claim, that needs to be disclosed, beyond general linking to a particular technological environment. Arguments that the claims require particular machines that are geographically differentiated computer environments engaged in live migrations are routine and normal for the technology. The role of the invention appears to be scheduling, and thus the invention merely links to the particular environment. In the absence of elements supporting a practical application, the Examiner notes that the invention performs the function of “rescheduling” and appears to be software generating a request for changing aspects of a job. It is not stated if this request is presented to a human operator or placed in the queue of another system. In either event, this appears to be a scheduling program performing a scheduling function, applied to a conventional processor.
In view of the above discussion of the claims reciting abstract ideas, and the absence of support for the improvement of the functioning of a computer system, a practical application, and the final output of the applied software being requests for rescheduling or requests for delivery of renewable energy supplies to a power grid authority, the arguments for allowable subject matter and a practical application are not compelling and the request for reconsideration under 35 U.S.C. § 101 and passage to allowance is denied.
Rejections under 35 U.S.C. § 103: Applicant’s argues an apparent absence of substantive reply to applicant’s traversals for claim 1. The Examiner notes that the claims present an overly broad and wide interpretation of the invention that is overcome with prior art using a Broadest Reasonable Interpretation of the substance of the claims and teachings of the prior art. The Examiner again notes the non-rejection of claims 2-3, 5-13, 16, and 19-20.
Claim 1 predicts future energy supply profiles, scheduling jobs defining an application workflow and deploying at least job according to the scheduling. The generalities supporting the claim fail to disclose detail enabling a person of ordinary skill in the art to be able to build upon the information provided in the claims. Cited prior art teaches the claims at the present level of breadth and depth. Arguments allege that Crosby teaches only local energy cost-based scheduling; Examiner notes that Crosby specifically states, the resources are not limited to local resources, [0064], and mesh network may determine the energy efficiency with which different resources can be used, especially for remote resources which are not local to the device, [0082].
Claims 4, 14-15, 18 are similarly rejected by prior art teaching features of the stated invention.
Amended independent claim 17 is rejected by additional prior art and additional detail drawn from the previous prior art.
Given the teachings of prior art, the Examiner finds that claims 1, 4, 14-15, 17, and 18 are not in condition for allowance and the rejections under 35 U.S.C. § 103 will not be withdrawn.
Claim Rejections – 35 U.S.C. § 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 non-statutory subject matter. The claims, 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-20 are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of planning software application job initiation in consideration of energy conditions across varied geographic regions across the network.
Claim 1 discloses a method, comprising: A method comprising:
predicting future energy supply profiles at respective ones of a plurality of geographically differentiated environments, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion), wherein the respective future energy supply profiles at the respective ones of the plurality of geographically differentiated environments express an attribute of supplied energy supplied to an environment attributable to renewable power generation; (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion),
scheduling a plurality of jobs defining an application workflow, wherein the application workflow is characterized by commencement of a second job of the plurality of jobs being dependent on completion of a first job of the plurality of jobs, (following rules or instructions, observation, evaluation, judgement, opinion), wherein the scheduling is performed in dependence on the predicting the future energy supply profiles at the respective ones of the plurality of geographically differentiated environments, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion), and in dependence on a forecasting of an execution time of the first job and the second job; (following rules or instructions, observation, evaluation, judgement, opinion), and
deploying at least one job of the plurality of jobs defining the application workflow
according to the scheduling, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion).
Additional limitations employ the method to schedule the first job in the first environment based on the energy profile at the first environment at the time of a first job, and scheduling a second job in a second environment based on energy profile at the second environment at the time of the second job, ((economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 2), forecasting the time of the first job includes a forecasting time for sending message data on the first job to a subsequent job including evaluating running the second job in geographically different environments, ((economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 3), generating a data structure that includes first and second nodes, referencing first and second jobs, and including an edge connecting the first and second node and scheduling includes first and second deployments and reading forecasted performance data written to the first and second node and the connecting edge, - claim 4), scheduling the first job in a first environment depending upon the predicted energy supply profile at the time of first job performance and a second job in a second environment with a second energy profile at the time of the second job, including dynamically rescheduling hosting one or more jobs so that an environment changes and the rescheduling is done based on a predicted change in energy supply profile, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 5), dynamically rescheduling hosting one or more jobs while running the workflow, changing an environment and based on one or more of change in energy supply profile in the environment, an attribute of weather forecasted at an environment, an energy generation incentive program, and a change in offered resource for hosting a job, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 6), rescheduling hosting of jobs as to change environments, depending upon one of a change in energy supply profile, an attribute of weather in the regional forecast, an energy incentive program, or a change in an offered resource where rescheduling live migration to the new environment, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 7), where scheduling includes scheduling the first job in the first environment, based on the energy profile at the first environment at the first time, and scheduling the second job in the second environment depending upon the energy profile at the second environment, when the time depends on the predicted data transfer between jobs, based on host environment locations, an offered resource for the second job and a baseline predicted execution run time from matching historical inputs for access to result data from the historical inputs, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 8), where scheduling depends upon on a predicted data transfer time between first and second job based on geographic distance between environments of the first and second job, (following rules or instructions, observation, evaluation, judgement, opinion – claim 9), where scheduling depends upon an offered resource for hosting the second job by processing an extraction image of a resource that emulates an attribute of performance of the second job, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 10), where scheduling depends upon a predicted execution run time method in which predicted inputs are matched to historical inputs for access to result data from the historical inputs, (following rules or instructions, observation, evaluation, judgement, opinion- claim 11), where scheduling includes scheduling a first job in a first environment based on a predicted energy supply profile at the first environment at the time of the first job and scheduling a second job in an environment based on predicted energy supply profile at the second environment at the time of the second job where scheduling is based on an offered resource for hosting the second job based on an extracted image that emulates performance of the and scheduling is performed based on a baseline predicted execution run time returned using a of predicted inputs is matched to historical inputs for access to results from historical inputs, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 12), where the method includes dynamically rescheduling hosting of one or more jobs during the running of the application so that a hosting environment is changed based on a predicted change in an energy supply profile of at least one environment, the predicted change resulting from an incentive program, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 13), where predicting is based on weather forecast data from a weather service system, (following rules or instructions, observation, evaluation, judgement, opinion- claim-14), where the energy supply profile at the environments express a percentage of supplied energy attributable to renewable power generation, (following rules or instructions, observation, evaluation, judgement, opinion- claim 15), where the method includes rescheduling one or more jobs so that an environment is changed based on predicted change in energy supply profile of at least one environment based upon an energy generation incentive program where the data is from an authority associated with one geographic region in common with one environment including generating request data based on detecting a specified renewable energy supply where the method includes sending the data to at least one environment to trigger messaging between the environment and a power grid authority, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 16).
Claim 17 comprises: A product for performing a method comprising:
predicting future energy supply profiles at respective ones of a plurality of geographically differentiated environments, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion), wherein the predicting is
performed by ingesting and producing data, wherein the respective future energy supply profiles at the respective ones of the plurality of geographically differentiated environments
express an attribute of supplied energy supplied to renewable power generation; (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion), scheduling a plurality of jobs defining an application workflow, wherein the application workflow is characterized by commencement of a second job of the plurality of jobs being dependent on completion of a first job of the plurality of jobs, (following rules or instructions, observation, evaluation, judgement, opinion), wherein the scheduling is performed in dependence on the predicting the future energy supply profiles at the respective ones of the plurality of geographically differentiated environments, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion), and in dependence on a forecasting of an execution time of the first job and the second job; (following rules or instructions, observation, evaluation, judgement, opinion), the forecasting including computation of inter-environment
communication overhead using message-passing latency models; (following rules or instructions, observation, evaluation, judgement, opinion), and deploying at least one job of the plurality of jobs defining the application workflow according to the scheduling, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion).
Each of these claimed limitations employ methods of organizing human behavior, fundamental economic principles or practices, interactions between people, following rules or instructions, and mental processes involving judgement, observation, evaluation and opinion.
Claims 18-20 recite similar abstract ideas as those identified with respect to claims 1-17.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-20 recite additional limitations which are hardware or software elements such as a computer implemented method, geographically differentiated computer environments, a first computer environment, a second computer environment, a hosting computer environment, an offered computer environment resource, a computer readable storage medium readable by one or more processing circuits and storing instructions for execution by one or more processors, a system comprising, a memory, one processor in communication with the memory, program instructions executable by one or more processor via the memory, natural language processing, hashing techniques, external telemetry streams, normalized machine-readable data, and geographically differentiated computer environments, and controlling job instantiation at a selected environment in real time as a function of the normalized profile data, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-20 are directed to abstract ideas.
Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 14-15, 18 are rejected under 35 U.S.C. § 103 as being taught by Crosby,
(US 20170242727 A1), hereafter Crosby, “Energy-Based Scheduling of Operations to be Executed by a Data Processing Apparatus,” in view of Zhang, “Prediction of Overall Energy Consumption of Data Centers in Different Locations,” Sensors 2022. 3704, https://doi.org/10.3390/s22103704, hereafter Zhang, in further view of Simonovic, (US 20180053328 A1), hereafter Simonovic, “Systems and Methods for Processing Computational Workflows, in further view of Cook, (GB 2616058 A), hereafter Cook, “Computer Implemented Methods of Executable Workflow Generation.”
Regarding Claim 1, Crosby teaches, A computer implemented method comprising:
predicting future energy supply profiles at respective ones of a plurality of geographically
differentiated computer environments, (the energy cost functions may represent an approximate estimate of the energy cost of a candidate schedule. Even if the energy costs are only approximate the scheduling based on the energy costs can still improve the overall energy efficiency of the processor, [0021]), and it is also possible to perform the scheduling ahead of time. For example, the scheduling can be done at compile time, or as a preliminary step performed by the processor just before executing the program. [ ] When selecting a candidate schedule to be executed at a current execution time, the current execution time refers to the time at which the candidate schedule will be executed (which may be at some point in the future), [0033]). In some cases, the energy cost function may depend on environmental conditions detected by the data processing apparatus or known to the data processing apparatus from another device. For example, the energy efficiency of some resources may vary with temperature conditions, or with the data processing apparatus's location, or its relative position in a network. [0019]).
Crosby does not teach, wherein the respective future energy supply profiles at the respective ones of the plurality of geographically differentiated computer environments express an attribute of supplied energy supplied to a computer environment attributable to renewable power generation; but Zhang teaches, (in addition to Power Usage Effectiveness (PUE), other energy efficiency metrics have been applied to the evaluation of data centers, such as pPUE [30] (i.e., local PUE) and RER [31] (i.e., renewable energy ratio). Greenpeace, an international environmental protection organization, believes that “green IT = energy efficiency + renewable energy“, which means the greening of the Internet not only needs to involve the reduction of costs by improving the energy efficiency but also requires the use of renewable energy to fundamentally reduce carbon emissions, [p. 4.]), and energy consumption models help to predict the consequences of operational decisions, allowing for more effective management and control of a system [5], [p.1], We planned to analyze energy consumption by investigating the following two aspects: First, we identified the essential factors affecting energy consumption, [ ], and second, we built mathematical models for the energy consumption of data centers, [p.2]).
Crosby and Zhang are both considered to be analogous to the claimed invention because they are both in the field of energy efficient operation planning. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy-based scheduling of Crosby with the renewable energy considerations of Zhang to reduce costs by improving the energy efficiency but also use renewable energy to fundamentally reduce carbon emissions, Zhang, [p.4.]
Crosby does not teach, scheduling a plurality of jobs defining an application workflow, wherein the application workflow is characterized by commencement of a second job of the plurality of jobs being dependent on completion of a first job of the plurality of jobs, but Simonovic teaches, (the instructions can further comprise receiving an indication that the first job has completed, updating variables, jobs, and links the control structure based on the received indication, and identifying a second actionable job from the control structure, wherein the input for the second actionable job depends from the output of the first actionable job, [0014]),
Crosby and Simonovic are both considered to be analogous to the claimed invention because they are both in the field of energy efficient operation planning. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy-based scheduling of Crosby with the workflow scheduling of Simonovic to meet the need for improvements in computational workflow execution, Simonovic, [0005].
wherein the scheduling is performed in dependence on the predicting the future energy supply profiles, Crosby teaches, (a method of scheduling operations to be executed by a data processing apparatus 2 includes determining energy cost functions for candidate schedules of operations, based on which resources of the data processing apparatus are required for execution of the operations. One of the candidate schedules is selected based on the energy cost functions, [Abstract]), and when selecting a candidate schedule to be executed at a current execution time, the current execution time refers to the time at which the candidate schedule will be executed (which may be at some point in the future), [0033]),
Crosby teaches at the respective ones of the plurality of geographically differentiated computer environments, (the energy cost function may depend on environmental conditions detected by the data processing apparatus or known to the data processing apparatus from another device. For example, the energy efficiency of some resources may vary with temperature conditions, or with the data processing apparatus's location, or its relative position in a network, [0019]), and in dependence on a forecasting of an execution time, Crosby teaches, (the associated information may identify a time window within which the at least one operation of a candidate schedule should be executed by the data processing apparatus. Again, this time window information may be specified at the granularity of the candidate schedule as a whole or for individual sets of one or more operations with the schedule. Specifying a window within which operations can be executed (rather than specifying an absolute time at which the operation should be executed) allows operations to be deferred if possible, which gives more freedom for reordering operations based on the energy cost functions, [0030]), of the first job and the second job; (the scheduler may then determine M of these N candidate schedules which have a time window which will already have started at the current execution time, [0031]), and deploying at least one job of the plurality of jobs defining the application workflow according to the scheduling, (as the apparatus executes a program it may have a scheduling task or a hardware scheduler which forms candidate schedules using operations for the program, and selects schedules for execution based on the energy cost functions as discussed above. [0033]).
Claim 18 is rejected for reasons similar to those of claim 1. In this claim, the addition of a memory, at least one processor in communication with the memory and program instructions executable by the one or more processors via the memory, does not change the rationale for the rejections under 35 U.S.C. § 103 or the reference prior art. (Crosby teaches the program may be stored on computer readable storage medium, which may be a non-transitory medium, [0045] and Crosby also teaches a data processing apparatus having a processor and various resources available for use when processing operations. For example, the resources may include data within a memory (which may include one or more caches), among others, [0063]).
Regarding claim 4, The computer implemented method of claim 1, wherein the method includes generating a data structure provided by workflow graph that includes first and second nodes referencing, respectively, the first and second jobs, and a plurality of edges including an edge that connects the first and second nodes, wherein the scheduling includes evaluating first and second candidate deployments, and wherein the evaluating includes reading forecasted performance data that has been written to the first and second nodes, and to the edge of the data structure. Crosby does not teach, Cook teaches, (generating an executable workflow, comprising: a. receiving a request for a first global variable to be output from a first scope of a workflow graph; b. defining the first global variable as a first node in the workflow graph; c. selecting a first model from a model repository, wherein the first model produces the first global variable as an output after the first model has received at least a first input; d. defining the selected first model as a second node in the workflow graph, wherein the second node is connected to the first node via a directed edge, and wherein the second node is assigned to the first scope, [ ] and whilst the method is generally described in the context of single inputs, single outputs, or at least a first input for example, the skilled person will appreciate that the method is applicable and scalable to models, sub-models, workflow graphs, and sub-graphs which may include a plurality of inputs and/or a plurality of outputs. Cook, [p.2]).
Crosby and Cook are both considered to be analogous to the claimed invention because they are both in the field of energy efficient operation planning. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy-based scheduling of Crosby with the
executable workflow of Cook, to make the assembly of the workflow more efficient, and create workflows which are in themselves more efficient to execute, [Abstract].
Regarding claim 14, the computer implemented method of claim 1, wherein the predicting is performed in dependence on weather forecast data Crosby teaches, (the energy cost function may depend on environmental conditions detected by the data processing apparatus or known to the data processing apparatus from another device. For example, the energy efficiency of some resources may vary with temperature conditions, or with the data processing apparatus's location, or its relative position in a network, [0019]), from a weather service system, Crosby does not teach, Zhang predict the overall energy consumption of HDCs with air-cooled IT equipment. According to the PUE predicted from the location and
the internal structure of data centers from the point of view of IT equipment energy
consumption, the total energy consumption can be calculated, and the carbon emissions
and electricity costs can be forecast. Using the hourly meteorological data in the NOAA
Integrated Surface Database (ISD) as climate parameters, [p.13]).
Crosby and Zhang are both considered to be analogous to the claimed invention because they are both in the field of energy efficient operation planning. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy-based scheduling of Crosby with the overall energy planning and meteorological data of Zhang to save energy costs and improve the economic efficiency.., Zhang, [p.4.]
Regarding claim 15, The computer implemented method of claim 1, wherein the respective energy supply profiles at the respective ones of the plurality of geographically differentiated computer environments express a percentage of supplied energy supplied to a computer environment attributable to renewable power generation, Crosby does not teach, Zhang teaches, (in addition to Power Usage Effectiveness (PUE), other energy efficiency metrics have been applied to the evaluation of data centers, such as pPUE [30] (i.e., local PUE) and RER [31] (i.e., renewable energy ratio), [p. 4.]).
Crosby and Zhang are both considered to be analogous to the claimed invention because they are both in the field of energy efficient operation planning. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy-based scheduling of Crosby with the renewable energy considerations of Zhang to reduce costs by improving the energy efficiency but also use renewable energy to fundamentally reduce carbon emissions, Zhang, [p.4.]
Claim 17 is rejected under 35 U.S.C. § 103 as being taught by Crosby,
(US 20170242727 A1), “Energy-Based Scheduling of Operations to be Executed by a Data Processing Apparatus,” in view of Zhang, “Prediction of Overall Energy Consumption of Data Centers in Different Locations,” Sensors 2022. 3704, https://doi.org/10.3390/s22103704, hereafter Zhang, in further view of Simonovic, (US 20180053328 A1), “Systems and Methods for Processing Computational Workflows,” in further view of Zamen, “Modeling of Processor Delay and Overall Reduction in Network Latencies for Real Time Interaction Applications, IEEE, 2002 Vol 1.”
Regarding claim 17, Crosby teaches, A computer program product comprising:
a computer readable storage medium readable by one or more processing circuit and
storing instructions for execution by one or more processor for performing a method, (The method may be implemented using a computer program for controlling a data processing
apparatus to perform the method described above. The program may be stored on computer readable storage medium, which may be a non-transitory medium, [0045]), comprising:
predicting future energy supply profiles at respective ones of a plurality of
geographically differentiated computer environments, (the energy cost functions may represent an approximate estimate of the energy cost of a candidate schedule. Even if the energy costs are only approximate the scheduling based on the energy costs can still improve the overall energy efficiency of the processor, [0021], and it is also possible to perform the scheduling ahead of time. For example, the scheduling can be done at compile time, or as a preliminary step performed by the processor just before executing the program. [ ] When selecting a candidate schedule to be executed at a current execution time, the current execution time refers to the time at which the candidate schedule will be executed (which may be at some point in the future), [0033]. In some cases, the energy cost function may depend on environmental conditions detected by the data processing apparatus or known to the data processing apparatus from another device. For example, the energy efficiency of some resources may vary with temperature conditions, or with the data processing apparatus's location, or its relative position in a network, [0019]), wherein the predicting is performed by ingesting external telemetry streams and producing normalized machine readable profile data, (the energy cost function may depend on a degree of similarity or correlation between the current use state of the resources and the resources identified by the associated information for the candidate schedule. [ ] An efficient way of determining the correlation maybe to represent the current use state of the resources using a prefix tree (also known as a trie) and to search the prefix tree for a value representing the resource(s) required to carry out the operations of the candidate schedule. For example, the prefix tree may have nodes representing each resource, and the branches between nodes may reflect dependencies between resources. For example, if one resource (e.g. a voltage regulator) requires another resource to be active in order to function properly(e.g. a battery must to be active to power the voltage regulator), then the dependent resource can be represented by a child node branching from a node corresponding to the parent resource. If a resource has several different states or modes then further nodes may be provided for each state/mode, branching from the node corresponding to the resource itself. The search of the prefix tree may return a common prefix representing a subset of common resources which are currently active and are also required by the candidate schedule. The common prefix may then be used as a factor for determining the energy cost function. This provides a more efficient algorithm for determining the energy cost function based on the current usage of resources, [0014]), wherein the scheduling is performed in dependence on the predicting the future energy supply profiles, at the respective ones of the plurality of geographically differentiated computer environments
and in dependence on a forecasting of an execution time of the first job and the second job, (a method of scheduling operations to be executed by a data processing apparatus 2 includes determining energy cost functions for candidate schedules of operations, based on which resources of the data processing apparatus are required for execution of the operations. One of the candidate schedules is selected based on the energy cost functions, [Abstract], and when selecting a candidate schedule to be executed at a current execution time, the current execution time refers to the time at which the candidate schedule will be executed (which may be at some point in the future), [0033], the energy cost function may depend on environmental conditions detected by the data processing apparatus or known to the data processing apparatus from another device. For example, the energy efficiency of some resources may vary with temperature conditions, or with the data processing apparatus's location, or its relative position in a network, [0019], the scheduler may then determine M of these N candidate schedules which have a time window which will already have started at the current execution time, [0031]), deploying at least one job of the plurality of jobs defining the application workflow according to the scheduling, wherein the deploying comprises controlling job instantiation at a selected environment in real time as a function of the normalized profile data, (the scheduling method may be performed by the data processing apparatus itself at execution time. Hence, as the apparatus executes a program it may have a scheduling task or a hardware scheduler which forms candidate schedules using operations for the program, and selects schedules for execution based on the energy cost functions as discussed above, [0033]).
Crosby does not teach, but Zhang teaches, wherein the respective future energy supply profiles at the respective ones of the plurality of geographically differentiated computer environments express an attribute of supplied energy supplied to a computer environment attributable to renewable power generation; (in addition to Power Usage Effectiveness (PUE), other energy efficiency metrics have been applied to the evaluation of data centers, such as pPUE [30] (i.e., local PUE) and RER [31] (i.e., renewable energy ratio). Greenpeace, an international environmental protection organization, believes that “green IT = energy efficiency + renewable energy“, which means the greening of the Internet not only needs to involve the reduction of costs by improving the energy efficiency but also requires the use of renewable energy to fundamentally reduce carbon emissions, [p. 4.], and energy consumption models help to predict the consequences of operational decisions, allowing for more effective management and control of a system [5], [p.1], We planned to analyze energy consumption by investigating the following two aspects: First, we identified the essential factors affecting energy consumption, [ ], and second, we built mathematical models for the energy consumption of data centers, [p.2]).
It would have been obvious before the earliest effective filing date of this application to modify Crosby’s energy cost function scheduling method with the teachings of Zhang in the use of renewable energy considerations with the with the motivation to reduce carbon emissions and take advantage of renewable sources (taking advantage of natural advantages), [p.2.].
Crosby does not teach, but Simonovic teaches, scheduling a plurality of jobs defining an application workflow, wherein the application workflow is characterized by commencement of a second job of the plurality of jobs being dependent on completion of a first job of the plurality of jobs, (the instructions can further comprise receiving an indication that the first job has completed, updating variables, jobs, and links the control structure based on the received indication, and identifying a second actionable job from the control structure, wherein the input for the second actionable job depends from the output of the first actionable job, [0014]).
It would have been obvious before the earliest effective filing date of this application to modify Crosby’s energy cost function scheduling method with the teachings of Simonovic in improved workflow scheduling with the motivation to precisely control job workflow to reproducibly execute a sequence of analyses on large volumes of data across multiple environments, [0005-6].
Crosby does not teach, but Zamen teaches, the forecasting including computation of inter-environment communication overhead using message-passing latency models; (a methodology for modeling processing delay of network packets for any protocol stack. Real-time, interactive applications such as desktop conferencing are sensitive to accumulated delay (latency). Our model is generic and can be used to model the processing delay on any processor and for any protocol. This is achieved through the definition of a Language for Processor and network Protocol - LPP, which allows the declaration of both processor and protocol characteristics that are required for our model. On the basis of empirical results, we are able to predict certain optimizations for edge routers causing minimal processing latencies, for an overall reduction in network latencies, [Abstract].
It would have been obvious before the earliest effective filing date of this application to modify Crosby’s energy cost function scheduling method with the teachings of Zamen in improved workflow scheduling with the motivation to precisely control job workflow to reproducibly execute a sequence of analyses on large volumes of data across multiple environments, [0005-6].
Conclusion
THIS ACTION IS MADE FINAL. 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.
Claims 2-3 are not rejected by prior art under 35 U.S.C. § 103. The closest prior art to
the invention Crosby, (US 20170242727 A1), “Energy Based Scheduling of Operations to be Executed by a Data Processing Apparatus,” and Simonovic, (US 20180053328 A1), “Systems and Methods for Processing Computational Workflows.” None of the prior art alone or in combination teaches the claimed invention as recited in these claims wherein the novelty is in the combination of all the limitations and not in a single limitation.
Regarding Claims 2-3, Although Crosby teaches predicting costs based on environmental data to include location, he does not teach workflows and separate job predictions. Simonovic teaches workflow but not the dependency on predicated energy supplies at each location for the time of the performance of each job. None of the prior art alone or in combination teaches all aspects of the claimed invention as recited in these claims wherein the novelty is in the combination of all the limitations and not in a single limitation.
Claims 5-7, 13, 16, and 19, are not rejected by prior art under 35 U.S.C. § 103. The
closest prior art to the invention is Li, (US 11544105 B2), “Recommendations for Scheduling Jobs On Distributed Computing Devices,” and Crosby, (US 20170242727 A1), “Energy Based Scheduling of Operations to be Executed by a Data Processing Apparatus.” None of the prior art alone or in combination teaches the claimed invention as recited in these claims wherein the novelty is in the combination of all the limitations and not in a single limitation.
Regarding Claim 5-7, 13, 16 and 19, specifically including dynamically rescheduling hosting of one or more job of the plurality of jobs during running of the application workflow, based on dependencies, the closest prior art to this claims included Li, where the recommendation engine is configured to generate recommendations for one or more types of computing device to schedule the job to, as well as a quantity specifying the amount of computing resources of each type that is recommended to be assigned, and Crosby, (the energy cost functions may represent an approximate estimate of the energy cost of a candidate schedule. [ ] Even if the energy costs are only approximate the scheduling based on the energy costs can still improve the overall energy efficiency of the processor). These individually or in combination did not teach the complete scope of the claim.
Claims 8, 9, and 20 are not rejected by prior art under 35 U.S.C. § 103. The closest prior
art to the invention is Zhang, Prediction of Overall Energy Consumption of Data Centers in Different Locations, Sensors 2022, 22, 3704. https://doi.org/10.3390/s22103704. None of the prior art alone or in combination teaches the claimed invention as recited in claims 8-9 and 20 wherein the novelty is in the combination of all the limitations and not in a single limitation.
Regarding claims 8-9, and 20, scheduling based on data transfer time between jobs as determined by geographical distance, Zhang promotes an energy consumption model that incorporates performance, scale, location, and other characteristics of data centers. This did not teach the complete scope of the claims,
Claims 10 and 12 are not rejected by prior art under 35 U.S.C. § 103. The closest prior
art to the invention is taught by Moorthi, (US 20120131591 A1), “Method and Apparatus for Clearing Cloud Compute Demand.” The prior art alone or in combination did not teach the claimed invention as recited in claim 10 wherein the novelty is in the combination of all the limitations and not in a single limitation.
Regarding claims 10 and 12, scheduling in dependence on an offered resource for hosting the second job as determined by processing an instantiation of a resource extraction image, Moorthi teaches scheduling for a job assigned to a resource and allocation based on cost, [0217], and acquiring a provider-offered resource, [0229]. This did not teach the complete scope of the claims.
Claim 11 is not rejected by prior art under 35 U.S.C. § 103. The closest prior art to the
invention is Lau, “Transition Phase Classification and Prediction,” IEEE Conference Papers, 11th International Symposium on High-Performance Computer Architecture, San Francisco, CA, USA, (2005, Page(s): 278-289), doi: 10.1109/HPCA.2005.39.
Regarding claim 11, where scheduling is based upon predicted execution run time using a hashing method, Lau examines run-time phase classification and prediction architecture and employ a hash in the estimation for the prediction. This did not teach the complete scope of the claim.
The prior art made of record and not relied upon is considered pertinent to applicants’
disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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