DETAILED ACTION
Claims 1-20 are pending. Applicant has amended claims 1, 6, 10, 15 and 19.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-14 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 11,740,942 B2) in view of Schulz (US 2023/0127026 A1) further in view of Di Balsamo et al. (US 2015/0277987 A1) and Norbeck, Jr. et al. (US 10,511,540 B1).
As to claim 1, Kumar teaches computing platform comprising:
at least one processor (processor; col. 13, lines 26-28);
a communication interface communicatively coupled to the at least one processor (I/O interface, network interface; col. 14, lines 17-24); and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to (memory 1020 may include program instructions that implement the various embodiments of the system; col. 14, lines 60-62):
receive a workload (The user input may also indicate the workload to be performed; col. 3, lines 49-50), wherein the workload identifies resources required to complete a task (a compute capacity constraint, storage capacity constraints; col. 6, lines 8-32);
receive, from a computing device associated with a user, one or more processing constraints (a user may provide any suitable constraint(s) for performance of the workload; col. 5, lines 20-21, the user may indicate a bandwidth constraint for performance of the workload; col. 5, lines 42-43, and maximum allowed latency as a constraint for performance of the workload; col. 5, lines 59-66);
receive contextual parameters associated with the workload, wherein the contextual parameters include geographic information (locations; col. 10, lines 27-39), weather related information (temperature measurement, humid, pressure measurements; col. 9, lines 38-50), and temporal information (For example, the user may indicate a maximum amount of data that can be uploaded from the network of the client to the provider network over a predefined period of time (e.g., daily limit, hourly limit, per second limit) and/or a maximum amount of data that can be downloaded to the network of the client from the provider network over a predefined period of time; col. 5, lines 43-49);
acquire availability data for a plurality of resources in a distributed computing environment, wherein each of the plurality of resources is capable of performing at least part of the workload (one or more available client resources of the remote network of the client, one or more available provider resources of the provider network; col. 3, line 64 – col. 4, line 23);
based on the one or more processing constraints, the contextual parameters, and the availability data, build, using an artificial intelligence algorithm, an optimization model for distributing the workload, wherein the optimization model optimizes distribution of available resources of the plurality of resources allocated to executing the workload (the service may determine, based on one or more performance measure … and to assign the other two portions to the client resources; col. 4, lines 24-64, col. 7, lines 32-45, col. 10, lines 9-39 and col. 11, lines 25-33);
based on the optimization model, identify one or more resource distribution options including an optimal distribution of the available resources for executing the workload (the workload adaptation may be used to determine an optimal … the workload adaptation service deploys the one or more provider resources according to the assignments of the workload deployment model; col. 11, lines 33-57); and
execute the workload based on the identified one or more resource distribution options (At block 712, the workload adaption service causes initiation of the execution of the workload; col. 57-60).
Kumar does not teach wherein the workload identifies a number of computer processing cycles required to complete a task; and contextual parameters include historical information, and stock market information.
However, Schulz teaches the workload identifies a number of computer processing cycles required to complete a task (CPU cycle; paragraphs [0017], [0052]-[0055]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Schulz to the system of Kumar because both are in the same field of endeavor and Schultz teaches an alternate method to identify resources needed to execute the workload.
Di Balsamo teaches contextual parameters include historical information (historical job information may be more efficiently used to estimate job completion dates and resource requirements of the batch jobs; paragraph [0065]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to apply the teaching of Di Balsamo to the system of Kumar as modified by Schulz because Di Balsamo teaches historical job information is used during resource allocation in job scheduling environment, which increase the efficiency of a job scheduling system and may reduce violations of SLAs (see paragraph [0001]).
Norbeck teaches contextual parameters include stock market information (the contextual information may identify that workload E (i.e. of the e-mail application) has peak usage in the late afternoon because Wall Street users tend access the e-mail at a high volume before stock markets close for the day; col. 4, lines 14-18). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to apply the teaching of Norbeck to the system of Kumar as modified by Schulz because Norbeck teaches a method directed to the automatic prediction and offering of cloud computing services to end users based on contextual aspects and/or information corresponding to the end user's existing set of cloud services.
As to claim 2, Kumar as modified by Schulz, Di Balsamo and Norbeck teaches the computing platform of claim 1, wherein acquiring the availability data for the plurality of resources includes identifying resources, of the plurality of resources, that are not being utilized (one or more available client resources of the remote network of the client, one or more available provider resources of the provider network; col. 3, line 64 – col. 4, line 23).
As to claim 3, Kumar as modified by Schulz, Di Balsamo and Norbeck teaches the computing platform of claim 1, wherein the computer processing cycles include processing cycles of a central processing unit or processing cycles of a graphical processing unit (see Schulz: CPU cycle; paragraphs [0017], [0052]).
As to claim 4, Kumar as modified by Schulz, Di Balsamo and Norbeck teaches the computing platform of claim 1, wherein the one or more processing constraints includes a time interval and a target budget for completion of the task (one or more constraints for performance of a workload (e.g., daily upload limit, maximum latency between different portion), bandwidth, storage capacity, etc.; col. 3, lines 47-49, col. 5, line 43- col. 6, line 37).
As to claim 5, Kumar as modified by Schulz, Di Balsamo and Norbeck teaches the computing platform of claim 1, wherein the one or more processing constraints includes a regulatory requirement associated with the task (maximum allowed latency; col. 5, line 59 – col. 6, line 7 and col. 10, lines 9-39).
As to claim 7, Kumar as modified by Schulz, Di Balsamo and Norbeck teaches the computing platform of claim 1, further including instructions that, when executed, cause the computing platform to: based on a type of the task, assign and apply weights to the optimization model to emphasize a selection of a central processing unit or a graphical processing unit for completing the task (see Schulz: CPU or GPU; paragraphs [0048] and [0080]) and (see Kumar: the service may determine, based on one or more performance measure … and to assign the other two portions to the client resources; col. 4, lines 24-64).
As to claim 8, Kumar as modified by Schulz, Di Balsamo and Norbeck teaches the computing platform of claim 1, further including instructions that, when executed, cause the computing platform to: determine a likelihood of completion of the workload within the one or more processing constraints (see Schulz: paragraphs [0023]-[0026]).
Kumar and Schulz do not clearly teach determine that the workload cannot be completed within the one or more processing constraints; and transmit a notification to the computing device associated with a user indicating the likelihood of completion of the workload outside the one or more processing constraints.
However, Schulz teaches the workload will be executed when the resources are available to perform the workload given the resources and timing requirement/constraints.
Kumar teaches a workload adaptation service can reallocates portions of workload to different resources prior and during execution of the workload to assure that the workload is executed within the required constraints (col. 4, lines 10-64 and Figs. 8-9 and associated text). Kumar further teaches the user can specify workload constraints via GUI (col. 9, lines 13-25).
It would have been obvious to one of ordinary skill in the art, given the teaching of Kumar and Schulz above, the system of Kumar as modified by Schultz could be modified to implement when determine that the workload cannot be completed within the one or more processing constraints; and transmit a notification to the computing device associated with a user indicating the likelihood of completion of the workload outside the one or more processing constraints, thus would allow the user to know potential error and given an option to modify the resources.
As to claim 9, Kumar as modified by Schulz, Di Balsamo and Norbeck teaches the computing platform of claim 1, further including instructions that, when executed, cause the computing platform to: prompt a user of the computing device to select of one of the identified one or more resource distribution options; and execute the workload based on the selection by the user of the computing device (see Kumar: col. 9, lines 13-25).
As to claim 10, see rejection of claim 1 above.
Kumar further teaches a method, comprising: at a computing platform (computing system 1000; Fig. 10) comprising at least one processor (one or more processors 1010; Fig. 10), a communication interface (I/O interface 1030; Fig. 10), and memory (a system memory 1020; Fig. 10).
As to claims 11-14 and 16-18, see rejections of claims 2-5 and 7-9 above, respectively.
As to claim 19, see rejection of claim 1 above.
Kumar further teaches one or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface (A computer-accessible medium may include non-transitory storage media or memory media such as magnetic or optical media, e.g., disk or CD/DVD-ROM coupled to computer system 1000 via I/O interface 1030. Program instructions and data stored via a computer-accessible medium may be transmitted by transmission media; paragraph [0079]).
As to claim 20, see rejection of claim 2 above.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 11,740,942 B2) in view of Schulz (US 2023/0127026 A1), Di Balsamo et al. (US 2015/0277987 A1) and Norbeck, Jr. et al. (US 10,511,540 B1) further in view of Kumbhare et al. (US 2023/0367653 A1).
As to claim 6, Kumar as modified by Schulz, Di Balsamo and Norbeck does not teach wherein the contextual parameters associated with the workload further include an impending storm or other major weather event expected to cause server outages in a particular area.
However, Kumbhare the contextual parameters associated with the workload further include an impending storm or other major weather event expected to cause server outages in a particular area (In some embodiments, the utilization patterns can inform the workload controller and/or the control service of the current or predicted future state of the workload on the co-location based at least partially on historical data and trends of resource utilization. For example, the utilization pattern may include process allocation, power draw, and/or computational load that is based at least partially on time of day, day of the week, day of the year, or correlation to other events, such as weather, holidays, or periodic events. In some embodiments, the workload controller and/or the control service may determine a trend or predicted future state of the workload based on the utilization patterns and pre-emptively change or adjust workload or power supply to at least partially compensate for the trend or predicted future state of the workload; paragraphs [0079] and [0033]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching Kumbhare to the system of Kumar as modified by Schulz, Di Balsamo and Norbeck because Kumbhare teaches a method for power management in a system based on at least infrastructure and weather information.
As to claim 15, see rejection of claim 6 above.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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/DIEM K CAO/Primary Examiner, Art Unit 2196
DC
January 23, 2026