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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over corresponding Claims 1-20 of copending Application No. 18/419,666 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the current application claims are anticipated by the corresponding reference application claims, the “completion time analysis” of the current application independent claims being representative of “latency analysis” of the reference application independent claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over corresponding Claims 1-20 of copending Application No. 18/419,780 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the current application claims are anticipated by the corresponding reference application claims, the “completion time analysis” of the current application independent claims being representative of “latency analysis” of the reference application independent claims. And the “parameter-efficient fine-tuning (PEFT) process” of the reference application dependent Claims 2, 9 and 16 being representative of the “training” of the corresponding current application dependent Claims 2, 9 and 16.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over corresponding Claims 1-20 of copending Application No. 18/419,790 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the current application claims are anticipated by the corresponding reference application claims, the “training workload” of the current application independent claims being representative of “model adaptation workload” of the reference application independent claims. And the “parameter-efficient fine-tuning (PEFT) process” of the reference application dependent Claims 2, 9 and 16 being representative of the “training” of the corresponding current application dependent Claims 2, 9 and 16.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
All claims are directed towards either a method, a system or a non-transitory computer readable medium and thus satisfies Step 1 as falling into one of the statutory categories.
Step 2A, Prong One:
Independent Claim 1 recites (the same analysis applies to similar independent Claims 8 and 15):
performing a completion time analysis using the telemetry data to generate a placement recommendation; making a determination that the placement recommendation specifies a second production environment of the plurality of production environments;
these limitations, under their broadest reasonable interpretation, covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is a person can analyze completion time data of various environments and make a determination as to which environment is most suitable for placing a workload using observation and evaluation.
Step 2A, Prong Two:
Claim 1 recites the additional elements of (the same analysis applies to similar independent Claims 8 and 15):
obtaining, by a workload placement service, a request for assigning a training workload to one of a plurality of production environments based on completion time;
this limitation is considered as adding insignificant extra-solution activity (obtaining data) to the judicial exception - see MPEP 2106.05(g).
performing an initial workload placement of the training workload to assign the training workload to a first production environment of the plurality of production environments;
this limitation is also considered as adding insignificant extra-solution activity (assigning data/workloads) to the judicial exception - see MPEP 2106.05(g).
monitoring: execution of the training workload on the first production environment, and performance of computing resource in the plurality of production environments to obtain telemetry data associated with the execution and the performance;
this limitation is also considered as adding insignificant extra-solution activity (obtaining data) to the judicial exception - see MPEP 2106.05(g).
and based on the determination, initiating deployment of the training workload to the second production environment.
this limitation is also considered as adding insignificant extra-solution activity (assigning or deploying data/workloads) to the judicial exception - see MPEP 2106.05(g).
The further additional elements of a “computer processor” and/or “processor” as recited in these independent claims are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are considered as appending well-understood, routine, conventional activities previously known to the industry (obtaining data, assigning or deploying data/workloads), specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d). The further additional elements of a “computer processor” and/or “processor” as recited in the independent claims amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are therefore not patent eligible.
Dependent Claims 2, 9, 16 are considered as using a generative AI model as a tool to perform an abstract idea, which also includes its training - see MPEP 2106.05(f).
Dependent Claims 3, 10, 17 are considered as appending well-understood, routine, conventional activities previously known to the industry (obtaining data), specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d).
Dependent Claims 4-7, 11-14, 18-20 are considered as generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Martinez, US 2023/0047295 A1.
Regarding Claim 1, Martinez teaches:
A method for managing workload placement, the method comprising (Abstract: “At a time of execution of a workload, a particular computer resource of the heterogeneous set of computer resources on which to dispatch the workload is dynamically determined”):
obtaining, by a workload placement service, a request for assigning a training workload to one of a plurality of production environments based on completion time (paragraph 26: “The execution framework 150 represents a workload execution framework for deploying workloads on computer resources (e.g., computer resources 160a-n). For example, a workload executor 155 of the execution framework 150 may receive workload requests, including the workload and specified constraints for the workload”; And, paragraph 37: “While in various examples described herein it is assumed the optimization goal is to maximize performance (e.g., complete execution in the least amount of time or with least latency) of workloads, it is to be understood the optimization goal and corresponding cost models may make use of different parameter sets to achieve other optimization goals”; And, paragraph 41: “When the workloads at issue represent ML inferences”. The machine learning (ML) inferences are training workloads);
in response to the request: performing an initial workload placement of the training workload to assign the training workload to a first production environment of the plurality of production environments (Abstract: “At a time of execution of a workload, a particular computer resource of the heterogeneous set of computer resources on which to dispatch the workload is dynamically determined”);
after performing the initial workload placement, monitoring: execution of the training workload on the first production environment (paragraph 14: “the recommendation system may first detect all the existing devices on the platform and thereafter dynamically monitor their respective utilization and availability”),
and performance of computing resource in the plurality of production environments to obtain telemetry data associated with the execution and the performance (paragraph 15: “telemetry samples may be collected in real-time from a computer system having a heterogeneous set of computer resources in which the telemetry samples are indicative of a state of the computer system (e.g., utilization of the individual computer resources). Based on the telemetry samples, one or more workload performance prediction models (e.g., a cloud-based federated learning model, a local statistical model, a local machine-learning model, and/or a network-based synthetic model) may be created or updated for a heterogeneous set of computer resources of the computer system with reference to one or more optimization goals (e.g., minimizing or maximizing one or more of performance, power, latency, throughput, etc.)”);
performing a completion time analysis using the telemetry data to generate a placement recommendation (paragraph 15: “Based on the telemetry samples, one or more workload performance prediction models (e.g., a cloud-based federated learning model, a local statistical model, a local machine-learning model, and/or a network-based synthetic model) may be created or updated for a heterogeneous set of computer resources of the computer system with reference to one or more optimization goals (e.g., minimizing or maximizing one or more of performance, power, latency, throughput, etc.). At a time of execution of a workload, a particular computer resource of the heterogeneous set of computer resources on which to dispatch the workload may be dynamically determined based on workload performance predictions for the computer resources”. And, paragraph 37: “While in various examples described herein it is assumed the optimization goal is to maximize performance (e.g., complete execution in the least amount of time or with least latency) of workloads, it is to be understood the optimization goal and corresponding cost models may make use of different parameter sets to achieve other optimization goals”);
making a determination that the placement recommendation specifies a second production environment of the plurality of production environments (paragraph 14: “the recommendation system may first detect all the existing devices on the platform and thereafter dynamically monitor their respective utilization and availability. Second, the performance of the workload on participating devices may be estimated. In the context of an ML workload, this may be accomplished (e.g., initially) with an innovative cost model that evaluates the network associated with the ML workload as well as device characteristics and availability. Finally, heuristics may be used to map the expected performance of the devices to the application requirement to determine the optimal device for the workload. This process may then be repeated continuously to identify the ideal device at any given time and for any active workload”. The dynamic identification of the ideal device from all the existing devices (including the currently used device) at any given time for the workload representative of the second production environment);
and based on the determination, initiating deployment of the training workload to the second production environment (paragraph 77: “At decision block 760, it is determined whether it is okay to switch from a currently used computer resource to the top ranked computer resource”. Examiner’s note: see also Featonby, US 2022/0413891 A1, for example Abstract).
Regarding Claim 2, Martinez further teaches:
The method of claim 1, wherein the training workload comprises the training of a generative artificial intelligence (AI) model using training data (paragraph 41: “the workloads at issue represent ML inferences”. The machine learning (ML) inferences comprises training data for the ML model and a generative artificial intelligence (AI) model is a ML model. Examiner’s note: see also Wyman, US 2024/0220831 A1, for example Abstract, paragraph 123).
Regarding Claim 3, Martinez further teaches:
The method of claim 2, wherein the generative AI model is utilized by a front-end environment to obtain an inferencing payload (paragraph 72: “At block 710, a workload steering request is received by the recommendation system, for example, from an execution framework (e.g., execution framework 150). In one embodiment, the workload steering request represents a query issued by a workload executor (e.g., workload executor 155) to the recommendation system via an API (e.g., API 305) exposed by the recommendation system. For example, the workload executor may issue a query for a recommended computer resource for a given workload”. The recommendation system including the API representative of the front-end environment and the workload representative of the inferencing payload).
Regarding Claim 4, Martinez further teaches:
The method of claim 1, wherein the completion time analysis is further based on causal variables associated with completion time (paragraph 37: “it is assumed the optimization goal is to maximize performance (e.g., complete execution in the least amount of time or with least latency) of workloads”; And, paragraph 70: “In the context of the present example, computer-resource-specific samples 510 indicative of actual performance (e.g., workload completion time) of a heterogeneous set of computer resources (e.g., computer resources 160a-n), including a CPU, a VPU, and a GPU, for various states (e.g., utilization percentages) have been regressed to generate corresponding predictions 520 that can be used to predicting workload completion time for a range of system states”).
Regarding Claim 5, Martinez further teaches:
The method of claim 4, wherein the causal variables comprise at least one of: clock speed of a graphics processing unit (GPU) of the first production environment, a number of GPUs used for the training workload in the first production environment, a second number of GPUs available in the second production environment, and interconnect speed between GPUs executing the training workload (paragraph 70: “In the context of the present example, computer-resource-specific samples 510 indicative of actual performance (e.g., workload completion time) of a heterogeneous set of computer resources (e.g., computer resources 160a-n), including a CPU, a VPU, and a GPU, for various states (e.g., utilization percentages) have been regressed to generate corresponding predictions 520 that can be used to predicting workload completion time for a range of system states. In this example, it can be seen based on the prior samples of actual performance by the CPU, the VPU, and the GPU, for all values of device utilization, the predicted workload completion time for the CPU is greater than that of the VPU and the GPU. Additionally, the predicted workload completion time for the GPU is lower than that of the VPU until about 25% device utilization at which point the predicted workload completion time for the VPU is lower than that of the GPU”. The completion time of the GPU indicative of its clock speed. Examiner’s note: see also Balle, US 2018/0027058 A1, for example paragraph 61).
Regarding Claim 6, Martinez further teaches:
The method of claim 1, wherein the production environment is a computing device of an on-premise environment (paragraph 23: “In the private cloud deployment model, the cloud infrastructure is provisioned for exclusive use by a single organization comprising multiple consumers (e.g., business units), may be owned, managed, and operated by the organization, a third party, or some combination of them, and may exist on or off premises”. Examiner’s note: see also Featonby, US 2022/0413891 A1, for example paragraph 59).
Regarding Claim 7, Martinez further teaches:
The method of claim 1, wherein the production environment is a computing device of a cloud environment (paragraph 23: “In the public cloud deployment model, the cloud infrastructure is provisioned for open use by the general public, may be owned, managed, and operated by a cloud provider (e.g., a business, academic, or government organization, or some combination of them), and exists on the premises of the cloud provider”).
Claims 8-14 are similar to Claims 1-7 and are rejected under the same rationale as stated above for those claims.
Claims 15-17 are similar to Claims 1-3 and are rejected under the same rationale as stated above for those claims.
Claim 18 is a combination of Claims 4 and 5 and is rejected under the same rationale as stated above for those claims.
Claims 19-20 are similar to Claims 6-7 and are rejected under the same rationale as stated above for those claims.
Examiner's Note:
The Examiner cites particular pages, sections, columns, line numbers, and/or paragraphs in the references as applied to the claims above 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 its 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 and the additional related prior arts made of record that are considered pertinent to applicant's disclosure to further show the general state of the art. The Examiner's interpretations in parenthesis are provided with the cited references to assist the applicants to better understand how the examiner interprets the prior art to read on the claims. Such comments are entirely consistent with the intent and spirit of compact prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 for the relevant prior art where for example Guim, US 2021/0144517 A1, teaches improvements to the configuration and functional capabilities of an edge computing architecture and an implementing edge computing system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVE MISIR whose telephone number is (571)272-5243. The examiner can normally be reached M-R 8-5 pm, F some hours.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at 5712703169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVE MISIR/Primary Examiner, Art Unit 2127
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