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 .
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 - 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract ide without significantly more.
Step 1: claim 1 recites “a computer-implemented method” which is a method/process claim 8 recites “a system comprising: one or more processors; and a memory coupled to the one or more processors, the memory storing a plurality of instructions executable by the one or more processors …” which is a machine. Claim 15 recites “a non-transitory computer-readable medium storing a plurality of instructions executable by one or more processors …” which is a manufacture.
Step 2A Prong 1:
Claims 1, 8 and 15 recite “determining,” “determining” and “comparing,” which specifically recite “determining a predicted resource limit for performing the operation based on the metadata”, “determining a GPU resource utilization value of each node of the plurality of nodes based on the status of each GPU resource, wherein the GPU resource utilization value indicates an amount of utilization of GPU resources of corresponding node;” and “comparing the GPU resource utilization value of each node with a pre-defined resource utilization threshold value;.” These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting (from claims 1, 8 and 15) “graphical processing unit (GPU) resources”, “a plurality of GPU resources,” “a plurality of nodes,” “a corresponding GPU resource” and “a set of GPU resources” (from claim 8) “a system,” “one or more processors”, “a memory,” (from claim 22) “a non-transitory computer-readable medium” and “one or more processors” nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “determining,” and “comparing,” in the context of this claim encompasses a user mentally, and/or with the aid of pen and paper writing the changes down on a sheet of paper and examine the items using mental judgement and/or evaluation.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claims recites the additional elements (from claims 1, 8 and 15) “graphical processing unit (GPU) resources”, “a plurality of GPU resources,” “a plurality of nodes,” “a corresponding GPU resource” and “a set of GPU resources” (from claim 8) “a system,” “one or more processors”, “a memory,” (from claim 22) “a non-transitory computer-readable medium” and “one or more processors.” These limitations are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts 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. (see MPEP 2106.05(f)). Therefore, this/these claim(s) is/are directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations (from claims 1, 8 and 15) “graphical processing unit (GPU) resources”, “a plurality of GPU resources,” “a plurality of nodes,” “a corresponding GPU resource” and “a set of GPU resources” (from claim 8) “a system,” “one or more processors”, “a memory,” (from claim 22) “a non-transitory computer-readable medium” and “one or more processors” these limitations are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts 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 and are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv)).
As such, claims 1, 12 and 22 are rejected under 35 U.S.C. 101.
Claims 2 – 7, 9 - 14 and 16 - 20 depend from claims 1 and 12 and do not add additional elements that would overcome the rejection of claims 1 or 12 and are rejected for at least this reason and the analysis below:
2. The method of claim 1, further comprising:
simulating the request on each node of the plurality of nodes (insignificant extra solution activity – generic computer processing);
determining a percentage of resource utilization for each node based on the simulation of the request (mental process – determining/evaluation/judgment);
identifying a node from the plurality of nodes having highest percentage of resource utilization; and allocating the set of GPU resources from the identified node to the client (mental process – determining/evaluation/judgment)
3. The method of claim 1, further comprising:
determining a type of the operation based on the request (mental process – determining/evaluation/judgment); and
allocating the set of GPU resources from the plurality of GPU resources based on the type of the operation (insignificant extra solution activity – generic data allocation)
4. The method of claim 1, further comprising:
generating a dedicated AI cluster by patching the set of GPU resources within a single cluster, wherein the dedicated AI cluster reserves a portion of a computation capacity of a computing system for a period of time (insignificant extra solution activity – generic data processing); and
allocating the dedicated AI cluster to the client associated with the client ID (insignificant extra solution activity – generic data allocation)
5. The method of claim 1, further comprising:
authenticating, prior to the allocation of the set of GPU resources, the request based on the client ID associated with the client, wherein the request is authenticated using a private key extracted from an asymmetric key pair associated with the client ID (insignificant extra solution activity – generic data processing)
6. The method of claim 1, further comprising:
determining a number of tokens associated with the request (mental process – determining/evaluation/judgment);
determining whether the number of tokens exceeds a pre-defined request limit corresponding to the client ID (mental process – determining/evaluation/judgment); and
blocking the request based on the determination that the number of tokens exceeds the pre-defined request limit (insignificant extra solution activity – generic data processing)
7. The method of claim 1, further comprising:
terminating patching of the set of GPU resources based on a pre-defined condition, wherein the pre-defined condition is one of (insignificant extra solution activity – generic data processing):
a failure of the set of GPU resources during launch (insignificant extra solution activity – generic data processing);
a workload failure of the set of GPU resources(insignificant extra solution activity – generic data processing); and
a software bug detected in the set of GPU resources (insignificant extra solution activity – generic data processing)
9. The system of claim 8, wherein the set of operations further includes:
simulating the request on each node of the plurality of nodes (insignificant extra solution activity – generic data processing);
determining a percentage of resource utilization for each node based on the simulation of the request (mental process – determining/evaluation/judgment);
identifying a node from the plurality of nodes having highest percentage of resource utilization (mental process – determining/evaluation/judgment); and
allocating the set of GPU resources from the identified node to the client (insignificant extra solution activity – generic data allocation)
10. The system of claim 8, wherein the set of operations further includes:
determining a type of the operation based on the request (mental process – determining/evaluation/judgment); and
allocating the set of GPU resources from the plurality of GPU resources based on the type of the operation (insignificant extra solution activity – generic data allocation)
11. The system of claim 8, wherein the set of operations further includes:
generating a dedicated AI cluster by patching the set of GPU resources within a single cluster, wherein the dedicated AI cluster reserves a portion of a computation capacity of a computing system for a period of time (insignificant extra solution activity – generic data processing); and
allocating the dedicated AI cluster to the client associated with the client ID (insignificant extra solution activity – generic data allocation)
12. The system of claim 8, wherein the set of operations further includes:
authenticating, prior to the allocation of the set of GPU resources, the request based on the client ID associated with the client, wherein the request is authenticated using a private key extracted from an asymmetric key pair associated with the client ID (insignificant extra solution activity – generic data processing)
13. The system of claim 8, wherein the set of operations further includes:
determining a number of tokens associated with the request (mental process – determining/evaluation/judgment);
determining whether the number of tokens exceeds a pre-defined request limit corresponding to the client ID (mental process – determining/evaluation/judgment); and
blocking the request based on the determination that the number of tokens exceeds the pre-defined request limit (insignificant extra solution activity – generic data processing)
14. The system of claim 8, wherein the set of operations further includes:
terminating patching of the set of GPU resources based on a pre-defined condition, wherein the pre-defined condition is one of (insignificant extra solution activity – generic data processing):
a failure of the set of GPU resources during launch (insignificant extra solution activity – generic data processing);
a workload failure of the set of GPU resources (insignificant extra solution activity – generic data processing); and
a software bug detected in the set of GPU resources (insignificant extra solution activity – generic data processing)
16. The non-transitory computer-readable medium of claim 15, wherein the set of operations further comprises:
simulating the request on each node of the plurality of nodes (insignificant extra solution activity – generic computer processing);
determining a percentage of resource utilization for each node based on the simulation of the request (mental process – determining/evaluation/judgment);
identifying a node from the plurality of nodes having highest percentage of resource utilization (mental process – determining/evaluation/judgement); and
allocating the set of GPU resources from the identified node to the client (insignificant extra solution activity – generic data allocation)
17. The non-transitory computer-readable medium of claim 15, wherein the set of operations further comprises:
determining a type of the operation based on the request; and allocating the set of GPU resources from the plurality of GPU resources based on the type of the operation (mental process – determining/evaluation/judgement)
18. The non-transitory computer-readable medium of claim 15, wherein the set of operations further comprises:
authenticating, prior to the allocation of the set of GPU resources, the request based on the client ID associated with the client, wherein the request is authenticated using a private key extracted from an asymmetric key pair associated with the client ID (insignificant extra solution activity – generic data allocation)
19. The non-transitory computer-readable medium of claim 15, wherein the set of operations further comprises:
determining a number of tokens associated with the request (mental process – determining/evaluation/judgement);
determining whether the number of tokens exceeds a pre-defined request limit corresponding to the client ID (mental process – determining/evaluation/judgement); and
blocking the request based on the determination that the number of tokens exceeds the pre-defined request limit (insignificant extra solution activity – generic computer processing)
20. The non-transitory computer-readable medium of claim 15, wherein the set of operations further comprises:
terminating patching of the set of GPU resources based on a pre-defined condition, wherein the pre-defined condition is one of (insignificant extra solution activity – generic computer processing):
a failure of the set of GPU resources during launch (insignificant extra solution activity – generic computer processing);
a workload failure of the set of GPU resources (insignificant extra solution activity – generic computer processing); and
a software bug detected in the set of GPU resources (insignificant extra solution activity – generic computer processing).
Other References Cited Not Relied Upon
Stein (US 2025/0097127) discloses considering latency in network communications that should be minimized for better network performance that incorporates metadata in its system.
Wilson et al. (US 2025/0138901) discloses a strategy of resource reservations in cloud/network computing to address latency and network performance while incorporating valuable metadata to achieve these aims.
Momchilov et al. (US 2020/0374238) discloses network packet stream management that addresses performance lag using user/client metadata as part of its solution.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CRAIG C DORAIS whose telephone number is (571)270-3371. The examiner can normally be reached M-F 9:00 am - 6:00pm.
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/CRAIG C DORAIS/Primary Examiner, Art Unit 2198
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