Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The action is in response to claims dated 7/25/2023.
Claims pending in the case: 1-20
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim 1 includes the following recitation of an abstract idea:
determine computational requirements associated with the ML model (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
determine a subset of computational resources from a pool of computational resources to execute the ML model based on the computational requirements associated with the ML model (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
the system comprising: a processing device; a non-transitory storage device containing instructions (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).);
receive a request to execute a machine learning (ML) model; (This amounts to a mere instruction to apply the abstract idea with generic computer components. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f)).
allocate the subset of computational resources to the ML model (This amounts to a mere instruction to apply the abstract idea with generic computer components. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f)); and
execute the ML model using the subset of computational resources (This high level recitation of the machine learning model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
These claimed limitations therefore do not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
Independent Claims 10 and 18, are similar in scope as claim 1 and therefore rejected under the same rationale. The additional elements of “a non-transitory computer-readable medium comprising code” in claim 10 also do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Dependent claim 2-4, 6 recites types of resources and is description of data (The use of data of a particular type or source is an attempt to limit the abstract idea to a particular field of use or technological environment. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05 (h).) .
Dependent claim 5 and 7 is similar in scope to claim 1 using one type of resource.
Dependent claim 8 recites, determine an occurrence of a trigger event during the execution of the ML model; capture information associated with the trigger event; determine an effect of the trigger event on the execution of the ML model (Determining or observing an event and determining its effect based on event information is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.); dynamically allocate additional computational resources to the ML model in response to determining the effect of the trigger event on the execution of the ML model; and execute the ML model using the subset of computational resources and the additional computational resources (Allocating resources based on specification amounts to a mere instruction to apply the abstract idea with generic computer components. This additional element is mere instructions to apply an exception because they recite no more than an idea of a solution or outcome. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f)).
Dependent claim 9 pertains to types of events and amounts to a mere instruction to apply the abstract idea with generic computer components.
The dependent claims therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea
Dependent Claims 11-17, 19-20 are similar in scope as claim 2-8, 2-3 respectively and therefore rejected under the same rationale.
Hence these claims are rejected as being abstract.
Claim Rejections - 35 USC § 102
(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.
Claim(s) 1-2, 10-11, 18-19 is/are rejected under 35 U.S.C. 102a(2) as being anticipated by Clemons (US 20240231928).
Regarding Claim 1, Clemons teaches, A system for dynamic allocation of computational resources for optimized performance of machine learning (ML) models, the system comprising: a processing device; a non-transitory storage device containing instructions when executed by the processing device (Clemons: [22]), causes the processing device to:
receive a request to execute a machine learning (ML) model (Clemons: [35]: scheduled task request to execute the task ML model);
determine computational requirements associated with the ML model (Clemons: Fig. 2, [30]: resource manager determines resources for the ML models);
determine a subset of computational resources from a pool of computational resources to execute the ML model based on the computational requirements associated with the ML model (Clemons: [36]: determine resource based on task requirement);
allocate the subset of computational resources to the ML model (Clemons: [36]: “sending, to each dynamic ML model 134, an amount of computational resources allocated to a corresponding task, show” - allocate resource based on task requirement); and
execute the ML model using the subset of computational resources (Clemons: Fig. 2, [38]: execute task).
Regarding claim 2, Clemons teach the invention as claimed in claim 1 above and,
wherein the computational requirements comprise at least processing power, memory, storage, network bandwidth, energy consumption, inference speed, numerical precision, and/or parallelism (Clemons: [64]: “computational resources, such as execution time, system memory, energy, or the like, on a computing system”; [19, 31]: performance requirement).
Regarding Claim(s) 10-11, this/these claim(s) is/are similar in scope as claim(s) 1-2 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Regarding Claim(s) 18-19, this/these claim(s) is/are similar in scope as claim(s) 1-2 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
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.
Claim(s) 3-7, 9, 12-16, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clemons (US 20240231928) in view of Henry (US 20210286650).
Regarding claim 3, Clemons teaches the invention as claimed in claim 1 above and,
wherein the pool of computational resources comprises a plurality of processing units, wherein each processing unit comprises a plurality of cores (Clemons: [64]: computational resources). It would have been obvious that computation resources include processing units with cores;
Nonetheless, Henry teaches, resources comprises a plurality of processing units, wherein each processing unit comprises a plurality of cores (Henry: [3, 18]: processor with cores as resource for ML tasks).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Clemons and Henry because these are analogous arts and the combination would enable taking into account other common resources such as processor cores and storage types during resource allocation. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would “better allocate computing resources for massive computing tasks” (see Henry [4-5]).
Regarding claim 4, Clemons and Henry teach the invention as claimed in claim 3 above and, wherein the plurality of processing units comprises at least central processing units (CPUs), graphics processing units (GPUs), tensor processing units (TPUs), field-programmable gate arrays (FPGAs), and/or application-specific integrated circuits (ASICs) (Henry: [2, 68]: CPU, GPU etc.).
Regarding claim 5, Clemons and Henry teach the invention as claimed in claim 4 above and,
wherein executing the instructions to determine the subset of computational resources further causes the processing device to:
determine a group of cores from the plurality of processing units; allocate the group of cores to the ML model; and execute the ML model using the group of cores (Henry: [3, 68]: allocate cores and execute ML tasks).
Regarding claim 6, Clemons teach the invention as claimed in claim 1 above and,
wherein the computational resources comprise one or more memory units, wherein the one or more memory units comprises at least a random access memory (RAM), a cache memory, a video RAM, a high bandwidth memory (HBM), a graphics double data rate (GDDR) memory, and/or a unified memory (Clemons: [64]: computational resources include storage)
Henry further teaches, types of memory units (Henry: [3, 59]:storage types of ROM, RAM etc.).
The same motivation to combine stated above applies.
Regarding claim 7, Clemons and Henry teach the invention as claimed in claim 6 above and, wherein executing the instructions to determine the subset of computational resources further causes the processing device to: determine a group of memory units; allocate the group of memory units to the ML model; and execute the ML model using the group of memory units (Clemons: [64]: “computational resources, such as execution time, system memory, energy, or the like, on a computing system”; [19, 31]: performance requirement) (Henry: [3, 59, 68]: allocate resources and execute ML tasks).
Regarding claim 9, Clemons teach the invention as claimed in claim 8 and,
Henry further teaches, wherein the trigger event comprises at least a change in dataset size, a change in model complexity, convergence issues, memory leaks, increase in concurrency, model ensembling, fault occurrences, and/or adversarial attacks (Henry: [31, 38, 42, 44, 49-50]: dynamic monitoring of sparsity (complexity/convergence), accuracy and other computation metric to adjust allocation of resources to neural network computing).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Clemons and Henry because these are analogous arts and the combination would enable taking into account events affecting resources to dynamically adjust resource allocation. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would “better allocate computing resources for massive computing tasks” (see Henry [4-5]).
Regarding Claim(s) 12-16, this/these claim(s) is/are similar in scope as claim(s) 3-7 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Regarding Claim(s) 20, this/these claim(s) is/are similar in scope as claim(s) 3. Therefore, this/these claim(s) is/are rejected under the same rationale.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clemons (US 20240231928).
Regarding claim 8, Clemons teach the invention as claimed in claim 1 above and,
wherein executing the instructions further causes the processing device to:
determine an occurrence of a trigger event during the execution of the ML model (Clemons: Fig. 7, [37, 47, 54, 58]: performance trigger; [37]: “depending on execution time availability and the current accuracy” – trigger event of a change in performance or accuracy; [31]: “task priorities can include static priorities …, as well as dynamic priorities that are computed based on how well the target and minimum performance requirements are being met”);
capture information associated with the trigger event (Clemons: Fig. 7, [37, 54, 58-59]: compute performance);
determine an effect of the trigger event on the execution of the ML model; dynamically allocate additional computational resources to the ML model in response to determining the effect of the trigger event on the execution of the ML model (Clemons: [58-59]: adjust based on computed performance); and
execute the ML model using the subset of computational resources and the additional computational resources (Clemons: [58-59]: adjust based on computed performance);
Although Clemons does not use the word “trigger event”, Clemons uses performance to dynamically allocate resources. A change in the performance reads on an event leading to a change in allocation and thus read on the limitation as claimed. Hence the limitation as claimed is obvious over the teachings in Clemons.
Regarding Claim(s) 17, this/these claim(s) is/are similar in scope as claim(s) 8. Therefore, this/these claim(s) is/are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in the attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
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