DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the amendment filed 29 June 2026.
Claims 1, 3-15, 24, 32, and 34-35 are pending. Claims 1, 24, and 32 are independent claims.
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, 3-15, 24, 32, and 34-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to Step 1 of the two Step analysis, claims 1-14 are directed toward an apparatus (machine). Claims 24 is directed toward a method (process). Claims 32-35 are directed toward a non-transitory machine readable medium (manufacture). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
With respect to claim 1, the claim recites:
determine whether a layer of a pre-trained machine learning model is of a type that can be converted to an elastic layer (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine that the layer can be converted to an elastic layer)
responsive to the determination that the layer is of a type that can be converted to the elastic layer, convert the layer to the elastic layer, and add the elastic layer to the super-network (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a determination that the layer may be converted to an elastic layer, converting the layer, and adding the layer to the observed super-network)
create a plurality of subnetworks based on the super-network (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses creation of a plurality of subnetworks based upon a user evaluation of the super-network)
search the plurality of subnetworks to select a subnetwork (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluation of a plurality of subnetworks using a set of search parameters to select a subnetwork)
Step 2A, Prong 2:
The claims disclose the following additional elements:
at least one memory
machine readable instructions
processor circuitry to at least one of instantiate or execute the machine readable instructions
These additional elements of a memory, machine readable instructions, and processing circuitry to at least one of instantiate or execute the machine readable instructions are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
The claims disclose the following additional elements:
at least one memory
machine readable instructions
processor circuitry to at least one of instantiate or execute the machine readable instructions
These additional elements of a memory, machine readable instructions, and processing circuitry to at least one of instantiate or execute the machine readable instructions are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 3:
With respect to dependent claim 3, the claim depends upon dependent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 3, the claim recites:
wherein the elastic layer includes at least one variable property (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine that the layer can be converted to an elastic layer based upon identifying at least variable property)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Claim 4:
With respect to dependent claim 4, the claim depends upon dependent claim 3. The analysis of claim 3 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 4, the claim recites:
wherein the variable property is a variable depth of the elastic layer (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine that the layer can be converted to an elastic layer based upon identifying at least variable property, wherein the variable property is a variable depth of the elastic layer)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Claim 5:
With respect to dependent claim 5, the claim depends upon dependent claim 3. The analysis of claim 3 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 5, the claim recites:
wherein the variable property is a variable width of the elastic layer (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine that the layer can be converted to an elastic layer based upon identifying at least variable property, wherein the variable property is a variable width of the elastic layer)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Claim 6:
With respect to dependent claim 6, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 6, the claim recites:
wherein… prior to extraction of the plurality of subnetworks, modify the super-network based on training data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The examiner notes that the claimed super-network has not been defined as a machine learning model. Therefore, for example, this limitation encompasses an evaluation of some training data to modify a network)
Step 2A, Prong 2:
The claim discloses the additional elements:
processor circuitry
These additional elements of a memory, machine readable instructions, and processing circuitry to at least one of instantiate or execute the machine readable instructions are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
The claim discloses the additional elements:
processor circuitry
These additional elements of a memory, machine readable instructions, and processing circuitry to at least one of instantiate or execute the machine readable instructions are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 7:
With respect to dependent claim 7, the claim depends upon dependent claim 6. The analysis of claim 6 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed to the same abstract idea identified with respect to claim 6.
Step 2A, Prong 2:
The claim discloses the additional elements:
wherein the modification of the super-network is performed using a training algorithm
Computer training is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
The claim discloses the additional elements:
wherein the modification of the super-network is performed using a training algorithm
Computer training is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 8:
With respect to dependent claim 8, the claim depends upon dependent claim 7. The analysis of claim 7 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed to the same abstract idea identified with respect to claim 7.
Step 2A, Prong 2:
The claim discloses the additional elements:
wherein the training algorithm is Progressive Shrinking
Computer training is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the additional elements:
wherein the training algorithm is Progressive Shrinking
Computer training is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 9:
With respect to dependent claim 9, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 9, the claim recites:
wherein the selection of the subnetwork is based at least on at least one of a performance of a performance characteristic of the subnetwork (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of a performance of a performance characteristic to enable selection)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Claim 10:
With respect to dependent claim 10, the claim depends upon dependent claim 9. The analysis of claim 9 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 10, the claim recites:
wherein the performance characteristic of the subnetwork is an estimated performance characteristic (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation, such as an estimation, of a performance of a performance characteristic to enable selection)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Claim 11:
With respect to dependent claim 11, the claim depends upon dependent claim 9. The analysis of claim 9 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 11, the claim recites:
wherein the selection of the subnetwork is based on the performance characteristic meeting or exceeding a corresponding performance characteristic of the pre-trained machine learning model (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of a performance of a performance characteristic to determine it meets or exceeds a threshold)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Claim 12:
With respect to dependent claim 12, the claim depends upon dependent claim 11. The analysis of claim 11 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 12, the claim recites:
wherein the performance characteristic is accuracy (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of a performance of a performance characteristic to determine accuracy)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Claim 13:
With respect to dependent claim 13, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed to the same abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The claims disclose the following additional elements:
wherein the processor is to distribute the selected subnetwork to a compute device for execution
These additional element of distributing a selected subnetwork to a compute device for execution is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
The claims disclose the following additional elements:
wherein the processor is to distribute the selected subnetwork to a compute device for execution
These additional element of distributing a selected subnetwork to a compute device for execution is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 14:
With respect to dependent claim 14, the claim depends upon dependent claim 13. The analysis of claim 13 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed to the same abstract idea identified with respect to claim 13.
Step 2A, Prong 2:
The claims disclose the following additional elements:
wherein the compute device is an edge device within an edge computing environment
These additional element of compute device is an Edge device within an Edge computing environment is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
The claims disclose the following additional elements:
wherein the compute device is an Edge device within an Edge computing environment
These additional element of compute device is an Edge device within an Edge computing environment is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 15:
With respect to dependent claim 15, the claim depends upon dependent claim 13. The analysis of claim 13 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 15, the claim recites:
select the subnetwork such that an operational characteristic of the subnetwork meets an operational requirement of the compute device (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of operational characteristics of a subnetwork and a computing device in order to select a subnetwork)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Claim 24:
Step 2A, Prong 1:
With respect to claim 24, the claim recites:
determine whether a layer of a pre-trained machine learning model is of a type that can be converted to an elastic layer (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine that the layer can be converted to an elastic layer)
responsive to the determination that the layer is of a type that can be converted to the elastic layer, convert the layer to the elastic layer, and add the elastic layer to the super-network (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a determination that the layer may be converted to an elastic layer, converting the layer, and adding the layer to the observed super-network)
extracting… a plurality of subnetworks from the super-network (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses creation of a plurality of subnetworks based upon a user evaluation of the super-network)
searching the plurality of subnetworks to select a subnetwork (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluation of a plurality of subnetworks using a set of search parameters to select a subnetwork)
Step 2A, Prong 2:
The claims disclose the following additional elements:
executing an instruction with at least one processor
These additional element executing an instruction with at least one processor is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
The claims disclose the following additional elements:
executing an instruction with at least one processor
These additional element executing an instruction with at least one processor is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 32:
Step 2A, Prong 1:
With respect to claim 32, the claim recites:
determine whether a layer of a pre-trained machine learning model is of a type that can be converted to an elastic layer (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine that the layer can be converted to an elastic layer)
responsive to the determination that the layer is of a type that can be converted to the elastic layer, convert the layer to the elastic layer, and add the elastic layer to the super-network (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a determination that the layer may be converted to an elastic layer, converting the layer, and adding the layer to the observed super-network)
create a plurality of subnetworks based on the super-network (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses creation of a plurality of subnetworks based upon a user evaluation of the super-network)
search the plurality of subnetworks to select a subnetwork (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluation of a plurality of subnetworks using a set of search parameters to select a subnetwork)
Step 2A, Prong 2:
The claims disclose the following additional elements:
a non-transitory machine readable storage medium comprising instructions that, when executed, cause the processor circuitry to…
These additional element of a non-transitory machine readable storage medium comprising instructions that, when executed, cause the processor circuitry is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
The claims disclose the following additional elements:
a non-transitory machine readable storage medium comprising instructions that, when executed, cause the processor circuitry to…
These additional element of a non-transitory machine readable storage medium comprising instructions that, when executed, cause the processor circuitry is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 34:
With respect to dependent claim 34, the claim depends upon dependent claim 32. The analysis of claim 32 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 34, the claim recites:
wherein the elastic layer includes at least one variable property (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine that the layer can be converted to an elastic layer based upon identifying at least variable property)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Claim 35:
With respect to dependent claim 35, the claim depends upon dependent claim 34. The analysis of claim 34 is incorporated herein by reference.
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 35, the claim recites:
wherein the variable property is a variable number of channels of the elastic layer (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine that the variable property is a variable number of channels of the elastic layer)
Step 2A, Prong 2:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
Step 2B:
The claim does not recite any additional elements that when considered alone or in combination provide a practical application or amount to significantly more than the abstract idea.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-15, 24, 32, and 34-35 remain rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization, provided by applicant on IDS filed 31 August 2023, published 31 March 2021, hereafter Yang) and further in view of Yu et al. (US 2022/0405579, provisional filed 5 March 2020, hereafter Yu).
As per independent claim 1, Yang discloses modifying pre-trained machine learning models, the instructions comprising:
determine whether a layer of a pre-trained machine learning model is of a type that can be converted to an elastic layer (Figure 3; Sections 2-2 and 2.3: Here, it is determined whether a layer may be removed by determining that the removal does not result in the number of output channels becoming zero)
responsive to the determination that the layer is of a type that can be converted to the elastic layer, convert the layer to the elastic layer and add the elastic layer to a super-network (Figures 2-3; Section 1; Sections 2.2 and 2.3: Here, a super network is trained to have the same architecture as an initial network. The initial network is a pre-trained machine learning model, deep neural network (Abstract; Section 1), having subnetworks (Figure 2) as input. The super-network shares weights across all the DNNs in a search space and is trained by minimizing loss across the DNNs When it is determined that the number of output channels will not become zero, filters may be removed and channels may be bypassed. If a channel may be bypassed, that channel is considered to be elastic)
create a plurality of subnetworks based on the super-network (Section 2.1: Here, a plurality of subnetworks with different layer widths, network depths, and kernel sizes are created to train and optimize the super-network)
search the plurality of subnetworks to select a subnetwork (Section 2.1: Here, the process of sampling the search space to generate samples and determining the next set of samples based upon the current performance continues until a stop criteria is met. At his point, the discovered DNN is trained until convergence)
Yang fails to specifically disclose:
an apparatus comprising:
at least one memory
machine readable instructions
processor circuitry to at least one of instantiate or execute the machine readable instructions
However, Yu, which is analogous to the claimed invention because it is directed to modifying machine learning models, discloses:
an apparatus (paragraph 0099: Here, a computer is an apparatus) comprising:
at least one memory (paragraph 0099: Here, the computer includes a memory)
machine readable instructions (paragraph 0099: Here, the computer includes a central processing unit for executing machine readable instructions)
processor circuitry to at least one of instantiate or execute the machine readable instructions (paragraph 0099: Here, the central processing unit is processing circuitry)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yoon with Yang, with a reasonable expectation of success, as it would have allowed for executing the modification of a machine learning model on a computer device (Yu: paragraph 0099).
As per dependent claim 3, Yang and Yu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Yang discloses wherein the elastic layer includes at least one variable property (Section 2.5: Here, the network depth, layer widths, and kernel sizes are variable properties).
As per dependent claim 4, Yang and Yu disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Yang discloses wherein the variable property is a variable depth of the elastic layer (Section 2.5: Here, the network depth is a variable depth of the elastic layer).
As per dependent claim 5, Yang and Yu disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Yang discloses wherein the variable property is a variable width of the elastic layer (Section 2.5: Here, the layer width is a variable width of the elastic layer).
As per dependent claim 6, Yang and Yu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Yang discloses prior to extraction of the plurality of subnetworks, modify the super-network based on training data (Section 2.1: Here, an initial network and uses the subnetworks by shrinking layers in the initial network. The subnetworks are trained by the super-network using shared weights).
As per dependent claim 7, Yang and Yu disclose the limitations similar to those in claim 6, and the same rejection is incorporated herein. Yang discloses wherein the modification of the super-network is performed using a training algorithm (Section 2.1; Figure 2: Here, a super-network is trained using a superkernel).
As per dependent claim 8, Yang and Yu disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein. Yang fails to specifically disclose wherein the training algorithm is Progressive Shrinking.
However, Yu, which is analogous to the claimed invention because it is directed toward training a super-network, discloses training using a progressive shrinking algorithm (paragraph 0050: Here, a large neural network is trained and progressively distilled to obtain smaller neural networks through additional training). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combine Yu with Yang-Yu, with a reasonable expectation of success, as it would have allowed for adjusting a neural network to satisfy specific network constraints (Yu: paragraph 0050).
As per dependent claim 9, Yang and Yu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Yu further discloses wherein the selection of the subnetwork is based on at least one of a performance characteristic of the subnetwork (paragraphs 0031-0032: Here, a neural network that satisfies a set of constraints, such as accuracy, and parameters, is selected).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yu with Yang-Yu, with a reasonable expectation of success, as it would have allowed for selecting subnetworks based upon performance constraints (Yu: paragraphs 0031-0032).
As per dependent claim 10, Yang and Yu disclose the limitations similar to those in claim 9, and the same rejection is incorporated herein. Yu further discloses wherein the performance characteristic of the subnetwork is an estimated performance characteristic (paragraphs 0031-0032: Here, a neural network that satisfies a set of constraints, such as accuracy. This constraint is a performance characteristics of the neural network).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yu with Yang-Yu, with a reasonable expectation of success, as it would have allowed for selecting subnetworks based upon performance characteristics (Yu: paragraphs 0031-0032).
As per dependent claim 11, Yang and Yu disclose the limitations similar to those in claim 9, and the same rejection is incorporated herein. Yu further discloses wherein the selection of the subnetwork is based on the performance characteristic meeting or exceeding a corresponding performance characteristic of the pre-trained machine learning model (paragraphs 0031-0032: Here, a neural network that satisfies a set of constraints, such as minimum accuracy. This constraint is a performance characteristics of the neural network).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yu with Yang-Yu, with a reasonable expectation of success, as it would have allowed for selecting subnetworks based upon performance characteristics (Yu: paragraphs 0031-0032).
As per dependent claim 12, Yang and Yu disclose the limitations similar to those in claim 11, and the same rejection is incorporated herein. Yu further discloses wherein the performance characteristic is accuracy (paragraphs 0031-0032: Here, a neural network that satisfies a set of constraints, such as accuracy. This constraint is a performance characteristics of the neural network).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yu with Yang-Yu, with a reasonable expectation of success, as it would have allowed for selecting subnetworks based upon performance characteristics, such as accuracy (Yu: paragraphs 0031-0032).
As per dependent claim 13, Yang and Yu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Yu discloses wherein the processor is to distribute the selected subnetwork to a compute device for execution (Figure 3, item 310; paragraph 0062: Here, the neural network is deployed for performing a machine learning task). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yu with Yang-Yu, with a reasonable expectation of success, as it would have allowed for deploying the neural network for performing a machine learning task (Yu: paragraph 0062).
As per dependent claim 14, Yang and Yu disclose the limitations similar to those in claim 13, and the same rejection is incorporated herein. Yu discloses wherein the compute device is an edge device within an edge computing environment (paragraph 0030: Here, an edge device is a hardware device on which to deploy the neural network). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yu with Yang-Yu, with a reasonable expectation of success, as it would have allowed for deploying the neural network for performing a machine learning task on an edge device (Yu: paragraphs 0030 and 0062).
As per dependent claim 15, Yang and Yu disclose the limitations similar to those in claim 13, and the same rejection is incorporated herein. Yu discloses wherein the processor is to select the subnetwork such that an operational characteristic of the subnetwork meets an operational requirement of the compute device (paragraphs 0031-0032: Here, a neural network that satisfies a set of constraints, such as accuracy. This constraint is a performance characteristics of the neural network. Additionally, operational characteristics such as FLOPs, the memory footprint, and runtime latency may be considered (paragraph 0028)).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yu with Yang-Yu, with a reasonable expectation of success, as it would have allowed for selecting subnetworks based upon operational characteristics (Yu: paragraphs 0028 and 0031-0032).
With respect to independent claim 24, the claim recites the limitations substantially similar to those in claim 1. Claim 24 is similarly rejected.
With respect to independent claim 32, the claim recites the limitations substantially similar to those in claim 1. The analysis of claim 1 is incorporated herein by reference.
Yu further discloses a non-transitory machine readable storage medium comprising instructions that, when executed, cause processor circuitry toe perform operations (paragraph 0100). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yoon with Yang, with a reasonable expectation of success, as it would have allowed for executing the modification of a machine learning model on a computer device (Yu: paragraph 0099).
With respect to claims 33 and 34, the claims disclose the limitations substantially similar to those in claims 2 and 3, respectively. Claims 33-34 are similarly rejected.
As per dependent claim 35, Yang and Yu disclose the limitations similar to those in claim 34, and the same rejection is incorporated herein. Yang discloses wherein the variable property is a variable number of channels of the elastic layer (Figure 3; Sections 2.2 and 2.3: Here, when it is determined that the number of output channels will not become zero, filters may be removed and channels may be bypassed. If a channel may be bypassed, that channel is considered to be elastic).
Response to Arguments
Applicant’s arguments with respect to the objection to claim 14 has been fully considered and are persuasive. The objection has been withdrawn.
Applicant’s arguments with respect to the rejection of claims under 35 USC 112 has been fully considered and are persuasive in view of the amendment filed 29 June 2026. The rejection has been withdrawn.
Applicant's arguments with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The applicant’s initial argument is based upon the belief that the limitations “determine whether a layer of a pre-trained machine learning model is of a type that can be converted to the elastic layer” and “responsive to the determination that the layer is of a type that can be converted to the elastic layer, convert the layer to the elastic layer and add the elastic layer to a super-network” cannot practically be performed in the human mind (page 7). The examiner respectfully disagrees.
The applicant indicates that “a model itself is elastic when the layer (or layers within the model) can have variable values in their properties and/or weighting values (page 7; Specification: paragraph 0044).” With that as the basis, the examiner maintains that “determining whether a layer of a pre-trained machine learning model is of a type that can be converted into the elastic layer,” as drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Determining whether a layer of a pre-trained machine learning model is of a type that can be converted into an elastic layer encompasses an observation. Specifically, the observation entails determining whether a layer of a pre-trained machine learning model can be converted into an elastic layer having variable values in their properties and/or weighing values.
Additionally, the examiner maintains that “responsive to the determination that the layer is of a type that can be converted to the elastic layer, convert the layer to the elastic layer, and add the elastic layer to the super-network,” as drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a determination that the layer may be converted to an elastic layer, converting the layer, and adding the layer to the observed super-network. This includes evaluating a pre-trained machine learning model to determine if layers can be converted into an elastic layer and converting the layer into an elastic layer having variable values in their properties and weighting values.
The applicant argues that the claim “requires manipulation of neural network data structures” and “output of modified data structures (a super-network containing elastic layers (pages 7-8).” However, it is noted that the claims, as amended, is silent with respect manipulation of neural network data structures and outputting modified data structures. Instead, the claim requires evaluation of a layer of a machine learning model and based upon the evaluation creating an elastic layer. Although it appears that the applicant is equating the “super network” with a neural network data structure, the claim itself does not require that the super network is a neural network data structure. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
For these reasons, this argument is not persuasive.
Applicant's arguments with respect to the rejection of claims under 35 USC 103 have been fully considered but they are not persuasive.
The applicant argues that Yang fails to disclose analyzing individual layers, determining their type, and converting suitable layers to elastic layers (pages 8-9). The examiner respectfully disagrees.
First, it is noted by the examiner that the claims fail to define the term “elastic layer.” Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
As noted by the examiner, Yang discloses training a super network to have the same architecture as an initial network. The initial network is a pre-trained machine learning model, deep neural network (Abstract; Section 1), having subnetworks (Figure 2) as input. The super-network shares weights across all the DNNs in a search space and is trained by minimizing loss across the DNNs When it is determined that the number of output channels will not become zero, filters may be removed and channels may be bypassed. If a channel may be bypassed, that channel is considered to be elastic (Figures 2-3; Section 1; Sections 2.2 and 2.3). For this reason, this argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zhou et al. (US 2020/0104699): Discloses training a super network using elastic weight consolidation (paragraph 0011)
Hussain et al. (US 2014/0044431): Discloses evolving a network toward an elastic network (paragraph 0004)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KYLE R STORK/Primary Examiner, Art Unit 2128
Prosecution Insights