DETAILED ACTION
This action is responsive to the Remarks filed on 09/11/2026. Claims 1 and 5-13 are pending in the case. Claims 1, 11 and 12 are independent claims. Claims 2-4 are cancelled. Claim 13 is new.
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 .
Response to Arguments
Applicant's arguments filed 09/11/2026 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant notes that the claim does not recite limitations which can be performed in the human mind, nor does the Applicant provide any explanation beyond merely quoting the claims as a whole and note they require hardware components and device level operations and therefore merely involve judicial exceptions.
Examiner disagrees.
The claim expressly recites mental evaluations such as calculation of a difference of weight values and a selection of candidates among other identified judicial exceptions. Such steps being performed by certain claimed hardware components or devices does not suggest such steps cannot be performed in the mind. The claims do not merely involve such activities but expressly recite performance of mental evaluations such as “determine a difference”.
Applicant notes the claims nevertheless integrate into a practical application. Without specific argumentation, Applicant notes the claims improve the functioning of the device or system itself thus constitute a technological solution.
Examiner disagrees. A mere improvement does not itself suggest the claims reflect any supposed improvement via additional elements. Critically, the claim must include additional elements which reflect the improvements. The improvements to functioning cannot be a result of the abstract idea alone. As noted in the rejection the additional elements in the claim merely recite data gathering and generic devices for performing the recited judicial exception. No Particular limitations alone nor in combination reflect an improvement to functioning.
Applicant asserts the claim recites a particular architecture and a particular way of achieving the result.
Examiner disagrees. Again Applicant provides no specific argument but for quoting the independent claim. The claim does not describe any particular architecture and instead describes labeled devices for performing a sequence of abstract ideas. The particular architecture of the system is not claimed nor described.
Applicant asserts, without any particular argumentation, that the claim as a whole is neither well-understood, routine nor conventional.
Examiner disagrees. Under Step 2B, the claim does not provide any additional elements beyond mere data gathering limitations (2106.05(d) and 2106.05(g)), “apply it” limitations (2106.05(f)), and generally linking limitations (2106.05(h)). Each when considered as a whole do not provide significantly more and are thus well understood routine and conventional. In order to provide significantly more the claim must recite specific additional element limitations other than what is well-understood, routine and conventional.
Applicant asserts a 101 rejection should not be made unless it is more likely than not the claim is ineligible.
Examiner notes the rejection is indeed made according to the cited standard.
Applicant's arguments and amendments filed 09/11/2026 with respect to the 35 U.S.C. 112 and 35 USC 102 rejection have been fully considered and have been withdrawn.
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 1 and 5-13 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Regarding Claim 1, 11 and 12
Under step 1, Claim 1 is directed to processing apparatus for optimizing a structure of a neural network which is directed to a machine, one of the statutory categories.
Under step 1, Claim 11 is directed to processing method for optimizing a structure of a neural network which is directed to a process, one of the statutory categories.
Under step 1, Claim 12 is directed to a non-transitory computer readable storage medium storing a computer executable program for optimizing a structure of a neural network which is directed to an article of manufacture, one of the statutory categories.
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluations:
“generate a plurality of candidates for an edge of the neural network…
calculate a loss of the neural network based on a specified candidate number K (K>2) which is the number of candidates to be selected from the plurality of candidates, and on the inference result…
update the weight coefficient for each of the plurality of candidates based on the loss;
select K candidates from the plurality of candidates based on the corresponding updated weight coefficient.
determines a difference between a K-th largest weight coefficient and a (K+1)-th largest weight coefficient among the plurality of candidates sorted in descending order of weight coefficient;
and calculates the loss according to the determined difference and increases the loss when the difference is smaller than a predetermined threshold value”
Each of these limitations describe mental steps which can be performed in the human mind. Updating, selecting, and generating candidates are decisions about selecting an organizing abstract data. No details in the claim confine these steps to processes which cannot be performed in the mind. Further, calculating a loss based on a number to be selected and a result is a decision about abstract idea. The loss being “of a neural network” does not tie the calculation to a computer confined step.
Therefore, the claim recites an abstract idea.
Under step 2A Prong 2, The claim recites the following additional element(s):
“a candidate generation unit configured to… a loss calculation unit configured to… an updating unit configured to… a selection unit configured to… with a weight coefficient set to each of the plurality of candidates for the edge… An information processing apparatus configured to learn an architecture for optimizing a structure of a neural network… An information processing method which is executed by an information processing apparatus configured to learn an architecture for optimizing a structure of a neural network (from claim 11)… A non-transitory computer-readable storage medium storing a computer-executable program for causing a computer to perform a method which is executed by an information processing apparatus configured to learn an architecture for optimizing a structure of a neural network (from claim 12)” (which amounts to mere instructions to apply a computer technology to an abstract idea, see MPEP 2106.05(f))
“with a weight coefficient set to each of the plurality of candidates for the edge… thereby training the neural network to increase the difference between the K-th largest weight coefficient and the (K+1)-th largest weight coefficient and to retain only K candidates for the edge” (which amounts to generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h). Examiner notes the training limitation does not describe how the training is performed, but rather describes the training being a consequence of performing the identified abstract ideas, subsequently having an intended result to increase the difference claimed.
And “an inference unit configured to obtain an inference result by inputting learning data to the neural network” (which amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g) because it does not impose meaningful limits on the claim and amounts to mere data gathering.)
The claim does not recite any further additional elements, therefore the claim is directed to the recited abstract idea
Under step 2B, the claim recites the additional elements of “an inference unit configured to obtain an inference result by inputting learning data to the neural network” are insignificant extra-solution activities that are considered well-understood, routine, conventional activities. This amounts to collecting information and sending information to a device over a network is well understood, routine, and conventional activity because it amounts to “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i).
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 5-7
Each of the limitations described in the claim, under Step 2A Prong 1, only serve to describe the abstract ideas addressed in the independent claim, in particular the limitations describe mental evaluations.
Regarding Claim 8
The claim does not recite the following limitation which further describes features of the previously recited abstract idea “into which the loss for the inference result of the neural network and the loss related to the neural network architecture are integrated, as the loss of the neural network “. This features merely describes the loss which may be calculated in the mind.
The claim recites the following additional element(s), in addition to those already identified in the parent claim:
“the loss calculation unit acquires a loss” (which amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g) because it does not impose meaningful limits on the claim and amounts to mere data gathering.)
Under step 2B, the claim recites the additional elements of “the loss calculation unit acquires a loss” are insignificant extra-solution activities that are considered well-understood, routine, conventional activities. This amounts to collecting information and sending information to a device over a network is well understood, routine, and conventional activity because it amounts to “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i).
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 9
Each of the limitations described in the claim, under Step 2A Prong 1, only serve to describe the abstract ideas addressed in the independent claim, in particular the limitations describe mental evaluations.
Regarding Claim 10
The claim does not recite further abstract idea to consider, beyond those recited in the parent claim.
The claim recites the following additional element(s), in addition to those already identified in the parent claim:
“wherein the neural network is a neural network for detecting a detection target or tracking a tracking target in an image” (which amounts to generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h))
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 13
The claim does not recite further abstract idea to consider, beyond those recited in the parent claim.
The claim recites the following additional element(s), in addition to those already identified in the parent claim:
“calculation of the loss based on the determined difference causes the K-th largest weight coefficient and the (K+1)-th largest weight coefficient to diverge during training such that, upon completion of training, the neural network architecture is transformed from an architecture containing the plurality of candidates for the edge into an inference architecture retaining only the K selected candidates for the edge” (which amounts to generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h). Examiner notes the claim limitations describes the transformed architecture being a result of the abstract idea, the claim does not describe how the training is performed.
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Allowable Subject Matter
The closest prior art of record is the Chen et al “Stabilizing differentiable architecture search via perturbation based Regularization” which describes training a neural network composed of a plurality of candidate operation in a plurality of edges, the architecture is trained to select a final candidate operation for each edge, the network does not select > 2 operations for each edge as claimed, nor does the system compute a loss function based on a difference between sorted weights among the candidates as claimed. Further, Wang et al. “Rethinking architecture selection in differentiable nas” describes a neural architecture search where each candidate operation of an edge has an associated weight, however the “best” edge is selected based on its impact on the accuracy when removed from the network not based on the claimed loss function calculation. Finally, Chen et al “One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking” describes selected candidates based on diversity guided edges selection. However, such edge selection is based on removing edges with a low diversity score rather than optimizing a difference of sorted weight values within a loss function as claimed.
The cited art either alone or in combination do not teach all of the limitations of claims 1, 11 and 12.
In particular, none of the cited references alone or in combination teach:
calculating a loss of the neural network based on a specified candidate number K (K > 2) which is the number of candidates to be selected from the plurality of candidates, and on the inference result;
determining a difference between a K-th largest weight coefficient and a (K+1)-th largest weight coefficient among the plurality of candidates sorted in descending order of weight coefficient;
and calculating the loss according to the determined difference and increases the loss when the difference is smaller than a predetermined threshold value,
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 JOHNATHAN R GERMICK whose telephone number is (571)272-8363. The examiner can normally be reached M-F 7:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.R.G./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
