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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a method claim. Claim 11 is a system claim. Therefore, claims 1 and 11 are directed to either a process, machine, manufacture or composition of matter.
With respect to Claim 1:
Step 2A Prong 1:
defining a plurality of computational cells of a neural network, each computational cell of the plurality of computational cells comprising a different directed graph of a predetermined number of nodes and edges and one or more respective computational cell hyper parameters, each node representing a respective neural network latent representation and each edge representing a respective operation that transforms a respective neural network latent representation (mental process – user can manually define a plurality of computational cells of a neural network, each computational cell of the plurality of computational cells comprising a different directed graph of a predetermined number of nodes and edges and one or more respective computational cell hyper parameters, each node representing a respective neural network latent representation and each edge representing a respective operation that transforms a respective neural network latent representation)
for each computational cell of the plurality of computational cells, optimizing a validation loss function subject to one or more computational resource constraints (mental process – user can manually for each computational cell of the plurality of computational cells, optimize a validation loss function subject to one or more computational resource constraints)
based on each optimized validation loss function, generating the neural network for performing a machine learning task using the respective one or more computational cell hyper parameters of each of the computational cells in the plurality of computational cells (mental process – user can manually, based on each optimized validation loss function, generate the neural network for performing a machine learning task using the respective one or more computational cell hyper parameters of each of the computational cells in the plurality of computational cells)
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements:
A computer-implemented method when executed by data processing hardware of a user device causes the data processing hardware to perform (mere instructions to apply the exception using a generic computer component)
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. Additional elements:
A computer-implemented method when executed by data processing hardware of a user device causes the data processing hardware to perform (mere instructions to apply the exception using a generic computer component)
Conclusion: The claim is not patent eligible.
Claim 11 is rejected on the same grounds as claim 1. Additionally claim 11 has the additional elements of a system comprising: data processing hardware; and memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations. These elements are mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B.
Regarding Claim 2: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually for each computational cell of the plurality of computational cells, replacing each respective operation that transforms a respective neural network latent representation with a respective linear combination of candidate operations from a predefined set of candidate operations, each candidate operation in a respective linear combination having a respective mixing weight that is parameterized by the respective one or more computational cell hyper parameters before optimizing the validation loss function subject to the one or more computational resource constraints.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 3: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually iteratively adjusting values of the respective one or more computational cell hyper parameters and computational cell weights.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 4: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually performing a bi-level optimization of the validation loss function and a training loss function that represents a measure of error obtained on training data, wherein the respective one or more computational cell hyper parameters comprise upper level parameters and the computational cell weights comprise lower level parameters.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 5: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually defining a respective cost function for each computational resource constraint, each defined cost function mapping the computational cell hyper parameters to a respective resource cost.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 6: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein a respective resource cost of an edge in each computational cell is calculated as a softmax over costs of operations in a candidate set of operations.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 7: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the operations further comprise setting lower and higher bound constraints for each defined cost function.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 8: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the validation loss function represents a measure of error obtained after running a validation dataset through each defined computational cell of the plurality of computational cells.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 9: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the one or more computational resource constraints comprise user defined constraints on one or more of memory, number of float point operations, or inference speed.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 10: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of training the generated neural network on training data to obtain a trained neural network; and performing the machine learning task using the trained neural network.
These judicial exceptions are not integrated into a practical application. The additional element(s) of training the generated neural network on training data to obtain a trained neural network; and performing the machine learning task using the trained neural network recite merely 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 - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of training the generated neural network on training data to obtain a trained neural network; and performing the machine learning task using the trained neural network recite 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 - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Claims 12-20 are rejected on the same grounds as claims 2-10 respectively.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 8, 10-13, 18, 20 is/are rejected under 35 U.S.C. 102(a) as being anticipated by Bender et al. (hereinafter Bender), Understanding and Simplifying One-Shot Architecture Search.
Regarding Claim 1, Bender discloses a computer-implemented method when executed by data processing hardware of a user device causes the data processing hardware to perform operations comprising:
defining a plurality of computational cells of a neural network [“our network is composed of several identical cells” §3.1 ¶5], each computational cell of the plurality of computational cells comprising a different directed graph of a predetermined number of nodes and edges and one or more respective computational cell hyper parameters [“Each cell is divided into a fixed number of choice blocks” §3.1 ¶5; “number of choice blocks within each cell, Nchoice, is a hyper-parameter of the search space.” §3.1 ¶6; Figs. 2, 3], each node representing a respective neural network latent representation and each edge representing a respective operation that transforms a respective neural network latent representation [Fig. 3; Examiner Note: the leftmost diagram indicates the nodes are latent representations (i.e., hidden layer nodes) and the edges as shown in the rightmost diagram are to operations];
for each computational cell of the plurality of computational cells, optimizing a validation loss function [“selectively zero out two of the three operations’ outputs at evaluation time in order to determine which operation leads to the best prediction accuracy” §1 ¶3; Fig. 1] subject to one or more computational resource constraints [“model must be small enough to train using limited compute resources (i.e., memory and time)” §3.1 ¶1]; and
based on each optimized validation loss function, generating the neural network for performing a machine learning task using the respective one or more computational cell hyper parameters of each of the computational cells in the plurality of computational cells [“output of the search is a list of candidate architectures ranked by one-shot accuracy” §3.4 ¶1].
Regarding Claim 2, Bender discloses the method of claim 1. Bender further discloses wherein the operations further comprise, for each computational cell of the plurality of computational cells [Cell 1, Cell 2, Cell 3 in Fig. 3], replacing each respective operation that transforms a respective neural network latent representation with a respective linear combination of candidate operations from a predefined set of candidate operations [Choice Block in Fig. 3], each candidate operation in a respective linear combination having a respective mixing weight that is parameterized by the respective one or more computational cell hyper parameters [Choice Block in Fig. 3] before optimizing the validation loss function subject to the one or more computational resource constraints [“selectively zero out two of the three operations’ outputs at evaluation time in order to determine which operation leads to the best prediction accuracy” §1 ¶3; Fig. 1; “model must be small enough to train using limited compute resources (i.e., memory and time)” §3.1 ¶1].
Regarding Claim 3, Bender discloses the method of claim 1. Bender further discloses wherein optimizing the validation loss function subject to the one or more computational resource constraints further comprises iteratively adjusting values of the respective one or more computational cell hyper parameters and computational cell weights [“one-shot model is a standard large neural network trained” §3.2 ¶1; Figs. 1-3].
Regarding Claim 8, Bender discloses the method of claim 1. Bender further discloses wherein the validation loss function represents a measure of error obtained after running a validation dataset through each defined computational cell of the plurality of computational cells [“selectively zero out two of the three operations’ outputs at evaluation time in order to determine which operation leads to the best prediction accuracy” §1 ¶3; Fig. 1].
Regarding Claim 10, Bender discloses the method of claim 1. Bender further discloses wherein the operations further comprise:
training the generated neural network on training data to obtain a trained neural network [“the best-performing architectures are retrained from scratch after the search is completed” §1 ¶4; “retrain the best-performing architectures from scratch” §3.4 ¶1]; and
performing the machine learning task using the trained neural network [“we used a 45,000 element training set, 5,000 element validation set, and 10,000 element test set.” §4 ¶2; Fig. 5].
Claims 11-13 are rejected on the same grounds as claims 1-3 respectively.
Claim 18 is rejected on the same grounds as claim 8.
Claim 20 is rejected on the same grounds as claim 10.
Claim Rejections - 35 USC § 103
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) 4-5, 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bender in view of Liu et al. (hereinafter Liu) DARTS: DIFFERENTIABLE ARCHITECTURE SEARCH.
Regarding Claim 4, Bender discloses the method of claim 3.
However, Bender fails to explicitly disclose wherein iteratively adjusting the values of the respective one or more computational cell hyper parameters and the computational cell weights comprises performing a bi-level optimization of the validation loss function and a training loss function that represents a measure of error obtained on training data, wherein the respective one or more computational cell hyper parameters comprise upper level parameters and the computational cell weights comprise lower level parameters.
Liu discloses wherein iteratively adjusting the values of the respective one or more computational cell hyper parameters and the computational cell weights comprises performing a bi-level optimization of the validation loss function and a training loss function that represents a measure of error obtained on training data, wherein the respective one or more computational cell hyper parameters comprise upper level parameters and the computational cell weights comprise lower level parameters [“Denote by Ltrain and Lval the training and the validation loss, respectively. Both losses are determined not only by the architecture α, but also the weights w in the network. The goal for architecture search is to find α* that minimizes the validation loss Lval(w*; α*), where the weights w* associated with the architecture are obtained by minimizing the training loss w* = argminw Ltrain(w; α*). This implies a bilevel optimization problem (Anandalingam & Friesz, 1992; Colson et al., 2007) with α as the upper-level variable and w as the lower-level variable” §2.2 ¶¶3-4; Figs. 1-2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Bender and Liu before him before the effective filing date of the claimed invention, to modify the method of Bender to incorporate bi-level optimization of Liu.
Given the advantage of using BLO in hierarchical decision making to obtain an optimal solution, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding claim 5, Bender and Liu disclose the method of claim 3.
However, Bender fails to explicitly disclose wherein iteratively adjusting the values of the computational cell hyper parameters and the computational cell weights comprises defining a respective cost function for each computational resource constraint, each defined cost function mapping the computational cell hyper parameters to a respective resource cost.
Liu discloses wherein iteratively adjusting the values of the computational cell hyper parameters and the computational cell weights comprises defining a respective cost function for each computational resource constraint, each defined cost function mapping the computational cell hyper parameters to a respective resource cost [“using three orders of magnitude less computation resources (i.e. 1.5 or 4 GPU days” §3.3 ¶1; GPU days in Tables 1-3].
It would have been obvious to one having ordinary skill in the art, having the teachings of Bender and Liu before him before the effective filing date of the claimed invention, to modify the combination to incorporate a cost function for the resource constraint of Liu.
Given the advantage of determining execution time to select a faster and more efficient approach, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claims 14-15 are rejected on the same grounds as claims 4-5 respectively.
Claim(s) 6-7, 9, 16-17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bender and Liu, in view of Zhang et al. (hereinafter Zhang), Customizable Architecture Search for Semantic Segmentation.
Regarding Claim 6, Bender and Liu disclose the method of claim 5.
However, Bender fails to explicitly disclose wherein a respective resource cost of an edge in each computational cell is calculated as a softmax over costs of operations in a candidate set of operations.
Zhang discloses wherein a respective resource cost of an edge in each computational cell is calculated as a softmax over costs of operations in a candidate set of operations [“The red edge denotes a heavy cost, and the green one has a light cost.” Fig. 2; Softmax operation of Equation 3].
It would have been obvious to one having ordinary skill in the art, having the teachings of Bender, Liu, and Zhang before him before the effective filing date of the claimed invention, to modify the combination to incorporate the edge cost functions and softmax calculations of Zhang.
Given the advantage of determining resource costs to permit limited resource devices to perform the action and for general faster processing, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 7, Bender and Liu disclose the method of claim 5.
However, Bender fails to explicitly disclose wherein the operations further comprise setting lower and higher bound constraints for each defined cost function.
Zhang discloses wherein the operations further comprise setting lower and higher bound constraints for each defined cost function [“Constraints” Fig. 1; “generating a computing cell with/without constraints” Fig. 2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Bender, Liu, and Zhang before him before the effective filing date of the claimed invention, to modify the combination to incorporate the cost constraints indicating limits of Zhang.
Given the advantage of determining resource costs to permit limited resource devices to perform the action, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 9, Bender discloses the method of claim 1.
However, Bender fails to explicitly disclose wherein the one or more computational resource constraints comprise user defined constraints on one or more of memory, number of float point operations, or inference speed.
Zhang discloses wherein the one or more computational resource constraints comprise user defined constraints on one or more of memory, number of float point operations, or inference speed [“customized constraints” Abstract; “GPU Time, CPU Time, Num of MAC, Num of Params” Fig. 1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Bender, Liu, and Zhang before him before the effective filing date of the claimed invention, to modify the combination to incorporate the user defined constraints of Zhang.
Given the advantage of determining resource costs to permit limited resource devices to perform the action, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claims 16-17 are rejected on the same grounds as claims 6-7 respectively.
Claim 19 is rejected on the same grounds as claim 9.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Response to Arguments
Regarding the §101 rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that 1) claim 1 is not directed to an abstract idea and provides a practical application due to technical improvements, and 2) claim 1 recites an inventive concept that amounts to significantly more than the judicial exception. Examiner disagrees for at least the following reasons.
First, claim 1 is directed to an abstract idea and does not provide a technical improvement for a practical application. Applicant’s recitation of specification passages is comprised of language which is narrower than the claim language. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. In reVan Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The claims must reflect any alleged improvement recited in the specification, which they currently do not. Under broadest reasonable interpretation, claim 1 provides mental process limitations (with the exception of being implemented in a computing environment – i.e., when executed by a data processing hardware of a user device). Therefore, any alleged improvement is to the abstract idea. Applicant’s attempt to inflate the claims by arguing the technique “typically requires the training of thousands of deep learning models” or a loss function is “a complex construct of potentially millions of parameters” is not persuasive under broadest reasonable interpretation of the current claim language.
Second, claim 1 does not recite an inventive concept that amounts to significantly more than the judicial exception. As stated above and in the rejection, the only additional element is the implementation of the abstract idea in a computing environment. An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). MPEP 2106.05. One example Applicant gives is that “‘optimizing a validation loss function subject to one or more computation resource constraints’ is not a generic, well-understood, or routine activity.” The standard of well-understood, routine, and conventional is applied to the additional elements or combinations thereof. Applicant is merely pointing to an abstract limitation. Additionally, the steps of claim 1 do not provide a concrete improvement to computer functionality. Rather, the computer functions the same. Any alleged inventive concept is to the abstract idea.
For at least these reasons, the rejections are maintained.
Regarding the prior art rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that claim 1 requires each computational cell comprises a different directed graph, while Bender network is comprised of several identical cells. Examiner disagrees with Applicant for at least the following reasons.
Exemplary claim 1 recites, each computational cell of the plurality of computational cells comprising a different directed graph of a predetermined number of nodes and edges and one or more respective computational cell hyper parameters. Bender recites, “our network is composed of several identical cells which are stacked on top of each other. Each cell is divided into a fixed number of choice blocks.” In §3.1 ¶5. Accordingly, each cell of Bender contains a different directed graph because there are a plurality of them, but those graphs can be identical.
For at least these reasons, the rejections are maintained.
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 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.
Guo et al. (Single Path One-Shot Neural Architecture Search with Uniform Sampling) discloses constructing a simplified supernet, Single Path Supernet, which is trained by an uniform path sampling method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T. Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
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