DETAILED ACTION
Claims 1,2,9 and 11,12,19 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 11,12,13,14,16,15,19 and 1,2,3,4,6,5,9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated1 by Gu et al. (AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising):
Claim(s) 12,13,14,16,15 and 2,3,4,6,5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gu et al. (AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising) in view of LIN et al. (CN 110555458 A) with machine translation:
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Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/11/2026 and 4/27/2026 has been entered. Claims 7,8,10,17,18,20 cancel; claims 1,2,3,4,5,6,9 and 11,12,13,14,15,16,19 pending:
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Priority
Receipt is acknowledged of certified copies (KOREA, REPUBLIC OF 10-2022-0188728 12/29/2022) of papers (a translation (filed 12/05/2025)) required by 37 CFR 1.55.
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 1,2,9 and 11,12,19 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
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Step 0: Broadest Reasonable Interpretation in footnotes in this Office action.
Step 1:
The claimed invention is directed to a machine and method: Claim 1 is to a machine and claim 11 is to a method
Step 2A, prong 1:
The claim(s) recite(s) math and mental process (--sample vectors from a first neural network generator…generate2 a second image from a second neural network generator … a feature map … a lightweight neural network target model…generator34… parameter5… distribution6… distribution value--) without significantly more:
1. An apparatus for generating training data, comprising:
at least one processor; and
a memory to store instructions for executing the at least one processor,
wherein upon being executed by the at least one processor, the instructions allow the at least one processor to:
output a first image for each of a plurality of sample vectors from a first neural network7 generator included in the apparatus, and
generate a second image from a second neural network generator included in the apparatus based on the first image and a feature map extracted from a convolution block for each stage of a lightweight neural network target model8 for the first image,
generate a third image from a third neural network generator included in the apparatus based on at least one of the first image or the second image,
wherein the third neural network generator generates a fourth image by applying a scaling parameter which adjusts an output channel distribution to the third image close to a channel distribution value of original training data of the lightweight target neural network model, and
input the fourth image to the lightweight target neural network model for optimization of at least one of the first neural network generator, the second neural network generator, and the third neural network generator,
wherein the plurality of sample vectors include a first sample vector and a second sample vector,
wherein the first neural network generator obtains a unique weight of the first neural network generator for each sample vector by updating a first weight corresponding to the first sample vector by iteratively generating the first image for the first sample vector a preset number of times, and upon the preset number of times being exceeded, initializing the first neural network generator and updating a second weight corresponding to the second sample vector by iteratively generating the first image for the second sample vector, wherein each of the first weight and the second weight is a weight of the first neural network generator.
Step 2A, prong 2:
This judicial exception is not integrated into a practical application because the additional elements (“processor” “memory” “neural network” “image” “output channel” “channel” and computer stuff) considered with the math/mental process do not improve computer-electronics technology or a technical field9 (“lightweight deep learning”10) in view of applicant’s disclosure.
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because each additional element:
(“processor” “memory” “neural network” “image” “output channel” “channel” and computer stuff)
considered individually or with the math and mental process:
(--one sample vector from a first neural network generator…generate11 a second image from a second neural network generator … a feature map … a lightweight neural network target model…generator12… parameter13… distribution14… distribution value--)
adheres to conventional practices as indicated in applicant’s specification’s background15:
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In contrast, claims 3,4,6,5 and 13,14,16,15 reflect the disclosed improvement of [0030]:
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Response to Arguments
The examiner acknowledges applicant’s remark, page 8, filed 4/27/2026:
“the translation and the "Oath or Declaration" was concurrently filed. Accordingly, Applicant requests that the Office reconsider their position with regard to the priority date Applicant is entitled to rely on for the purposes of overcoming the prior art rejection in view of Kim.”
Claim Objections
Applicant’s arguments, see remarks, page 8, filed 4/27/2026, with respect to the claim objection have been fully considered and are persuasive. The claim objection of claims 1,2,3,4,5,6,9,10 and 19 has been withdrawn.
Rejections under 35 USC 101
Applicant's arguments filed 4/27/2026 have been fully considered but they are not persuasive.
Applicant’s state, page 10, 2nd para, 2nd S: “However, the claims require "input the fourth image to the lightweight target
neural network model for optimization," which is performed by the apparatus using generated image data and cannot be performed as a mental process.”
The examiner respectfully disagrees since an image can be mentally generated/produced/created.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., remarks, page 10, 2nd para, last S: “ modifies internal parameters of the first neural network generator based on generated image outputs.” ) are not recited in the rejected claim(s). 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).
Applicants state, page 10, penult para:
“Further, Applicant submits that the claims integrate any exception into a practical application because the claims require inputting the fourth image to the lightweight target neural network model for optimization and require updating and initializing the first neural network generator to obtain a unique weight for each sample vector.”
The examiner respectfully disagrees since an improvement16 to technology or an improvement to the functioning of a computer or improvement to a technical field is not “apparent to one of ordinary skill in the art”17 in said remarks. Thus “the examiner should not determine the claim improves technology”.
Rejections under 35 USC 103
Applicant's arguments filed 4/27/2026 have been fully considered but they are not persuasive.
Applicants state in page 11, penult para, 1st S:
“Gu discloses generating images using a generator, but does not disclose inputting a generated image to a lightweight neural network target model for optimization of at least one of a first neural network generator, a second neural network generator, and a third neural network generator.”
The examiner respectfully disagree since Gu (AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising) teaches inputting an image (X or y) to a 18 neural network19 20 model, i.e., a neural network generator G or neural network F, via fig. 1:
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In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., remarks, page 11, penult para, last S: “optimize multiple neural network generators”) are not recited in the rejected claim(s). 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).
Regarding applicant’s remarks, page 11,12 of Liu and David, Liu (Very Lightweight Photo Retouching Network With Conditional Sequential Modulation) and David (US 2022/0012595 A1) are not being applied in the current prior art rejections under 35 USC 102 and 35 USC 103 since they are redundant teachings to that of Gu (AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising) as applied under 35 USC 102 and 35 USC 103.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., remarks, page 12, 4th para: ”and then21 initializing”) are not recited in the rejected claim(s). 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). In contrast, claim 1 states “upon22 the preset number of times being exceeded, initializing”.
Regarding applicant’s remarks, page 12 of Jung, Jung (Learning to Avoid Errors in GANs by Manipulating Input Spaces) are not being applied in the current prior art rejections under 35 USC 102 and 35 USC 103 since they are redundant teachings to that of Gu (AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising) as applied under 35 USC 102 and 35 USC 103.
Claim Rejections - 35 USC § 102
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) 11,12,13,14,16,15,19 and 1,2,3,4,6,5,9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated23 by Gu et al. (AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising):
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Re 11. (Currently Amended), Gu discloses A method for generating training data, performed by an apparatus for generating training data, including at least one processor and a memory to store instructions for executing the at least one process, the method comprising:
outputting a first image for each24 of a plurality of sample vectors (or “mean25 vector and a variance vector”, pg. 75, A. Switchable Generator Using AdaIN Layers, 2nd para, 4th S) from a first neural network generator included in the apparatus; and
generating a second image from a second neural network generator included in the apparatus based on the first image 26 27 for each stage of a lightweight28 (“dubbed AdaIN code generator”, pg. 74, lcol, 2nd para, 4th S) (&) 29 (&) neural network30 model (or “AdaIN layers”-fig. 2: layers-“feature maps”31, pg. 78, C. Implementation Details, 1st para, last S: fig. 5: -target-map-function: “G”) for the first image (fig. fig. 5:”y” or “x”),
generating a third image from a third neural network generator included in the apparatus based on at least one of the first image or the second image (via fig. 1), and
which adjusts an output32 (&) 33 distribution34 (or likewise setting loss parameters35 lambda (eqn (9)) for a neural network weighted “G”-output via “output…loss weights…were set36”, pg. 78, C. Implementation Details, 2nd para, penult S & pg. 78, rcol, 1st para, 2nd S) to the third image (via fig. 1 & fig. 4:
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inputting the fourth image to the neural network model for optimization (or likewise “The generators, discriminators and Asian code generator were trained by solving (8)…using ADAM optimization”, pg. 78, lcol, last para, 1st two Ss) of the first neural network generator
wherein the plurality of sample vectors include a sample vector and a (or likewise said “mean37 vector and a variance vector”, pg. 75, A. Switchable Generator Using AdaIN Layers, 2nd para, 4th S),
wherein the generating of the first image comprises obtaining a unique weight38 (or likewise the quantity of fig. 1(b): “X” being the output of neural network weighting) of the first neural network generator for each sample vector by updating a first weight (or likewise updating a neural network39 via “The generator and discriminators are updated”, pg. 77, lcol, penul para, 2nd S) corresponding to the sample vector by iteratively40 generating the first image for the sample vector a preset number of times (or likewise: “the low-dose CT image is decomposed repeatedly using wavelet transform…set to 6 after extensive experiments”, pg. 77, lcol, last para, penult S & pg. 78, rcol, last para, 4th S) 41 42 wherein each of the first weight is a weight of the first neural network generator (via fig. 3:
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).
Claim 19 is rejected like claim 9:
19. (Currently Amended) The method for generating training data according to claim 11, wherein the scaling parameter is learned such that a channel distribution value of the third image is close to a channel distribution value of original training data of the lightweight target neural network model.
Claim 1 rejected like claim 11:
Re 1. (Currently Amended), Gu discloses An apparatus for generating training data, comprising:
at least one (“GPU” , pg. 78, C. Implementation Details, 3rd para, last S) processor; and
a memory to store instructions for executing the at least one processor,
wherein upon being executed by the at least one processor, the instructions allow the at least one processor to:
output a first image (fig. 1: “X” or “Y”) for each of a plurality of sample vectors from a first neural network generator included in the apparatus, and
generate a second image (fig. 1: “X” or “Y”) from a second neural network generator (fig. 2: “G”) included in the apparatus based on the first image and a (“decoder”, pg. 78, C. Implementation Details, 1st para, 4th S) feature map extracted (decoded) from a convolution block (via fig. 2: “Convolution layer”: “convolution layer” “feature map”43, pg. 78, C. Implementation Details, 2nd para, 4th S) for each stage (“of 4 stages” , pg. 78, C. Implementation Details, 1st para, 1st S) of a (“very”, pg. 74, lcol, 1st full para, 4th S) lightweight44 (&) neural network45 (&) target46 model (Fig. 2: “AdaIN Code Generator”: see rejection claim 11) for the first image,
generate a third image from a third neural network generator included in the apparatus based on at least one of the first image or the second image (see rejection claim 11),
wherein the third neural network generator generates a fourth image by applying a scaling parameter (see rejection claim 11) which adjusts47 (fittingly) an output 48 to a channel distribution value (or likewise “fig. 1(b) visualizes the learning49 scheme of…Px and Py…the associated probability distribution …which…’push-forwards’ the probability measure Py to Px”, pg. 76, rcol, 1st, 2nd 4th Ss, via fig. 1(b): “G”) of training data (or likewise “trained…original CT image”, pg. 74, lcol, 3rd para, last S) of the
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input the fourth image to the neural network model for optimization of at least one of the first neural network generator
wherein the plurality of sample vectors include a first sample vector
wherein the first neural network generator obtains a unique weight of the first neural network generator for each sample vector by updating a first weight corresponding to the first sample vector by iteratively generating the first image for the first sample vector a preset number of times, and upon the preset number of times being50 exceeded (as indicated in figures 1 and 3 by doubling generator “G” hence doubling the set wavelet repetitions/iterations of 6 to 12), initializing the first neural network generator is a weight of the first neural network generator (via figures 1 and 2:
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Re claim 9, Gu discloses The apparatus for generating training data according to claim 1, wherein the scaling parameter is learned (or likewise, pg 77, lcol, 2nd para: “The training of our CycleGAN can be done by solving…
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such that a 51 scheme of…Px and Py…the associated probability distribution …which…’push-forwards’52 the probability measure Py to Px”, pg. 76, rcol, 1st, 2nd 4th Ss, via fig. 1(b): “G”) of original training data of the .
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 12,13,14,16,15 and 2,3,4,6,5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gu et al. (AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising) in view of LIN et al. (CN 110555458 A) with machine translation:
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Claim 12 is rejected like claim 2, below:
12. (Previously Presented) The method for generating training data according to claim 11, wherein the lightweight target neural network model includes at least one first convolution block to generate the feature map, and
wherein the second neural network generator includes at least one second convolution block to generate a feature enhancement map.
Claim 13 is rejected like claim 3:
13. (Previously presented) The method for generating training data according to claim 12, wherein the feature map of the first convolution block mapped with the second convolution block is combined with the feature enhancement map of the second convolution block.
Claim 14 is rejected like claim 4:
14. (Previously presented) The method for generating training data according to claim 13, wherein the first convolution block mapped with the second convolution block includes a remaining first convolution block except the first convolution block of a last stage of the lightweight target neural network model.
Claim 16 is rejected like claim 6:
16. (Previous ) The method for generating training data according to claim 14, wherein the feature map of the first convolution block of the last stage is used as an input value of the second neural network generator.
Claim 15 is rejected like claim 5:
15. (Original) The method for generating training data according to claim 13, wherein in case of the at least one second convolution block being a plurality of second convolution blocks, the feature enhancement map of a previous second convolution block in combination with the feature map of the first convolution block corresponding to the previous second convolution block is included in an input value of a next second convolution block.
Re claim 2 (Currently Amended), Gu teaches The apparatus for generating training data according to claim 1, wherein the
wherein the a feature map.
Gu does not teach, under “a narrow subset of claim scope”5354, the difference of claim 2:
a feature enhancement map.
Lin teaches “the multi-channel feature enhancement map”, 3rd page, 1st S.
Since Gu teaches a feature map it would have been obvious to make the feature map as Lin’s predictably recognizing the change as being enhanced or increased in quality.
Re claim 3 (Original), Gu of the combination of Gu-Lin teaches The apparatus for generating training data according to claim 2, wherein the feature map of the first convolution block (via Gu’s fig. 2: “Convolution layer”) mapped with the second convolution block (via Gu’s fig. 2: “Convolution layer”) is combined (or “concatenated”, Gu: pg. 78, C. Implementation Details, 1st para, 4th S) with the feature enhancement map of the second convolution block.
Re claim 4 (Previously presented), Gu of the combination of Gu-Lin teaches The apparatus for generating training data according to claim 3, wherein the first convolution block (via Gu’s fig. 2: “Convolution layer”) mapped with the second convolution block (via Gu’s fig. 2: “Convolution layer”) includes a remaining first convolution block (via Gu’s fig. 2: “Convolution layer”) except the first convolution block (via Gu’s fig. 2: “Convolution layer”) of a last stage (“of 4 stages”, Gu: pg. 78, C. Implementation Details, 1st para, 1st S) of the (“very”, Gu: pg. 74, lcol, 1st full para, 4th S) lightweight target neural network model (Gu: Fig. 2: “AdaIN Code Generator”).
Re claim 6 (Currently Amended), Gu of the combination of Gu-Lin teaches The apparatus for generating training data according to claim 4, wherein the feature map (via Gu’s fig. 2: “Convolution layer”) of the first convolution block (via Gu’s fig. 2: “Convolution layer”) of the last stage is used as an input value (represented as arrows in fig. 2) of the second neural network generator (Gu’s fig. 2: “G”).
Re claim 5 (Previously Presented), Gu of the combination of Gu-Lin teaches The apparatus for generating training data according to claim 3, wherein in case of the at least one second convolution block (via Gu’s fig. 2: “Convolution layer”) being a plurality of second convolution blocks (via Gu’s fig. 2: “Convolution layer”), the feature enhancement map (via Gu’s fig. 2: “Convolution layer”) of a previous second convolution block (via Gu’s fig. 2: “Convolution layer”) in combination with the feature map (via Gu’s fig. 2: “Convolution layer”) of the first convolution block (via Gu’s fig. 2: “Convolution layer”) corresponding to the previous second convolution block (via Gu’s fig. 2: “Convolution layer”) is included in an input value (represented as arrows in fig. 2) of a next second convolution block (via Gu’s fig. 2: “Convolution layer”).
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
WEI et al. (CN 115063329 A) with SEARCH machine translation
WEI teaches “fusion network, updating the semantic loss weight β” (pg. 25, 1st txt blk) and a “lightweight bilateral attention decoder” (pg. 20, last txt blk: fig. 7) fusion of feature maps via figures 7,8:
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as the closest to the claimed “lightweight…updating a first weight” of claim 1 and “feature map…combined” of claim 3.
Xu et al. (US 2022/0027672 A1)
Xu teaches a “lightweight…network N…to generate fused label 234”, [0074] 2nd S, and feature map fusion via fig. 2:232 and “parameters (e.g. weights and/or biases)…may be updated”:
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[0550] In at least one embodiment, model training 3614 may include retraining or updating an initial model 4004 (e.g., a pre-trained model) using new training data (e.g., new input data, such as customer dataset 4006, and/or new ground truth data associated with input data). In at least one embodiment, to retrain, or update, initial model 4004, output or loss layer(s) of initial model 4004 may be reset, or deleted, and/or replaced with an updated or new output or loss layer(s). In at least one embodiment, initial model 4004 may have previously fine-tuned parameters (e.g., weights and/or biases) that remain from prior training, so training or retraining 3614 may not take as long or require as much processing as training a model from scratch. In at least one embodiment, during model training 3614, by having reset or replaced output or loss layer(s) of initial model 4004, parameters may be updated and re-tuned for a new data set based on loss calculations associated with accuracy of output or loss layer(s) at generating predictions on new, customer dataset 4006 (e.g., image data 3608 of FIG. 36).
as the closest to the claimed “lightweight…updating a first weight” of claim 1 and “feature map…combined” of claim 3.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM EST.
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, Henok Shiferaw can be reached at 571-272-4637. 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.
/DENNIS ROSARIO/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
1 MPEP 2131 Anticipation — Application of 35 U.S.C. 102 [R-08.2017], last para, 2nd to last S: The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).
2 generate: to bring into existence; cause to be; produce, wherein produce is defined: to bring into existence by intellectual or creative ability, wherein intellectual is defined: possessing or showing intellect or mental capacity, especially to a high degree. (Dictionary.com)
3 generator: Mathematics.
A. an element or one of a set of elements from which a specified mathematical object can be formed by applying certain operations.
B. an element, as a line, that generates a figure. (Dictionary.com)
4 generator: a person or thing that generates. (Dictionary.com)
5 parameter Mathematics.
A. a constant or variable term in a function that determines the specific form of the function but not its general nature, as a in f (x ) = ax, where a determines only the slope of the line described by f (x ).
B. one of the independent variables in a set of parametric equations. (Dictionary.com)
6 distribution: Mathematics. a generalized function used especially in solving differential equations. (Dictionary.com)
7 neural network: Also called neural net. Computers. a hardware or software system in which weighted connections between data nodes are refined to produce increasingly accurate results in information processing, as in pattern recognition or problem solving, with the goal of algorithmic computing that requires minimal human intervention. (Dictionary.com)
8 Applicant’s disclosure: [0058] The lightweight target model 40 refers to a model that is a target to be lightweight,and may be a trained classifier based on the original training data to be reproduced.
9 Applicant’s Disclosure:
1. Field
[0002]The disclosed embodiments relate to an apparatus and method for generating training data. More particularly, the disclosed embodiments relate to technology for generating training data similar to original training data without access to the original training data.
wherein for is defined: (lightweight deep learning) intended to belong to, or be used in connection with (generating training data similar to original training data without access to the original training data) (Dictionary.com)
2. Description of the Related Art
[0005]Lightweight deep learning is essential for deep learning in mobile, edge or cloud environment. Here, lightweight deep learning refers to technology that generates a compression model with a similar level of performance to the original model and a smaller amount of computational resources.
wherein refers to is defined: to relate to; apply to; mean or denote. (Dictionary.com)
10 deep learning: Computers. an advanced type of machine learning that uses multilayered neural networks to establish nested hierarchical models for data processing and analysis, as in image recognition or natural language processing, with the goal of self-directed information processing, wherein neural network is defined: Also called neural net. Computers. a hardware or software system in which weighted connections between data nodes are refined to produce increasingly accurate results in information processing, as in pattern recognition or problem solving, with the goal of algorithmic computing that requires minimal human intervention. (Dictionary.com)
11 generate: to bring into existence; cause to be; produce, wherein produce is defined: to bring into existence by intellectual or creative ability, wherein intellectual is defined: possessing or showing intellect or mental capacity, especially to a high degree. (Dictionary.com)
12 generator: Mathematics.
A. an element or one of a set of elements from which a specified mathematical object can be formed by applying certain operations.
B. an element, as a line, that generates a figure. (Dictionary.com)
13 parameter Mathematics.
A. a constant or variable term in a function that determines the specific form of the function but not its general nature, as a in f (x ) = ax, where a determines only the slope of the line described by f (x ).
B. one of the independent variables in a set of parametric equations. (Dictionary.com)
14 distribution: Mathematics. a generalized function used especially in solving differential equations. (Dictionary.com)
15 background: one's origin, education, experience, etc., in relation to one's present character, status, etc., wherein experience is defined: knowledge or practical wisdom gained from what one has observed, encountered, or undergone, wherein practical is defined: of or relating to practice or action, wherein practice is defined: custom, wherein custom is defined: convention, wherein convention is defined: conventionalism, wherein conventionalism is defined: adherence to or advocacy of conventional attitudes or practices (Dictionary.com)
16 MPEP 2106.04(d)(1) Evaluating Improvements in the Functioning of a Computer, or an Improvement to Any Other Technology or Technical Field in Step 2A Prong Two [R-10.2019], 2nd para:
The courts have not provided an explicit test for this consideration, but have instead illustrated how it is evaluated in numerous decisions. These decisions, and a detailed explanation of how examiners should evaluate this consideration are provided in MPEP § 2106.05(a). In short, first the specification should be evaluated to determine if the disclosure provides sufficient details ([0030] The disclosed embodiments may generate similar training data with fidelity to the distribution of original training data by combining the feature map for each stage of the lightweight target model with the feature map of the second image in the reverse order.) such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent (for example applicant’s fig. 2 of combining/ plus (+) signs) to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel").
17
18 an alternative AND coordinate adjective:
a lightweight AND neural network AND target model or
a target AND neural network AND lightweight model or etc,
19 neural network: Also called: neural net. an analogous network of electronic components, esp one in a computer designed to mimic the operation of the human brain, wherein mimic is defined: to imitate (a person, a manner, etc), esp for satirical effect; ape, wherein imitate is defined: to try to follow the manner, style, character, etc, of or take as a model (Dictionary.com)
20 an alternative AND coordinate adjective: a lightweight AND neural network AND target model
21 then: at that time, wherein time is defined: duration regarded as belonging to the present life as distinct from the life to come or from eternity; finite duration. (Dictionary.com)
22 upon: on the occasion of (Dictionary.com)
23 MPEP 2131 Anticipation — Application of 35 U.S.C. 102 [R-08.2017], last para, 2nd to last S: The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).
24BROAD CLAIM LANGUAGE: each PRONOUN every one (what?) individually; each one. (Dictionary.com)
25 mean: maths another name for average See also geometric mean, wherein average is defined: the typical or normal amount, quality, degree, etc, wherein typical is defined: being or serving as a representative example of a particular type; characteristic, wherein example is defined: a specimen or instance that is typical of the group or set of which it forms part; sample (Dictionary.com)
26 and: (used to connect alternatives). (Dictionary.com)
27 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para:
As a general matter, the grammar and ordinary meaning of terms (“and”) as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
28 coordinate adjective, wherein coordinate is defined: Grammar. of the same rank in grammatical construction, as Jack and Jill in the phrase Jack and Jill, or got up and shook hands in the sentence He got up and shook hands. (Dictionary.com)
29 coordinate adjective
30 coordinate adjective
31 map: a maplike delineation, representation, or reflection of anything, wherein representation is defined: the act of representing, wherein represent is defined: to serve as an example or specimen of; exemplify, wherein example is defined: a pattern or model, as of something to be imitated or avoided. (Dictionary.com): “the generator makes…the fake image”, pg. 78, rcol, 2nd para, 3rd S.
32 coordinate adjective
33 coordinate adjective
34 distribution: an act or instance of distributing. (Dictionary.com)
35 parameter: Statistics. a variable entering into the mathematical form of any distribution such that the possible values of the variable correspond to different distributions. (Dictionary.com)
36 set: to adjust (a mechanism) so as to control its performance. (Dictionary.com)
37 mean: maths another name for average See also geometric mean, wherein average is defined: the typical or normal amount, quality, degree, etc, wherein typical is defined: being or serving as a representative example of a particular type; characteristic, wherein example is defined: a specimen or instance that is typical of the group or set of which it forms part; sample (Dictionary.com)
38 weight: a specific quantity of a substance that is determined by weighing or that weighs a fixed amount, wherein weighting is defined: to bias or slant toward a particular goal or direction; manipulate. (Dictionary.com)
39 neural network: Also called neural net. Computers. a hardware or software system in which weighted connections between data nodes are refined to produce increasingly accurate results in information processing, as in pattern recognition or problem solving, with the goal of algorithmic computing that requires minimal human intervention. (Dictionary.com)
40 iteratively: a word derived from iterative, wherein iterative is defined: repeating; making repetition; repetitious. (Dictionary.com)
41 upon: on the occasion of, wherein on is defined: used to indicate support, subsistence, contingency, etc (Dictionary.com)
42The crossed text “are not required” via MPEP 2111/04 II. CONTINGENT LIMITATIONS, last para:
See Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016) for an analysis of contingent claim limitations in the context of both method claims and system claims. In Schulhauser, both method claims and system claims recited the same contingent step. When analyzing the claimed method as a whole, the PTAB determined that giving the claim its broadest reasonable interpretation, "[i]f the condition for performing a contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed" (quotation omitted). Schulhauser at 10. When analyzing the claimed system as a whole, the PTAB determined that "[t]he broadest reasonable interpretation of a system claim having structure that performs a function, which only needs to occur if a condition precedent is met, still requires structure for performing the function should the condition occur." Schulhauser at 14. Therefore "[t]he Examiner did not need to present evidence of the obviousness of the [ ] method steps of claim 1 that are not required to be performed under a broadest reasonable interpretation of the claim (e.g., instances in which the electrocardiac signal data is not within the threshold electrocardiac criteria such that the condition precedent for the determining step and the remaining steps of claim 1 has not been met);" however to render the claimed system obvious, the prior art must teach the structure that performs the function of the contingent step along with the other recited claim limitations. Schulhauser at 9, 14.
43 map: Mathematics., function., wherein function is defined: Mathematics. Also called correspondence, map, mapping, transformation. a relation between two sets in which one element of the second (data) set is assigned to each element of the first (data) set, as the expression (data set) y = (data set) x 2 ; operator, wherein data set is defined: Computers. a collection of data records for computer processing, wherein record is defined: Computers., a group of related fields, or a single field, treated as a unit and comprising part of a file or data set, for purposes of input, processing, output, or storage by a computer, wherein file is defined: a collection of papers, records, etc., arranged in convenient order, wherein order is defined: formal disposition or array, wherein array is defined: Computers., a block of related data elements, each of which is usually identified by one or more subscripts (see equations (1) & (2): x1 & y1). (Dictionary.com)
44 coordinate adjective
45 coordinate adjective
46 coordinate adjective
47 “adjusts” is further modified by the adverb “close to”
48 “close to” is a adverb further modifying the claimed “adjusts”
49 learn: to acquire knowledge of or skill in by study, instruction, or experience, wherein instruction is defined: the act or practice of instructing or teaching; education., wherein instructing is defined: . to furnish with knowledge, especially by a systematic method; teach; train; educate, wherein train is defined: to treat or manipulate so as to bring into some desired form, position, direction, etc.., wherein manipulate is defined: to adapt or change (accounts, figures, etc.) to suit one's purpose or advantage, wherein adapt is defined: to make suitable to requirements or conditions; adjust or modify fittingly. (Dictionary.com)
50 “being” essentially means look at a figure
51 learn: to acquire knowledge of or skill in by study, instruction, or experience, wherein instruction is defined: the act or practice of instructing or teaching; education., wherein instructing is defined: . to furnish with knowledge, especially by a systematic method; teach; train; educate, wherein train is defined: to treat or manipulate so as to bring into some desired form, position, direction, etc.., wherein manipulate is defined: to adapt or change (accounts, figures, etc.) to suit one's purpose or advantage, wherein adapt is defined: to make suitable to requirements or conditions; adjust or modify fittingly. (Dictionary.com)
52 forwards: toward or at a place, point, or time in advance; onward; ahead, wherein toward is defined: shortly before; close to. (Dictionary.com)
53 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 2nd para: Examiners must consider all claim limitations when determining patentability of an invention over the prior art. In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 403-04 (Fed. Cir. 1983). The subject matter of a properly construed claim is defined by the terms that limit the scope of the claim when given their broadest reasonable interpretation. In Axonics, Inc. v. Medtronic, Inc., 73 F.4th 950, 958-59, 2023 USPQ2d 795 (Fed. Cir. 2023), the court found the claims were improperly narrowed based on a preferred embodiment to sacral anatomy or sacral neuromodulation, whereas the patent claims made no reference to sacral anatomy or sacral neuromodulation. Thus, the relevant prior art was improperly limited to a narrow subset of claim scope. See also MPEP § 2111 et seq. It is the subject matter of the properly construed claim that must be examined. The determination of whether particular language is a limitation in a claim depends on the specific facts of the case. See, e.g., Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002).
54 Thus Gu discloses claims 2,3,4,6,5 in the above 35 USC 102 rejection:
Claim(s) 11,12,13,14,16,15,19 and 1,2,3,4,6,5,9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gu et al. (AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising)
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