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 .
Status of Claims
Claims 1-7 were canceled.
Claims 8-27 are pending and are examined herein.
Claims 8-27 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Claims 8-27 are rejected under 35 USC 103.
Priority
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-
(d) prior to declaration of an interference, a certified English translation of the foreign
application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the
non-English application.
Information Disclosure Statement
The attached information disclosure statements (IDS) submitted on 10/01/2021, 06/07/2023, and 08/16/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the attached information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101 – Abstract Idea
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 8-27 rejected under 35 USC 101 because the claimed invention is directed to an
abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined
whether the claim is directed to one of the four statutory categories of invention, i.e., process,
machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the
statutory categories, the second step in the analysis is to determine whether the claim is directed
to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first
prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception
(e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If
it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis
proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the
claims integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical application, the
analysis proceeds to determining whether the claim is a patent-eligible application of the
exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception
into a practical application, or else amounts to significantly more than the abstract idea itself.
Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis
According to the first part of the analysis, in the instant case Claims 8-14 are directed to a method, Claims 15-21 are directed to an apparatus, and Claims 22-27 are directed towards non-transitory memory; consequently, these claims fall within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2 Analysis (Combined Step 2A Prong 1-2 and Step 2B Analysis)
Claim 8 includes the following recitation of an abstract idea:
applying adaptive pattern transformation to the target data for adaptation with the at least one candidate data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. The adaptive pattern transformation could also fall within being a mathematical concept.)
Claim 8 recites the following additional elements which, considered individually and as
an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
transferring the target data to which the adaptive pattern transformation is applied, to the deep neural network model (This is insignificant extra-solution activity. See
MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional
as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
retrieving at least one candidate data having a highest similarity to the target data from the learning data (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
Claim 8 does not reflect an improvement to computer technology or any other
technology.
Claim 9 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 9 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
acquiring the output value based on the target data to which the adaptive pattern transformation is applied, from the deep neural network model (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
Claim 9 does not reflect an improvement to computer technology or any other
technology.
Claim 10 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 10 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the at least one candidate is retrieved from reference data of the learning data (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, storing and retrieving information in memory is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example iv. Storing and retrieving information.)
Claim 10 does not reflect an improvement to computer technology or any other
technology.
Claim 11 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 11 includes the following recitation of an additional abstract idea:
when the similarity between the target data and the reference data is less than a first threshold value, terminating the adaptive pattern transformation with classifying the target data as impossible to judge (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 11 recites no further additional elements which, considered individually and as an
ordered combination with the additional elements from the claim upon which it depends, that integrate the abstract idea into a practical application or amount to significantly more than the abstract ideas.
Claim 11 does not reflect an improvement to computer technology or any other
technology.
Claim 12 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 12 includes the following recitation of an additional abstract idea:
the reference data represents data having a lower similarity related to latent features between the learning data than a second threshold value, among the learning data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 12 recites no further additional elements which, considered individually and as an
ordered combination with the additional elements from the claim upon which it depends, that integrate the abstract idea into a practical application or amount to significantly more than the abstract ideas.
Claim 12 does not reflect an improvement to computer technology or any other
technology.
Claim 13 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 13 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
transforming a pattern of the target data to have a qualitative pattern of the at least one candidate data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Claim 13 does not reflect an improvement to computer technology or any other
technology.
Claim 14 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 14 includes the following recitation of an additional abstract idea:
wherein a number of the at least one candidate data is greater than 2, wherein the qualitative pattern of the at least one candidate data is based on a combination of the at least one candidate data or an average value of the at least one candidate data, in a latent space of the deep neural network model (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 14 recites no further additional elements which, considered individually and as an
ordered combination with the additional elements from the claim upon which it depends, that integrate the abstract idea into a practical application or amount to significantly more than the abstract ideas.
Claim 14 does not reflect an improvement to computer technology or any other
technology.
Claim 15 recites at least the abstract idea identified above in Claim 8.
Claim 15 recites substantially similar subject matter to Claims 8 except it is a computer
system that performs the method instead of being the method itself, respectively, and are rejected
with the same rationale, mutatis mutandis.
Claim 15 recites the following additional elements aside from those described which,
considered individually and as an ordered combination with the additional elements from the
claim upon which it depends, do not integrate the abstract idea into a practical application or
amount to significantly more than the abstract idea:
A computing apparatus for improving reproduction performance of an output value for target data having a different qualitative pattern from learning data related to a deep neural network model, the computing apparatus comprising: a processor configured to perform processes (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea and falls under mere instructions to apply. See MPEP 2106.05(f).)
Claim 15 does not reflect an improvement to computer technology or any other
technology.
Claims 16-21 recite at least the abstract idea identified above in the claim upon which
they depend.
Claims 16-21 recite substantially similar subject matter to Claims 9-14, respectively, and
are rejected with the same rationale, mutatis mutandis.
Claims 16-21 do not reflect an improvement to computer technology or any other
technology.
Claim 22 recites at least the abstract idea identified above in Claim 8.
Claim 22 recites substantially similar subject matter to Claims 8 except it is a computer
system that performs the method instead of being the method itself, respectively, and are rejected
with the same rationale, mutatis mutandis.
Claim 22 recites the following additional elements aside from those described which,
considered individually and as an ordered combination with the additional elements from the
claim upon which it depends, do not integrate the abstract idea into a practical application or
amount to significantly more than the abstract idea:
A computer program stored in a non-transitory machine-readable recording medium, including instructions that cause a computing apparatus to perform a method of improving reproduction performance of an output value for target data having a different qualitative pattern from learning data related to a deep neural network model (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea and falls under mere instructions to apply. See MPEP 2106.05(f).)
Claim 22 does not reflect an improvement to computer technology or any other
technology.
Claims 23-27 recite at least the abstract idea identified above in the claim upon which
they depend.
Claims 23-27 recite substantially similar subject matter to Claims 9-13, respectively, and
are rejected with the same rationale, mutatis mutandis.
Claims 23-27 do not reflect an improvement to computer technology or any other
technology.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 8-11, 13, 15-18, 20, 22-25, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over “Date” (“Fashioning with Networks: Neural Style Transfer to Design Clothes”, Published 2017) in view of “Wang” (US 20220138398 A1, “Style Transfer”).
Regarding Claim 8, Date teaches
applying adaptive pattern transformation to the target data for adaptation with the at least one candidate data (Date, 1 Introduction recites “Figure 1 shows a sample clothing item generated using neural style transfer. The first clothing item given by the user provides the shape for the new dress. The second is initially provided by the user from his/her closet to learn their preference.” Examiner interprets using the neural style transfer as applying an adaptive pattern transformation and the preference of shape as the candidate data.)
transferring the target data to which the adaptive pattern transformation is applied, to the deep neural network model (Date, 1 Introduction recites “Figure 1 shows a sample clothing item generated using neural style transfer. The first clothing item given by the user provides the shape for the new dress. The second is initially provided by the user from his/her closet to learn their preference. The third is the final generated design for the user (the generated sample contains styles from multiple pieces of the user's clothing).” Examiner recognizes the use of a deep neural network model being used at the end of the process as indicated by ‘generate the design for the user’. Date, 3 Preliminaries confirms this fact, “Consider an input image x and convolutional neural network N N. Every convolution layer l in the convolutional network has Nl distinct filters.”)
Date does not appear to explicitly teach
retrieving at least one candidate data having a highest similarity to the target data from the learning data
However, Wang, directed to analogous art (explicitly on transferring a style to an image), teaches
retrieving at least one candidate data having a highest similarity to the target data from the learning data (Wang, Paragraph [0046] recites “In some implementations, a plurality of predefined editable objects can be associated with a plurality of various categories, such as science category, finance category and the like. In such case, one or more data sets whose similarity with the target data set is below a predefined threshold can be respectively determined from the plurality of categories. Thus, styles in various categories can be recommended to the users.” Examiner interprets the one or more data sets as potential candidate states. Wang, Paragraph [0047] proceeds to state “Alternatively or additionally, the style corresponding to the data set with the highest similarity can be directly applied into the target editable object.” Examiner recognizes the word ‘retrieving’ in the instant limitation as continuing with the rest of the method having this information. Examiner sees that ‘can be directly applied’ found within Wang as to also having this context of having the information readily available.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Date in view of Wang to apply only candidate date with the highest similarity to the target data in the process of generating the image. Date, Conclusions & Future Work recites “In the future we will like to improve the performance of the pipeline as it is time consuming to generate a new design. Also, we plan to experiment with better methods to personalize and generate designs with higher resolutions.” The reader is lead to improve Date specifically in generating the image closer to the user’s choice. Wang, Paragraph [0049] states “In this way, some better styles can be conveniently recommended to the user for them to choose, thereby enhancing the convenient level of the style transfer.” In Wang, the context is a recommendation. However, said person of ordinary skill in the art would recognize that this same process could be used for other inventions in the art. Wang, Paragraph [0044] recites “The predefined editable objects have respective styles, which can be the styles matching the data at a higher aesthetic degree.” Some styles are more natural with certain images and look better. It would be obvious to said person of ordinary skill to use the combination to generate images that have better quality and match better to the chosen styles.
Regarding Claim 9, the rejection of Claim 8 is incorporated herein. Date teaches
acquiring the output value based on the target data to which the adaptive pattern transformation is applied, from the deep neural network model (Date, 1 Introduction recites “Figure 1 shows a sample clothing item generated using neural style transfer. The first clothing item given by the user provides the shape for the new dress. The second is initially provided by the user from his/her closet to learn their preference. The third is the final generated design for the user (the generated sample contains styles from multiple pieces of the user's clothing).” The generated design for the user is derived by using a neural style transfer process that takes in the user’s closet via preferences.)
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to modify Date in view of Wang as described above with respect to Claim 8.
Regarding Claim 10, the rejection of Claim 8 is incorporated herein. Date teaches
wherein the at least one candidate is retrieved from reference data of the learning data (Date, 4.2 Creating a Personal Style Store recites “To learn the user's fashion preferences, the user initially provides the set of clothes from his / her closet. The Gram matrices Gl (eq.1) of all the clothes with their annotated attributes are calculated.” Figure 2 shows “User’s Personal Style Store” to the right, and the same user inputting “User Chosen Outline” to the left. Examiner interprets this to mean that the user must input candidate data that is the same as the preferences trained on.)
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to modify Date in view of Wang as described above with respect to Claim 8.
Regarding Claim 11, the rejection of Claim 10 is incorporated herein. Date teaches
when the similarity between the target data and the reference data is less than a first threshold value, terminating the adaptive pattern transformation with classifying the target data as impossible to judge (Date, 3.1 Style Extraction recites “The dot product computes the similarities between feature maps. Thus the Gram matrix Gl invariably contains image points that are consistent between the maps while inconsistent features become 0. Consider two images x (input image used to transfer the style) and ^x (a randomly generated image from white noise).” The first threshold value is 0 as that is when inconsistent features become nulled in the adaptive pattern transformation process.)
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to modify Date in view of Wang as described above with respect to Claim 8.
Regarding Claim 13, the rejection of Claim 8 is incorporated herein. Date teaches
transforming a pattern of the target data to have a qualitative pattern of the at least one candidate data (Date, 4.2 Creating a Personal Style Store recites “The Gram matrices Gl (eq.1) of all the clothes with their annotated attributes are calculated. Tensor ow [1] allows us to get the partially computed functions El in 2 (where the gram matrices for Gl are computed first and then Gl later). The style losses El are thus stored in a dictionary with the associated attributes.” Date, 4.3 Style Transfer states “Although the style's extracted from the user's closet as a whole represent the his/her fashion sense, we pick the style functions of the chosen attributes because we assume the user's mental model of dress is likely to be similar to the styles extracted for those attributes.” The qualitative patterns in this art are the styles learned from the closet. These same styles are picked by the user when selecting a certain target.)
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to modify Date in view of Wang as described above with respect to Claim 8.
Claims 15-18 and 20 recite a system which performs substantially similar steps as listed by the method in Claims 8-11 and 13, respectively, and are rejected with the same rationale, mutatis mutandis. Additionally, Date teaches
A computing apparatus for improving reproduction performance of an output value for target data having a different qualitative pattern from learning data related to a deep neural network model, the computing apparatus comprising: a processor configured to perform processes (Date, System Architecture recites “Figure 2 shows the entire pipeline to personalize and de-sign custom clothes for the user. There are four modules to the architecture, namely, preprocessing, personal style store creation, style transfer and post-processing to generate the final design.” Date, Abstract states “In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes.” Examiner sees no explicit definition of the equipment used in order to perform the disclosed method; thus, the broadest reasonable interruption of the equipment is used. Among such equipment would be a computer system having a processor in order to process the algorithm.)
Claims 22-25 and 27 recite a non-transitory medium which stores substantially similar as listed by the method in Claims 8-11 and 13, respectively, and are rejected with the same rationale, mutatis mutandis. Additionally, Date teaches
A computer program stored in a non-transitory machine-readable recording medium, including instructions that cause a computing apparatus to perform a method of improving reproduction performance of an output value for target data having a different qualitative pattern from learning data related to a deep neural network model (Date, System Architecture recites “Figure 2 shows the entire pipeline to personalize and de-sign custom clothes for the user. There are four modules to the architecture, namely, preprocessing, personal style store creation, style transfer and post-processing to generate the final design.” Date, Abstract states “In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes.” Examiner sees no explicit definition of the equipment used in order to perform the disclosed method; thus, the broadest reasonable interruption of the equipment is used. Among such equipment would be a computer system having a processor in order to process the algorithm. The algorithm would have to be stored on a machine-readable medium in order to conduct the steps.)
Claims 12, 14, 19, 21, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over “Date” (“Fashioning with Networks: Neural Style Transfer to Design Clothes”, Published 2017) in view of “Wang” (US 20220138398 A1, “Style Transfer”) and “Chen” (“Unsupervised Stylish Image Description Generation via Domain Layer Norm”, Published in 2018).
Regarding Claim 12, the rejection of Claim 10 is incorporated herein. Date teaches
the reference data represents data having a lower similarity (Date, 3.1 Style Extraction recites “The dot product computes the similarities between feature maps. Thus the Gram matrix Gl invariably contains image points that are consistent between the maps while inconsistent features become 0. Consider two images x (input image used to transfer the style) and ^x (a randomly generated image from white noise).”)
Date does not appear to explicitly teach
related to latent features between the learning data than a second threshold value, among the learning data
However, Chen, directed to analogous art (explicitly on transferring a style to an image), teaches
related to latent features between the learning data than a second threshold value, among the learning data (Chen, Figure 2 has a description stating “We make several assumptions to deal with the challenging unsupervised stylish image description generation problem. We first assume there exists a shared latent space Z so that a latent code z ∈ Z can be mapped to the source description space DS and the target stylish description space DT via GS and GT. We also assume there exists a stylish image description embedding function that can map a stylish description to a latent code. Finally, we assume there exists an image embedding.” Figure 2 shows a latent space Z having a latent point z which is a specific similarity between the source image and target description. Examiner interprets the context of Date to be similarities between the characteristics/features. The combination of Chen with Date would arrive at what the Examiner would classify as latent features between the reference data and learning data. In that same figure, Examiner interprets the line E1 that indicates what is an acceptable generated image as a threshold. If the latent features are distant, then the image would not be generated with those features.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Date in view of Wang and Chen. The modifications would be to apply only candidate date with the highest similarity to the target data in the process of generating the image and to form latent spaces representing each feature. The rational for combining with Wang is described in Claim 8. Chen, Abstract recites “It can learn to generate stylish image descriptions that are more related to image content and can be trained with the arbitrary monolingual corpus without collecting new paired image and stylish descriptions. Moreover, it enables users to generate various stylish descriptions by plugging in style-specific parameters to include new styles into the existing model.” This would customize the style they want even if it is not a trained/known style yet, giving the user more personalization of the process. Examiner interprets the modifications to be congruent in personalizing images generated from Date’s art: Said person of ordinary skill in the art looking to generate images that match the user’s intentions would see the obvious combination of these arts.
Regarding Claim 14, the rejection of Claim 13 is incorporated herein. Date teaches
a number of the at least one candidate data is greater than 2 wherein the qualitative pattern of the at least one candidate data is based on a combination of the at least one candidate data (Date, 4.3 Style Transfer recites “The number of images for every attribute picked depends on the distribution of the particular attribute across the entire list of images present. The higher the frequency of the attribute in the distribution, the higher is the bias towards a certain label and suppresses the effect of the others. This makes certain image characteristics more pronounced in the final dress than others.” Examiner interprets the images belonging to the user’s closet as candidate data while the characteristics as the qualitative patterns.)
Date does not appear to explicitly teach
or an average value of the at least one candidate data, in a latent space of the deep neural network model
However, Chen, directed to analogous art (explicitly on transferring a style to an image), teaches
or an average value of the at least one candidate data, in a latent space of the deep neural network model (Chen, Figure 2 has a description stating “We make several assumptions to deal with the challenging unsupervised stylish image description generation problem. We first assume there exists a shared latent space Z so that a latent code z ∈ Z can be mapped to the source description space DS and the target stylish description space DT via GS and GT. We also assume there exists a stylish image description embedding function that can map a stylish description to a latent code. Finally, we assume there exists an image embedding.” Figure 2 shows a latent space Z having a latent point z which is a specific similarity between the source image and target description. Chen, Experiment recites “The overall content similarity score is averaged over the testing data. This is because we assume stylish descriptions should at least contain objects which appear in the image.” Examiner recognizes that the latent space of a certain space is averaged. The latent space belong to the candidate data would also follow this implementation and be averaged together within the combination of the two arts.)
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to modify Date in view of Wang and Chen as described above with respect to Claim 12.
Claims 19 and 21 recite a system which performs substantially similar steps as listed by the method in Claims 12 and 14, respectively, and are rejected with the same rationale, mutatis mutandis. Additionally, Date teaches
A computing apparatus for improving reproduction performance of an output value for target data having a different qualitative pattern from learning data related to a deep neural network model, the computing apparatus comprising: a processor configured to perform processes (Date, System Architecture recites “Figure 2 shows the entire pipeline to personalize and de-sign custom clothes for the user. There are four modules to the architecture, namely, preprocessing, personal style store creation, style transfer and post-processing to generate the final design.” Date, Abstract states “In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes.” Examiner sees no explicit definition of the equipment used in order to perform the disclosed method; thus, the broadest reasonable interruption of the equipment is used. Among such equipment would be a computer system having a processor in order to process the algorithm.)
Claim 26 recites a non-transitory medium which stores substantially similar as listed by the method in Claim 12, respectively, and is rejected with the same rationale, mutatis mutandis. Additionally, Date teaches
A computer program stored in a non-transitory machine-readable recording medium, including instructions that cause a computing apparatus to perform a method of improving reproduction performance of an output value for target data having a different qualitative pattern from learning data related to a deep neural network model (Date, System Architecture recites “Figure 2 shows the entire pipeline to personalize and de-sign custom clothes for the user. There are four modules to the architecture, namely, preprocessing, personal style store creation, style transfer and post-processing to generate the final design.” Date, Abstract states “In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes.” Examiner sees no explicit definition of the equipment used in order to perform the disclosed method; thus, the broadest reasonable interruption of the equipment is used. Among such equipment would be a computer system having a processor in order to process the algorithm. The algorithm would have to be stored on a machine-readable medium in order to conduct the steps.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN C PRESLEY whose telephone number is (571)272-2682. The examiner can normally be reached Monday-Friday: 9:00 am - 4:00 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUSTIN C PRESLEY/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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