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 .
This Office Action is in response to amendment filed on February 23, 2026
Claims 1, 2, 4, 5 and 10 – 12 have been amended.
The rejections from the prior correspondence that are not restated herein are withdrawn.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/23/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to the rejection of the claims 35 U.S.C. 101 have been fully considered but they are not persuasive:
With respect to 101:
Applicant argues:
Applicant argues that the claims allegedly provide an improvement to the technological field of neural network computations and manufacturing of articles using surface-treating agent and base materials, as stated on pages 12 – 14 of the remarks.
Examiner’s answer:
Regarding the Applicant’s assertation that the claim as a whole is directed to an improvement to training a neural network computations and manufacturing of articles using surface-treating agent and base materials. The Applicant directs to [0176 - 0177] of their specification for support for these claims. [0176 - 177], recites the training of the machine learning model and evaluation of surface-treating agents for manufacturing textile at a high-level of generality and does not provide specific steps that an ordinary artisan would see as an improvement to the art. Thus, the judicial exceptions are not integrated into a practical application.
With respect to 103:
Applicant argues:
Applicant argues that the newly added limitation is not taught by the prior art Namligoz, as stated on pages 15 – 17. The Applicant states that Namligoz fails to teach “using these prediction to select at least one of a base material and a surface-treating agent to manufacture a first article.”
Examiner’s answer:
As further seen in the body of this rejection, Namligoz does in fact teach on the newly added limitation:(See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water, oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process of the fabrics.)
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 – 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following sections follow the 2019 PEG guidelines for analyzing subject matter eligibility.
The analysis below of the claims’ subject matter eligibility follows the 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”)
and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial
Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
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.
Claim 1
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1:
determining, a first evaluation of a first article in which a first surface-treating agent is fixed onto a first base material, the learning model generation method comprising: (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
generating the first evaluation using the learning model, wherein the first evaluation includes at least one of antifouling property information regarding an antifouling property of the first article, and processing stability information regarding processing stability of the first article; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
selecting at least one of the first surface-treating agent and the first base material to be used to manufacture the first article based on the first evaluation, wherein: the first article is obtained by fixing the first surface-treating agent onto the first base material; the second article is obtained by fixing the second surface-treating agent onto the second base material; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
A learning model generation method of generating a learning model for determining, by a processor (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
obtaining, by the processor, as teacher data, information including at least second base material information regarding a second base material, second treatment agent information regarding a second surface-treating agent, and a second evaluation of a second article; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
learning, by the processor, based on the teacher data; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
generating, by the processor, the learning model based on the learning; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
generating the first evaluation using the learning model (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the learning model is configured to receive input information, which is different from the teacher data, as an input, and output the first evaluation of the first article; (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the input information includes at least first base material information regarding the first base material, and first treatment agent information regarding the first surface-treating agent. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
A learning model generation method of generating a learning model for determining, by a processor (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
obtaining, by the processor, as teacher data, information including at least second base material information regarding a second base material, second treatment agent information regarding a second surface-treating agent, and a second evaluation of a second article; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
learning, by the processor, based on the teacher data; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
generating, by the processor, the learning model based on the learning; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
generating the first evaluation using the learning model (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the learning model is configured to receive input information, which is different from the teacher data, as an input, and output the first evaluation of the first article; (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the input information includes at least first base material information regarding the first base material, and first treatment agent information regarding the first surface-treating agent. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to method of applying and evaluating surface
treatments to base fabrics, this is nothing more than using machine learning models to generating
output data results. Nothing in the claim provides significantly more than this. As such, the claim
is not patent eligible.
Claim 2
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1:
selecting at least one of a second surface-treating agent and a second base material to be used to manufacture a second article based on an output of the learning model, wherein: the first article is obtained by fixing the first surface-treating agent onto the first base material; the second article is obtained by fixing the second surface-treating agent onto a-the second base material; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
obtaining, by a processor, as teacher data, information including at least first base material information regarding a first base material, first treatment agent information regarding a first surface-treating agent to be fixed onto the first base material, and a first evaluation of a first article in which the first surface-treating agent is fixed onto the first base material, wherein the first evaluation includes at least one of antifouling property information regarding an antifouling property of the first article, and processing stability information regarding processing stability of the first article; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
learning, by the processor, based on the teacher data; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
generating, by the processor, a learning model based on the learning; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the learning model is configured to receive input information, which is different from the teacher data, as an input, and output second treatment agent information for the second base material; (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the input information includes at least second base material information regarding the second base material, and information regarding a second evaluation of the second base material. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
obtaining, by a processor, as teacher data, information including at least first base material information regarding a first base material, first treatment agent information regarding a first surface-treating agent to be fixed onto the first base material, and a first evaluation of a first article in which the first surface-treating agent is fixed onto the first base material, wherein the first evaluation includes at least one of antifouling property information regarding an antifouling property of the first article, and processing stability information regarding processing stability of the first article; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
learning, by the processor, based on the teacher data; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
generating, by the processor, a learning model based on the learning; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the learning model is configured to receive input information, which is different from the teacher data, as an input, and output second treatment agent information for the second base material; (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the input information includes at least second base material information regarding the second base material, and information regarding a second evaluation of the second base material. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to method of applying and evaluating surface treatments to base fabrics, this is nothing more than using machine learning models to generating output data results. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 3 incorporates the rejection of claim 1.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
The learning model generation method as claimed in claim 1, wherein the learning is performed by a regression analysis or ensemble learning that is a combination of a plurality of regression analyses. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical application. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “Preforming encoding and decoding on data based on a machine learning model” of base claim 1) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. Mere instructions to apply an exception (i.e., the evaluation of surface treatments: method of base claim 1) cannot provide an inventive concept. The claim is not patent eligible.
Claim 4
Step 1: The claim recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
determining, a first evaluation of a first article in which a first surface-treating agent is fixed onto a first base material (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
determine, the first evaluation of the first article in which the first surface-treating agent is fixed onto the first base material, wherein the first evaluation includes at least one of antifouling property information regarding an antifouling property of the first article, and processing stability information regarding processing stability of the first article; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
select at least one of the first surface-treating agent and the first base material to be used to manufacture the first article based on the first evaluation, wherein: the first article is obtained by fixing the first surface-treating agent onto the first base material; a second article is obtained by fixing a second surface-treating agent onto a second base material;
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
A device for determining, by using a learning model, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the device comprising: a memory configured to store a program (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a processor configured to execute the program to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
receive input information as an input (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
determine, using the input information and the learning model (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the first evaluation (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
the learning model is configured to learn using teacher data including information including at least second base material information regarding the second base material, second treatment agent information regarding the second surface-treating agent, and a second evaluation of the second article (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the input information is different from the teacher data, and includes at least the first base material information and the first treatment agent information. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
A device for determining, by using a learning model, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the device comprising: a memory configured to store a program (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a processor configured to execute the program to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
receive input information as an input (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
determine, using the input information and the learning model (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the first evaluation (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
the learning model is configured to learn using teacher data including information including at least second base material information regarding the second base material, second treatment agent information regarding the second surface-treating agent, and a second evaluation of the second article (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the input information is different from the teacher data, and includes at least the first base material information and the first treatment agent information. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to method of applying and evaluating surface treatments to base fabrics, this is nothing more than using machine learning models to generating output data results. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 5
Step 1: The claim recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
determining, first treatment agent information regarding a first surface-treating agent to be fixed onto a first base material of a first article (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
determine, the first treatment agent information (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
based on the first treatment agent information, select the first surface-treating agent to be used to manufacture the first article, wherein: the learning model is configured to learn using teacher data including information including at least second base material information regarding a second base material, second treatment agent information regarding a second surface-treating agent to be fixed onto the second base material, and a second evaluation of a second article in which the second surface-treating agent is fixed onto the second base material (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
A device for determining, using a learning model, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the device comprising: a memory configured to store a program (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a processor configured to execute the program to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
receive input information as an input (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
determine, using the input information and the learning model (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the first treatment agent information (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
the first article is obtained by fixing the first surface-treating agent onto the first base material; and (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second article is obtained by fixing the second surface-treating agent onto the second base material. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
A device for determining, using a learning model, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the device comprising: a memory configured to store a program (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a processor configured to execute the program to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
receive input information as an input (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
determine, using the input information and the learning model (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the first treatment agent information (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
the first article is obtained by fixing the first surface-treating agent onto the first base material; and (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second article is obtained by fixing the second surface-treating agent onto the second base material. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to method of applying and evaluating surface treatments to base fabrics, this is nothing more than using machine learning models to generating output data results. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 6 incorporates the rejection of claim 4.
Step 1: The claim recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 4 are incorporated. Please see the analysis of
claim 4 above. Regarding the system steps recited in claim 4, these steps cover mental processes
based on generate variable selection weights.
Therefore, claim 6 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
The device as claimed in claim 4, wherein the first evaluation further includes at least one of water-repellency information regarding water- repellency of the first article, and oil-repellency information regarding oil-repellency of the first article (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The device as claimed in claim 4, wherein the first evaluation further includes at least one of water-repellency information regarding water- repellency of the first article, and oil-repellency information regarding oil-repellency of the first article (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
Claim 7 incorporates the rejection of claim 4.
Step 1: The claim recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 4 are incorporated. Please see the analysis of
claim 4 above. Regarding the system steps recited in claim 4, these steps cover mental processes
based on generate variable selection weights.
Therefore, claim 7 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
The device as claimed in claim 4, wherein the first base material is a textile product. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The device as claimed in claim 4, wherein the first base material is a textile product. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
Claim 8 incorporates the rejection of claim 7.
Step 1: The claim recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 7 are incorporated. Please see the analysis of
claim 7 above. Regarding the system steps recited in claim 7, these steps cover mental processes
based on generate variable selection weights.
Therefore, claim 8 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein: the first base material information comprises information regarding at least a type of the textile product and a type of a dye; (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the first treatment agent information comprises information regarding at least a type of a monomer constituting a repellent polymer contained in the first surface-treating agent, a content of the monomer in the repellent polymer, a content of the repellent polymer in the first surface- treating agent, a type of a solvent and a content of the solvent in the first surface-treating agent, and a type of a surfactant and a content of the surfactant in the first surface-treating agent. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein: the first base material information comprises information regarding at least a type of the textile product and a type of a dye; (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the first treatment agent information comprises information regarding at least a type of a monomer constituting a repellent polymer contained in the first surface-treating agent, a content of the monomer in the repellent polymer, a content of the repellent polymer in the first surface- treating agent, a type of a solvent and a content of the solvent in the first surface-treating agent, and a type of a surfactant and a content of the surfactant in the first surface-treating agent. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
Claim 9 incorporates the rejection of claim 8.
Step 1: The claim recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 8 are incorporated. Please see the analysis of
claim 8 above. Regarding the system steps recited in claim 8, these steps cover mental processes
based on generate variable selection weights.
Therefore, claim 9 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein: the teacher data further comprises environment information regarding an environment during processing of the second base material (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the environment information comprises information regarding at least one of a concentration of the second surface-treating agent in a treatment tank, a temperature of the environment, a humidity of the environment, a curing temperature, or a processing speed during the processing of the second base material (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the second base material information further comprises information regarding at least one of a color, a weave, a basis weight, a yarn thickness, or a zeta potential of a second textile product (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the second treatment agent information further comprises information regarding at least one of a type and a content of an additive to be added to the second surface-treating agent, a pH of the second surface-treating agent, or a zeta potential of the second surface-treating agent. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein: the teacher data further comprises environment information regarding an environment during processing of the second base material (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the environment information comprises information regarding at least one of a concentration of the second surface-treating agent in a treatment tank, a temperature of the environment, a humidity of the environment, a curing temperature, or a processing speed during the processing of the second base material (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the second base material information further comprises information regarding at least one of a color, a weave, a basis weight, a yarn thickness, or a zeta potential of a second textile product (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
the second treatment agent information further comprises information regarding at least one of a type and a content of an additive to be added to the second surface-treating agent, a pH of the second surface-treating agent, or a zeta potential of the second surface-treating agent. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
Claim 10
Step 1: The claim recites a non-transitory computer-readable medium, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
determining, a first evaluation of a first article in which a first surface-treating agent is fixed onto a first base material, (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
determine, the first evaluation of the first article in which the first surface-treating agent is fixed onto the first base material (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
select at least one of the first surface-treating agent and the first base material to be used to manufacture the first article based on the first evaluation, wherein: the first article is obtained by fixing the first surface-treating agent onto the first base material, a second article is obtained by fixing a second surface-treating agent onto a second base material (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
A non-transitory computer-readable medium storing a program for determining, by using a learning model, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the program being configured to cause a processor to: receive input information as an input; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
determine, using the input information and the learning model, wherein the first evaluation includes at least one of antifouling property information regarding an antifouling property of the first article, and processing stability information regarding processing stability of the first article (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the first evaluation (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
the learning model is configured to learn using teacher data including information including at least second base material information regarding the second base material, second treatment agent information regarding the second surface-treating agent, and a second evaluation of the second article (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the input information is different from the teacher data, and includes at least the second base material information and the second treatment agent information. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
A non-transitory computer-readable medium storing a program for determining, by using a learning model, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the program being configured to cause a processor to: receive input information as an input; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
determine, using the input information and the learning model, wherein the first evaluation includes at least one of antifouling property information regarding an antifouling property of the first article, and processing stability information regarding processing stability of the first article (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the first evaluation (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
the learning model is configured to learn using teacher data including information including at least second base material information regarding the second base material, second treatment agent information regarding the second surface-treating agent, and a second evaluation of the second article (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the input information is different from the teacher data, and includes at least the second base material information and the second treatment agent information. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to method of applying and evaluating surface treatments to base fabrics, this is nothing more than using machine learning models to generating output data results. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 11
Step 1: The claim recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
select at least one of the first surface-treating agent and the first base material to be used to manufacture the first article based on the first evaluation, wherein the first evaluation includes at least one of antifouling property information regarding an antifouling property of the first article (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
A device comprising: a memory configured to store a learned model (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a processor configured to, using the learned model, perform calculation based on a weighting coefficient of a neural network with respect to first base material information regarding a first base material and first treatment agent information regarding a first surface- treating agent being input to an input layer of the neural network (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output a first evaluation of a first article from an output layer of the neural network (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
processing stability information regarding processing stability of the first article, wherein: the weighting coefficient is obtained through learning of the learned model using at least second base material information, second treatment agent information, and a second evaluation as teacher data; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second base material information is information regarding a second base material; the second treatment agent information is information regarding a second surface-treating agent to be fixed onto the second base material; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second evaluation is regarding a second article in which the second surface-treating agent is fixed onto the second base material; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the first article is obtained by fixing the first surface-treating agent onto the first base material; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
the second article is obtained by fixing the second surface-treating agent onto the second base material. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
A device comprising: a memory configured to store a learned model (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a processor configured to, using the learned model, perform calculation based on a weighting coefficient of a neural network with respect to first base material information regarding a first base material and first treatment agent information regarding a first surface- treating agent being input to an input layer of the neural network (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output a first evaluation of a first article from an output layer of the neural network (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
processing stability information regarding processing stability of the first article, wherein: the weighting coefficient is obtained through learning of the learned model using at least second base material information, second treatment agent information, and a second evaluation as teacher data; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second base material information is information regarding a second base material; the second treatment agent information is information regarding a second surface-treating agent to be fixed onto the second base material; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second evaluation is regarding a second article in which the second surface-treating agent is fixed onto the second base material; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the first article is obtained by fixing the first surface-treating agent onto the first base material; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
the second article is obtained by fixing the second surface-treating agent onto the second base material. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to method of applying and evaluating surface treatments to base fabrics, this is nothing more than using machine learning models to generating output data results. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 12
Step 1: The claim recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
select the first surface-treating agent to be used to manufacture the first article based on the first treatment agent information, wherein the first evaluation includes at least one of antifouling property information regarding an antifouling property of the first article (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
A device comprising: a memory configured to store a learned model; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a processor configured to, using the learned model, perform calculation based on a weighting coefficient of a neural network with respect to first base material information regarding a first base material and information regarding a first evaluation of a first article being input to an input layer of the neural network (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output first treatment agent information regarding a first surface-treating agent to be fixed onto the first base material from an output layer of the neural network (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
processing stability information regarding processing stability of the first article, wherein: the weighting coefficient is obtained through learning of the learned model using at least second base material information, second treatment agent information, and a second evaluation as teacher data; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second base material information is information regarding a second base material; the second treatment agent information is information regarding a second surface-treating agent to be fixed onto the second base material; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second evaluation is regarding a second article in which the second surface-treating agent is fixed onto the second base material; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the first article is obtained by fixing the first surface-treating agent onto the first base material; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
the second article is obtained by fixing the second surface-treating agent onto the second base material. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
A device comprising: a memory configured to store a learned model; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a processor configured to, using the learned model, perform calculation based on a weighting coefficient of a neural network with respect to first base material information regarding a first base material and information regarding a first evaluation of a first article being input to an input layer of the neural network (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output first treatment agent information regarding a first surface-treating agent to be fixed onto the first base material from an output layer of the neural network (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
processing stability information regarding processing stability of the first article, wherein: the weighting coefficient is obtained through learning of the learned model using at least second base material information, second treatment agent information, and a second evaluation as teacher data; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second base material information is information regarding a second base material; the second treatment agent information is information regarding a second surface-treating agent to be fixed onto the second base material; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the second evaluation is regarding a second article in which the second surface-treating agent is fixed onto the second base material; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the first article is obtained by fixing the first surface-treating agent onto the first base material; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
the second article is obtained by fixing the second surface-treating agent onto the second base material. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to method of applying and evaluating surface treatments to base fabrics, this is nothing more than using machine learning models to generating output data results. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 7 and 10 – 12 are rejected under 35 U.S.C. 103 as being unpatentable over Namligoz (NPL DOI 10.1007 /s 12221-011-0414-8) in view of TSUNO (US 20180342077 A1) further in view of Chen (NPL: Two-Step Approach for Fabrication of Durable Superamphiphobic Fabrics for Self-Cleaning, Antifouling, and On-Demand Oil/Water Separation)
Regarding claim 1, Namligoz teaches, A learning model generation method of generating a learning model for determining, [by a processor], a first evaluation of a first article in which a first surface-treating agent is fixed onto a first base material, the learning model generation method comprising: (See e.g. [P418:C1], The results of the best neural network developed [learning model] to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage [A learning model generation method of generating a learning model for determining], 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.) (See e.g. [P414:C2] In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [a first surface-treating agent] were applied to 21 different fabrics [a first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics were measured.)
obtaining, [by the processor, as teacher data], information including at least second base material information regarding a second base material, second treatment agent information regarding a second surface-treating agent, and a second evaluation of a second article; (See e.g. [P414:C2] In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations were applied [second treatment agent information regarding a second surface-treating agent] to 21 different fabrics [at least second base material information] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [second article] were measured.) (See e.g. [P419:C1] In Table 12, the results of the neural network developed for the prediction of oil repellency are given. [obtaining, a second evaluation of a second article])
generating, [by the processor], the learning model based on the learning (See e.g. [P416:C1] In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water, oil repellencies and wrinkle recovery angle properties of the treated fabrics… As a result, 336, 168 and 189 experimental results were used to form the neural network [generating] architecture since the mean values of each repeats for each test were considered.)
generating the first evaluation using the learning model, wherein the first evaluation includes [at least one of antifouling property information regarding an antifouling property of] the first article, and processing [stability information regarding processing stability] of the first article; (See e.g. [P418:C1], The results of the best neural network developed [learning model] to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.)
selecting at least one of the first surface-treating agent and the first base material to be used to manufacture the first article based on the first evaluation (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.) (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water [a first evaluation], oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process [to be used to manufacture] of the fabrics [a first base material]. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables.)
wherein: the first article is obtained by fixing the first surface-treating agent onto the first base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [the first article is obtained] were measured.)
the second article is obtained by fixing the second surface-treating agent onto the second base material; (See e.g. [P414:C2] In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the second surface-treating agent] were applied to 21 different fabrics [the second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [the second article is obtained] were measured.)
the input information includes at least first base material information regarding the first base material, and first treatment agent information regarding the first surface-treating agent. (See e.g. [P416:C1], The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process of the fabrics [the first base material]. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano) [first treatment agent information regarding the first surface-treating agent.], and chemical concentration parameters are used as variables. [the input information])
Namligoz does not teach a processor, obtaining, by the processor, as teacher data, generating, by the processor, the learning model based on the learning. generating, by the
processor, the learning model based on the learning
TSUNO teaches A learning model generation method of generating a learning model for determining, by a processor…(See e.g. [Claim 5], A teacher data generation method for generating teacher data used for object detection for detecting a specific identifying target, the teacher data generation method comprising: learning the specific identifying target by an object recognition method using reference data including the specific identifying target to generate an identification model of the specific identifying target, by a processor;)
obtaining, by the processor, as teacher data (See e.g. [Claim 5], A teacher data generation method for generating teacher data used for object detection for detecting a specific identifying target, the teacher data generation method comprising: learning the specific identifying target by an object recognition method using reference data including the specific identifying target to generate an identification model of the specific identifying target, by a processor;)
learning, by the processor, based on the teacher data (See e.g. [Claim 5], A teacher data generation method for generating teacher data used for object detection for detecting a specific identifying target, the teacher data generation method comprising: learning the specific identifying target by an object recognition method using reference data including the specific identifying target to generate an identification model of the specific identifying target, by a processor;)
generating, by the processor, the learning model based on the learning (See e.g. [Claim 5], A teacher data generation method for generating teacher data used for object detection for detecting a specific identifying target, the teacher data generation method comprising: learning the specific identifying target by an object recognition method using reference data including the specific identifying target to generate an identification model of the specific identifying target, by a processor;)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz and
TSUNO before them, to include TSUNO’s system featuring teacher data in Namligoz’s system
that has a learning model with multiple inputs of different base materials, surface treating agents
and articles. One would have been motivated to make such a combination in order to better
verify the results of the repellency using the identification models generated with teacher data
taught by TSUNO(US 20180342077 A1) (0011)
Namligoz and TSUNO do not teach at least one of antifouling property information regarding an antifouling property and processing stability information regarding processing stability
Chen teaches at least one of antifouling property information regarding an antifouling property (See e.g. [P5497:S3.3:C2], To test antifouling properties, the SiO2−SH@fabric and SiO2−S-FMA@fabric were separately dipped into rapeseed oil which has low surface tension and high viscosity for a moment and then taken out (Figure 10e,f and Videos S5 and S6). [information regarding an antifouling property])
processing stability information regarding processing stability (See e.g. [P5495:S3.2:C1], The sand impact test and tape-peeling test were used to assess the mechanical stability of the fabric. After the fabric suffered from 160g sand impact, the WCAs, HCAs, WSAs, and HSAs of the fabric changed from 159° to 153°, 152° to 146°, 2.0° to 6.6°, and 8.3° to 13.5°, respectively (Figure 9a). [processing stability information])
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz, TSUNO and Chen before them, to include Chen’s system antifouling and stability properties in Namligoz and TSUNO’s system that has a learning model with multiple inputs of different base materials, surface treating agents and articles. One would have been motivated to make such a combination in order to increase the durability of the fabric taught by Chen (NPL: Two-Step Approach for Fabrication of Durable Superamphiphobic Fabrics for Self-Cleaning, Antifouling, and On-Demand Oil/Water Separation) (P5492:S3.1:C2)
Regarding claim 2, Namligoz teaches, A learning model generation method comprising: obtaining, by a processor, as teacher data, information including at least first base material information regarding a first base material, first treatment agent information regarding a first surface-treating agent to be fixed onto the first base material, and a first evaluation of a first article in which the first surface-treating agent is fixed onto the first base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [first treatment agent information] were applied to 21 different fabrics [information including at least first base material information regarding a first base material] with two application methods.) (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property of the fabrics are presented in Table 9. [first evaluation of a first article in which the first surface-treating agent is fixed onto the first base material])
wherein the first evaluation includes at least [one of antifouling property information regarding an antifouling property of] the first article, and processing [stability information regarding processing stability] of the first article (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [first evaluation] of the fabrics [first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.)
selecting at least one of a second surface-treating agent and a second base material to be used to manufacture a second article based on an output of the learning model (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the second surface-treating agent] were applied to 21 different fabrics [the second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [a second article] were measured.) (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water, oil repellencies and wrinkle recovery angle properties [the second surface-treating agent] of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process [to be used to manufacture] of the fabrics [a second base material]. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables.)
wherein: the first article is obtained by fixing the first surface-treating agent onto the first base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [the first article is obtained] were measured.)
the second article is obtained by fixing the second surface-treating agent onto a second base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the second surface-treating agent] were applied to 21 different fabrics [the second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [the second article is obtained] were measured.)
the learning model is configured to receive input information, [which is different from the teacher data], as an input, and output second treatment agent information for the second base material (See e.g. [P421:C1], The aim of this study is to predict [the learning model] these aforementioned added properties [as an input] of the fabrics [the second base material] based on the blend, treatment method, used chemicals and chemical concentrations by using artificial neural networks before manufacturing. [the learning model is configured to receive input information]) (See e.g. [P419:C1], In Table 12, the results of the neural network developed for the prediction of oil repellency are given. [output second treatment agent information for the second base material])
the input information includes at least second base material information regarding the second base material, and information regarding a second evaluation of the second base material. (See e.g. [P416:C1], The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process of the fabrics [the second base material]. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), [second base material information] method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables. [input information]) (See e.g. [P419:C1], In Table 12, the results of the neural network developed for the prediction of oil repellency are given. [a second evaluation of the second base material.])
Namligoz does not teach by a processor, as teacher data, learning, by the processor, based on the teacher data; and generating, by the processor, a learning model based on the learning, which is different from the teacher data
TSUNO teaches A learning model generation method comprising: obtaining, by a processor, as teacher data… (See e.g. [Claim 5], A teacher data generation method for generating teacher data used for object detection for detecting a specific identifying target, the teacher data generation method comprising: learning the specific identifying target by an object recognition method using reference data including the specific identifying target to generate an identification model of the specific identifying target, by a processor;)
learning, by the processor, based on the teacher data; and (See e.g. [Claim 5], A teacher data generation method for generating teacher data used for object detection for detecting a specific identifying target, the teacher data generation method comprising: learning the specific identifying target by an object recognition method using reference data including the specific identifying target to generate an identification model of the specific identifying target, by a processor;)
generating, by the processor, a learning model based on the learning (See e.g. [Claim 5], A teacher data generation method for generating teacher data used for object detection for detecting a specific identifying target, the teacher data generation method comprising: learning the specific identifying target by an object recognition method using reference data including the specific identifying target to generate an identification model of the specific identifying target, by a processor;)
which is different from the teacher data, as an input (See e.g. [0091], By the “input data” being input to a neural network including many parameters, deep learning training is performed in a manner to update the difference (a weight during training) between a deduced label and the right answer label, to thereby obtain a trained weight. (0245): The test data storage part 301 is configured to store test data for deduction. The test data includes only input data (image). [which is different from the teacher data])
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz and
TSUNO before them, to include TSUNO’s system featuring teacher data in Namligoz’s system
that has a learning model with multiple inputs of different base materials, surface treating agents
and articles. One would have been motivated to make such a combination in order to better
verify the results of the repellency using the identification models generated with teacher data
taught by TSUNO(US 20180342077 A1) (0011)
Namligoz and TSUNO do not teach at least one of antifouling property information regarding an antifouling property and processing stability information regarding processing stability
Chen teaches at least one of antifouling property information regarding an antifouling property (See e.g. [P5497:S3.3:C2], To test antifouling properties, the SiO2−SH@fabric and SiO2−S-FMA@fabric were separately dipped into rapeseed oil which has low surface tension and high viscosity for a moment and then taken out (Figure 10e,f and Videos S5 and S6). [information regarding an antifouling property])
processing stability information regarding processing stability (See e.g. [P5495:S3.2:C1], The sand impact test and tape-peeling test were used to assess the mechanical stability of the fabric. After the fabric suffered from 160g sand impact, the WCAs, HCAs, WSAs, and HSAs of the fabric changed from 159° to 153°, 152° to 146°, 2.0° to 6.6°, and 8.3° to 13.5°, respectively (Figure 9a). [processing stability information])
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz, TSUNO and Chen before them, to include Chen’s system antifouling and stability properties in Namligoz and TSUNO’s system that has a learning model with multiple inputs of different base materials, surface treating agents and articles. One would have been motivated to make such a combination in order to increase the durability of the fabric taught by Chen (NPL: Two-Step Approach for Fabrication of Durable Superamphiphobic Fabrics for Self-Cleaning, Antifouling, and On-Demand Oil/Water Separation) (P5492:S3.1:C2)
Regarding claim 3, Namligoz, TSUNO and Chen teach the method of claim 1. Namligoz further teaches, The learning model generation method as claimed in claim 1, wherein the learning is performed by a regression analysis or ensemble learning that is a combination of a plurality of regression analyses. (See e.g. [P414:C1], There are different types of prediction methods such as; mathematical modeling, regression and artificial neural networks.)
Regarding claim 4, Namligoz teaches, [A device] for determining, by using a learning model, a first evaluation of a first article in which a first surface-treating agent is fixed onto a first base material, [the device comprising]: (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations were applied to 21 different fabrics with two application methods.) (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency [a first evaluation] property of the fabrics [a first article] are presented in Table 9.) (See e.g. [P415:C2], In these treatments, the various water-oil repellents [a first surface-treating agent] and cross-linking agents were applied to all fabrics [a first base material] at the same bath with two different application methods.)
receive input information as an input (See e.g. [P416:C2], In the input layer 39 inputs are presented to the system [receive input information as an input], whereas in the hidden layer 8 neurons are present. In the output layer there are three different outputs encoded such as mean, good and best.)
determine, using the input information and the learning model, the first evaluation of the first article in which the first surface-treating agent is fixed onto the first base material (See e.g. [P418:C1], The results of the best neural network [the learning model] developed to predict the water repellency [a first evaluation] property of the fabrics [the input information] are presented in Table 9.) (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [first article] were measured.)
wherein the first evaluation includes at least one [of antifouling property information regarding an antifouling property] of the first article, and processing [stability information regarding processing stability] of the first article; (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.)
output the first evaluation (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property of the fabrics are presented in Table 9.)
select at least one of the first surface-treating agent and the first base material to be used to manufacture the first article based on the first evaluation (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.) (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water [a first evaluation], oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process [to be used to manufacture] of the fabrics [a first base material]. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables.)
wherein: the first article is obtained by fixing the first surface-treating agent onto the first base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [first article] were measured.)
a second article is obtained by fixing a second surface-treating agent onto a second base material; […], regarding the second base material, second treatment agent information regarding the second surface-treating agent, and a second evaluation of the second article; (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [a second surface-treating agent] were applied to 21 different fabrics [the second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [second article] were measured.) (See e.g. [P419:C1], In Table 12, the results of the neural network developed for the prediction of oil repellency are given. [a second evaluation])
[the input information is different from the teacher data], and includes at least the first base material information and the first treatment agent information. (See e.g. [P416:C1], The aim of this study is to estimate these aforementioned properties of the fabrics [the first base material information] before manufacturing process of the fabrics. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration [the first treatment agent information] parameters are used as variables.)
Namligoz does not teach a device, a memory configured to store a program, a
processor configured to execute the program to, the learning model is configured to learn using
teacher data including information including information including at least second base material
information and the input information is different from the teacher data;
TSUNO teaches A device (See e.g. [0101], Examples of the connection part 97 include a device configured to communicate with an external device through an arbitrary network (a line or a transmission medium) such as a LAN (Local Area Network) and a WAN (Wide Area Network) and perform data conversion accompanying the communication.)
a memory configured to store a program; and (See e.g. [0096], an external memory device 95 described below is configured to store a teacher data generation program)
a processor configured to execute the program to: (See e.g. [0096], a CPU (Central Processing Unit) 91 described below is configured to read out the program and execute the program)
the learning model is configured to learn using teacher data including information
including information including [at least second base material information] (See e.g. [Abstract], A teacher data generation apparatus configured to generate teacher data used for object detection for detecting a specific identifying target includes a processor configured to execute a process including learning the specific identifying target…)
the input information is different from the teacher data; (See e.g. [0091], By the “input data” being input to a neural network including many parameters, deep learning training is performed in a manner to update the difference (a weight during training) between a deduced label and the right answer label, to thereby obtain a trained weight.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz and
TSUNO before them, to include TSUNO’s system featuring teacher data in Namligoz’s system
that has a learning model with multiple inputs of different base materials, surface treating agents
and articles. One would have been motivated to make such a combination in order to better
verify the results of the repellency using the identification models generated with teacher data
taught by TSUNO(US 20180342077 A1) (0011)
Namligoz and TSUNO do not teach at least one of antifouling property information regarding an antifouling property and processing stability information regarding processing stability
Chen teaches at least one of antifouling property information regarding an antifouling property (See. e.g. [P5497:S3.3:C2], To test antifouling properties, the SiO2−SH@fabric and SiO2−S-FMA@fabric were separately dipped into rapeseed oil which has low surface tension and high viscosity for a moment and then taken out (Figure 10e,f and Videos S5 and S6). [information regarding an antifouling property])
processing stability information regarding processing stability (See e.g. [P5495:S3.2:C1] The sand impact test and tape-peeling test were used to assess the mechanical stability of the fabric. After the fabric suffered from 160g sand impact, the WCAs, HCAs, WSAs, and HSAs of the fabric changed from 159° to 153°, 152° to 146°, 2.0° to 6.6°, and 8.3° to 13.5°, respectively (Figure 9a). [processing stability information])
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz, TSUNO and Chen before them, to include Chen’s system antifouling and stability properties in Namligoz and TSUNO’s system that has a learning model with multiple inputs of different base materials, surface treating agents and articles. One would have been motivated to make such a combination in order to increase the durability of the fabric taught by Chen (NPL: Two-Step Approach for Fabrication of Durable Superamphiphobic Fabrics for Self-Cleaning, Antifouling, and On-Demand Oil/Water Separation) (P5492:S3.1:C2)
Regarding claim 5, Namligoz teaches, [A device for determining], using a learning model, first treatment agent information regarding a first surface-treating agent to be fixed onto a first base material of a first article, [the device comprising]: (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) [using a learning model] with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water, oil repellencies and wrinkle recovery angle properties of the treated fabrics. [a first article] The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process of the fabrics. In the estimation of these properties, fabric [a first base material] type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration [first treatment agent information] parameters are used as variables.)
receive input information as an input (See e.g. [P416:C2], In the input layer 39 inputs are presented to the system [receive input information as an input], whereas in the hidden layer 8 neurons are present. In the output layer there are three different outputs encoded such as mean, good and best.)
determine, using the input information and the learning model, the first treatment agent information (See e.g. [P416:C1], In this study, a multilayer feed-forward network
(MLP) [the learning model] with one hidden layer trained in several phases of back propagation
and conjugate gradient descent algorithms was used to predict the water, oil repellencies and
wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these
aforementioned properties of the fabrics before manufacturing process of the fabrics. In the
estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer),
chemicals (classic/nano) [the first treatment agent information], and chemical concentration
parameters are used as variables. [the input information])
output the first treatment agent information; (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property of the fabrics are presented in Table 9.)
based on the first treatment agent information, select the first surface-treating agent to be used to manufacture the first article (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.) (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water [a first evaluation], oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process [to be used to manufacture] of the fabrics [a first base material]. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables.) (See e.g. [P415:C2], In these treatments, the various water-oil repellents [a first surface-treating agent] and cross-linking agents were applied to all fabrics [a first base material] at the same bath with two different application methods.)
…regarding a second base material, second treatment agent information regarding a second surface-treating agent to be fixed onto the second base material, and a second evaluation of a second article in which the second surface-treating agent is fixed onto the second base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [a second surface-treating agent] were applied to 21 different fabrics [the second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [second article] were measured.) (See e.g. [P419:C1], In Table 12, the results of the neural network developed for the prediction of oil repellency are given. [a second evaluation])
[the input information is different from the teacher data] and includes at least first base material information regarding the first base material and information regarding a first evaluation of the first article; (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods.) (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property of the fabrics [the first article] are presented in Table 9.) (See e.g. [P416:C1], The aim of this study is to estimate these aforementioned properties of the fabrics [the first base material information] before manufacturing process of the fabrics. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables.)
wherein the first evaluation includes [at least one of antifouling property information regarding an antifouling property of] the first article, and processing [stability information regarding processing stability of] the first article; (See e.g. [P416:C1], The aim of this study is to estimate these aforementioned properties of the fabrics [the first base material information] before manufacturing process of the fabrics. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration [the first treatment agent information] parameters are used as variables.) (See e.g. [P:418:C1], The results of the best neural network developed to predict the water repellency property [a first evaluation] of the fabrics [the first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.)
the first article is obtained by fixing the first surface-treating agent onto the first base material; (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [first article] were measured.)
the second article is obtained by fixing the second surface-treating agent onto the second base material. (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [second surface-treating agent] were applied to 21 different fabrics [the second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [second article] were measured.)
Namligoz does not teach A device, a memory configured to store a program;
and a processor configured to execute the program to, wherein: the learning model, is configured to learn using teacher data including information including at least second base material information, the input information is different from the teacher data
TSUNO teaches A device (See e.g. [0101], Examples of the connection part 97 include a device configured to communicate with an external device through an arbitrary network (a line or a transmission medium) such as a LAN (Local Area Network) and a WAN (Wide Area Network) and perform data conversion accompanying the communication.)
a memory configured to store a program; and (See. e.g. [0096], an external memory device 95 described below is configured to store a teacher data generation program)
a processor configured to execute the program to: (See e.g. [0096], a CPU (Central Processing Unit) 91 described below is configured to read out the program and execute the program)
wherein: the learning model, is configured to learn using teacher data including
information including at least second base material information, (See e.g. [0011], a teacher data generation apparatus configured to generate teacher data used for object detection for detecting a specific identifying target)
the input information is different from the teacher data, (See e.g. [0091], By the “input data” being input to a neural network including many parameters, deep learning training is performed in a manner to update the difference (a weight during training) between a deduced label and the right answer label, to thereby obtain a trained weight.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz and
TSUNO before them, to include TSUNO’s system featuring teacher data in Namligoz’s system
that has a learning model with multiple inputs of different base materials, surface treating agents
and articles. One would have been motivated to make such a combination in order to better
verify the results of the repellency using the identification models generated with teacher data
taught by TSUNO(US 20180342077 A1) (0011)
Namligoz and TSUNO do not teach at least one of antifouling property information regarding an antifouling property and processing stability information regarding processing stability
Chen teaches at least one of antifouling property information regarding an antifouling property (See e.g. [P5497:S3.3:C2], To test antifouling properties, the SiO2−SH@fabric and SiO2−S-FMA@fabric were separately dipped into rapeseed oil which has low surface tension and high viscosity for a moment and then taken out (Figure 10e,f and Videos S5 and S6). [information regarding an antifouling property])
processing stability information regarding processing stability (See e.g. [P5495:S3.2:C1], The sand impact test and tape-peeling test were used to assess the mechanical stability of the fabric. After the fabric suffered from 160g sand impact, the WCAs, HCAs, WSAs, and HSAs of the fabric changed from 159° to 153°, 152° to 146°, 2.0° to 6.6°, and 8.3° to 13.5°, respectively (Figure 9a). [processing stability information])
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz, TSUNO and Chen before them, to include Chen’s system antifouling and stability properties in Namligoz and TSUNO’s system that has a learning model with multiple inputs of different base materials, surface treating agents and articles. One would have been motivated to make such a combination in order to increase the durability of the fabric taught by Chen (NPL: Two-Step Approach for Fabrication of Durable Superamphiphobic Fabrics for Self-Cleaning, Antifouling, and On-Demand Oil/Water Separation) (P5492:S3.1:C2)
Regarding claim 6, Namligoz, TSUNO and Chen teach the device of claim 4. Namligoz further teaches, wherein the first evaluation further includes at least one of water-repellency information regarding water-repellency of the first article, and oil-repellency information regarding oil-repellency of the first article (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [first evaluation] of the fabrics [first article] are presented in Table 9.) (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency [water-repellency information regarding water- repellency of the first article, and oil-repellency information regarding oil-repellency] and wrinkle angle recovery properties of the fabrics [the first article] were measured.)
Regarding claim 7, Namligoz, TSUNO and Chen teach the device of claim 4. Namligoz further teaches, The device as claimed in claim 4, wherein the first base material is a textile product. (See e.g. [Introduction], In a similar way, anti-bacterial finish, flame retardant, water repellent, water proof, antistatic finish, peach finish are some of the important finishes applied to textile fabrics.)
Regarding claim 10, Namligoz teaches, [A non-transitory computer-readable medium storing a program] for determining, by using a learning model, a first evaluation of a first article in which a first surface-treating agent is fixed onto a first base material, [the program being configured to cause a processor to]: (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property of the fabrics are presented in Table 9. [a first evaluation]) (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [first surface-treating agent] were applied to 21 different fabrics [first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [first article] were measured.)
receive input information as an input; (See e.g. [P416:C2], In the input layer 39 inputs are presented to the system [receive input information as an input], whereas in the hidden layer 8 neurons are present. In the output layer there are three different outputs encoded such as mean, good and best.)
determine, using the input information and the learning model, the first evaluation of the first article in which the first surface-treating agent is fixed onto the first base material (See e.g. [P418:C1], The results of the best neural network [the learning model] developed to predict the water repellency [a first evaluation] property of the fabrics [the input information] are presented in Table 9.) (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [first article] were measured.)
wherein the first evaluation includes at least [one of antifouling property information regarding an antifouling property of] the first article, and processing [stability information regarding processing stability of] the first article; (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.)
output the first evaluation; (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property of the fabrics are presented in Table 9.)
select at least one of the first surface-treating agent and the first base material to be used to manufacture the first article based on the first evaluation (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.) (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water [a first evaluation], oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process [to be used to manufacture] of the fabrics [a first base material]. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables.)
wherein: the first article is obtained by fixing the first surface-treating agent onto the first base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [first article] were measured.)
a second article is obtained by fixing a second surface-treating agent onto a second base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [second surface-treating agent] were applied to 21 different fabrics [second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [second article is obtained] were measured.)
the learning model is [configured to learn using teacher data] including information including at least second base material information regarding the second base material, second treatment agent information regarding the second surface-treating agent, and a second evaluation of the second article (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [second surface-treating agent] were applied to 21 different fabrics [second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [second article] were measured.) (See e.g. [P419:C1], In Table 12, the results of the neural network developed for the prediction of oil repellency are given. [a second evaluation])
…and includes at least the second base material information and the second treatment agent information. (See e.g. [P416:C1], The aim of this study is to estimate these aforementioned properties of the fabrics [second base material] before manufacturing process of the fabrics. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration [second treatment agent information] parameters are used as variables.)
Namligoz does not teach A non-transitory computer-readable medium storing a
program, configured to learn using teacher data, the input information is different from the teacher data,
TSUNO teaches A non-transitory computer-readable medium storing a program (See e.g. [0041], a non-transitory computer-readable recording medium having stored therein a teacher data generation program,)
configured to learn using teacher data (See e.g. [0011], a teacher data generation apparatus configured to generate teacher data used for object detection for detecting a specific identifying target includes: an identification model generation part configured to learn a specific identifying target by an object)
the input information is different from the teacher data, (See e.g. [0091], By the “input data” being input to a neural network including many parameters, deep learning training is performed in a manner to update the difference (a weight during training) between a deduced label and the right answer label, to thereby obtain a trained weight.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz and
TSUNO before them, to include TSUNO’s system featuring teacher data in Namligoz’s system
that has a learning model with multiple inputs of different base materials, surface treating agents
and articles. One would have been motivated to make such a combination in order to better
verify the results of the repellency using the identification models generated with teacher data
taught by TSUNO(US 20180342077 A1) (0011)
Namligoz and TSUNO do not teach at least one of antifouling property information regarding an antifouling property and processing stability information regarding processing stability
Chen teaches at least one of antifouling property information regarding an antifouling property (See e.g. [P5497:S3.3:C2], To test antifouling properties, the SiO2−SH@fabric and SiO2−S-FMA@fabric were separately dipped into rapeseed oil which has low surface tension and high viscosity for a moment and then taken out (Figure 10e,f and Videos S5 and S6). [information regarding an antifouling property])
processing stability information regarding processing stability (See e.g. [P5495:S3.2:C1], The sand impact test and tape-peeling test were used to assess the mechanical stability of the fabric. After the fabric suffered from 160g sand impact, the WCAs, HCAs, WSAs, and HSAs of the fabric changed from 159° to 153°, 152° to 146°, 2.0° to 6.6°, and 8.3° to 13.5°, respectively (Figure 9a). [processing stability information])
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz, TSUNO and Chen before them, to include Chen’s system antifouling and stability properties in Namligoz and TSUNO’s system that has a learning model with multiple inputs of different base materials, surface treating agents and articles. One would have been motivated to make such a combination in order to increase the durability of the fabric taught by Chen (NPL: Two-Step Approach for Fabrication of Durable Superamphiphobic Fabrics for Self-Cleaning, Antifouling, and On-Demand Oil/Water Separation) (P5492:S3.1:C2)
Regarding claim 11, Namligoz teaches, [a processor configured to], using the learned model, perform calculation based on a weighting coefficient of a neural network with respect to first base material information regarding a first base material and first treatment agent information regarding a first surface-treating agent being input to an input layer of the neural network, output a first evaluation of a first article from an output layer of the neural network (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) [learned model, perform calculation based on a weighting coefficient of a neural network] with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water, oil repellencies and wrinkle recovery angle properties of the treated fabrics. [first article] The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process of the fabrics. In the estimation of these properties, fabric [first base material] type (Co/Co, PES/Co etc.) [first base material information], method (padding or transfer), chemicals (classic/nano) [first surface-treating agent], and chemical concentration [first treatment agent information] parameters are used as variables. [input to an input layer of the neural network]) (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property of the fabrics are presented in Table 9. [output a first evaluation of a first article from an output layer of the neural network])
select at least one of the first surface-treating agent and the first base material to be used to manufacture the first article based on the first evaluation (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.) (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water [a first evaluation], oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process [to be used to manufacture] of the fabrics [a first base material]. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables.)
wherein: the weighting coefficient is obtained through learning of the learned model using at least second base material information, second treatment agent information,… (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation [weighting coefficient is obtained through learning of the learned model] and conjugate gradient descent algorithms was used to predict the water, oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics [second base material information] before manufacturing process of the fabrics. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration [second treatment agent information] parameters are used as variables.)
the second base material information is information regarding a second base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [second base material information] were applied to 21 different fabrics [second base material] with two application methods.)
the second treatment agent information is information regarding a second surface-treating agent to be fixed onto the second base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [second surface-treating agent] were applied to 21 different fabrics [second base material] two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics were measured.)
the second evaluation is regarding a second article in which the second surface- treating agent is fixed onto the second base material (See e.g. [P419:C1], In Table 12, the results of the neural network developed for the prediction of oil repellency are given.)
the first article is obtained by fixing the first surface-treating agent onto the first base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [the first article is obtained] were measured.)
the second article is obtained by fixing the second surface-treating agent onto the second base material. (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the second surface-treating agent] were applied to 21 different fabrics [the second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [the second article is obtained] were measured.)
Namligoz does not teach A device comprising: a memory configured to store a
learned model; and, a processor configured to, and a second evaluation as teacher data
TSUNO teaches A device comprising: a memory configured to store a learned model; and (See e.g. [0098], The CPU 91 is a unit configured to execute various programs of the reference data generation part 61, the identification model generation part 81, the teacher data generation part 82, and the selection part 83 that are stored in, for example, the external memory device 95.)
a processor configured to (See e.g. [0096], a CPU (Central Processing Unit) 91 described below is configured to read out the program and execute the program)
and a second evaluation as teacher data (See e.g. [0011], a teacher data generation apparatus configured to generate teacher data used for object detection for detecting a specific identifying target)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz and
TSUNO before them, to include TSUNO’s system featuring teacher data in Namligoz’s system
that has a learning model with multiple inputs of different base materials, surface treating agents
and articles. One would have been motivated to make such a combination in order to better
verify the results of the repellency using the identification models generated with teacher data
taught by TSUNO(US 20180342077 A1) (0011)
Namligoz and TSUNO do not teach at least one of antifouling property information regarding an antifouling property and processing stability information regarding processing stability
Chen teaches at least one of antifouling property information regarding an antifouling property (See e.g. [P5497:S3.3:C2], To test antifouling properties, the SiO2−SH@fabric and SiO2−S-FMA@fabric were separately dipped into rapeseed oil which has low surface tension and high viscosity for a moment and then taken out (Figure 10e,f and Videos S5 and S6). [information regarding an antifouling property])
processing stability information regarding processing stability (See e.g. [P5495:S3.2:C1], The sand impact test and tape-peeling test were used to assess the mechanical stability of the fabric. After the fabric suffered from 160g sand impact, the WCAs, HCAs, WSAs, and HSAs of the fabric changed from 159° to 153°, 152° to 146°, 2.0° to 6.6°, and 8.3° to 13.5°, respectively (Figure 9a). [processing stability information])
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz, TSUNO and Chen before them, to include Chen’s system antifouling and stability properties in Namligoz and TSUNO’s system that has a learning model with multiple inputs of different base materials, surface treating agents and articles. One would have been motivated to make such a combination in order to increase the durability of the fabric taught by Chen (NPL: Two-Step Approach for Fabrication of Durable Superamphiphobic Fabrics for Self-Cleaning, Antifouling, and On-Demand Oil/Water Separation) (P5492:S3.1:C2)
Regarding claim 12, Namligoz teaches, [a processor configured to], using the learned model, perform calculation based on a weighting coefficient of a neural network with respect to first base material information regarding a first base material and information regarding a first evaluation of a first article being input to an input layer of the neural network, output first treatment agent information regarding a first surface-treating agent to be fixed onto the first base material from an output layer of the neural network, (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms [using the learned model, perform calculation based on a weighting coefficient of a neural network] was used to predict the water, oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process of the fabrics. In the estimation of these properties, fabric [first base material] type (Co/Co, PES/Co etc.) [first base material information], method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables.) (See e.g. [P418:C1], The results of the best neural network developed [learning model] to predict the water repellency [first evaluation] property of the fabrics are presented in Table 9.) (See e.g. [P416:C2], Based on 336 experimental results totally, the best neural network for the prediction of water repellency is multi linear perceptron [a first evaluation of a first article being input to an input layer of the neural network] having 4 inputs and one output (Figure 1). [output first treatment agent information regarding a first surface-treating agent to be fixed onto the first base material from an output layer of the neural network])
select the first surface-treating agent to be used to manufacture the first article based on the first treatment agent information (See e.g. [P418:C1], The results of the best neural network developed to predict the water repellency property [a first evaluation] of the fabrics [a first article] are presented in Table 9. For the prediction of this property, 252 data in training stage, 84 data in testing stage are used. Table 9 shows how many cases are correctly classified, incorrectly classified, or unclassified.) (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation and conjugate gradient descent algorithms was used to predict the water [a first evaluation], oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process [to be used to manufacture] of the fabrics [a first base material]. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.), method (padding or transfer), chemicals (classic/nano), and chemical concentration parameters are used as variables.) (See e.g. [P415:C2], In these treatments, the various water-oil repellents [a first surface-treating agent] and cross-linking agents were applied to all fabrics [a first base material] at the same bath with two different application methods.)
wherein: the weighting coefficient is obtained through learning of the learned model using at least second base material information, second treatment agent information, and a second evaluation [as teacher data] (See e.g. [P416:C1], In this study, a multilayer feed-forward network (MLP) with one hidden layer trained in several phases of back propagation [the weighting coefficient is obtained through learning of the learned model] and conjugate gradient descent algorithms was used to predict the water, oil repellencies and wrinkle recovery angle properties of the treated fabrics. The aim of this study is to estimate these aforementioned properties of the fabrics before manufacturing process of the fabrics. In the estimation of these properties, fabric type (Co/Co, PES/Co etc.) [at least second base material information], method (padding or transfer), chemicals (classic/nano), and chemical concentration [second treatment agent information] parameters are used as variables.) (See e.g. [P419:C1], In Table 12, the results of the neural network developed for the prediction of oil repellency are given. [second evaluation])
the second base material information is information regarding a second base material; (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations were applied to 21 different fabrics [regarding a second base material] with two application methods.)
the second treatment agent information is information regarding a second surface-treating agent to be fixed onto the second base material; (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the second treatment agent information] were applied to 21 different fabrics with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics were measured.)
the second evaluation is regarding a second article in which the second surface-treating agent is fixed onto the second base material (See e.g. [P419:C1], In Table 12, the results of the neural network developed for the prediction of oil repellency are given.)
the first article is obtained by fixing the first surface-treating agent onto the first base material (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the first surface-treating agent] were applied to 21 different fabrics [the first base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [the first article] were measured.)
the second article is obtained by fixing the second surface-treating agent onto the second base material. (See e.g. [P414:C2], In this study, the conventional and nano crosslinking agents and water-oil repellents and wrinkle resistant agents with different chemical concentrations [the second surface-treating agent] were applied to 21 different fabrics [the second base material] with two application methods. After the finishing processes were completed, water, oil repellency and wrinkle angle recovery properties of the fabrics [the second article] were measured.)
Namligoz does not teach A device comprising: a memory configured to store a learned model; and, A processor configured to, teacher data
TSUNO teaches A device comprising: a memory configured to store a learned model; and (See e.g. [0098], The CPU 91 is a unit configured to execute various programs of the reference data generation part 61, the identification model generation part 81, the teacher data
generation part 82, and the selection part 83 that are stored in, for example, the external memory
device 95.)
a processor configured to… (See e.g. [0096], a CPU (Central Processing Unit) 91 described below is configured to read out the program and execute the program)
… teacher data (See e.g. [0011], a teacher data generation apparatus configured to generate teacher data used for object detection for detecting a specific identifying target)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz and
TSUNO before them, to include TSUNO’s system featuring teacher data in Namligoz’s system
that has a learning model with multiple inputs of different base materials, surface treating agents
and articles. One would have been motivated to make such a combination in order to better
verify the results of the repellency using the identification models generated with teacher data
taught by TSUNO(US 20180342077 A1) (0011)
Namligoz and TSUNO do not teach at least one of antifouling property information regarding an antifouling property and processing stability information regarding processing stability
Chen teaches at least one of antifouling property information regarding an antifouling property (See e.g. [P5497:S3.3:C2], To test antifouling properties, the SiO2−SH@fabric and SiO2−S-FMA@fabric were separately dipped into rapeseed oil which has low surface tension and high viscosity for a moment and then taken out (Figure 10e,f and Videos S5 and S6). [information regarding an antifouling property])
processing stability information regarding processing stability (See e.g. [P5495:S3.2:C1], The sand impact test and tape-peeling test were used to assess the mechanical stability of the fabric. After the fabric suffered from 160g sand impact, the WCAs, HCAs, WSAs, and HSAs of the fabric changed from 159° to 153°, 152° to 146°, 2.0° to 6.6°, and 8.3° to 13.5°, respectively (Figure 9a). [processing stability information])
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz, TSUNO and Chen before them, to include Chen’s system antifouling and stability properties in Namligoz and TSUNO’s system that has a learning model with multiple inputs of different base materials, surface treating agents and articles. One would have been motivated to make such a combination in order to increase the durability of the fabric taught by Chen (NPL: Two-Step Approach for Fabrication of Durable Superamphiphobic Fabrics for Self-Cleaning, Antifouling, and On-Demand Oil/Water Separation) (P5492:S3.1:C2)
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Namligoz (NPL DOI 10.1007 /s 12221-011-0414-8) in view of TSUNO (US 20180342077 A1) further in view of Chen (NPL: Two-Step Approach for Fabrication of Durable Superamphiphobic Fabrics for Self-Cleaning, Antifouling, and On-Demand Oil/Water Separation) further in view of Malik (NPL Indian Journal of Fibre & Textile Research Vol. 41 March 2016)
Regarding claim 8, Namligoz, TSUNO and Chen teach the device of claim 7. Namligoz further teaches, wherein: the first base material information comprises information regarding at least a type of the textile product [and a type of a dye]; (P414:C1): In a similar way, anti bacterial finish, flame retardant, water repellent, water proof, antistatic finish, peach finish are some of the important finishes applied to textile fabrics. [at least a type of the textile product]
the first treatment agent information comprises information regarding at least a type of a monomer constituting a repellent polymer contained in the first surface-treating agent, a content of the monomer in the repellent polymer, a content of the repellent polymer in the first surface- treating agent, [a type of a solvent and a content of the solvent in the first surface-treating agent], and a type of a surfactant and a content of the surfactant in the first surface-treating agent.
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Table 2 being a makeup of the used chemicals in the study provides a water and oil repellent
which is a fluorocarbon compound. Tetrafluoroethylene is a fluorocarbon compound which is
also, a monomer (https://pubchem.ncbi.nlm.nih.gov/compound/Tetrafluoroethylene) [least a type of a monomer constituting a repellent polymer contained in the first surface-treating agent] Polytetrafluoroethylene is a fluorocarbon which is a polymer of Tetrafluoroethylene
(https://pubchem.ncbi.nlm.nih.gov/compound/Polytetrafluoroethylene) [a content of the
repellent polymer in the surface-treating agent] Table 2 to also provides Softeners which are a
sub-category of surfactants [and a type of a surfactant and a content of the surfactant in the
first surface-treating agent.]
Namligoz, Chen and TSUNO do not teach a type of dye and a type of a solvent and a content of the solvent in the first surface-treating agent
Malik teaches a type of dye (See e.g. [Abstract], Empirical and statistical models have been proposed to predict dyed cotton fabric hydrophobicity.)
a type of a solvent and a content of the solvent in the first surface-treating agent (See e.g. [P68:C1], The nanosol solution was prepared from silica nanoparticals (Aerosil® 200), silane hydrophobe (n-octyltriethoxysilane) with tetraethoxysilane (TEOS) and/or tetramethoxysilane (TMOS) as cross-linker, while ethanol (96%) and 0.01 N hydrochloric acid were used as solvent [a type of a solvent and a content of the solvent] and pH controller respectively.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz, Chen, TSUNO and Malik before them, to include Malik’s features a type of solvent and a dye in Namligoz, Chen and TSUNO’s system that provides base materials including monomers, polymers and surfactants. One would have been motivated to make such a combination in order to provide optimization, and fabric quality characteristics prediction and computer match prediction of fluorescent dyes. It is important to judge the efficiency of ANN models, and for this purpose several researchers13-15 have compared ANN with MLR technique… hydrochloric acid were used as solvent and pH controller respectively taught by Malik (NPL Indian Journal of Fibre & Textile Research Vol. 41 March 2016) (P68:C1)
Regarding claim 9, Namligoz, TSUNO, Chen and Malik teach the method of claim 8. Namligoz further teaches, wherein: [the teacher data] further comprises environment information regarding an environment during processing of the second base material; (P415:C2): All the measurements were managed after conditioning of
the fabrics for 24 h under the standard atmosphere conditions (20 °C±2 temperature, 65±4 % relative humidity).
the environment information comprises information regarding at least one of a concentration of the second surface-treating agent in a treatment tank, a temperature of the environment, a humidity of the environment, a curing temperature, or a processing speed during the processing of the second base material
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Tables 3 and 4 both cover curing temperatures of the surface-treating agents application methods and conditions [at least one of a concentration of the second surface-treating agent, a curing temperature]
the second base material information further comprises information regarding at least one of a color, a weave, a basis weight, a yarn thickness, or a zeta potential of a second textile product; (See e.g. [P414:C1], Fabric [the second base] sett was kept constant which was 48 ends/cm for warp yarns, 31 picks/cm for weft yarns. [a weave])
the second treatment agent information further comprises information regarding at least one of a type and a content of an additive to be added to the second surface-treating agent, a pH of the second surface-treating agent, or a zeta potential of the surface-treating agent. (See e.g. [See tables 3 and 4], above for [a pH of the second surface-treating agent])
Namligoz, Chen and Malik does not teach teacher data
TSUNO teaches … teacher data (0011): a teacher data generation apparatus configured to generate teacher data used for object detection for detecting a specific identifying target
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teachings of Namligoz, Chen and Malik and TSUNO before them, to include TSUNO’s system featuring teacher data in Namligoz, Chen and Malik’s system that has a learning model with multiple inputs of different base materials, surface treating agents and articles. One would have been motivated to make such a combination in order to better verify the results of the repellency using the identification models generated with teacher data taught by TSUNO(US 20180342077 A1) (0011)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ALLMAN THOMPSON whose telephone number is (571)272-3671. The examiner can normally be reached Monday - Thursday, 6 a.m. - 3 p.m. ET..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.A.T./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125