CTNF 18/762,464 CTNF 99705 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file. Information Disclosure Statement The information disclosure statement(s) (IDS) submitted on 07/02/2024 and 01/27/2026 have been considered by the Examiner. Examiners Remark(s) The Office did not issue a 35 U.S.C. 101 rejection for claim 10 on the basis of non-statutory transitory forms of signal transmission. Claim 10 states “A storage medium configured to store a program, the program, when executed by a computer, causing the computer to perform the determination method according to claim 9.” The Office understands the “storage medium” as only being directed to a non-transitory storage medium as stated in the specification at Page [24], Line(s) [24-29]: “The processing of the various data described above may be recorded, as a program that can be executed by a computer, in a magnetic disk (a flexible disk, a hard disk, etc.), an optical disk (CD-ROM, CD-R, CD-RW, DVD-ROM, DVD±R, DVD±RW, etc.), semiconductor memory, or another non-transitory computer-readable storage medium.” 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Claims 1, 4 and 8-9 recite limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f): Claim 1 recites the limitation, “the determination device being configured to…” [Line 2]. Claim 1 and 9 recites the limitation, “the classification model being configured to…” [Line 5 and Line 3-4]. Claim 4 recites the limitation, “the determination device calculates …” [Line 2]. Claim 8 recites the limitation, “an inspection device configured to…” [Line 5]. Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1, 4 and 8-9 : (i) “a determination device” (Fig. 1, and 14, #40, Page 4, 15 and 24, Line 9-15, 11-20 and 14-24-a determination device is described as being configured to acquire intermediate data, acquire multiple sets of reference data and determine the suitability of a classification model #100 using the data and output a determination result. Fig. 1 shows an inspection system 10 with multiple connected black boxes for an inspection device #30, a determination device #40, a storage device #20 and a classification model #100, which is depicted as being within the storage device #20. Fig. 12 shows an inspection system 2 and multiple connected black boxes, including an inspection device #30, a determination device #40, a training device #50 and a classification model #100 within a storage device #20. The inspection device #30, determination device #40 and training device #50 are further described as being implemented using one or more computers with a CPU (wherein the determination device #40 has sufficient structure associated with it, and it is understood as a computer linked to algorithms for performing a determination function). (ii) “a classification model” (Fig. 1 and 14, #100. Page 4, 15 and 24, Line 15-17, 11-20 and Line 14-24-a classification model #100 is described as a convolutional neural network for classifying articles in an image. The classification model is further described as being used by the inspection device #30 to inspect articles within an image. Fig. 1 shows an inspection system 10 with multiple connected black boxes for an inspection device #30, a determination device #40, a storage device #20 and a classification model #100, which is depicted as being within the storage device #20. Fig. 12 shows an inspection system 2 and multiple connected black boxes, including an inspection device #30, a determination device #40, a training device #50 and a classification model #100 within a storage device #20. The classification model is also described as being trained by a training device #50 and stored on a storage device #20. The inspection device #30, determination device #40 and training device #50 are further described as being implemented using one or more computers with a CPU (wherein the classification model #100 has sufficient structure associated with it, and it is understood as a computer linked to algorithms for performing a classification function). (iii) “an inspection device” (Fig. 1 and 14, #30. Page 3 and 24, Line 24-30 and Line 14-24-a inspection device #30 is described as a device configured to inspect an article using a classification model #100. The inspection model is further described as performing the function of inputting an image into the classification model #100, acquiring a classification result of an article visible within the input image and outputting the classification result. Fig. 1 shows an inspection system 10 with multiple connected black boxes for an inspection device #30, a determination device #40, a storage device #20 and a classification model #100, which is depicted as being within the storage device #20. Fig. 12 shows an inspection system 2 and multiple connected black boxes, including an inspection device #30, a determination device #40, a training device #50 and a classification model #100 within a storage device #20. The inspection device #30, determination device #40 and training device #50 are further described as being implemented using one or more computers with a CPU (wherein the inspection device #30 has sufficient structure associated with it, and it is understood as a computer linked to algorithms for performing an inspection function). If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-06 AIA 15-10-15 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 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. 07-15 AIA Claim s 1 and 3-11 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by KWON et al. (US 20190370661 A1), hereinafter referenced as KWON . Regarding claim 1, KWON explicitly teaches a determination device (Fig. 1, #100 called a machine learning device. Paragraph [0028] . Further in paragraph [0028]-KWON discloses FIG. 1 is a block diagram illustrating a machine learning device 100 (wherein machine learning device 100 includes a bus 110, a processor 120, a neuromorphic processor 130, a random access memory 140, a modem 150, an image database 160, storage 170, and a user interface 180. In paragraph [0029]-KWON discloses the processor 120 may control the machine learning device 100. In paragraph [0031]-KWON discloses the neuromorphic processor 130 may perform machine learning under control of the processor 120. Please also see Fig. 15 and read paragraph [0128]) , the determination device being configured to determine a suitability of a classification model (Fig. 1, #130 and #131/171 called a neuromorphic processor and a machine learning classifier, respectively. Paragraph [0030-0033 and 0047-0048]) , the classification model including a neural network (Fig. 1. Paragraph [0031]-KWON discloses the neuromorphic processor 130 may include a machine learning classifier 131. In paragraph [0044]-KWON discloses the machine learning classifier 131 which is generated by the neuromorphic processor 130 may be stored to the storage 170 as the machine learning classifier 171. The machine learning classifier 171 may be transmitted to a test device for testing a defect of a semiconductor device. In paragraph [0074]-KWON discloses the machine learning classifier 131 is a convolutional neural network (CNN). The inventive concept may be applied to various other neural networks or machine learning systems) , the classification model being configured to output a classification result according to an input of an image (Fig. 1, #IMG called an image. Paragraph [0049]. In paragraph [0032]-KWON discloses the neuromorphic processor 130 may receive images and pieces of class information (e.g., pieces of first class information) of the images from the image database 160. The neuromorphic processor 130 may perform machine learning by using the images. In paragraph [0033]-KWON discloses the neuromorphic processor 130 may generate the pieces of class information of the images by using the machine learning classifier 131. The neuromorphic processor 130 may compare the pieces of class information (e.g., pieces of second class information) generated from the images with the pieces of first class information. Depending on a result of the comparison, the neuromorphic processor 130 may update the machine learning classifier 131. Please also see Fig. 15 and read paragraph [0053, 0064-0070 and 0111-0114]) , the determination device being configured to: acquire intermediate data (Fig. 1, #DINT1, DINT2, and #DINT3, called intermediate pixel data or first intermediate pixel data, second intermediate pixel data and third intermediate pixel data, respectively. Paragraph [0047-0051]) of an intermediate layer (Fig. 1, #132_1, #132_2, and #132_3, called a first convolutional layer, a second convolutional layer and a third convolutional layer, respectively. Paragraph [0047-0051]) of the neural network (Fig. 1. Paragraph [0047]-KWON discloses the neuromorphic processor 130 includes first to third convolution layers 132_1 to 132_3, a classify layer 133, a loss layer 134, a resize block 135, first to third activation map generation blocks 136_1 to 136_3, and a class activation map generation block 137. In paragraph [0048]-KWON discloses the first to third convolution layers 132_1 to 132_3 and the classify layer 133 may constitute the machine learning classifier 131. In paragraph [0074]-KWON discloses the machine learning classifier 131 is a convolutional neural network (CNN). However, the inventive concept is not limited to CNNs, and may be applied to various other neural networks or machine learning systems) when an input image (Fig. 1, #IMG called an image. Paragraph [0049]. In paragraph [0049]-KWON discloses the image IMG may include pixel data including pixel values respectively corresponding to pixels of a camera. Further in paragraph [0043]-KWON discloses the neuromorphic processor 130 may perform machine learning by using the guide maps, thus preventing machine learning from being hindered and supporting learning. That is, the reliability of the machine learning classifier 131 is improved. Please also read paragraph [0053 and 0111-0114]) is input to the classification model (Fig. 1. Paragraph [0041]-KWON discloses the image database 160 may store images of semiconductor devices. In paragraph [0049]-KWON discloses the first convolution layer 132_1 may receive an image IMG from the image database 160. The first convolution layer 132_1 may generate first intermediate pixel data DINT1 by convolving its own kernels with the pixel data of the image IMG. In paragraph [0050]-KWON disclose the second convolution layer 132_2 may generate second intermediate pixel data DINT2 by convolving its own kernels with the first intermediate pixel data DINT1. The third convolution layer 132_3 may generate third intermediate pixel data DINT3 by convolving its own kernels with the second intermediate pixel data DINT2. In paragraph [0051]-KWON discloses data which the first to third convolution layers 132_1 to 132_3 output are referenced as intermediate pixel data. In paragraph [0052]-KWON discloses the classify layer 133 may output second class information ICLS2 by performing an operation including the third intermediate pixel data DINT3 and class parameters. Please also read paragraph [0053 and 0058-0061]) ; and determine the suitability (Fig. 1. Paragraph [0069]-KWON discloses the neuromorphic processor 130 may perform back propagation by updating weights of the machine learning classifier 131 based on a result of comparing the first class information ICLS1 and the second class information ICLS2. The neuromorphic processor 130 may also perform back propagation by individually updating weights of each layer of the machine learning classifier 131 by using the guide map GDM. In paragraph [0070]-KWON discloses additional information for machine learning may be obtained from the first to third intermediate pixel data DINT1 to DINT3 generated in the process in which the machine learning classifier 131 generates the second class information ICLS2. In paragraph [0071]-KWON discloses an update direction may be intended by the guide map GDM upon updating weights of each layer of the machine learning classifier 131. Depending on the guide map GDM, the machine learning classifier 131 may learn that the pixel data from which the pattern is excluded is preferential or dominant. Accordingly, since the machine learning is prevented from being hindered and the learning is supported, the reliability of the machine learning classifier 131 is improved. Please also read paragraph [0041, 0043, 0063-0068 and 0111-0114]) by using a plurality of sets of the intermediate data and a plurality of sets of reference data (Fig. 1. Paragraph [0063]-KWON discloses the loss layer 134 may receive the second class information ICLS2 from the classify layer 133 and may receive first class information ICLS1 from the image database 160. In the case where the guide map GDM is not provided from the image database 160, the loss layer 134 may compare the second class information ICLS2 and the first class information ICLS1 to perform machine learning. In paragraph [0064]-KWON discloses when the guide map GDM is provided from the image database 160, the loss layer 134 may perform machine learning by comparing the second class information ICLS2, the first class information ICLS1, and the differences D1 to D4 transferred from the first to third activation map generation blocks 136_1 to 136_3 and the class activation map generation block 137 (wherein the first to third activation map generation block 136_1-3 may generate a first to third activation map from the first to third intermediate pixel data DINT1-3 and generate difference D1-D3 values between intermediate pixel data and guide maps GDM1-3, and a difference value D4 may be generated from a difference between a fourth guide map GDM4 and the class activation map)) , the plurality of sets of reference data being prepared beforehand (Fig. 1. Paragraph [0041]-KWON discloses the image database 160 may store images of semiconductor devices. Pieces of first class information of the images may include information indicating fault types or normality (i.e., an indication of the absence of faults) of the semiconductor devices. In paragraph [0043]-KWON discloses images stored in the image database 160 may include patterns hindering machine learning. The images which have patterns hindering machine learning or making it difficult to perform learning may be transferred to the neuromorphic processor 130 together with guide maps. The neuromorphic processor 130 may perform machine learning by using the guide maps, thus preventing machine learning from being hindered and supporting learning. That is, the reliability of the machine learning classifier 131 is improved. Please also read paragraph [0049-0052 and 0058-0061]) . Regarding claim 3, KWON explicitly teaches the determination device according to claim 1, KWON further teaches wherein the determination of the suitability includes: calculating an evaluation value (Fig. 1. Paragraph [0052]-KWON discloses the classify layer 133 may output second class information ICLS2 by performing an operation including the third intermediate pixel data DINT3 and class parameters. In paragraph [0063]-KWON discloses the loss layer 134 may receive the second class information ICLS2 from the classify layer 133 and may receive first class information ICLS1 from the image database 160. In the case where the guide map GDM is not provided from the image database 160, the loss layer 134 may compare the second class information ICLS2 and the first class information ICLS1 to perform machine learning. In paragraph [0064]-KWON discloses when the guide map GDM is provided from the image database 160, the loss layer 134 may perform machine learning by comparing the second class information ICLS2, the first class information ICLS1, and the differences D1 to D4 transferred from the first to third activation map generation blocks 136_1 to 136_3 and the class activation map generation block 137) by using the plurality of sets of intermediate data (Fig. 1. Paragraph [0049]-KWON discloses the first convolution layer 132_1 may receive an image IMG from the image database 160. The first convolution layer 132_1 may generate first intermediate pixel data DINT1 by convolving its own kernels with the pixel data of the image IMG. In paragraph [0050]-KWON discloses the second convolution layer 132_2 may generate second intermediate pixel data DINT2 by convolving its own kernels with the first intermediate pixel data DINT1. The third convolution layer 132_3 may generate third intermediate pixel data DINT3 by convolving its own kernels with the second intermediate pixel data DINT2. In paragraph [0052]-KWON discloses the classify layer 133 may output second class information ICLS2 by performing an operation including the third intermediate pixel data DINT3 and class parameters (wherein the first to third activation map generation block 136_1-3 may generate a first to third activation map from the first to third intermediate pixel data DINT1-3 and generate difference D1-D3 values between intermediate pixel data and guide maps GDM1-3, and a difference value D4 may be generated from a difference between a fourth guide map GDM4 and the class activation map). Please also read paragraph [0058-0063 and 0111-0114]) ; and determining the suitability by comparing the evaluation value and a comparison value, and the comparison value is set based on the plurality of sets of reference data (Fig. 1. Paragraph [0069]-KWON discloses the neuromorphic processor 130 may perform back propagation by updating weights of the machine learning classifier 131 based on a result of comparing the first class information ICLS1 and the second class information ICLS2. The neuromorphic processor 130 may also perform back propagation by individually updating weights of each layer of the machine learning classifier 131 by using the guide map GDM. In paragraph [0070]-KWON discloses additional information for machine learning may be obtained from the first to third intermediate pixel data DINT1 to DINT3 generated in the process in which the machine learning classifier 131 generates the second class information ICLS2. In paragraph [0071]-KWON discloses an update direction may be intended by the guide map GDM upon updating weights of each layer of the machine learning classifier 131. The machine learning classifier 131 may learn that the pixel data from which the pattern is excluded is preferential or dominant. Accordingly, since the machine learning is prevented from being hindered and the learning is supported, the reliability of the machine learning classifier 131 is improved. Please also read paragraph [0063-0068 and 0111-0114]). Regarding claim 4, KWON explicitly teaches the determination device according to claim 3, KWON further teaches wherein the determination device (Fig. 1, #100 called a machine learning device. Paragraph [0028]. Please also see Fig. 15 and read paragraph [0031-0032]) calculates the evaluation value to represent a similarity between the plurality of sets of intermediate data and the plurality of sets of reference data by using: a typical value calculated using the plurality of sets of intermediate data (Fig. 5. Paragraph [0082]-KWON discloses FIG. 4 is a diagram of an example in which at least one of the convolution layers 132_1 to 132_3 applies input pixel data DIN to kernels K1 to K8 to generate output pixel data DOUT (wherein the output pixel data DOUT may be first through third intermediate pixel data DINT1-3). In paragraph [0090]-KWON discloses the classify layer 133 may calculate an average of pixel values of each channel of the third intermediate pixel data DINT3. In paragraph [0091]-KWON discloses an average value of the fourth pixel data Ps4 of the third intermediate pixel data DINT3 may form a fourth pixel value P4 of the pooled data PD. An average value of a fifth pixel data Ps5 of the third intermediate pixel data DINT3 may form a fourth pixel value P5 of the pooled data PD. In paragraph [0097]-KWON discloses an activation map generation block may generate the activation map ACTM from the output pixel data DOUT. In paragraph [0098]-KWON discloses the activation map generation block may generate the activation map ACTM by performing a pixel wise operation (e.g., a sum or an average) on the output pixel data DOUT. Please also read paragraph [0064-0070, 0100, and 0111-0114]) ; and a reference value calculated using the plurality of sets of reference data (Fig. 1. Paragraph [0063]-KWON discloses the loss layer 134 may receive the second class information ICLS2 from the classify layer 133 and may receive first class information ICLS1 from the image database 160. In the case where the guide map GDM is not provided from the image database 160, the loss layer 134 may compare the second class information ICLS2 and the first class information ICLS1 to perform machine learning. In paragraph [0064]-KWON discloses when the guide map GDM is provided from the image database 160, the loss layer 134 may perform machine learning by comparing the second class information ICLS2, the first class information ICLS1, and the differences D1 to D4 transferred from the first to third activation map generation blocks 136_1 to 136_3 and the class activation map generation block 137 (wherein the first to third activation map generation block 136_1-3 may generate a first to third activation map from the first to third intermediate pixel data DINT1-3 and generate difference values D1-D3 between intermediate pixel data and guide maps GDM1-3, and a difference value D4 may be generated from a difference between a fourth guide map GDM4 and the class activation map). Please also read paragraph [0054-0061 and 0111-0114]). Regarding claim 5, KWON explicitly teaches the determination device according to claim 3, KWON further teaches wherein a plurality of the input images (Fig. 1, #IMG called an image. Paragraph [0049]. Please also read paragraph [0053 and 0111-0114]) includes a plurality of first input images classified into a first class by the classification model (Fig. 1, #130 and #131/171 called a neuromorphic processor and a machine learning classifier, respectively. Paragraph [0030-0033 and 0047-0048]. In paragraph [0032]-KWON discloses the neuromorphic processor 130 may receive images and pieces of class information (e.g., pieces of first class information) of the images from the image database 160. The neuromorphic processor 130 may perform machine learning by using the images. In paragraph [0033]-KWON discloses the neuromorphic processor 130 may generate the pieces of class information of the images by using the machine learning classifier 131. The neuromorphic processor 130 may compare the pieces of class information (e.g., pieces of second class information) generated from the images with the pieces of first class information. Depending on a result of the comparison, the neuromorphic processor 130 may update the machine learning classifier 131). Please also read paragraph [0049]) , the plurality of sets of intermediate data includes a plurality of sets of first intermediate data (Fig. 1, #DINT1, DINT2, and #DINT3, called intermediate pixel data or first intermediate pixel data, second intermediate pixel data and third intermediate pixel data, respectively. Paragraph [0047-0051 and 0058-0061]) respectively based on inputs of the plurality of first input images to the classification model (Fig. 1. Paragraph [0049]-KWON discloses the first convolution layer 132_1 may receive an image IMG from the image database 160. The image IMG may include pixel data including pixel values respectively corresponding to pixels of a camera. The first convolution layer 132_1 may generate first intermediate pixel data DINT1 by convolving its own kernels with the pixel data of the image IMG. In paragraph [0050]-KWON disclose the second convolution layer 132_2 may generate second intermediate pixel data DINT2 by convolving its own kernels with the first intermediate pixel data DINT1. The third convolution layer 132_3 may generate third intermediate pixel data DINT3 by convolving its own kernels with the second intermediate pixel data DINT2. In paragraph [0051]-KWON discloses data which the first to third convolution layers 132_1 to 132_3 output are referenced as intermediate pixel data. In paragraph [0052]-KWON discloses the classify layer 133 may output second class information ICLS2 by performing an operation including the third intermediate pixel data DINT3 and class parameters. The classify layer 133 may calculate values respectively corresponding to classes which the classify layer 133 generates. The values of the classes may indicate the probability that the image IMG belongs to each class. The classify layer 133 may output information of a class having the highest probability as the second class information ICLS2. Please also read paragraph [0058-0061and 0111-0114]) , and the determination of the suitability includes calculating a first evaluation value as the evaluation value by using the plurality of sets of first intermediate data (Fig. 1. Paragraph [0033]-KWON discloses the neuromorphic processor 130 may compare the pieces of class information (e.g., pieces of second class information) generated from the images with the pieces of first class information. Depending on a result of the comparison, the neuromorphic processor 130 may update the machine learning classifier 131). In paragraph [0064]-KWON discloses the loss layer 134 may perform machine learning by comparing the second class information ICLS2, the first class information ICLS1, and the differences D1 to D4 transferred from the first to third activation map generation blocks 136_1 to 136_3 and the class activation map generation block 137. In paragraph [0065]-KWON discloses the loss layer 134 may calculate a difference between the first class information ICLS1 and the second class information ICLS2. Depending on the calculated difference, the loss layer 134 may update weights (e.g., the values of synapses in a neural network) of the first to third convolution layers 132_1 to 132_3 and the classify layer 133. The loss layer 134 may update weights such that the second class information ICLS2 becomes closer to the first class information ICLS1). Regarding claim 6, KWON explicitly teaches the determination device according to claim 5, KWON further teaches wherein the plurality of sets of reference data is acquired from the intermediate layer respectively when a plurality of reference images (Fig. 1, #IMG called an image. Paragraph [0049]. Please also read paragraph [0035 and 0111-0114]) is input to the classification model (Fig. 1, #130 and #131/171 called a neuromorphic processor and a machine learning classifier, respectively. Paragraph [0030-0033 and 0047-0048]. In paragraph [0047]-KWON discloses the neuromorphic processor 130 includes first to third convolution layers 132_1 to 132_3, a classify layer 133, a loss layer 134, a resize block 135, first to third activation map generation blocks 136_1 to 136_3, and a class activation map generation block 137 (wherein the neuromorphic processor includes a machine learning classifier #131 and the first to third convolution layers 132_1 to 132_3 and the classify layer 133 may constitute the machine learning classifier 131). In paragraph [0049]-KWON disclose the first convolution layer 132_1 may receive an image IMG from the image database 160. Please also read paragraph [0053]) , the plurality of reference images includes a plurality of first reference images classified into the first class by the classification model (Fig. 1. Paragraph [0032]-KWON discloses the neuromorphic processor 130 may receive images and pieces of class information (e.g., pieces of first class information) of the images from the image database 160. The neuromorphic processor 130 may perform machine learning by using the images. In paragraph [0033]-KWON discloses the neuromorphic processor 130 may generate the pieces of class information of the images by using the machine learning classifier 131. The neuromorphic processor 130 may compare the pieces of class information (e.g., pieces of second class information) generated from the images with the pieces of first class information. Depending on a result of the comparison, the neuromorphic processor 130 may update the machine learning classifier 131). Please also read paragraph [0075-0076 and 0111-0114]]) , the plurality of sets of reference data includes a plurality of sets of first reference data respectively based on inputs of the plurality of first reference images to the classification model (Fig. 1. Paragraph [0049]-KWON discloses the first convolution layer 132_1 may generate first intermediate pixel data DINT1 by convolving its own kernels with the pixel data of the image IMG. In paragraph [0050]-KWON discloses the second convolution layer 132_2 may generate second intermediate pixel data DINT2 by convolving its own kernels with the first intermediate pixel data DINT1. The third convolution layer 132_3 may generate third intermediate pixel data DINT3 by convolving its own kernels with the second intermediate pixel data DINT2. In paragraph [0052]-KWON discloses the classify layer 133 may output second class information ICLS2 by performing an operation including the third intermediate pixel data DINT3 and class parameters (wherein a first to third activation map generation block 136_1-3 may generate a first to third activation map from the first to third intermediate pixel data DINT1-3 and generate difference D1-D3 values between intermediate pixel data and guide maps GDM1-3, and a difference value D4 may be generated from a difference between a fourth guide map GDM4 and the class activation map). Please also read paragraph [0058-0063]) , and a first comparison value calculated using the plurality of sets of first reference data is set as the comparison value (Fig. 1. Paragraph [0063]-KWON discloses the loss layer 134 may receive the second class information ICLS2 from the classify layer 133 and may receive first class information ICLS1 from the image database 160. In the case where the guide map GDM is not provided from the image database 160, the loss layer 134 may compare the second class information ICLS2 and the first class information ICLS1 to perform machine learning. In paragraph [0064]-KWON discloses when the guide map GDM is provided from the image database 160, the loss layer 134 may perform machine learning by comparing the second class information ICLS2, the first class information ICLS1, and the differences D1 to D4 transferred from the first to third activation map generation blocks 136_1 to 136_3 and the class activation map generation block 137. Please also read paragraph [0041, 0043, 0054-0063, 0111-0114]) . Regarding claim 7, KWON explicitly teaches the determination device according to claim 6, KWON further teaches wherein the plurality of input images (Fig. 1. Paragraph [0049]-KWON discloses the first convolution layer 132_1 may receive an image IMG from the image database 160 (wherein the images may be received with Guide maps GDM, which may include information about whether to perform machine learning more preferentially (or dominantly) based on any portion of an image). In paragraph [0053]-KWON discloses the resize block 135 may receive a guide map GDM from the image database 160. The guide map GDM may have the same size as the image IMG. The guide map GDM may include pixel data including weights respectively corresponding to the pixels of the image IMG. Please also read paragraph [0035, 0114, 0122]) further includes a plurality of second input images classified into a second class by the classification model (Fig. 1, #130 and #131/171 called a neuromorphic processor and a machine learning classifier, respectively. Paragraph [0030-0031 and 0047-0048]) , the plurality of sets of intermediate data further includes a plurality of sets of second intermediate data respectively based on inputs of the plurality of second input images to the classification model (Fig. 1. Paragraph [0049]-KWON discloses the first convolution layer 132_1 may generate first intermediate pixel data DINT1 by convolving its own kernels with the pixel data of the image IMG. In paragraph [0050]-KWON discloses the second convolution layer 132_2 may generate second intermediate pixel data DINT2 by convolving its own kernels with the first intermediate pixel data DINT1. The third convolution layer 132_3 may generate third intermediate pixel data DINT3 by convolving its own kernels with the second intermediate pixel data DINT2. In paragraph [0052]-KWON discloses the classify layer 133 may output second class information ICLS2 by performing an operation including the third intermediate pixel data DINT3 and class parameters (wherein a first to third activation map generation block 136_1-3 may generate a first to third activation map from the first to third intermediate pixel data DINT1-3 and generate difference D1-D3 values between intermediate pixel data and guide maps GDM1-3, and a difference value D4 may be generated from a difference between a fourth guide map GDM4 and the class activation map). Please also read paragraph [0053, 0058-0063]) , the plurality of reference images further includes a plurality of second reference images classified into the second class by the classification model (Fig. 1. Paragraph [0114]-KWON discloses FIG. 11 is a diagram illustrating an example of first images IMG1 and second images IMG2 used for machine learning. The first images IMG1 may have the first class information ICLS1 corresponding to a circle (i.e., first class information), and the second images IMG2 may have the first class information ICLS1 corresponding to a square (i.e., third class information). The neuromorphic processor 130 may perform machine learning such that the machine learning classifier 131 classifies the first images IMG1 as a circle-shaped class (i.e., second class information) and classifies the second images IMG2 as a square-shaped class (i.e., fourth class information). Please also read paragraph [0041, 0043, and 0064-0070]) , the plurality of sets of reference data further includes a plurality of sets of second reference data respectively based on inputs of the plurality of second reference images to the classification model (Fig. 1. Paragraph [0111]-KWON discloses the machine learning classifier 131 may be trained such that the image IMG is a class (e.g., an original class) pointed out by the first class information ICLS1. Accordingly, by generating a class activation map corresponding to the first class information ICLS1, whether any portion of the third intermediate pixel data DINT3 is referenced may be determined upon classifying the image IMG as belonging to the original class. In paragraph [0112]-KWON discloses the class activation map generation block 137 may calculate the guide difference D4 between a class activation map of the original class and the fourth guide map GDM4. In paragraph [0113]-KWON discloses the class activation map generation block 137 may calculate 20 class activation maps corresponding to 20 classes. The class activation map generation block 137 may calculate 20 differences D4 by comparing the fourth guide map GDM4 with the 20 class activation maps, respectively. The loss layer 134 may update the class parameters CLSP based on the 20 differences D4. Please also read paragraph [0058-0070]) , the determination of the suitability further includes: calculating a second evaluation value (Fig. 1. Paragraph [0052]-KWON discloses the classify layer 133 may output second class information ICLS2 by performing an operation including the third intermediate pixel data DINT3 and class parameters. The classify layer 133 may calculate values respectively corresponding to classes which the classify layer 133 generates. The values of the classes may indicate the probability that the image IMG belongs to each class. The classify layer 133 may output information of a class having the highest probability as the second class information ICLS2. Please also read paragraph [0058-0065 and 0111-0114]) by using the plurality of sets of second intermediate data (Fig. 1. Paragraph [0049]-KWON discloses the first convolution layer 132_1 may generate first intermediate pixel data DINT1 by convolving its own kernels with the pixel data of the image IMG. In paragraph [0050]-KWON discloses the second convolution layer 132_2 may generate second intermediate pixel data DINT2 by convolving its own kernels with the first intermediate pixel data DINT1. The third convolution layer 132_3 may generate third intermediate pixel data DINT3 by convolving its own kernels with the second intermediate pixel data DINT2. In paragraph [0052]-KWON discloses the classify layer 133 may output second class information ICLS2 by performing an operation including the third intermediate pixel data DINT3 and class parameters (wherein a first to third activation map generation block 136_1-3 may generate a first to third activation map from the first to third intermediate pixel data DINT1-3 and generate difference D1-D3 values between intermediate pixel data and guide maps GDM1-3, and a difference value D4 may be generated from a difference between a fourth guide map GDM4 and the class activation map). Please also read paragraph [0058-0063 and 0111-0114]) ; and determining the suitability by comparing the second evaluation value and a second comparison value, and the second comparison value is calculated using the plurality of sets of second reference data (Fig. 1. Paragraph [0063]-KWON discloses the loss layer 134 may receive the second class information ICLS2 from the classify layer 133 and may receive first class information ICLS1 from the image database 160. In the case where the guide map GDM is not provided from the image database 160, the loss layer 134 may compare the second class information ICLS2 and the first class information ICLS1 to perform machine learning. In paragraph [0064]-KWON discloses when the guide map GDM is provided from the image database 160, the loss layer 134 may perform machine learning by comparing the second class information ICLS2, the first class information ICLS1, and the differences D1 to D4 transferred from the first to third activation map generation blocks 136_1 to 136_3 and the class activation map generation block 137. In paragraph [0065]-KWON discloses the loss layer 134 may calculate a difference between the first class information ICLS1 and the second class information ICLS2. Depending on the calculated difference, the loss layer 134 may update weights (e.g., the values of synapses in a neural network) of the first to third convolution layers 132_1 to 132_3 and the classify layer 133. The loss layer 134 may update weights such that the second class information ICLS2 becomes closer to the first class information ICLS1. Please also read paragraph [0111-0114]) . Regarding claim 8, KWON explicitly teaches an inspection system (Fig. 15, #200 called a semiconductor defect classification system. Paragraph [0128]. (wherein the machine learning device includes a bus 110, a processor 120, a neuromorphic processor 130, a random access memory 140, a modem 150, an image database 160, storage 170, and a user interface 180, and the semiconductor defect classification system 200 includes a wafer 210, a manufacture device 220, an automatic defect review device 230, an imaging device 240, image storage 250, a semiconductor defect classification device 260, and a defect image database 270). Please also see Fig. 1 and read paragraph [0028-0032]) , comprising: the determination device (Fig. 1, #100 called a machine learning device. Paragraph [0028]. Please also read paragraph [0031-0032]) according to claim 1 (Please see the rejection for claim 1 above) ; an imaging device (Fig. 15, #240 called an imaging device. Paragraph [0128]. In paragraph [0135]-KWON discloses the imaging device 240 may produce images of locations, which are predicted as a defect is present in semiconductor patterns on the wafer 210, based on the location information LI. The imaging device 240 may include an SEM (Scanning Electron Microscopy) device or an OM (Optical Microscopy) device) configured to acquire the input image (Fig. 1 and 15, #IMG, #OI, #HR, LRI and #RI called an image, an optical image, a high resolution image, a low resolution image and a reference image, respectively. Paragraph [0041 and 0135-0136]. In paragraph [0136]-KWON disclose the imaging device 240 may output a high resolution image HRI, a low resolution image LRI, and a reference image RI, based on the SEM imaging. The imaging device 240 may output an optical image OI based on the OM imaging. Please also see Fig. 1) by imaging an article (Fig. 15, #210 called a wafer. Paragraph [0128]. Please also see Fig. 1 and read paragraph [0035 and 0039]) ; and an inspection device (Fig. 15, #260 called a defect classification device. Paragraph [0142]-KWON discloses the semiconductor defect classification device 260 may receive the high resolution image HRI, the low resolution image LRI, the reference image RI, and the optical image OI from the image storage 250. The semiconductor defect classification device 260 may receive the first meta information MI1 from the manufacture device 220 and may receive the second meta information M12 from the imaging device 240. Please also see Fig. 1) configured to inspect the article (Fig. 15, #210 called a wafer. Paragraph [0128]) visible in the input image (Fig. 1 and 15, #IMG, #OI, #HR, LRI and #RI called an image, an optical image, a high resolution image, a low resolution image and a reference image, respectively. Paragraph [0041 and 0135-0136]) by using the classification model (Fig. 1, #130 and #131/171 called a neuromorphic processor and a machine learning classifier, respectively. Paragraph [0030-0033 and 0047-0048]. In paragraph [0143]-KWON discloses the semiconductor defect classification device 260 may classify (or determine), based on machine learning, whether semiconductor patterns of the wafer 210 associated with images have a defect, by using the high resolution image HRI, the low resolution image LRI, the reference image RI, the optical image OI, the first meta information MI1, the second meta information MI2, and third meta information MI3.the neuromorphic processor 130 may include a machine learning classifier 131. In paragraph [0145]-KWON discloses the semiconductor defect classification device 260 may include a classifier (e.g., 171) which identifies and/or classifies defects, based on the machine learning, using the high resolution image HRI, the low resolution image LRI, the reference image RI, the optical image OI, and the first to third meta information MI1 to MI3. The semiconductor defect classification device 260 may output a classification result CR. Please also see Fig. 15) . Regarding claim 9, KWON explicitly teaches a determination method (Fig. 3. Paragraph [0075]-KWON discloses FIG. 3 is a flowchart illustrating an operating method of the neuromorphic processor 130. Please also see Fig. 1-2 and 15 and read paragraph [0064-0070 and 0111-0114]) , comprising: determining a suitability of a classification model (Fig. 1, #130, #131 and #171 called a neuromorphic processor and classifier, respectively. Paragraph [0031-0033]. Further in paragraph [0047]-KWON discloses the neuromorphic processor 130 includes first to third convolution layers 132_1 to 132_3, a classify layer 133, a loss layer 134, a resize block 135, first to third activation map generation blocks 136_1 to 136_3, and a class activation map generation block 137. In paragraph [0033]-KWON discloses the neuromorphic processor 130 may generate the pieces of class information of the images by using the machine learning classifier 131. The neuromorphic processor 130 may compare the pieces of class information (e.g., pieces of second class information) generated from the images with the pieces of first class information. Depending on a result of the comparison, the neuromorphic processor 130 may update the machine learning classifier 131. Please also read paragraph [0064-0076, 0111-0114 and 0140-0150]) , the classification model including a neural network (Fig. 1. Paragraph [0031]-KWON discloses the neuromorphic processor 130 may include a machine learning classifier 131. In paragraph [0044]-KWON discloses the machine learning classifier 131 which is generated by the neuromorphic processor 130 may be stored to the storage 170 as the machine learning classifier 171. The machine learning classifier 171 may be transmitted to a test device for testing a defect of a semiconductor device. Further in paragraph [0048]-KWON discloses the first to third convolution layers 132_1 to 132_3 and the classify layer 133 may constitute the machine learning classifier 131. In paragraph [0074]-KWON discloses the machine learning classifier 131 is a convolutional neural network (CNN). The inventive concept may be applied to various other neural networks or machine learning systems) , the classification model being configured to output a classification result according to an input of an image (Fig. 1, #IMG called an image. Paragraph [0049]. In paragraph [0032]-KWON discloses the neuromorphic processor 130 may receive images and pieces of class information (e.g., pieces of first class information) of the images from the image database 160. The neuromorphic processor 130 may perform machine learning by using the images. In paragraph [0063]-KWON discloses the loss layer 134 may receive the second class information ICLS2 from the classify layer 133 and may receive first class information ICLS1 from the image database 160. In paragraph [0064]-KWON discloses when the guide map GDM is provided from the image database 160, the loss layer 134 may perform machine learning by comparing the second class information ICLS2, the first class information ICLS1, and the differences D1 to D4 transferred from the first to third activation map generation blocks 136_1 to 136_3 and the class activation map generation block 137. Please also read paragraph [0035 and 0053]) ; acquiring intermediate data (Fig. 1, #DINT1, DINT2, and #DINT3, called intermediate pixel data or first intermediate pixel data, second intermediate pixel data and third intermediate pixel data, respectively. Paragraph [0047-0051 and 0058-0061]) of an intermediate layer (Fig. 1, #132_1, #132_2, and #132_3, called a first convolutional layer, a second convolutional layer and a third convolutional layer, respectively. Paragraph [0047-0051 and 0058-0061]) of the neural network (Fig. 1. Paragraph [0074]-KWON discloses the machine learning classifier 131 is a convolutional neural network (CNN). However, the inventive concept is not limited to CNNs, and may be applied to various other neural networks or machine learning systems) when an input image (Fig. 1, #IMG called an image. Paragraph [0049]. In paragraph [0049]-KWON discloses the image IMG may include pixel data including pixel values respectively corresponding to pixels of a camera. Please also read paragraph [0035, 0111-0114 and 0140-0150]) is input to the classification model (Fig. 1, #130 called a neuromorphic processor. Paragraph [0031]. In paragraph [0049]-KWON discloses the first convolution layer 132_1 may receive an image IMG from the image database 160. The first convolution layer 132_1 may generate first intermediate pixel data DINT1 by convolving its own kernels with the pixel data of the image IMG. In paragraph [0050]-KWON discloses the second convolution layer 132_2 may generate second intermediate pixel data DINT2 by convolving its own kernels with the first intermediate pixel data DINT1. The third convolution layer 132_3 may generate third intermediate pixel data DINT3 by convolving its own kernels with the second intermediate pixel data DINT2. Please also read paragraph [0053 and 0058-0062]) ; and determining the suitability (Fig. 1. Paragraph [0063]-KWON discloses the loss layer 134 may receive the second class information ICLS2 from the classify layer 133 and may receive first class information ICLS1 from the image database 160. In paragraph [0064]-KWON discloses when the guide map GDM is provided from the image database 160, the loss layer 134 may perform machine learning by comparing the second class information ICLS2, the first class information ICLS1, and the differences D1 to D4 transferred from the first to third activation map generation blocks 136_1 to 136_3 and the class activation map generation block 137. In paragraph [0065]-KWON discloses the loss layer 134 may calculate a difference between the first class information ICLS1 and the second class information ICLS2. Depending on the calculated difference, the loss layer 134 may update weights (e.g., the values of synapses in a neural network) of the first to third convolution layers 132_1 to 132_3 and the classify layer 133. The loss layer 134 may update weights such that the second class information ICLS2 becomes closer to the first class information ICLS1. Please also read paragraph [0041, 0043, 0063-0068 and 0111-0114]) by using a plurality of sets of the intermediate data and a plurality of sets of reference data (Fig. 1. Paragraph [0052]-KWON discloses the classify layer 133 may output second class information ICLS2 by performing an operation including the third intermediate pixel data DINT3 and class parameters. In paragraph [0058]-KWON discloses the first activation map generation block 136_1 may generate a first activation map from the first intermediate pixel data DINT1. The first activation map generation block 136_1 may provide a difference (e.g., D1) between the first activation map and the first guide map GDM1 to the loss layer 134. In paragraph [0059]-KWON discloses the second activation map generation block 136_2 may generate a second activation map from the second intermediate pixel data DINT2. The second activation map generation block 136_2 may provide a difference (e.g., D2) between the second activation map and the second guide map GDM2 to the loss layer 134. In paragraph [0060]-KWON discloses the third activation map generation block 136_3 may generate a third activation map from the third intermediate pixel data DINT3. The third activation map generation block 136_3 may provide a difference (e.g., D3) between the third activation map and the third guide map GDM3 to the loss layer 134. In paragraph [0061]-KWON disclose the class activation map generation block 137 may generate a class activation map from the third intermediate pixel data DINT3 and the class parameters CLSP. Please also read paragraph [0049-0051]) , the plurality of sets of reference data being prepared beforehand (Fig. 1. Paragraph [0041]-KWON discloses the image database 160 may store images of semiconductor devices. Pieces of first class information of the images may include information indicating fault types or normality (i.e., an indication of the absence of faults) of the semiconductor devices. In paragraph [0043]-KWON discloses images stored in the image database 160 may include patterns hindering machine learning. The images which have patterns hindering machine learning or making it difficult to perform learning may be transferred to the neuromorphic processor 130 together with guide maps. The neuromorphic processor 130 may perform machine learning by using the guide maps, thus preventing machine learning from being hindered and supporting learning. That is, the reliability of the machine learning classifier 131 is improved. Please also read paragraph [0049-0052, 0058-0062 and 0111-0114]) . Regarding claim 10, KWON explicitly teaches a storage medium (Fig. 1, #170 called storage. Paragraph [0033]) configured to store a program (Fig. 1. Paragraph [0039]-KWON discloses the storage 170 may store data generated by the processor 120. The storage 170 may store an operating system or firmware code which the processor 120 executes. The storage 170 may store the machine learning classifier 131 (e.g., a first machine learning classifier) generated by the neuromorphic processor 130 as a machine learning classifier 171 (e.g., a second machine learning classifier). The storage 170 may include a nonvolatile memory. Please also see Fig. 15 and read paragraph [0031-0038]) , the program, when executed by a computer, causing the computer to perform the determination method according to claim 9 (Please see the rejection for claim 9 above) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over KWON et al. (US 20190370661 A1), hereinafter referenced as KWON in view of AIZAWA et al. (US 20190362233 A1), hereinafter referenced as AIZAWA . Regarding claim 2, KWON explicitly teaches the determination device according to claim 1, KWON fails to explicitly teach wherein the classification model is trained by metric learning. However, AIZAWA explicitly teaches wherein the classification model (Fig. 4-5 and 7, #440, #540, and #740 called a neural network. Paragraph [0058, 0080 and 0084]. Further in paragraph [0080]-AIZAWA discloses FIG. 5 illustrates a process 500 of computing visual similarity rankings using a trained neural network 540, e.g., for use in a recommendation engine that recommends articles of clothing. Before the process 500 begins, a plurality of triplets is first generated by classifying reference images of clothing items as three positive images of similar clothing items and randomly selecting three negative images from the set of all images in the dataset. In paragraph [0082]-AIZAWA discloses the process 500 shown in FIG. 5 can be used in an e-commerce platform to identify and recommend similar items in response to user queries) is trained (Fig. 1. Paragraph [0044]-AIZAWA discloses FIG. 1 illustrates a process 100 for training a neural network for a similarity ranking engine or recommendation engine using training data, such as images, in the form of triplets. The process 100 begins with collecting data (110) for forming into triplets. This data may include images, waveform representations of audio clips, bag-of-words representations of text, or any other quantity that can be expressed numerically. In paragraph [0045]-AIZAWA discloses each triplet comprises one reference (anchor) data point, at least one positive (similar) data point, and at least one negative (dissimilar) data point (wherein each reference data point may correspond to multiple positive and/or negative data points). In paragraph [0046]-AIZAWA discloses the reference data point and positive data point(s) may be classified beforehand as data corresponding to similar items. In paragraph [0048]-AIZAWA discloses the reference data point and the negative data point(s) may or may not have been classified beforehand as data corresponding to a dissimilar item(s)) by metric learning (Fig. 1. Paragraph [0029]-AIZAWA discloses the neural network might be optimized using a metric learning objective function (e.g., triplet loss, n-pair loss, or another suitable metric learning objective) that uses relative positive and negative pairings of training examples. In paragraph [0042]-AIZAWA discloses a recommendation engine with a neural network trained on similar triplets enables a bridging among content-based recommenders, collaborative filtering, and deep metric learning. Please also see Fig. 7 and read paragraph [0056 and 0086-0088]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KWON of having a determination device, the determination device being configured to determine a suitability of a classification model, the classification model including a neural network, the classification model being configured to output a classification result according to an input of an image, the determination device being configured to: acquire intermediate data of an intermediate layer of the neural network when an input image is input to the classification model, with the teachings of AIZAWA of having wherein the classification model is trained by metric learning. Wherein KWON’s method having wherein the classification model is trained by metric learning. The motivation behind the modification would have been to obtain a method that improves the performance of recommendation and classification models, and provides advantages over other machine learning models, since both KWON and AIZAWA concern image classification. Wherein KWON’s provides methods and systems that improve the reliability of machine learning, while AIZAWA’s methods and systems improve the performance of a collaborative-filtering recommender system, reduces overfitting, increases training speed and provides advantages over other machine learning models. Please see KWON et al. (US 20190370661 A1), Abstract and Paragraph [0043 and 0127] and AIZAWA et al. (US 20190362233 A1), Abstract and Paragraph [0042 and 0065]. Conclusion Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant`s disclosure. SUTIC et al. (US 20190050682 A1)- In an example method for training image signal processors, a reconstructed image is generated via an image signal processor based on a sensor image. An intermediate loss function is generated based on a comparison of an output of one or more corresponding layers of a computer vision network and a copy of the computer vision network. The output of the computer vision network is based on the reconstructed image. An image signal processor is trained based on the intermediate loss function................................... Please see Fig. 1-5. Abstract. HU et al. (US 20220398783 A1)- Disclosed are an image processing method, an image processing device, a neutral network and a training method thereof, and a storage medium. The image processing method includes: obtaining an input image; performing a segmentation process on the input image via a first encoding-decoding network, to obtain a first output feature map and the first segmented image; concatenating the first output feature map with at least one selected from the group consisting of the input image and the first segmented image, to obtain an input of the second encoding-decoding network; and performing a segmentation process on the input of the second encoding-decoding network via a second encoding-decoding network, to obtain the second segmented image. And the first encoding-decoding network and the second encoding-decoding network forms a neural network....................................... Please see Fig. 2-3 and 7 and para. [0089-0091, 0125-0127]. Abstract. DAS et al. (WO 2023282569 A1)- A method for generating an optimal neural network (NN) model may include determining intermediate outputs of the NN model by passing an input dataset through each intermediate exit gate of the plurality of intermediate exit gates, determining an accuracy score for each intermediate exit gate of the plurality of intermediate exit gates based on a comparison of the final output of the NN model with the intermediate output, identifying an earliest intermediate exit gate that produces the intermediate output closer to the final output based on the accuracy score, and generating the optimal NN model by removing remaining layers of the plurality of layers and remaining intermediate exit gates of the plurality of intermediate exit gates located after the determined earliest intermediate exit gate...................................... Please see Fig. 3-6 and 8. Abstract. TSUNODA et al. (US 20220366242 A1)- An information processing apparatus is operable to train a machine learning model that has a hierarchical structure configured by a plurality of hierarchical layers and that is used for recognizing a recognition target in inputted data. An obtaining unit obtains input data and data indicating a ground truth of an output from the machine learning model regarding the input data. A learning unit trains the machine learning model based on an error between the data indicating the ground truth of the output from the machine learning model regarding a specific domain of the input data and at least one output in an intermediate layer of the machine learning model with respect to the input data….................................. Please see Fig. 1-3 and 9-11, and para. [0058]. Abstract. Ukishima et al. (US 20210114368 A1)- There is provided a machine learning model generation device, a machine learning model generation method, a program, an inspection device, an inspection method, and a printing device for inspecting a defect of a printed matter with high accuracy. A machine learning model for detecting the defect of the printed matter is generated by using, as learning input information, at least learning inspection data based on a captured image obtained by capturing an image of a printed matter as an inspection target and second learning reference data based on print digital data and using, as learning output information, at least learning defect information of the learning inspection data estimated by performing comparison processing of the learning inspection data and first learning reference data based on a captured image obtained by capturing the image of the printed matter..................................... Please see Fig. 1-7. Abstract. LILLO (US 20220284261 A1)- Machine learning models are provided that consider, during the process of producing output, various aspects of the training data and/or training process from which the models are created. A machine learning model may generate output (e.g., classification determinations or regression output) that is augmented with information regarding the distribution(s) of the corpus of training data upon which the model was trained, the features extracted from the training data, the resulting determinations made by the model, and/or other information. The augmentation may occur internally while generating the model output, or the output itself may be augmented to include distribution-based data in addition to a model output................................... Please see Fig. 1-2 and 4-6. Abstract. Makhijani et al. (US 11080596 B1)- The present disclosure is directed to filtering co-occurrence data. In one embodiment, a machine learning model can be trained. An output of an intermediate structure of the machine learning model (e.g., an output of an internal layer of a neural network) can be used as a representation of an event. Similarities between representations of events can be determined and used to generate, augment, or modify co-occurrence data.................................. Please see Fig. 1, 4-5. Abstract. Rodríguez-Serrano et al. (US 20170083792 A1)- A system and method provide object localization in a query image based on a global representation of the image generated with a model derived from a convolutional neural network. Representations of annotated images and a query image are each generated based on activations output by a layer of the model which precedes the fully-connected layers of the neural network. A similarity is computed between the query image representation and each of the annotated image representations to identify a subset of the annotated images having the highest computed similarity. Object location information from at least one of the subset of annotated images is transferred to the query image and information is output, based on the transferred object location information................................. Please see Fig. 1-4 and para. [0058-0064 and 0088-0090] Abstract. TESHIMA et al. (US 20220129702 A1)- An image searching apparatus includes: a processor; and a memory, wherein the processor is configured to attach, to an image with a first correct label attached thereto, a second correct label, the first correct label being a correct label attached to each image included in an image dataset for training for use in supervised training, the second correct label being a correct label based on a degree of similarity from a predetermined standpoint; execute main training processing to train a classifier by using the images and one of the first correct label and the second correct label, fine-tune a training state of the classifier; trained by the main training processing, by using the images and the other one of the first correct label and the second correct label; and search, by using the classifier that is fine-tuned, for images similar to a query image..................................... Please see Fig. 1-5. Abstract Gonzales et al. (US 20220383128 A1)- An object analysis system is disclosed herein. The object analysis system may receive an input image that depicts an object. The object analysis system may determine, using a feature extraction model and from the input image, a first feature output that is associated with one or more features of the object. The feature extraction model may be trained based on reference images that depict reference objects that are a type of the object. The object analysis system may determine, using a classification model, that an anomaly status of the object is indicative of the object including an anomaly. The classification model may be trained based on the reference images. The object analysis system may determine, using an anomaly localization model, a location of the anomaly in the input image based on a second feature output of the convolutional neural network encoder. The anomaly localization model may be trained based on the reference images. The object analysis system may perform an action associated with the location of the anomaly...................................... Please see Fig. 1-2 and para. [0034-0042 and 0094]. Abstract. FROLOVA et al. (US 20210081754 A1)- Systems and methods are disclosed for error correction in convolutional neural networks. In one implementation, a first image is received. A first activation map is generated with respect to the first image within a first layer of the convolutional neural network. A correlation is computed between data reflected in the first activation map and data reflected in a second activation map associated with a second image. Based on the computed correlation, a linear combination of the first activation map and the second activation map is used to process the first image within a second layer of the convolutional neural network. An output is provided based on the processing of the first image within the second layer of the convolutional neural network................................. Please see Fig. 1-4. Abstract. OKAWA et al. (US 20220222581 A1)- A creation method for a computer to execute a process includes training a first detection model by using a first training data set; acquiring each of scores of a plurality of pieces of training data included in the first training data set by using the first detection model; creating a second training data set by excluding a part of the training data from the first training data set based on the scores; and training a second detection model by using the second training data set................................... Please see Fig. 1, 9-13 and 18. Abstract. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /AARON TIMOTHY BONANSINGA/Examiner, Art Unit 2673 /CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673 Application/Control Number: 18/762,464 Page 2 Art Unit: 2673 Application/Control Number: 18/762,464 Page 3 Art Unit: 2673 Application/Control Number: 18/762,464 Page 4 Art Unit: 2673 Application/Control Number: 18/762,464 Page 5 Art Unit: 2673 Application/Control Number: 18/762,464 Page 6 Art Unit: 2673 Application/Control Number: 18/762,464 Page 7 Art Unit: 2673 Application/Control Number: 18/762,464 Page 8 Art Unit: 2673 Application/Control Number: 18/762,464 Page 9 Art Unit: 2673 Application/Control Number: 18/762,464 Page 10 Art Unit: 2673 Application/Control Number: 18/762,464 Page 11 Art Unit: 2673 Application/Control Number: 18/762,464 Page 12 Art Unit: 2673 Application/Control Number: 18/762,464 Page 13 Art Unit: 2673 Application/Control Number: 18/762,464 Page 14 Art Unit: 2673 Application/Control Number: 18/762,464 Page 15 Art Unit: 2673 Application/Control Number: 18/762,464 Page 16 Art Unit: 2673 Application/Control Number: 18/762,464 Page 17 Art Unit: 2673 Application/Control Number: 18/762,464 Page 18 Art Unit: 2673 Application/Control Number: 18/762,464 Page 19 Art Unit: 2673 Application/Control Number: 18/762,464 Page 20 Art Unit: 2673 Application/Control Number: 18/762,464 Page 21 Art Unit: 2673 Application/Control Number: 18/762,464 Page 22 Art Unit: 2673 Application/Control Number: 18/762,464 Page 23 Art Unit: 2673 Application/Control Number: 18/762,464 Page 24 Art Unit: 2673 Application/Control Number: 18/762,464 Page 25 Art Unit: 2673 Application/Control Number: 18/762,464 Page 26 Art Unit: 2673 Application/Control Number: 18/762,464 Page 27 Art Unit: 2673 Application/Control Number: 18/762,464 Page 28 Art Unit: 2673 Application/Control Number: 18/762,464 Page 29 Art Unit: 2673 Application/Control Number: 18/762,464 Page 30 Art Unit: 2673 Application/Control Number: 18/762,464 Page 31 Art Unit: 2673 Application/Control Number: 18/762,464 Page 32 Art Unit: 2673
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