CTNF 18/458,209 CTNF 97128 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. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Step 1: According to the first part of the analysis, in the instant case, claim 1 is directed to a learning apparatus (machine) and claim 10 is directed to a learning method (method) and claim 11 is directed to a non-transitory media for leaning method (machine. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A Prong 1 : “ generate a first converted feature values and a second converted feature values by stochastically converting at least one of the first feature values and the second feature values” is a mental step of data manipulation. Additional Elements Step 2A Prong 2 : “ In re learning apparatus comprising processing circuitry configured to: extract first feature values from input data by processing based on one or more first parameters” recited in the preamble do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ extract second feature values from the input data by processing based on one or more second parameters different from the first parameters” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ calculate a first loss related to similarity between the first converted feature values and the second converted feature value” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ obtain a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters” “ and update a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B : “ In re learning apparatus comprising processing circuitry configured to: extract first feature values from input data by processing based on one or more first parameters” recited in the preamble does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ extract second feature values from the input data by processing based on one or more second parameters different from the first parameters” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ calculate a first loss related to similarity between the first converted feature values and the second converted feature value” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ obtain a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ and update a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 2: Step 2A Prong 1 : “ by replacing at least one element of the first feature values and the second feature values selected by a random number with a predetermined value” is a mental step of data manipulation. Additional Elements Step 2A Prong 2 : “ wherein the processing circuitry generates the first converted feature values and the second converted feature values” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B : “ wherein the processing circuitry generates the first converted feature values and the second converted feature values” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 3: Step 2A Prong 2 : “ wherein the processing circuitry generates the first converted feature values and the second converted feature values by (a) adding at least one of the first feature values and the second feature values to a pattern generated based on a random number or (b) multiplying at least one of the first feature values and the second feature values by the pattern” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B: “ wherein the processing circuitry generates the first converted feature values and the second converted feature values by (a) adding at least one of the first feature values and the second feature values to a pattern generated based on a random number or (b) multiplying at least one of the first feature values and the second feature values by the pattern” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 4: Step 2A Prong 2 : “ wherein the processing circuitry generates the first converted feature values or the second converted feature values by a weighted average of the first feature values and the second feature values” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B : “ wherein the processing circuitry generates the first converted feature values or the second converted feature values by a weighted average of the first feature values and the second feature values” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 5: Step 2A Prong 2 : “ wherein at least one of first processing and second processing is executed a plurality of times, the first processing performing processing of generation of the first converted feature values and the second converted feature values after processing of extraction of the first feature values, the second processing performing processing of the processing of generation of the first converted feature values and the second converted feature values after processing of extraction of the second feature values” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B : “ wherein at least one of first processing and second processing is executed a plurality of times, the first processing performing processing of generation of the first converted feature values and the second converted feature values after processing of extraction of the first feature values, the second processing performing processing of the processing of generation of the first converted feature values and the second converted feature values after processing of extraction of the second feature values” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 6: Step 2A Prong 2 : “ wherein the processing circuitry executes preprocessing including one or more conversions on the input data” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B: “ wherein the processing circuitry executes preprocessing including one or more conversions on the input data” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 7: Step 2A Prong 1 : “ and update a parameter of at least one of the second parameters and the fourth parameters such that a value based on a third loss calculated from the second processing result and a label is minimized” is a mental step of data manipulation. Additional Elements Step 2A Prong 2 : “wherein the processing circuitry is further configured to: obtain a second processing result by executing processing based on one or more fourth parameters different from the first to the third parameters, with respect to the second converted feature values” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B: “wherein the processing circuitry is further configured to: obtain a second processing result by executing processing based on one or more fourth parameters different from the first to the third parameters, with respect to the second converted feature values” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 8: Step 2A Prong 2 : “ wherein the processing circuitry updates the first parameters and the third parameters after completion of updating of the second parameters and the fourth parameters” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B : “ wherein the processing circuitry updates the first parameters and the third parameters after completion of updating of the second parameters and the fourth parameters” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 9: Step 2A Prong 2 : “ wherein the processing circuitry updates the second parameters based on updated first parameter” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B : “ wherein the processing circuitry updates the second parameters based on updated first parameter” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 10: Step 2A Prong 1 : “ generating a first converted feature values and a second converted feature values by stochastically converting at least one of the first feature values and the second feature values” is a mental step of data manipulation. Additional Elements Step 2A Prong 2 : “ A learning method, comprising : extracting first feature values from input data by processing based on one or more first parameters” recited in the preamble do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ extracting second feature values from the input data by processing based on one or more second parameters different from the first parameters” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ calculating a first loss related to similarity between the first converted feature values and the second converted feature value” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ obtaining a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters” “ and updating a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B : “ A learning method comprising : extracting first feature values from input data by processing based on one or more first parameters” recited in the preamble does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ extracting second feature values from the input data by processing based on one or more second parameters different from the first parameters” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ calculating a first loss related to similarity between the first converted feature values and the second converted feature value” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ obtaining a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ and updating a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). In regard to claim 11: Step 2A Prong 1 : “ generating a first converted feature values and a second converted feature values by stochastically converting at least one of the first feature values and the second feature values” is a mental step of data manipulation. Additional Elements Step 2A Prong 2 : “A non-transitory computer readable medium including computer executable Instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising:” recited in the preamble do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ extracting second feature values from the input data by processing based on one or more second parameters different from the first parameters” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ calculating a first loss related to similarity between the first converted feature values and the second converted feature value” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ obtaining a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters” “ and updating a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B : “A non-transitory computer readable medium including computer executable Instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising:” recited in the preamble does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ extracting second feature values from the input data by processing based on one or more second parameters different from the first parameters” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ calculating a first loss related to similarity between the first converted feature values and the second converted feature value” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ obtaining a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ and updating a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). do not integrate the judicial exception into a practical application. These additional elements In regard to claim 12: Step 2A Prong 1 : “the first network including a parameter group of the first parameters and the third parameters, the second network including a parameter group of the second parameters and the fourth parameters”(except the inference apparatus comprising processing circuitry configured to: “ is a mental step of topology construction using pen and paper. Additional Elements Step 2A Prong 2 : “ An inference apparatus using a first network and a second network trained by the learning according to claim 7 ” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ input processing target data to the first network and generate a first processing result; input processing target data to the second network and generate a second processing result ” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ and calculate at least one of a weighted average of the first processing result and the second processing result and reliability based on a difference between the first processing result and the second processing result” do not integrate the judicial exception into a practical application. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Step 2B: “ An inference apparatus using a first network and a second network trained by the learning according to claim 7” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ input processing target data to the first network and generate a first processing result; input processing target data to the second network and generate a second processing result” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). “ and calculate at least one of a weighted average of the first processing result and the second processing result and reliability based on a difference between the first processing result and the second processing result” does not amount to more than the judicial exception in the claim. These additional elements are merely directed to using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h). Claim Rejections - 35 USC § 103 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, 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. Claims 1, 4-5, 7-12 are rejected under 35 U.S.C. 103 unpatentable over Masahiro OGINO e t.al. (hereinafter OGINO) US 2021/0272277A1, in view of Utkarsh Ojha et.al. (hereinafter Ojha) US 2022/0254071 A1, in view of Hongsheng Wang et.al. (hereinafter Wang) US 11810366 B1. In regard to claim 1: OGINO d iscloses: - learning apparatus comprising processing circuitry configured to: extract first feature values from input data by processing based on one or more first parameters In [Abstract]: A medical imaging apparatus includes an imaging unit In [Abstract]: The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that abstracts the first feature quantity to extract a second feature quantity In [0028]: FIG. 14 is a diagram for description of a relationship between feature quantities in the feature quantity space; PNG media_image1.png 580 785 media_image1.png Greyscale In [0058]: The feature quantity A, which is the output of the predictive model 232M, expresses a plurality of classifications necessary for diagnosis of a feature of an image as a vector of a plurality of dimensions (for example, 1,024 dimensions), and a feature related to a parameter (for example, whether a tumor is benign or malignant) is extracted. Such a feature quantity A is obtained for each patch. Note that in FIG. 4, the feature quantity A 410 is an output of a final layer of all the combined layers. However, the invention is not limited thereto. Even though the deeper the layer, the greater the degree of feature abstraction, it is possible to use an output of a layer shallower than the final layer as the feature quantity. (BRI: A feature quantity space with feature of an image represented as a vector of multiple dimensions does represent the plurality of feature values where each dimensions value is a specific measurable property (value) of the image) In [0083]: When data of the feature quantity A and data of the feature quantity B for learning are set to an input (teacher data) A.sub.k and an output B.sub.k, respectively In [0084]: By adding a distance r between the teacher data A.sub.k and the output B.sub.k on the space ε (for example, between the centers of gravity of the respective data sets) to the error function of Formula (1), an error function is set so that an error of the distance r on the space ε becomes small In [ 0095 ]: For example, in FIG. 15 illustrating a feature quantity space, an error function in which weights are given to a distance between medically important teacher data A1 and an output B1 and a distance between next important teacher data group (data set S) Ai and an output Bi is set. (BRI: the weights given to the distance r represents a first parameter) - generate a first converted feature values and a second converted feature values by stochastically converting at least one of the first feature values and the second feature values; In [0083]: When data of the feature quantity A and data of the feature quantity B for learning are set to an input (teacher data) A.sub.k and an output B.sub.k, respectively, each of the teacher data A.sub.k and the output B.sub.k is dimensionally transformed and compressed and mapped to a predetermined space ε as illustrated in FIG. 12 . As a mapping method, for example, principal component analysis (PCA) or t-distributed Stochastic Neighbor Embedding (tSNE) is used . Note that even though the space ε is set to a two-dimensional space in this figure, the invention is not limited thereto. In [ 0071 ]: FIG. 10 illustrates an example of a result of feature quantity conversion in the present embodiment. A feature quantity map 1001 is a map of the feature quantity B , a feature quantity map 1002 is a map of the feature quantity C , and a feature quantity map 1003 is a map generated from the feature quantity B by the feature quantity conversion . These maps are obtained by compression from multidimensional (1,024 dimensions) ones to two-dimensional ones by t-distributed Stochastic Neighbor Embedding (tSNE ). From FIG. 10, it can be understood that the map 1003 obtained by the feature quantity conversion has characteristics close to those of the map 1002 of the feature quantity C that is teacher data. In [0094]: The accuracy of learning can be further improved by weighting a spatial distance between highly related regions to reduce an error. Processing is similar to the predetermined spatial distance error , in which coordinates (centers of gravity) of patch images (groups) highly related to each other are obtained from medical knowledge, and an error between the coordinates (centers of gravity) is defined as an error function . (BRI: the patch images that are highly related to each other represents “similarity” ) In [ 0093 ]: This error function is a combination of the above-mentioned predetermined spatial distance error and medical knowledge. The predetermined spatial distance error defines an error function that brings the entire space closer , using a center of gravity of a feature quantity space as a parameter . In this error function, a space to be matched is weighted based on medical knowledge and importance. Specifically, as illustrated in FIG. 14, in the feature quantity space (feature quantity map), a relationship in image data among teacher data 1002, image data before conversion 1001 and after conversion 1003 from the feature quantity B to the feature quantity C is analyzed. For example, a feature quantity space error reduction is particularly weighted to the distance between a patch image (group) 1402 having a deep relation to determination of the presence or absence of a disease in a pathological image and an MRI image (group) 1401 corresponding thereto. In [0097] : By using the error function as described above, it is possible to reduce the error of the feature quantity conversion model or the identification model and realize a more accurate predictive model. Alternatively, the error functions (2) and (5) may be combined and weighted to form an error function represented by the following (BRI: an error function that reduces the error of the conversion model to provide more accurate predictive model does represent a loss function. This error function quantifies the difference between the predicted quantile and the actual observed value) OGINI does not explicitly disclose: - calculate a first loss related to similarity between the first converted feature values and the second converted feature values; However, Ojha discloses: - calculate a first loss related to similarity between the first converted feature values and the second converted feature values; In [0009]: FIG. 3 illustrates a schematic diagram illustrating a process for GAN translation that preserves relative feature distances in accordance with one or more implementations; PNG media_image2.png 572 747 media_image2.png Greyscale (BRI: a GAN to GAN translation can indeed provide feature value conversion in which one type of in age is converted into another such as sketch or photo which may enable the tasks related to image processing) In [ 0033 ]: the GAN translation system determines relative pairwise distances or relative feature distances between digital images (or more specifically between digital image feature vectors). As used herein, the term “relative feature distance” refers to a relative distance or difference between feature vectors (e.g., within a feature space) corresponding to digital images. For example, a relative feature distance indicates a measure of similarity (or difference) between digital images or their corresponding feature vectors . In [0035]: the GAN translation system preserves relative feature distances or relative pairwise distances by utilizing a cross-domain distance consistency constraint or loss . As used herein, the term “ cross-domain distance consistency loss ” refers to a loss function that enforces similarity in the distribution of pairwise distances of generated samples (e.g., digital images) before and after adaptation. For example, the GAN translation system implements a cross-domain distance consistency loss to encourage or enforce preservation of relative pairwise distances for a generative adversarial neural network before and after adaptation from a source domain to a target domain. In [0051]: further illustrated in FIG. 2, the GAN translation system 102 utilizes an adaptation process 206 to modify, update, or adapt the parameters of the source generative adversarial neural network 202 to generate a modified or target generative adversarial neural network 204 (represented by G.sub.t) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, and Ojha . OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Ojha that can improve the accuracy of the GAN using GAN translation ( Ojha [0086]). OGINI and Ojha do not explicitly disclose: - extract second feature values from the input data by processing based on one or more second parameters different from the first parameters; - obtain a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters; - and update a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized. However, Wang discloses: - extract second feature values from the input data by processing based on one or more second parameters different from the first parameters; In [Col 10, lines 29-30]: extract local image features of pedestrians ; a module for extracting local features of pedestrians. In [Col 7, lines 63-67]: inputting the images into the three-channel image convolutional neural network to capture image channel features of pedestrians , and referring to FIG. 3, the fourth stage is divided into the following sub-steps: In [Col 8, lines 1-16]: Step 1: with regard to the three channels of the input images, constructing a three-channel image convolutional neural network which includes three convolution kernels that corre spond to the three channels of the images , respectively; allowing the three convolution kernels to learn weight parameters of corre sponding image channels respectively , so as to output three groups of different weight parameters, each of the convolution kernels having a size of 1×1×3, where 3 is the number of channels of the input images; inputting the images into the three-channel image convolutional neural network, where the input images are weighted and combined in a convolution depth direction and, after going through the three convolution kernels of 1×1×3, output three local features which contain the weight parameters among the three channels (BRI: the weight parameters with different weights corresponding to image channels do represent feature parameters that are different as the weight parameters in CNN is designed to extract specific features from the input data and to learn most relevant features during training) - obtain a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters; In [Col 7, lines 64-67]: inputting the images into the three-channel image convolutional neural network to capture image channel features of pedestrians, and referring to FIG. 3, the fourth stage is divided into the following sub-steps: In [Col 8, lines 64-67]: Step 1: with regard to the three channels of the input images, constructing a three-channel image convolutional neural network which includes three convolution kernels that correspond to the three channels of the images , respectively; allowing the three convolution kernels to learn weight parameters of corresponding image channels respectively, so as to output three groups of different weight parameters , each of the convolution kernels having a size of 1×1×3, where 3 is the number of channels of the input images; inputting the images into the three-channel image convolutional neural network, where the input images are weighted and combined in a convolution depth direction and, after going through the three convolution kernels of 1×1×3, output three local features which contain the weight parameters among the three channels, and the calculation formula is as follows: in [Col 8, lines 1-42]: PNG media_image3.png 60 378 media_image3.png Greyscale where: O( i, j) is an output matrix, I is an input matrix, K is a convolution kernel matrix, and the convolution kernel matrix K has a shape of m x n; I (i+m, j+n) K (m,n) K(m,n) represents that elements of the input matrix I( i+m,j+n) are multiplied by elements of the kernel matrix K(m, n), and PNG media_image4.png 33 152 media_image4.png Greyscale is accumulated and summed in horizontal and vertical directions of the matrix, respectively; and Step 2: allowing the three convolution kernels to calculate independently, and to learn differential parameter weights among the three channels, and obtaining feature space maps of the three channels, which are then calculated interactively to obtain image channel features of pedestrians . - and update a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized. In [Col 14, lines 37-54]: S7.1: adopting the feed-forward neural network and the activation function, inputting the obtained enhanced local image features of pedestrians into the feed-forward neural network, allowing it to go through linear layer transformation, and mapping the probability distribution of pedestrians into categories to recognize pedestrians by using the activation function; and S7.2: calculating an intersection ratio of coordinates of the recognized pedestrians and the image label ed sample in the original surveillance video image data set, and calculating an accuracy rate and a recall rate, where the accuracy rate refers to the recognized pedestrians , indicating a proportion of real pedestrians in the sample predicted to be positive, and the recall rate refers to the image labeled sample in the original surveillance video image data set, indicating a proportion of correctly recognized pedestrians in the positive examples in the sample. In [Col 10, lines 50-53]: configured to construct a feed-forward neural network , where the enhanced local image features of pedestrians go through linear transformation and are then mapped into a pedestrian probability output; a module for model training, configured to iteratively train the neural network obtained by joint modeling and update model parameters until the model training converges, It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, Ojha and Wang. OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Wang that can enhance local features to improve the recognition rate of the object (pedestrians) ( Wang [Col 5, lines 18-19]) In regard to claim 4: Ogino discloses : - wherein the processing circuitry generates the first converted feature values or the second converted feature values by a weighted average of the first feature values and the second feature values. In [ 0095 ]: in FIG. 15 illustrating a feature quantity space , an error function i n which weights are given to a distance between medically important teacher data A1 and an output B1 and a distance between next important teacher data group (data set S) Ai and an output Bi is set. When a conversion function to the feature quantity space ε is set to g and the center of gravity ( average value of coordinates of each piece of data ) on the space ε is represented by C , the error function is represented by the following Formula (2). PNG media_image5.png 92 375 media_image5.png Greyscale in [0093]: This error function is a combination of the above-mentioned predetermined spatial distance error and medical knowledge . The predetermined spatial distance error defines an error function that brings the entire space closer, using a center of gravity of a feature quantity space as a parameter. In this error function, a space to be matched is weighted based on medical knowledge and importance In regard to claim 5: OGINO discloses : - wherein at least one of first processing and second processing is executed a plurality of times, the first processing performing processing of generation of the first converted feature values and the second converted feature values after processing of extraction of the first feature values, the second processing performing processing of the processing of generation of the first converted feature values and the second converted feature values after processing of extraction of the second feature values. In [Abstract]: The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data , a feature quantity abstraction unit that abstracts the first feature quantity to extract a second feature quantity , a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data, and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value. In [0028]: FIG. 14 is a diagram for description of a relationship between feature quantities in the feature quantity space; PNG media_image1.png 580 785 media_image1.png Greyscale In [0058]: The feature quantity A, which is the output of the predictive model 232M, expresses a plurality of classifications necessary for diagnosis of a feature of an image as a vector of a plurality of dimensions (for example, 1,024 dimensions), and a feature related to a parameter (for example, whether a tumor is benign or malignant) is extracted. Such a feature quantity A is obtained for each patch. Note that in FIG. 4, the feature quantity A 410 is an output of a final layer of all the combined layers. However, the invention is not limited thereto. Even though the deeper the layer, the greater the degree of feature abstraction, it is possible to use an output of a layer shallower than the final layer as the feature quantity. (BRI: A feature quantity space with feature of an image represented as a vector of multiple dimensions does represent the plurality of feature values where each dimensions value is a specific measurable property (value) of the image) In [0055]: As schematically illustrated in FIG. 4, a CNN 40 of the predictive model 232M is a computing unit constructed on a computer configured to repeat a convolution operation and pooling 43 a plurality of times between an input layer 41 and an output layer 44 on a multi-layer network . In FIG. 4, a number in front of a block indicating each layer is the number of layers, and a number in each layer represents a size processed by each layer. The CNN of this predictive model is learned t o extract a feature quantity A 410 for accurately identifying the presence or absence of the lesion of an input image 400 by the CNN repeat ing the convolution calculation and pooling on input data of the input image 400 for learning divided into a plurality of patches by the patch processing unit 231. In [0061]: The predictive model 233M receives a feature quantity corresponding to the number of patches output from the feature quantity extraction unit 232 as an input, and extracts a main feature quantity In [0056]: Learning is performed until an error between an output and teacher data falls within a predetermined range. An error function used at this time will be described after the structure of the learning model. In [0053]: the feature quantity extraction unit 232 extracts the feature quantity A from image data of an input image, a second model is a model for the feature quantity abstraction unit 233 to extract the feature quantity B abstracted from the feature quantity A , a third model is a feature quantity conversion model for the feature quantity conversion unit 234 to convert the feature quantity B into the feature quantity C , (BRI: the conversion to C after extracting A and B ) In [0120]: as illustrated in FIG. 20, for example, a relevant part of the lesion is cut out by a patch from a plurality of T1 weighted images of the MRI to extract the feature quantity A, and each feature quantity A obtained by each patch is combined to extract the feature quantity B In [0108]: the input image may be generated from the signal collected by the imaging unit 100, and the feature quantity A and the feature quantity B extracted from the input image can be converted into the feature quantity C of the image having more detailed information to calculate the parameter value used for more accurate diagnosis from the feature quantity C. In this way, it is possible to present more accurate diagnosis support information using the medical imaging apparatus. In [0074]: The identification model 235M is incorporated in the identification unit 235 such that such a CNN is trained using a plurality of combinations of the feature quantity (feature quantity C ) after conversion In [129]: Further, the conversion of the feature quantity in the feature quantity conversion unit 234 is not limited to two captured images, and can be applied to a plurality of different types of captured images . In [ 0095 ]: For example, in FIG. 15 illustrating a feature quantity space, an error function in which weights are given to a distance between medically important teacher data A1 and an output B1 and a distance between next important teacher data group (data set S) Ai and an output Bi is set. W hen a conversion function to the feature quantity space ε is set to g and the center of gravity (average value of coordinates of each piece of data) on the space ε is represented by C, the error function is represented by the following Formula (6) PNG media_image6.png 107 363 media_image6.png Greyscale In [0096] Here, α, β, and γ are weighting factors, for example, α=0.5, β=0.4, and γ=0.1. In regard to claim 7: OGINO discloses : - wherein the processing circuitry is further configured to: obtain a second processing result by executing processing based on one or more fourth parameters different from the first to the third parameters, with respect to the second converted feature values; In [ 0013 ]: The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that extracts (abstracts) a more important second feature quantity from the first feature quantity, a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data different from the first image data , and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value , and performs prediction. OGINO and Ojha do not explicitly disclose: - and update a parameter of at least one of the second parameters and the fourth parameters such that a value based on a third loss calculated from the second processing result and a label is minimized. However, Wang discloses: - and update a parameter of at least one of the second parameters and the fourth parameters such that a value based on a third loss calculated from the second processing result and a label is minimized. In [Col 14, lines 37-54]: S7.1: adopting the feed-forward neural network and the activation function, inputting the obtained enhanced local image features of pedestrians into the feed-forward neural network, allowing it to go through linear layer transformation, and mapping the probability distribution of pedestrians into categories to recognize pedestrians by using the activation function; and S7. 2: calculating an intersection ratio of coordinates of the recognized pedestrians and the image label ed sample in the original surveillance video image data set , and calculating an accuracy rate and a recall rate, where the accuracy rate refers to the recognized pedestrians, indicating a proportion of real pedestrians in the sample predicted to be positive, and the recall rate refers to the image labeled sample in the original surveillance video image data set, indicating a proportion of correctly recognized pedestrians in the positive examples in the sample. It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, Ojha and Wang. OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Wang that can enhance local features to improve the recognition rate of the object (pedestrians) ( Wang [Col 5, lines 18-19]) In regard to claim 8: Odino and Ojha do not explicitly disclose : - wherein the processing circuitry updates the first parameters and the third parameters after completion of updating of the second parameters and the fourth parameters. However, Wang discloses: - wherein the processing circuitry updates the first parameters and the third parameters after completion of updating of the second parameters and the fourth parameters. In [Col 14, lines 37-54]: S7.1: adopting the feed-forward neural network and the activation function, inputting the obtained enhanced local image features of pedestrians into the feed-forward neural network, allowing it to go through linear layer transformation, and mapping the probability distribution of pedestrians into categories to recognize pedestrians by using the activation function; and S7. 2: calculating an intersection ratio of coordinates of the recognized pedestrians and the image label ed sample in the original surveillance video image data set , and calculating an accuracy rate and a recall rate, where the accuracy rate refers to the recognized pedestrians, indicating a proportion of real pedestrians in the sample predicted to be positive, and the recall rate refers to the image labeled sample in the original surveillance video image data set, indicating a proportion of correctly recognized pedestrians in the positive examples in the sample. It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, Ojha and Wang. OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Wang that can enhance local features to improve the recognition rate of the object (pedestrians) ( Wang [Col 5, lines 18-19]) In regard to claim 9: Odino and Ojha do not explicitly disclose: - wherein the processing circuitry updates the second parameters based on updated first parameters. However, Wang discloses: In [Abstract]: weight parameters of image channels are learned by channel convolution kernels, spatial features on the images are scanned through spatial convolution in [Col 3, lines 33-38]: S62: an interactive concatenation of the multi-head attention neural network and the enhanced channel feature neural network: the output of the calculation for multi-head attention going through the first-layer three-channel convolution, learning different weight parameters among the three channels In [Col 14, lines 37-54]: S7.1: adopting the feed-forward neural network and the activation function, inputting the obtained enhanced local image features of pedestrians into the feed-forward neural network, allowing it to go through linear layer transformation, and mapping the probability distribution of pedestrians into categories to recognize pedestrians by using the activation function; and S7. 2: calculating an intersection ratio of coordinates of the recognized pedestrians and the image label ed sample in the original surveillance video image data set , and calculating an accuracy rate and a recall rate, where the accuracy rate refers to the recognized pedestrians, indicating a proportion of real pedestrians in the sample predicted to be positive, and the recall rate refers to the image labeled sample in the original surveillance video image data set, indicating a proportion of correctly recognized pedestrians in the positive examples in the sample, In [Col 4, lines 63-67]: a module for model training, configured to iteratively train the neural network obtained by joint modeling and update model parameters until the model training converges, It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, Ojha and Wang. OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Wang that can enhance local features to improve the recognition rate of the object (pedestrians) ( Wang [Col 5, lines 18-19]) In regard to claim 10: OGINO discloses: - A learning method, comprising: extracting first feature values from input data by processing based on one or more first parameters; In [Abstract]: A medical imaging apparatus includes an imaging unit In [Abstract]: The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that abstracts the first feature quantity to extract a second feature quantity In [0028]: FIG. 14 is a diagram for description of a relationship between feature quantities in the feature quantity space; PNG media_image1.png 580 785 media_image1.png Greyscale In [0058]: The feature quantity A, which is the output of the predictive model 232M, expresses a plurality of classifications necessary for diagnosis of a feature of an image as a vector of a plurality of dimensions (for example, 1,024 dimensions), and a feature related to a parameter (for example, whether a tumor is benign or malignant) is extracted. Such a feature quantity A is obtained for each patch. Note that in FIG. 4, the feature quantity A 410 is an output of a final layer of all the combined layers. However, the invention is not limited thereto. Even though the deeper the layer, the greater the degree of feature abstraction, it is possible to use an output of a layer shallower than the final layer as the feature quantity. (BRI: A feature quantity space with feature of an image represented as a vector of multiple dimensions does represent the plurality of feature values where each dimensions value is a specific measurable property (value) of the image) In [0083]: When data of the feature quantity A and data of the feature quantity B for learning are set to an input (teacher data) A.sub.k and an output B.sub.k, respectively In [0084]: By adding a distance r between the teacher data A.sub.k and the output B.sub.k on the space ε (for example, between the centers of gravity of the respective data sets) to the error function of Formula (1), an error function is set so that an error of the distance r on the space ε becomes small In [ 0095 ]: For example, in FIG. 15 illustrating a feature quantity space, an error function in which weights are given to a distance between medically important teacher data A1 and an output B1 and a distance between next important teacher data group (data set S) Ai and an output Bi is set. (BRI: the weights given to the distance r represents a first parameter) - generating a first converted feature values and a second converted feature values by stochastically converting at least one of the first feature values and the second feature values; In [0083]: When data of the feature quantity A and data of the feature quantity B for learning are set to an input (teacher data) A.sub.k and an output B.sub.k, respectively, each of the teacher data A.sub.k and the output B.sub.k is dimensionally transformed and compressed and mapped to a predetermined space ε as illustrated in FIG. 12 . As a mapping method, for example, principal component analysis (PCA) or t-distributed Stochastic Neighbor Embedding (tSNE) is used . Note that even though the space ε is set to a two-dimensional space in this figure, the invention is not limited thereto. In [ 0071 ]: FIG. 10 illustrates an example of a result of feature quantity conversion in the present embodiment. A feature quantity map 1001 is a map of the feature quantity B , a feature quantity map 1002 is a map of the feature quantity C , and a feature quantity map 1003 is a map generated from the feature quantity B by the feature quantity conversion . These maps are obtained by compression from multidimensional (1,024 dimensions) ones to two-dimensional ones by t-distributed Stochastic Neighbor Embedding (tSNE ). From FIG. 10, it can be understood that the map 1003 obtained by the feature quantity conversion has characteristics close to those of the map 1002 of the feature quantity C that is teacher data. In [0094]: The accuracy of learning can be further improved by weighting a spatial distance between highly related regions to reduce an error. Processing is similar to the predetermined spatial distance error , in which coordinates (centers of gravity) of patch images (groups) highly related to each other are obtained from medical knowledge, and an error between the coordinates (centers of gravity) is defined as an error function . (BRI: the patch images that are highly related to each other represents “similarity” ) In [ 0093 ]: This error function is a combination of the above-mentioned predetermined spatial distance error and medical knowledge. The predetermined spatial distance error defines an error function that brings the entire space closer , using a center of gravity of a feature quantity space as a parameter . In this error function, a space to be matched is weighted based on medical knowledge and importance. Specifically, as illustrated in FIG. 14, in the feature quantity space (feature quantity map), a relationship in image data among teacher data 1002, image data before conversion 1001 and after conversion 1003 from the feature quantity B to the feature quantity C is analyzed. For example, a feature quantity space error reduction is particularly weighted to the distance between a patch image (group) 1402 having a deep relation to determination of the presence or absence of a disease in a pathological image and an MRI image (group) 1401 corresponding thereto. In [0097] : By using the error function as described above, it is possible to reduce the error of the feature quantity conversion model or the identification model and realize a more accurate predictive model. Alternatively, the error functions (2) and (5) may be combined and weighted to form an error function represented by the following (BRI: an error function that reduces the error of the conversion model to provide more accurate predictive model does represent a loss function. This error function quantifies the difference between the predicted quantile and the actual observed value) OGINI does not explicitly disclose: - calculating a first loss related to similarity between the first converted feature values and the second converted feature values; However, Ojha discloses: - calculating a first loss related to similarity between the first converted feature values and the second converted feature values; In [0009]: FIG. 3 illustrates a schematic diagram illustrating a process for GAN translation that preserves relative feature distances in accordance with one or more implementations; PNG media_image2.png 572 747 media_image2.png Greyscale (BRI: a GAN to GAN translation can indeed provide feature value conversion in which one type of in age is converted into another such as sketch or photo which may enable the tasks related to image processing) In [ 0033 ]: the GAN translation system determines relative pairwise distances or relative feature distances between digital images (or more specifically between digital image feature vectors). As used herein, the term “relative feature distance” refers to a relative distance or difference between feature vectors (e.g., within a feature space) corresponding to digital images. For example, a relative feature distance indicates a measure of similarity (or difference) between digital images or their corresponding feature vectors . In [0035]: the GAN translation system preserves relative feature distances or relative pairwise distances by utilizing a cross-domain distance consistency constraint or loss . As used herein, the term “ cross-domain distance consistency loss ” refers to a loss function that enforces similarity in the distribution of pairwise distances of generated samples (e.g., digital images) before and after adaptation. For example, the GAN translation system implements a cross-domain distance consistency loss to encourage or enforce preservation of relative pairwise distances for a generative adversarial neural network before and after adaptation from a source domain to a target domain. In [0051]: further illustrated in FIG. 2, the GAN translation system 102 utilizes an adaptation process 206 to modify, update, or adapt the parameters of the source generative adversarial neural network 202 to generate a modified or target generative adversarial neural network 204 (represented by G.sub.t) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, and Ojha . OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Ojha that can improve the accuracy of the GAN using GAN translation ( Ojha [0086]). OGINI and Ojha do not explicitly disclose: - extracting first feature values from input data by processing based on one or more first parameters; extracting second feature values from the input data by processing based on one or more second parameters different from the first parameters; - obtaining a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters; - and updating a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized. However, Wang discloses: - extracting first feature values from input data by processing based on one or more first parameters; extracting second feature values from the input data by processing based on one or more second parameters different from the first parameters; In [Col 10, lines 29-30]: extract local image features of pedestrians ; a module for extracting local features of pedestrians. In [Col 7, lines 63-67]: inputting the images into the three-channel image convolutional neural network to capture image channel features of pedestrians , and referring to FIG. 3, the fourth stage is divided into the following sub-steps: In [Col 8, lines 1-16]: Step 1: with regard to the three channels of the input images, constructing a three-channel image convolutional neural network which includes three convolution kernels that corre spond to the three channels of the images , respectively; allowing the three convolution kernels to learn weight parameters of corre sponding image channels respectively , so as to output three groups of different weight parameters, each of the convolution kernels having a size of 1×1×3, where 3 is the number of channels of the input images; inputting the images into the three-channel image convolutional neural network, where the input images are weighted and combined in a convolution depth direction and, after going through the three convolution kernels of 1×1×3, output three local features which contain the weight parameters among the three channels (BRI: the weight parameters with different weights corresponding to image channels do represent feature parameters that are different as the weight parameters in CNN is designed to extract specific features from the input data and to learn most relevant features during training) - obtaining a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters; In [Col 7, lines 64-67]: inputting the images into the three-channel image convolutional neural network to capture image channel features of pedestrians, and referring to FIG. 3, the fourth stage is divided into the following sub-steps: In [Col 8, lines 64-67]: Step 1: with regard to the three channels of the input images, constructing a three-channel image convolutional neural network which includes three convolution kernels that correspond to the three channels of the images , respectively; allowing the three convolution kernels to learn weight parameters of corresponding image channels respectively, so as to output three groups of different weight parameters , each of the convolution kernels having a size of 1×1×3, where 3 is the number of channels of the input images; inputting the images into the three-channel image convolutional neural network, where the input images are weighted and combined in a convolution depth direction and, after going through the three convolution kernels of 1×1×3, output three local features which contain the weight parameters among the three channels, and the calculation formula is as follows: in [Col 8, lines 1-42]: PNG media_image3.png 60 378 media_image3.png Greyscale where: O( i, j) is an output matrix, I is an input matrix, K is a convolution kernel matrix, and the convolution kernel matrix K has a shape of m x n; I (i+m, j+n) K (m,n) K(m,n) represents that elements of the input matrix I( i+m,j+n) are multiplied by elements of the kernel matrix K(m, n), and PNG media_image4.png 33 152 media_image4.png Greyscale is accumulated and summed in horizontal and vertical directions of the matrix, respectively; and Step 2: allowing the three convolution kernels to calculate independently, and to learn differential parameter weights among the three channels, and obtaining feature space maps of the three channels, which are then calculated interactively to obtain image channel features of pedestrians . - and updating a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized. In [Col 14, lines 37-54]: S7.1: adopting the feed-forward neural network and the activation function, inputting the obtained enhanced local image features of pedestrians into the feed-forward neural network, allowing it to go through linear layer transformation, and mapping the probability distribution of pedestrians into categories to recognize pedestrians by using the activation function; and S7.2: calculating an intersection ratio of coordinates of the recognized pedestrians and the image label ed sample in the original surveillance video image data set, and calculating an accuracy rate and a recall rate, where the accuracy rate refers to the recognized pedestrians , indicating a proportion of real pedestrians in the sample predicted to be positive, and the recall rate refers to the image labeled sample in the original surveillance video image data set, indicating a proportion of correctly recognized pedestrians in the positive examples in the sample. In [Col 10, lines 50-53]: configured to construct a feed-forward neural network , where the enhanced local image features of pedestrians go through linear transformation and are then mapped into a pedestrian probability output; a module for model training, configured to iteratively train the neural network obtained by joint modeling and update model parameters until the model training converges, It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, Ojha and Wang. OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Wang that can enhance local features to improve the recognition rate of the object (pedestrians) ( Wang [Col 5, lines 18-19]) In regard to claim 11: OGINO discloses: - generating a first converted feature values and a second converted feature values by stochastically converting at least one of the first feature values and the second feature values In [0028]: FIG. 14 is a diagram for description of a relationship between feature quantities in the feature quantity space; PNG media_image1.png 580 785 media_image1.png Greyscale In [0058]: The feature quantity A, which is the output of the predictive model 232M, expresses a plurality of classifications necessary for diagnosis of a feature of an image as a vector of a plurality of dimensions (for example, 1,024 dimensions), and a feature related to a parameter (for example, whether a tumor is benign or malignant) is extracted. Such a feature quantity A is obtained for each patch. Note that in FIG. 4, the feature quantity A 410 is an output of a final layer of all the combined layers. However, the invention is not limited thereto. Even though the deeper the layer, the greater the degree of feature abstraction, it is possible to use an output of a layer shallower than the final layer as the feature quantity. (BRI: A feature quantity space with feature of an image represented as a vector of multiple dimensions does represent the plurality of feature values where each dimensions value is a specific measurable property (value) of the image) In [0083]: When data of the feature quantity A and data of the feature quantity B for learning are set to an input (teacher data) A.sub.k and an output B.sub.k, respectively, each of the teacher data A.sub.k and the output B.sub.k is dimensionally transformed and compressed and mapped to a predetermined space ε as illustrated in FIG. 12 . As a mapping method, for example, principal component analysis (PCA) or t-distributed Stochastic Neighbor Embedding (tSNE) is used . Note that even though the space ε is set to a two-dimensional space in this figure, the invention is not limited thereto. In [ 0071 ]: FIG. 10 illustrates an example of a result of feature quantity conversion in the present embodiment. A feature quantity map 1001 is a map of the feature quantity B , a feature quantity map 1002 is a map of the feature quantity C , and a feature quantity map 1003 is a map generated from the feature quantity B by the feature quantity conversion . These maps are obtained by compression from multidimensional (1,024 dimensions) ones to two-dimensional ones by t-distributed Stochastic Neighbor Embedding (tSNE ). From FIG. 10, it can be understood that the map 1003 obtained by the feature quantity conversion has characteristics close to those of the map 1002 of the feature quantity C that is teacher data. In [0094]: The accuracy of learning can be further improved by weighting a spatial distance between highly related regions to reduce an error. Processing is similar to the predetermined spatial distance error , in which coordinates (centers of gravity) of patch images (groups) highly related to each other are obtained from medical knowledge, and an error between the coordinates (centers of gravity) is defined as an error function . (BRI: the patch images that are highly related to each other represents “similarity” ) In [ 0093 ]: This error function is a combination of the above-mentioned predetermined spatial distance error and medical knowledge. The predetermined spatial distance error defines an error function that brings the entire space closer , using a center of gravity of a feature quantity space as a parameter . In this error function, a space to be matched is weighted based on medical knowledge and importance. Specifically, as illustrated in FIG. 14, in the feature quantity space (feature quantity map), a relationship in image data among teacher data 1002, image data before conversion 1001 and after conversion 1003 from the feature quantity B to the feature quantity C is analyzed. For example, a feature quantity space error reduction is particularly weighted to the distance between a patch image (group) 1402 having a deep relation to determination of the presence or absence of a disease in a pathological image and an MRI image (group) 1401 corresponding thereto. In [0097] : By using the error function as described above, it is possible to reduce the error of the feature quantity conversion model or the identification model and realize a more accurate predictive model. Alternatively, the error functions (2) and (5) may be combined and weighted to form an error function represented by the following (BRI: an error function that reduces the error of the conversion model to provide more accurate predictive model does represent a loss function. This error function quantifies the difference between the predicted quantile and the actual observed value) OGINO does not explicitly disclose: - calculating a first loss related to similarity between the first converted feature values and the second converted feature values; However, Ojha discloses: calculating a first loss related to similarity between the first converted feature values and the second converted feature values; In [0009]: FIG. 3 illustrates a schematic diagram illustrating a process for GAN translation that preserves relative feature distances in accordance with one or more implementations; PNG media_image2.png 572 747 media_image2.png Greyscale (BRI: a GAN to GAN translation can indeed provide feature value conversion in which one type of in age is converted into another such as sketch or photo which may enable the tasks related to image processing) In [ 0033 ]: the GAN translation system determines relative pairwise distances or relative feature distances between digital images (or more specifically between digital image feature vectors). As used herein, the term “relative feature distance” refers to a relative distance or difference between feature vectors (e.g., within a feature space) corresponding to digital images. For example, a relative feature distance indicates a measure of similarity (or difference) between digital images or their corresponding feature vectors . In [0035]: the GAN translation system preserves relative feature distances or relative pairwise distances by utilizing a cross-domain distance consistency constraint or loss . As used herein, the term “ cross-domain distance consistency loss ” refers to a loss function that enforces similarity in the distribution of pairwise distances of generated samples (e.g., digital images) before and after adaptation. For example, the GAN translation system implements a cross-domain distance consistency loss to encourage or enforce preservation of relative pairwise distances for a generative adversarial neural network before and after adaptation from a source domain to a target domain. In [0051]: further illustrated in FIG. 2, the GAN translation system 102 utilizes an adaptation process 206 to modify, update, or adapt the parameters of the source generative adversarial neural network 202 to generate a modified or target generative adversarial neural network 204 (represented by G.sub.t) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, and Ojha . OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Ojha that can improve the accuracy of the GAN using GAN translation ( Ojha [0086]). Odino and Ojha do not explicitly disclose: - A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising: - obtaining a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters; - and updating a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized. However , Wang discloses: - A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising: In [Col 11, lines 58-67], in [Col 12, lines 1-5]: (BRI: a computer readable storage is a non-transitory) - extracting first feature values from input data by processing based on one or more first parameters; extracting second feature values from the input data by processing based on one or more second parameters different from the first parameters; In [Col 10, lines 29-30]: extract local image features of pedestrians ; a module for extracting local features of pedestrians. In [Col 7, lines 63-67]: inputting the images into the three-channel image convolutional neural network to capture image channel features of pedestrians , and referring to FIG. 3, the fourth stage is divided into the following sub-steps: In [Col 8, lines 1-16]: Step 1: with regard to the three channels of the input images, constructing a three-channel image convolutional neural network which includes three convolution kernels that corre spond to the three channels of the images , respectively; allowing the three convolution kernels to learn weight parameters of corre sponding image channels respectively , so as to output three groups of different weight parameters, each of the convolution kernels having a size of 1×1×3, where 3 is the number of channels of the input images; inputting the images into the three-channel image convolutional neural network, where the input images are weighted and combined in a convolution depth direction and, after going through the three convolution kernels of 1×1×3, output three local features which contain the weight parameters among the three channels (BRI: the weight parameters with different weights corresponding to image channels do represent feature parameters that are different as the weight parameters in CNN is designed to extract specific features from the input data and to learn most relevant features during training) - obtaining a first processing result by processing based on one or more third parameters with respect to the first converted feature values, the one or more third parameters being different from the first parameters and the second parameters; In [Col 7, lines 64-67]: inputting the images into the three-channel image convolutional neural network to capture image channel features of pedestrians, and referring to FIG. 3, the fourth stage is divided into the following sub-steps: In [Col 8, lines 64-67]: Step 1: with regard to the three channels of the input images, constructing a three-channel image convolutional neural network which includes three convolution kernels that correspond to the three channels of the images , respectively; allowing the three convolution kernels to learn weight parameters of corresponding image channels respectively, so as to output three groups of different weight parameters , each of the convolution kernels having a size of 1×1×3, where 3 is the number of channels of the input images; inputting the images into the three-channel image convolutional neural network, where the input images are weighted and combined in a convolution depth direction and, after going through the three convolution kernels of 1×1×3, output three local features which contain the weight parameters among the three channels, and the calculation formula is as follows: in [Col 8, lines 1-42]: PNG media_image3.png 60 378 media_image3.png Greyscale where: O( i, j) is an output matrix, I is an input matrix, K is a convolution kernel matrix, and the convolution kernel matrix K has a shape of m x n; I (i+m, j+n) K (m,n) K(m,n) represents that elements of the input matrix I( i+m,j+n) are multiplied by elements of the kernel matrix K(m, n), and PNG media_image4.png 33 152 media_image4.png Greyscale is accumulated and summed in horizontal and vertical directions of the matrix, respectively; and Step 2: allowing the three convolution kernels to calculate independently, and to learn differential parameter weights among the three channels, and obtaining feature space maps of the three channels, which are then calculated interactively to obtain image channel features of pedestrians . - and updating a parameter of at least one of the first parameters and the third parameters such that a value based on the first loss and a second loss calculated from the first processing result and a label is minimized. In [Col 14, lines 37-54]: S7.1: adopting the feed-forward neural network and the activation function, inputting the obtained enhanced local image features of pedestrians into the feed-forward neural network, allowing it to go through linear layer transformation, and mapping the probability distribution of pedestrians into categories to recognize pedestrians by using the activation function; and S7.2: calculating an intersection ratio of coordinates of the recognized pedestrians and the image label ed sample in the original surveillance video image data set, and calculating an accuracy rate and a recall rate, where the accuracy rate refers to the recognized pedestrians , indicating a proportion of real pedestrians in the sample predicted to be positive, and the recall rate refers to the image labeled sample in the original surveillance video image data set, indicating a proportion of correctly recognized pedestrians in the positive examples in the sample. In [Col 10, lines 50-53]: configured to construct a feed-forward neural network , where the enhanced local image features of pedestrians go through linear transformation and are then mapped into a pedestrian probability output; a module for model training, configured to iteratively train the neural network obtained by joint modeling and update model parameters until the model training converges, It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, Ojha and Wang. OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Wang that can enhance local features to improve the recognition rate of the object (pedestrians) ( Wang [Col 5, lines 18-19]) In regard to claim 12: OGINO discloses: - An inference apparatus using a first network and a second network trained by the learning apparatus In [Abstract]: A medical imaging apparatus includes an imaging unit that collects an image signal of an inspection target, and an image processing unit that generates first image data from the image signal and performs image processing of the first image data. In [Abstract]: To obtain a predict ive model that shows a diagnostic predict ion result with higher accuracy and high medical validity. In [0063]: The CNN is trained so that a feature quantity that most contributes to the parameter is output, and is used as the predictive model 233M of the feature quantity abstraction unit 233. - the first network including a parameter group of the first parameters and the third parameters In [0118]: a plurality of patterns of learning models such as a learning model according to an examination site of the subject (BRI: the plurality of patterns in a leaning model can be considered as plurality of networks provide different patterns and representation of data) In [0093]: in the feature quantity space (feature quantity map), a relationship in image data among teacher data 1002, image data before conversion 1001 and after conversion 1003 from the feature quantity B to the feature quantity C is analyzed. For example, a feature quantity space error reduction is particularly weighted to the distance between a patch image (group) 1402 having a deep relation to determination of the presence or absence of a disease in a pathological image and an MRI image (group) 1401 corresponding thereto. In [0104]: the feature quantity conversion unit 234 uses the feature quantity conversion model 234M (FIG. 6) to convert the feature quantity B into the feature quantity C (S5). The identification unit 235 uses the identification model 235M to calculate a parameter value for predicting - the second network including a parameter group of the second parameters and the fourth parameters, the inference apparatus comprising` processing circuitry configured to: In [0118]: a plurality of patterns of learning models such as a learning model according to an examination site of the subject In [0093]: in the feature quantity space (feature quantity map), a relationship in image data among teacher data 1002, image data before conversion 1001 and after conversion 1003 from the feature quantity B to the feature quantity C is analyzed. For example, a feature quantity space error reduction is particularly weighted to the distance between a patch image (group) 1402 having a deep relation to determination of the presence or absence of a disease in a pathological image and an MRI image (group) 1401 corresponding thereto. In [0104]: the feature quantity conversion unit 234 uses the feature quantity conversion model 234M (FIG. 6) to convert the feature quantity B into the feature quantity C (S5). The identification unit 235 uses the identification model 235M to calculate a parameter value for predicting - input processing target data to the first network and generate a first processing result; In [0118]: a plurality of patterns of learning models such as a learning model according to an examination site of the subject (BRI: first network) In [0041]: As illustrated in FIG. 1, a medical imaging apparatus 10 according to the present embodiment includes an imaging unit 100 that collects an image signal necessary for image reconstruction from a subject and an image processing unit 200 that performs image processing of the subject imaged by the imaging unit 100. The medical imaging apparatus 10 further includes an input unit 110. In [ 0013 ]: Specifically, a medical imaging apparatus includes an imaging unit that collects an image signal of an inspection target , and an image processing unit that generates first image data from the image signal and performs image processing of the first image data. The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that extracts (abstracts) a more important second feature quantity from the first feature quantity, a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data different from the first image data, and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value, and performs prediction . - input processing target data to the second network and generate a second processing result; In [0118]: a plurality of patterns of learning models such as a learning model according to an examination site of the subject (BRI: second network) In [0041]: As illustrated in FIG. 1, a medical imaging apparatus 10 according to the present embodiment includes an imaging unit 100 that collects an image signal necessary for image reconstruction from a subject and an image processing unit 200 that performs image processing of the subject imaged by the imaging unit 100. The medical imaging apparatus 10 further includes an input unit 110. In [ 0013 ]: Specifically, a medical imaging apparatus includes an imaging unit that collects an image signal of an inspection target , and an image processing unit that generates first image data from the image signal and performs image processing of the first image data. The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that extracts (abstracts) a more important second feature quantity from the first feature quantity, a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data different from the first image data, and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value, and performs prediction . - and calculate at least one of a weighted average of the first processing result and the second processing result and reliability based on a difference between the first processing result and the second processing result. In [ 0095 ] : in FIG. 15 illustrating a feature quantity space, an error function in which weights are given to a distance between medically important teacher data A1 and an output B1 and a distance between next important teacher data group (data set S) Ai and an output Bi is set. When a conversion function to the feature quantity space ε is set to g and the center of gravity ( average value of coordinates of each piece of data ) on the space ε is represented by C , the error function is represented by the following Formula (6). PNG media_image6.png 107 363 media_image6.png Greyscale In [0096]: Here, α, β, and γ are weighting factors, for example, α=0.5, β=0.4, and γ=0.1. In [0097]: By using the error function as described above, it is possible to reduce the error of the feature quantity conversion model or the identification model and realize a more accurate predictive model. Alternatively, the error functions (2) and (5) may be combined and weighted to form an error function represented by the following Formula (7). PNG media_image7.png 26 377 media_image7.png Greyscale In [0098]: Here, w1 and w2 are weighting factors (for example, w1=0.5, w2=0.5). Claim 2 is rejected under 35 U.S.C. 103 unpatentable over Masahiro OGINO e t.al. (hereinafter OGINO) US 2021/0272277A1, in view of Utkarsh Ojha et.al. (hereinafter Ojha) US 2022/0254071 A1, in view of Hongsheng Wang et.al. (hereinafter Wang) US 11819366 B1, further in view of ROMA Cambray et.al. (hereinafter Cambray) US 2023/0197248 A1. In regard to claim 2: OGINO does not explicitly disclose: - wherein the processing circuitry generates the first converted feature values and the second converted feature values by replacing at least one element of the first feature values and the second feature values selected by a random number with a predetermined value However, Ojha discloses: - wherein the processing circuitry generates the first converted feature values and the second converted feature values by replacing at least one element of the first feature values and the second feature values selected by a random number with a predetermined value In [0075]: To define the anchor region 406, the GAN translation system 102 selects k random points (e.g., corresponding to the number of example digital images within a few-shot target set 408) within the latent space 402. In addition, the GAN translation system 102 samples from these fixed points with a small added Gaussian noise (σ=0.5 ). Further, the GAN translation system 102 utilizes shared parameters (e.g., weights) between the two discriminators by defining D.sub.patch as a subset of the larger D.sub.img neural network. In various embodiments, network size depends on the network architecture and layer. In some cases, the GAN translation system 102 reads off a set of layers with effective patch size ranging from 22×22 to 61×61. (BRI: the selection of k rando points corresponding to the number of digital image features is indeed a method of randomization that serve as a initial cluster centroids) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, and Ojha . OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. One of ordinary skill would have motivation to combine OGINO and Ojha that can improve the accuracy of the GAN using GAN translation ( Ojha [0086]). Odino, Ojha and Wang do not explicitly disclose: - wherein the processing circuitry generates the first converted feature values and the second converted feature values by replacing at least one element of the first feature values and the second feature values selected by a random number with a predetermined value However, Cambray discloses: - wherein the processing circuitry generates the first converted feature values and the second converted feature values by replacing at least one element of the first feature values and the second feature values selected by a random number with a predetermined value In [0081]: In an embodiment of this invention, the CADx device (120) incorporates the ability to adapt to the medical image(s) scan protocol information (116). In some cases, it is expected that part or all of the scan protocol information (116) may be missing. It would therefore be advantageous to incorporate the ability to handle such scenario, where the scan protocol information is incomplete. In [0082]: a specific point in that encoding space such as for example, the zero vector, may be chosen to represent a missing scan protocol parameter. During training, the stochastic drop-out step (801) sets protocol parameters as missing according to some policy, preferably by a random process with a pre-determined probability for each scan protocol parameter. When a scan protocol parameter is removed, it is simply substituted by the zero-vector. Note that outside of training, at the time of making predictions, this stochastic drop-out step is not applied and instead, the model is able to handle missing parameters by converting the missing parameter(s) to the zero-vector. It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, Ojha, Wang and Cambray. OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. Cambray teaches replacing an element of the feature values selected by a random number with a predetermined value One of ordinary skill would have motivation to combine OGINO, Ojha, Wang and Cambray that can provide minimum error metric using trainable machine learning model ( Cambray [ 0059]). Claims 3 and 6 are rejected under 35 U.S.C. 103 unpatentable over Masahiro OGINO e t.al. (hereinafter OGINO) US 2021/0272277 A1, in view of Utkarsh Ojha et.al. (hereinafter Ojha) US 2022/0254071 A1, in view of Hongsheng Wang et.al. (hereinafter Wang) US 11819366 B1, further in view of Atsushi YAGUCHI et.al. (hereinafter YAGUCHI) US 2019/0005644 A1. In regard to claim 3: OGINO, Ojha and Wang do not explicitly disclose: - wherein the processing circuitry generates the first converted feature values and the second converted feature values by (a) adding at least one of the first feature values and the second feature values to a pattern generated based on a random number or (b) multiplying at least one of the first feature values and the second feature values by the pattern. However , YAGUCHI discloses: - wherein the processing circuitry generates the first converted feature values and the second converted feature values by (a) adding at least one of the first feature values and the second feature values to a pattern generated based on a random number or (b) multiplying at least one of the first feature values and the second feature values by the pattern. In [Abstract]: an image processing apparatus includes processing circuitry. The processing circuitry is configured to acquire medical image data. The processing circuitry is configured to obtain spatial distribution of likelihood values representing a likelihood of corresponding to a textual pattern in a predetermined region of a medical image for each of a plurality of textual patterns based on the medical image data. The processing circuitry is configured to calculate feature values in the predetermined region of the medical image based on the spatial distribution obtained for the each of the plurality of textual patterns. in [ 0063 ]: A weighting factor to be multiplied to a likelihood value is determined by various techniques . For example , as a weighting factor, a determined value, such as a Gaussian filter , a Gabor filter, an average value filter, and a box filter, may be used. In [0063]: a network is prepared, which connects, at all coupling layers, output units having been matched in advance with the number of textual patterns to be identified , and feature values acquired by being multiplied by a weighting factor . Note that an initial value of the weighting factor is preferably set randomly from Gaussian distribution , uniform distribution, etc. For example, the weighting factor is repeatedly updated using an error inverse propagation method . In the case of using the machine learning, a weighting factor is automatically determined in line with a problem to be identified. It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, Ojha, Wang and YAGUCHI. OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. YAGACHI teaches multiplying at least one of the first feature values and the second feature values by the pattern. One of ordinary skill would have motivation to combine OGINO, Ojha, Wang and YAGUCHI that can provide improved accuracy ( Yaguchi [ 0063]). In regard to claim 6: Odino, Ojha and Wang do not explicitly disclose: - wherein the processing circuitry executes preprocessing including one or more conversions on the input data. However, Yaguchi discloses: - wherein the processing circuitry executes preprocessing including one or more conversions on the input data. In [0032]: The processing circuitry 11 according to the present embodiment executes a program according to the present embodiment to calculate a feature value using a likelihood that an element included in a medical image is likely to be classified into a classification item corresponding to a predetermined feature . (BRI: calculating a feature value from a likely to be classified based on a predetermined feature is a “preprocessing”). It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine OGINO, Ojha, Wang and YAGUCHI. OGINO teaches extracting features and generating a converted feature values by stochastically converting the feature values. Ojha teaches loss related to similarity of converted features. Wang teaches processing the feature parameters and updating parameters to minimize the loss. YAGACHI teaches multiplying at least one of the first feature values and the second feature values by the pattern. One of ordinary skill would have motivation to combine OGINO, Ojha, Wang and YAGUCHI that can provide improved accuracy ( Yaguchi [ 0063]). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIRUMALE KRISHNASWAMY RAMESH whose telephone number is (571)272-4605. The examiner can normally be reached by phone. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached on phone (571-272-3768). The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TIRUMALE K RAMESH/ Examiner, Art Unit 2121 /Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121 Application/Control Number: 18/458,209 Page 2 Art Unit: 2121 Application/Control Number: 18/458,209 Page 3 Art Unit: 2121 Application/Control Number: 18/458,209 Page 4 Art Unit: 2121 Application/Control Number: 18/458,209 Page 5 Art Unit: 2121 Application/Control Number: 18/458,209 Page 6 Art Unit: 2121 Application/Control Number: 18/458,209 Page 7 Art Unit: 2121 Application/Control Number: 18/458,209 Page 8 Art Unit: 2121 Application/Control Number: 18/458,209 Page 9 Art Unit: 2121 Application/Control Number: 18/458,209 Page 10 Art Unit: 2121 Application/Control Number: 18/458,209 Page 11 Art Unit: 2121 Application/Control Number: 18/458,209 Page 12 Art Unit: 2121 Application/Control Number: 18/458,209 Page 13 Art Unit: 2121 Application/Control Number: 18/458,209 Page 14 Art Unit: 2121 Application/Control Number: 18/458,209 Page 15 Art Unit: 2121 Application/Control Number: 18/458,209 Page 16 Art Unit: 2121 Application/Control Number: 18/458,209 Page 17 Art Unit: 2121 Application/Control Number: 18/458,209 Page 18 Art Unit: 2121 Application/Control Number: 18/458,209 Page 19 Art Unit: 2121 Application/Control Number: 18/458,209 Page 20 Art Unit: 2121 Application/Control Number: 18/458,209 Page 21 Art Unit: 2121 Application/Control Number: 18/458,209 Page 22 Art Unit: 2121 Application/Control Number: 18/458,209 Page 23 Art Unit: 2121 Application/Control Number: 18/458,209 Page 24 Art Unit: 2121 Application/Control Number: 18/458,209 Page 25 Art Unit: 2121 Application/Control Number: 18/458,209 Page 26 Art Unit: 2121 Application/Control Number: 18/458,209 Page 27 Art Unit: 2121 Application/Control Number: 18/458,209 Page 28 Art Unit: 2121 Application/Control Number: 18/458,209 Page 29 Art Unit: 2121 Application/Control Number: 18/458,209 Page 30 Art Unit: 2121 Application/Control Number: 18/458,209 Page 31 Art Unit: 2121 Application/Control Number: 18/458,209 Page 32 Art Unit: 2121 Application/Control Number: 18/458,209 Page 33 Art Unit: 2121 Application/Control Number: 18/458,209 Page 34 Art Unit: 2121 Application/Control Number: 18/458,209 Page 35 Art Unit: 2121 Application/Control Number: 18/458,209 Page 36 Art Unit: 2121 Application/Control Number: 18/458,209 Page 37 Art Unit: 2121 Application/Control Number: 18/458,209 Page 38 Art Unit: 2121 Application/Control Number: 18/458,209 Page 39 Art Unit: 2121 Application/Control Number: 18/458,209 Page 40 Art Unit: 2121 Application/Control Number: 18/458,209 Page 41 Art Unit: 2121 Application/Control Number: 18/458,209 Page 42 Art Unit: 2121 Application/Control Number: 18/458,209 Page 43 Art Unit: 2121 Application/Control Number: 18/458,209 Page 44 Art Unit: 2121 Application/Control Number: 18/458,209 Page 45 Art Unit: 2121 Application/Control Number: 18/458,209 Page 46 Art Unit: 2121 Application/Control Number: 18/458,209 Page 47 Art Unit: 2121 Application/Control Number: 18/458,209 Page 48 Art Unit: 2121
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