Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicants
This communication is in response to the application filed on 09/27/2024
Claims 1-13 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 09/27/2024 has been fully considered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-2, 7, 12-13 are rejected under 35 § U.S.C. 103 as being obvious over US 2022/0254071 A1 to OJHA et al. (hereinafter “OJHA”) in view of US 2005/0102246 A1 to MOVELLAN et al. (hereinafter “MOVELLAN”).
As per claim 1, OJHA discloses a learning system (a computing system adapted to perform a teaching/learning method; abstract; figs 1, 11; paragraph [0099]), comprising at least one processor configured to (the system comprising a computer with a computing processor; abstract; figs 1, 11; paragraph [0099]): acquire, for each feature of a generative adversarial network (GAN) which allows a user to control a plurality of features relating to a generated image, an anchor discrimination image (the computing system is adapted to acquire a plurality of image features for use in a generative adversarial neural network and includes a generated anchoring discriminator image used to train the GAN; figs 1, 4, 11; paragraphs [0030], [0046], [0062], [0067-0073], [0085-0088], [0099], [0109]), a positive discrimination image changed in the each feature from the anchor discrimination image (the computing system is adapted to change image feature/parameters in order to generate a positive discrimination image from the anchor image by discriminator neural network also causes the GAN translation system to modify parameters of the encoder neural network and/or the generator neural network to learn to eventually generate digital images that fool the discriminator neural network into indicating that a generated digital image is a real digital image; figs 1, 4, 11; paragraphs [0030], [0046], [0062], [0067-0073], [0085-0088], [0099], [0109]), and a negative discrimination image changed in another feature from the anchor discrimination image (and further the system is adapted to generate negative anchor discrimination images which the neural network can determine are fake or generated by the computer based on the features being analyzed; figs 6-7; paragraphs [0085-0088]); calculate, for each feature space corresponding to each of the plurality of features (as seen in table 700 of fig 7 the computing system calculates performance metrics related to image features of the GAN translation systems ability in rendering the digital images; figs 6-7; paragraphs [0085-0088]), based on a discriminator of the GAN (based on comparison to the generated discriminator image of the GAN; figs 6-7; paragraphs [0085-0088]), an anchor discrimination vector relating to the anchor discrimination image of the each of the plurality of features (using a plurality of digital image feature vectors to compare to the discriminator anchor image vectors; fig 9; paragraphs [0092-0097]), a positive discrimination vector relating to the positive discrimination image of the each of the plurality of features (for example as discussed in the prior art regarding fig 10A the source domain vector includes a set of digital images acting as the anchor discrimination vector, and the GAN is used to generate a first set of images acting as the positive discriminator images based on adjustment of specific image features; figs 9-10A; paragraphs [0088], [0092-0097], [0103-0107]), and a negative discrimination vector relating to the negative discrimination image of the each of the plurality of features (the computing system is further adapted to using the GAN network generate a set of images based on the domain vector in which as seen in 10B the GAN can be used to generate a second set of images which act substantially as the negative discrimination images after the GAN adjusts the plurality of image features related to the target/desired feature vectors; figs 9-10A; paragraphs [0088], [0092-0097], [0103-0107], [0110-0116]). OJHA fails to disclose and execute learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the positive discrimination vector approach each other, and the anchor discrimination vector and the negative discrimination vector become distant from each other.
MOVELLAN discloses and execute learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the positive discrimination vector approach each other, and the anchor discrimination vector and the negative discrimination vector become distant from each other (the learning model is taught using weak hypothesis’s which are tested via a selecting unit to determine if the weak hypothesis is a positive discriminator or a negative discriminator and is determined via a weak hypothesis selection unit 35 and tested via a reliability unit to determine if the model is updated using the image/feature data by comparing the data to an abort threshold 37 wherein the positive discriminators are above the threshold value and negative discriminators are below said threshold value; figs 10-12, 14; paragraphs [0138-0145], [0163-0166]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify OJHA to have execute learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the positive discrimination vector approach each other, and the anchor discrimination vector and the negative discrimination vector become distant from each other of MOVELLAN reference. The Suggestion/motivation for doing so would have been to provide a way to track reliability of a training hypothesis expressed by the equation (6), where the lower the weighted error rate Et is, the higher the reliability at of the weak hypothesis is as suggested by paragraphs [0166] to MOVELLAN. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MOVELLAN with OJHA to obtain the invention as specified in claim 1.
As per claim 2, OJHA in view of MOVELLAN discloses the learning system according to claim 1. Modified OJHA fails to disclose wherein the plurality of features are three or more features, wherein the at least one processor configured to: acquire, for the each feature, a plurality of the negative discrimination images changed in other features different from one another, calculate, for each feature space corresponding to each of the plurality of features, the negative discrimination vector of each of the plurality of the negative discrimination images of the each of the plurality of features, and execute the learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the negative discrimination vector of each of the plurality of the negative discrimination images become distant from each other.
MOVELLAN discloses wherein the plurality of features are three or more features, wherein the at least one processor configured to: acquire, for the each feature, a plurality of the negative discrimination images changed in other features different from one another, calculate, for each feature space corresponding to each of the plurality of features, the negative discrimination vector of each of the plurality of the negative discrimination images of the each of the plurality of features, and execute the learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the negative discrimination vector of each of the plurality of the negative discrimination images become distant from each other (the system generates using the GAN weak hypothesis which are determined to be either positive or negative discriminators used to further train the recognition model providing the data as learning data to be used by the face detection apparatus of the prior art, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of calculated statistical feature/characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is acquired, this is done, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition in this provided example; figs 10-12, 14; paragraphs [0138-0145], [0163-0166]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify OJHA to have execute the learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the negative discrimination vector of each of the plurality of the negative discrimination images become distant from each other of MOVELLAN reference. The Suggestion/motivation for doing so would have been to provide in the case of discriminating a face from an input image and any other use case to which the method is applied by using a final hypothesis made up of many weak hypotheses acquired by learning, as discrimination is made by a classifier made up of plural weak hypotheses, as described above, the quantity of arithmetic operation is reduced, compared with the case of making weighted vote among sum values of all the weak hypotheses, and the discrimination processing speed can be further improved as suggested by paragraphs [0016], and [0236] of MOVELLAN. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MOVELLAN with OJHA to obtain the invention as specified in claim 2.
As per claim 7, OJHA in view of MOVELLAN discloses the learning system according to claim 1, wherein the at least one processor configured to: acquire, for the each feature, an anchor latent code (digital images are generated using latent vectors as the vectors are sampled from anchor regions of the digital image generating a first set of digital images from a first set of latent vectors sampled from a first region of a latent space; figs 10A-10B; paragraphs [0102-0116]), a positive latent code obtained by changing a portion of the anchor latent code corresponding to the each feature (a positive latent vector would be generated in relation to the positive discriminator image; figs 10A-10B; paragraphs [0102-0116]), and a negative latent code obtained by changing a portion of the anchor latent code corresponding to another feature (and vice versa a negative latent vector acting as the negative latent code would be generated and related to the negative discriminator image generated from the anchor; figs 10A-10B; paragraphs [0102-0116]); generate, for the each feature, based on a generator of the GAN, an anchor generated image corresponding to the anchor latent code of the each feature, a positive generated image corresponding to the positive latent code of the each feature, and a negative generated image corresponding to the negative latent code of the each feature (; figs 10A-10B; paragraphs [0102-0116]); calculate, for each feature space corresponding to each of the plurality of features (as seen in table 700 of fig 7 the computing system calculates performance metrics related to image features of the GAN translation systems ability in rendering the digital images; figs 6-7; paragraphs [0085-0088]), based on the discriminator (based on comparison to the generated discriminator image of the GAN; figs 6-7; paragraphs [0085-0088]), an anchor generation vector relating to the anchor generated image of the each of the plurality of features (using a plurality of digital image feature vectors to compare to the discriminator anchor image vectors; fig 9; paragraphs [0092-0097]), a positive generation vector relating to the positive generated image of the each of the plurality of features (for example as discussed in the prior art regarding fig 10A the source domain vector includes a set of digital images acting as the anchor discrimination vector, and the GAN is used to generate a first set of images acting as the positive discriminator images based on adjustment of specific image features; figs 9-10A; paragraphs [0088], [0092-0097], [0103-0107]), and a negative generation vector relating to the negative generated image of the each of the plurality of features (the computing system is further adapted to using the GAN network generate a set of images based on the domain vector in which as seen in 10B the GAN can be used to generate a second set of images which act substantially as the negative discrimination images after the GAN adjusts the plurality of image features related to the target/desired feature vectors; figs 9-10A; paragraphs [0088], [0092-0097], [0103-0107], [0110-0116]). Modified OJHA fails to disclose and execute learning of the generator such that, in the feature space corresponding to each of the plurality of changes, the anchor generation vector and the positive generation vector approach each other, and the anchor generation vector and the negative generation vector become distant from each other.
MOVELLAN discloses and execute learning of the generator such that, in the feature space corresponding to each of the plurality of changes, the anchor generation vector and the positive generation vector approach each other, and the anchor generation vector and the negative generation vector become distant from each other (the learning model is taught using weak hypothesis’s which are tested via a selecting unit to determine if the weak hypothesis is a positive discriminator or a negative discriminator and is determined via a weak hypothesis selection unit 35 and tested via a reliability unit to determine if the model is updated using the image/feature data by comparing the data to an abort threshold 37 wherein the positive discriminators are above the threshold value and negative discriminators are below said threshold value which would result in the model GAN being trained towards values that meet the threshold and away from values that do not meet said threshold; figs 10-12, 14; paragraphs [0138-0145], [0163-0166]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify OJHA to have execute learning of the generator such that, in the feature space corresponding to each of the plurality of changes, the anchor generation vector and the positive generation vector approach each other, and the anchor generation vector and the negative generation vector become distant from each other of MOVELLAN reference. The Suggestion/motivation for doing so would have been to provide a way to track reliability of a training hypothesis expressed by the equation (6), where the lower the weighted error rate Et is, the higher the reliability at of the weak hypothesis is as suggested by paragraphs [0166] to MOVELLAN. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MOVELLAN with OJHA to obtain the invention as specified in claim 7.
As per claim 12, OJHA discloses a learning method executed by a computer (a computing system adapted to perform a teaching/learning method; abstract; figs 1, 11; paragraph [0099]), comprising: acquiring, for each feature of a generative adversarial network (GAN) which allows a user to control a plurality of features relating to a generated image, an anchor discrimination image (the computing system is adapted to acquire a plurality of image features for use in a generative adversarial neural network and includes a generated anchoring discriminator image used to train the GAN; figs 1, 4, 11; paragraphs [0030], [0046], [0062], [0067-0073], [0085-0088], [0099], [0109]), a positive discrimination image changed in the each feature from the anchor discrimination image (the computing system is adapted to change image feature/parameters in order to generate a positive discrimination image from the anchor image by discriminator neural network also causes the GAN translation system to modify parameters of the encoder neural network and/or the generator neural network to learn to eventually generate digital images that fool the discriminator neural network into indicating that a generated digital image is a real digital image; figs 1, 4, 11; paragraphs [0030], [0046], [0062], [0067-0073], [0085-0088], [0099], [0109]), and a negative discrimination image changed in another feature from the anchor discrimination image (and further the system is adapted to generate negative anchor discrimination images which the neural network can determine are fake or generated by the computer based on the features being analyzed; figs 6-7; paragraphs [0085-0088]); calculating, for each feature space corresponding to each of the plurality of features (as seen in table 700 of fig 7 the computing system calculates performance metrics related to image features of the GAN translation systems ability in rendering the digital images; figs 6-7; paragraphs [0085-0088]), based on a discriminator of the GAN (based on comparison to the generated discriminator image of the GAN; figs 6-7; paragraphs [0085-0088]), an anchor discrimination vector relating to the anchor discrimination image of the each of the plurality of features (using a plurality of digital image feature vectors to compare to the discriminator anchor image vectors; fig 9; paragraphs [0092-0097]), a positive discrimination vector relating to the positive discrimination image of the each of the plurality of features (for example as discussed in the prior art regarding fig 10A the source domain vector includes a set of digital images acting as the anchor discrimination vector, and the GAN is used to generate a first set of images acting as the positive discriminator images based on adjustment of specific image features; figs 9-10A; paragraphs [0088], [0092-0097], [0103-0107]), and a negative discrimination vector relating to the negative discrimination image of the each of the plurality of features (the computing system is further adapted to using the GAN network generate a set of images based on the domain vector in which as seen in 10B the GAN can be used to generate a second set of images which act substantially as the negative discrimination images after the GAN adjusts the plurality of image features related to the target/desired feature vectors; figs 9-10A; paragraphs [0088], [0092-0097], [0103-0107], [0110-0116]). OJHA fails to disclose and executing learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the positive discrimination vector approach each other, and the anchor discrimination vector and the negative discrimination vector become distant from each other.
MOVELLAN discloses and executing learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the positive discrimination vector approach each other, and the anchor discrimination vector and the negative discrimination vector become distant from each other (the learning model is taught using weak hypothesis’s which are tested via a selecting unit to determine if the weak hypothesis is a positive discriminator or a negative discriminator and is determined via a weak hypothesis selection unit 35 and tested via a reliability unit to determine if the model is updated using the image/feature data by comparing the data to an abort threshold 37 wherein the positive discriminators are above the threshold value and negative discriminators are below said threshold value; figs 10-12, 14; paragraphs [0138-0145], [0163-0166]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify OJHA to have execute learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the positive discrimination vector approach each other, and the anchor discrimination vector and the negative discrimination vector become distant from each other of MOVELLAN reference. The Suggestion/motivation for doing so would have been to provide a way to track reliability of a training hypothesis expressed by the equation (6), where the lower the weighted error rate Et is, the higher the reliability at of the weak hypothesis is as suggested by paragraphs [0166] to MOVELLAN. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MOVELLAN with OJHA to obtain the invention as specified in claim 12.
As per claim 13, OJHA discloses a non-transitory computer-readable information storage medium storing a program for causing a computer to (a computing system adapted to perform a teaching/learning method the system comprising a computer with a computing processor and memory component adapted to store and execute programs, instructions, and data related to the method; abstract; figs 1, 11; paragraph [0099]): acquire, for each feature of a generative adversarial network (GAN) which allows a user to control a plurality of features relating to a generated image, an anchor discrimination image (the computing system is adapted to acquire a plurality of image features for use in a generative adversarial neural network and includes a generated anchoring discriminator image used to train the GAN; figs 1, 4, 11; paragraphs [0030], [0046], [0062], [0067-0073], [0085-0088], [0099], [0109]), a positive discrimination image changed in the each feature from the anchor discrimination image (the computing system is adapted to change image feature/parameters in order to generate a positive discrimination image from the anchor image by discriminator neural network also causes the GAN translation system to modify parameters of the encoder neural network and/or the generator neural network to learn to eventually generate digital images that fool the discriminator neural network into indicating that a generated digital image is a real digital image; figs 1, 4, 11; paragraphs [0030], [0046], [0062], [0067-0073], [0085-0088], [0099], [0109]), and a negative discrimination image changed in another feature from the anchor discrimination image (and further the system is adapted to generate negative anchor discrimination images which the neural network can determine are fake or generated by the computer based on the features being analyzed; figs 6-7; paragraphs [0085-0088]); calculate, for each feature space corresponding to each of the plurality of features (as seen in table 700 of fig 7 the computing system calculates performance metrics related to image features of the GAN translation systems ability in rendering the digital images; figs 6-7; paragraphs [0085-0088]), based on a discriminator of the GAN (based on comparison to the generated discriminator image of the GAN; figs 6-7; paragraphs [0085-0088]), an anchor discrimination vector relating to the anchor discrimination image of the each of the plurality of features (using a plurality of digital image feature vectors to compare to the discriminator anchor image vectors; fig 9; paragraphs [0092-0097]), a positive discrimination vector relating to the positive discrimination image of the each of the plurality of features (for example as discussed in the prior art regarding fig 10A the source domain vector includes a set of digital images acting as the anchor discrimination vector, and the GAN is used to generate a first set of images acting as the positive discriminator images based on adjustment of specific image features; figs 9-10A; paragraphs [0088], [0092-0097], [0103-0107]), and a negative discrimination vector relating to the negative discrimination image of the each of the plurality of features (the computing system is further adapted to using the GAN network generate a set of images based on the domain vector in which as seen in 10B the GAN can be used to generate a second set of images which act substantially as the negative discrimination images after the GAN adjusts the plurality of image features related to the target/desired feature vectors; figs 9-10A; paragraphs [0088], [0092-0097], [0103-0107], [0110-0116]). OJHA fails to disclose and execute learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the positive discrimination vector approach each other, and the anchor discrimination vector and the negative discrimination vector become distant from each other.
MOVELLAN discloses and execute learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the positive discrimination vector approach each other, and the anchor discrimination vector and the negative discrimination vector become distant from each other (the learning model is taught using weak hypothesis’s which are tested via a selecting unit to determine if the weak hypothesis is a positive discriminator or a negative discriminator and is determined via a weak hypothesis selection unit 35 and tested via a reliability unit to determine if the model is updated using the image/feature data by comparing the data to an abort threshold 37 wherein the positive discriminators are above the threshold value and negative discriminators are below said threshold value; figs 10-12, 14; paragraphs [0138-0145], [0163-0166]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify OJHA to have execute learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector and the positive discrimination vector approach each other, and the anchor discrimination vector and the negative discrimination vector become distant from each other of MOVELLAN reference. The Suggestion/motivation for doing so would have been to provide a way to track reliability of a training hypothesis expressed by the equation (6), where the lower the weighted error rate Et is, the higher the reliability at of the weak hypothesis is as suggested by paragraphs [0166] to MOVELLAN. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MOVELLAN with OJHA to obtain the invention as specified in claim 13.
Claims 3-5, and 8-11 are rejected under 35 § U.S.C. 103 as being obvious over US 2022/0254071 A1 to OJHA et al. (hereinafter “OJHA”) in view of US 2005/0102246 A1 to MOVELLAN et al. (hereinafter “MOVELLAN”) in view of US 2025/0022096 A1 to DONG (hereinafter “DONG”).
As per claim 3, OJHA in view of MOVELLAN discloses the learning system according to claim 1. Modified OJHA fails to disclose wherein the at least one processor configured to: acquire, for the each feature, a plurality of the anchor discrimination images, a plurality of the positive discrimination images, and a plurality of the negative discrimination images, calculate, for each feature space corresponding to each of the plurality of features, the anchor discrimination vector of each of the plurality of the anchor discrimination images of the each of the plurality of features, the positive discrimination vector of each of the plurality of the positive discrimination images of the each of the plurality of features, and the negative discrimination vector of each of the plurality of the negative discrimination images of the each of the plurality of features, calculate, for each anchor discrimination image, a contrastive discrimination loss relating to closeness between the anchor discrimination vector of the each anchor discrimination image and the positive discrimination vector of the positive discrimination image of the each anchor discrimination image and closeness between the anchor discrimination vector of the each anchor discrimination image and the negative discrimination vector of the negative discrimination image of the each anchor discrimination image; calculate a batchwise discrimination loss relating to an average of the contrastive discrimination losses each calculated for one of the plurality of the anchor discrimination images; and execute the learning of the discriminator based on the batchwise discrimination loss.
DONG discloses wherein the at least one processor configured to: acquire, for the each feature, a plurality of the anchor discrimination images, a plurality of the positive discrimination images, and a plurality of the negative discrimination images (the computing system is adapted to acquire for each image feature vector a plurality of discrimination image features in step 102 and the GAN is to using a discriminative model of the GAN, separately perform discrimination on the first feature and the third feature to obtain a first score corresponding to the positive sample image and a second score corresponding to the reference sample image, and determine a binary cross entropy loss function based on the first score and the second score and further at step 103 the second contrastive learning loss function is used for enabling a feature of the reference sample image to be close to a feature of tl1e positive sample image and far away from a feature of the negative sample image acting as the positive and negative discrimination images; abstract; fig 1; paragraphs [0048-0060]), calculate, for each feature space corresponding to each of the plurality of features, the anchor discrimination vector of each of the plurality of the anchor discrimination images of the each of the plurality of features, the positive discrimination vector of each of the plurality of the positive discrimination images of the each of the plurality of features, and the negative discrimination vector of each of the plurality of the negative discrimination images of the each of the plurality of features (the computing system is adapted to compute vector based value for each image feature of the corresponding generated anchor discrimination image and the positive and negative discrimination images; abstract; fig 1; paragraphs [0048-0060]), calculate, for each anchor discrimination image, a contrastive discrimination loss relating to closeness between the anchor discrimination vector of the each anchor discrimination image and the positive discrimination vector of the positive discrimination image of the each anchor discrimination image and closeness between the anchor discrimination vector of the each anchor discrimination image and the negative discrimination vector of the negative discrimination image of the each anchor discrimination image (using the anchor vector, the positive and negative discrimination vectors the GAN prediction model is applied to a contrastive loss function which records the discriminator loss for the generated training images and includes up to a sixth extracted feature of the positive image see step 103 paragraph [0054]; abstract; fig 1; paragraphs [0048-0060]); calculate a batchwise discrimination loss relating to an average of the contrastive discrimination losses each calculated for one of the plurality of the anchor discrimination images (the super-resolution network is trained based on the calculation of loss values of the positive and negative samples (including both positive and negative samples is batch wise) in the feature dimension, in this embodiment, the output of the network is not only enabled to be close to the ground truths (positive sample images), but also distanced from some defective negative samples, thereby reducing the introduced artifacts and noise; paragraphs [0057-0060]); and execute the learning of the discriminator based on the batchwise discrimination loss (the super-resolution network is trained based on the calculation of loss values of the positive and negative samples in the feature dimension, in this embodiment, the output of the network is not only enabled to be close to the ground truths (positive sample images), but also distanced from some defective negative samples, thereby reducing the introduced artifacts and noise; paragraphs [0057-0060]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify OJHA to have calculate, for each feature space corresponding to each of the plurality of features, the anchor discrimination vector of each of the plurality of the anchor discrimination images of the each of the plurality of features of DONG reference. The Suggestion/motivation for doing so would have been to provide the ability to reducing the introduced artifacts and noise as suggested by paragraph [0059] of DONG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine DONG with modified OJHA to obtain the invention as specified in claim 3.
As per claim 4, OJHA in view of MOVELLAN discloses the learning system according to claim 1. Modified OJHA fails to disclose wherein the at least one processor configured to: acquire, for the each feature, a plurality of the anchor discrimination images, a plurality of the positive discrimination images, and a plurality of the negative discrimination images, calculate, for each feature space corresponding to each of the plurality of features, the anchor discrimination vector of each of the plurality of the anchor discrimination images of the each of the plurality of features, the positive discrimination vector of each of the plurality of the positive discrimination images of the each of the plurality of features, and the negative discrimination vector of each of the plurality of the negative discrimination images of the each of the plurality of features, and execute the learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector of each of the plurality of the anchor discrimination images and the positive discrimination vector of the positive discrimination image of the each of the plurality of the anchor discrimination images approach each other, the anchor discrimination vector of the each of the plurality of the anchor discrimination images and the negative discrimination vector of the negative discrimination image of the each of the plurality of the anchor discrimination images become distant from each other, and the anchor discrimination vector of the each of the plurality of the anchor discrimination images and the anchor discrimination vector of another anchor discrimination image become distant from each other.
DONG discloses wherein the at least one processor configured to: acquire, for the each feature, a plurality of the anchor discrimination images, a plurality of the positive discrimination images, and a plurality of the negative discrimination images (the computing system is adapted to acquire for each image feature vector a plurality of discrimination image features in step 102 and the GAN is to using a discriminative model of the GAN, separately perform discrimination on the first feature and the third feature to obtain a first score corresponding to the positive sample image and a second score corresponding to the reference sample image, and determine a binary cross entropy loss function based on the first score and the second score and further at step 103 the second contrastive learning loss function is used for enabling a feature of the reference sample image to be close to a feature of tl1e positive sample image and far away from a feature of the negative sample image acting as the positive and negative discrimination images; abstract; fig 1; paragraphs [0048-0060]), calculate, for each feature space corresponding to each of the plurality of features, the anchor discrimination vector of each of the plurality of the anchor discrimination images of the each of the plurality of features, the positive discrimination vector of each of the plurality of the positive discrimination images of the each of the plurality of features, and the negative discrimination vector of each of the plurality of the negative discrimination images of the each of the plurality of features (the computing system is adapted to compute vector based value for each image feature of the corresponding generated anchor discrimination image and the positive and negative discrimination images; abstract; fig 1; paragraphs [0048-0060]), and execute the learning of the discriminator such that, in the feature space corresponding to each of the plurality of features, the anchor discrimination vector of each of the plurality of the anchor discrimination images and the positive discrimination vector of the positive discrimination image of the each of the plurality of the anchor discrimination images approach each other (the super-resolution network is trained based on the calculation of loss values of the positive and negative samples (including both positive and negative samples is batch wise) in the feature dimension, in this embodiment, the output of the network is not only enabled to be close to the ground truths (positive sample images), but also distanced from some defective negative samples, thereby reducing the introduced artifacts and noise; paragraphs [0057-0060]), the anchor discrimination vector of the each of the plurality of the anchor discrimination images and the negative discrimination vector of the negative discrimination image of the each of the plurality of the anchor discrimination images become distant from each other, and the anchor discrimination vector of the each of the plurality of the anchor discrimination images and the anchor discrimination vector of another anchor discrimination image become distant from each other (the super-resolution network is trained based on the calculation of loss values of the positive and negative samples (including both positive and negative samples is batch wise) in the feature dimension, in this embodiment, the output of the network is not only enabled to be close to the ground truths (positive sample images), but also distanced from some defective negative samples, thereby reducing the introduced artifacts and noise; paragraphs [0057-0060]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify OJHA to have the negative discrimination vector of the negative discrimination image of the each of the plurality of the anchor discrimination images become distant from each other of DONG reference. The Suggestion/motivation for doing so would have been to provide the ability to reducing the introduced artifacts and noise as suggested by paragraph [0059] of DONG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine DONG with OJHA to obtain the invention as specified in claim 4.
As per claim 5, OJHA in view of MOVELLAN discloses the learning system according to claim 1. Modified OJHA fails to disclose wherein the at least one processor configured to: cause the discriminator to estimate authenticity of the anchor discrimination image and authenticity of the generated image generated by a generator of the GAN, and execute the learning of the discriminator based further on an estimation result of the authenticity of the anchor discrimination image and an estimation result of the authenticity of the generated image generated by the generator.
DONG discloses wherein the at least one processor configured to: cause the discriminator to estimate authenticity of the anchor discrimination image and authenticity of the generated image generated by a generator of the GAN, and execute the learning of the discriminator based further on an estimation result of the authenticity of the anchor discrimination image and an estimation result of the authenticity of the generated image generated by the generator (Gaussian random noise may be added to generate the negative sample image, thereby improving the authenticity of the negative sample image and ensuring the training effect of the GAN neural network is adapted to perform even in the presence of noise; fig 3; paragraphs [0046-0048]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify OJHA to have execute the learning of the discriminator based further on an estimation result of the authenticity of the anchor discrimination image of DONG reference. The Suggestion/motivation for doing so would have been to provide real world noise values to the fused image once the noise is applied to arrive at the negative image as suggested by paragraphs [0046-0047] of DONG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine DONG with modified OJHA to obtain the invention as specified in claim 5.
As per claim 8, OJHA in view of MOVELLAN discloses the learning system according to claim 7. Modified OJHA fails to disclose wherein the plurality of features are three or more features, wherein the at least one processor configured to: generate, for the each feature, a plurality of the negative generated images changed in other features different from one another, calculate, for each feature space corresponding to each of the plurality of features, the negative generation vector of each of the plurality of the negative generated images of the each of the plurality of features, and execute the learning of the generator such that, in the feature space corresponding to each of the plurality of features, the anchor generation vector and the negative generation vector of each of the plurality of the negative generated images become distant from each other.
DONG discloses wherein the plurality of features are three or more features, wherein the at least one processor configured to: generate, for the each feature, a plurality of the negative generated images changed in other features different from one another, calculate, for each feature space corresponding to each of the plurality of features, the negative generation vector of each of the plurality of the negative generated images of the each of the plurality of features, and execute the learning of the generator such that, in the feature space corresponding to each of the plurality of features, the anchor generation vector and the negative generation vector of each of the plurality of the negative generated images become distant from each other (the super-resolution network is trained based on the calculation of loss values of the positive and negative samples (including both positive and negative samples is batch wise) in the feature dimension, in this embodiment, the output of the network is not only enabled to be close to the ground truths (positive sample images), but also distanced from some defective negative samples, thereby reducing the introduced artifacts and noise; paragraphs [0057-0060]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify OJHA to have the anchor generation vector and the negative generation vector of each of the plurality of the negative generated images become distant from each other of DONG reference. The Suggestion/motivation for doing so would have been to provide real world noise values to the fused image once the noise is applied to arrive at the negative image as suggested by paragraphs [0046-0047] of DONG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine DONG with OJHA to obtain the invention as specified in claim 8.
As per claim 9, OJHA in view of MOVELLAN discloses the learning system according to claim 7. Modified OJHA fails to disclose wherein the at least one processor configured to: acquire, for the each feature, a plurality of the anchor latent codes, a plurality of the positive latent codes, and a plurality of the negative latent codes, generate, for the each feature, the anchor generated image corresponding to each of the plurality of the anchor latent codes of the each feature, the positive generated image corresponding to each of the plurality of the positive latent codes of the each feature, and the negative generated image corresponding to each of the plurality of the negative latent codes of the each feature, calculate, for each feature space corresponding to each of the plurality of features, the anchor generation vector of the anchor generated image corresponding to each of the plurality of the anchor latent codes of the each of the plurality of features, the positive generation vector of each of the plurality of the positive generated images of the each of the plurality of features, and the negative generation vector of each of the plurality of the negative generated images of the each of the plurality of features, and calculate, for each anchor generated image, a contrastive generation loss relating to closeness between the anchor generation vector of the each anchor generated image and the positive generation vector of the positive generated image of the each anchor generated image and closeness between the anchor generation vector of the each anchor generated image and the negative generation vector of the negative generated image of the each anchor generated image; calculate a batchwise generation loss relating to an average of the contrastive generation losses each calculated for one of the plurality of anchor generated images; and execute the learning of the generator based on the batchwise generation loss.
DONG discloses wherein the at least one processor configured to: acquire, for the each feature, a plurality of the anchor latent codes, a plurality of the positive latent codes, and a plurality of the negative latent codes, generate, for the each feature, the anchor generated image corresponding to each of the plurality of the anchor latent codes of the each feature, the positive generated image corresponding to each of the plurality of the positive latent codes of the each feature, and the negative generated image corresponding to each of the plurality of the negative latent codes of the each feature (the computing system is adapted to acquire for each image feature vector a plurality of discrimination image features in step 102 and the GAN is to using a discriminative model of the GAN, separately perform discrimination on the first feature and the third feature to obtain a first score corresponding to the positive sample image and a second score corresponding to the reference sample image, and determine a binary cross entropy loss function based on the first score and the second score and further at step 103 the second contrastive learning loss function is used for enabling a feature of the reference sample image to be close to a feature of tl1e positive sample image and far away from a feature of the negative sample image acting as the positive and negative discrimination images; abstract; fig 1; paragraphs [0048-0060]), calculate, for each feature space corresponding to each of the plurality of features, the anchor generation vector of the anchor generated image corresponding to each of the plurality of the anchor latent codes of the each of the plurality of features, the positive generation vector of each of the plurality of the positive generated images of the each of the plurality of features, and the negative generation vector of each of the plurality of the negative generated images of the each of the plurality of features (the computing system is adapted to compute vector based value for each image feature of the corresponding generated anchor discrimination image and the positive and negative discrimination images; abstract; fig 1; paragraphs [0048-0060]), and calculate, for each anchor generated image, a contrastive generation loss relating to closeness between the anchor generation vector of the each anchor generated image and the positive generation vector of the positive generated image of the each anchor generated image and closeness between the anchor generation vector of the each anchor generated image and the negative generation vector of the negative generated image of the each anchor generated image (using the anchor vector, the positive and negative discrimination vectors the GAN prediction model is applied to a contrastive loss function which records the discriminator loss for the generated training images and includes up to a sixth extracted feature of the positive image see step 103 paragraph [0054]; abstract; fig 1; paragraphs [0048-0060]); calculate a batchwise generation loss relating to an average of the contrastive generation losses each calculated for one of the plurality of anchor generated images (the super-resolution network is trained based on the calculation of loss values of the positive and negative samples (including both positive and negative samples is batch wise) in the feature dimension, in this embodiment, the output of the network is not only enabled to be close to the ground truths (positive sample images), but also distanced from some defective negative samples, thereby reducing the introduced artifacts and noise; paragraphs [0057-0060]); and execute the learning of the generator based on the batchwise generation loss the super-resolution network is trained based on the calculation of loss values of the positive and negative samples in the feature dimension, in this embodiment, the output of the network is not only enabled to be close to the ground truths (positive sample images), but also distanced from some defective negative samples, thereby reducing the introduced artifacts and noise; paragraphs [0057-0060]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify OJHA to have calculate, for each feature space corresponding to each of the plurality of features, the anchor discrimination vector of each of the plurality of the anchor discrimination images of the each of the plurality of features of DONG reference. The Suggestion/motivation for doing so would have been to provide real world noise values to the fused image once the noise is applied to arrive at the negative image as suggested by paragraphs [0046-0047] of DONG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine DONG with OJHA to obtain the invention as specified in claim 9.
As per claim 10, OJHA in view of MOVELLAN discloses the learning system according to claim 7. Modified OJHA fails to disclose wherein the at least one processor configured to: acquire, for the each feature, a plurality of the anchor generated images, a plurality of the positive generated images, and a plurality of the negative generated images, calculate, for each feature space corresponding to each of the plurality of features, the anchor generation vector of each of the plurality of the anchor generated images of the each of the plurality of features, the positive generation vector of each of the plurality of the positive generated images of the each of the plurality of features, and the negative generation vector of each of the plurality of the negative generated images of the each of the plurality of features, and execute the learning of the generator such that, in the feature space corresponding to each of the plurality of features, the anchor generation vector of each of the plurality of the anchor generated images and the positive generation vector of the positive generated image of the each of the plurality of the anchor generated images approach each other, the anchor generation vector of the each of the plurality of the anchor generated images and the negative generation vector of the negative generated image of the each of the plurality of the anchor generated images become distant from each other, and the anchor generation vector of the each of the plurality of the anchor generated images and the anchor generation vector of another anchor generated image become distant from each other.
DONG discloses wherein the at least one processor configured to: acquire, for the each feature, a plurality of the anchor generated images, a plurality of the positive generated images (), and a plurality of the negative generated images (the computing system is adapted to acquire for each image feature vector a plurality of discrimination image features in step 102 and the GAN is to using a discriminative model of the GAN, separately perform discrimination on the first feature and the third feature to obtain a first score corresponding to the positive sample image and a second score corresponding to the reference sample image, and determine a binary cross entropy loss function based on the first score and the second score and further at step 103 the second contrastive learning loss function is used for enabling a feature of the reference sample image to be close to a feature of tl1e positive sample image and far away from a feature of the negative sample image acting as the positive and negative discrimination images; abstract; fig 1; paragraphs [0048-0060]), calculate, for each feature space corresponding to each of the plurality of features, the anchor generation vector of each of the plurality of the anchor generated images of the each of the plurality of features (the computing system is adapted to compute vector based value for each image feature of the corresponding generated anchor discrimination image and the positive and negative discrimination images; abstract; fig 1; paragraphs [0048-0060]), the positive generation vector of each of the plurality of the positive generated images of the each of the plurality of features, and the negative generation vector of each of the plurality of the negative generated images of the each of the plurality of features, and execute the learning of the generator such that, in the feature space corresponding to each of the plurality of features, the anchor generation vector of each of the plurality of the anchor generated images and the positive generation vector of the positive generated image of the each of the plurality of the anchor generated images approach each other (the super-resolution network is trained based on the calculation of loss values of the positive and negative samples (including both positive and negative samples is batch wise) in the feature dimension, in this embodiment, the output of the network is not only enabled to be close to the ground truths (positive sample images), but also distanced from some defective negative samples, thereby reducing the introduced artifacts and noise; paragraphs [0057-0060]), the anchor generation vector of the each of the plurality of the anchor generated images and the negative generation vector of the negative generated image of the each of the plurality of the anchor generated images become distant from each other, and the anchor generation vector of the each of the plurality of the anchor generated images and the anchor generation vector of another anchor generated image become distant from each other (the super-resolution network is trained based on the calculation of loss values of the positive and negative samples (including both positive and negative samples is batch wise) in the feature dimension, in this embodiment, the output of the network is not only enabled to be close to the ground truths (positive sample images), but also distanced from some defective negative samples, thereby reducing the introduced artifacts and noise; paragraphs [0057-0060]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify OJHA to have the anchor generation vector of the each of the plurality of the anchor generated images and the anchor generation vector of another anchor generated image become distant from each other of DONG reference. The Suggestion/motivation for doing so would have been to provide the ability to reducing the introduced artifacts and noise as suggested by paragraph [0059] of DONG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine DONG with OJHA to obtain the invention as specified in claim 10.
As per claim 11, OJHA in view of MOVELLAN discloses the learning system according to claim 7. Modified OJHA fails to disclose wherein the at least one processor configured to: cause the discriminator to estimate authenticity of the anchor generated image, and execute the learning of the generator based further on an estimation result of the authenticity of the anchor generated image.
DONG discloses wherein the at least one processor configured to: cause the discriminator to estimate authenticity of the anchor generated image, and execute the learning of the generator based further on an estimation result of the authenticity of the anchor generated image (Gaussian random noise may be added to generate the negative sample image, thereby improving the authenticity of the negative sample image and ensuring the training effect of the GAN neural network is adapted to perform even in the presence of noise; fig 3; paragraphs [0046-0048]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify OJHA to have the discriminator to estimate authenticity of the anchor generated image of DONG reference. The Suggestion/motivation for doing so would have been to provide real world noise values to the fused image once the noise is applied to arrive at the negative image as suggested by paragraphs [0046-0047] of DONG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine DONG with modified OJHA to obtain the invention as specified in claim 11.
Claim 6 is rejected under 35 § U.S.C. 103 as being obvious over US 2022/0254071 A1 to OJHA et al. (hereinafter “OJHA”) in view of US 2005/0102246 A1 to MOVELLAN et al. (hereinafter “MOVELLAN”) in view of Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification to DENG et al. (hereinafter “DENG”)
As per claim 6, OJHA in view of MOVELLAN discloses the learning system according to claim 1. Modified OJHA fails to disclose wherein the at least one processor configured to: cause the discriminator to estimate authenticity of each of a plurality of the anchor discrimination images, and execute normalization relating to an estimation result of the authenticity of each of the plurality of the anchor discrimination images, and to execute the learning of the discriminator based further on an execution result of the normalization.
DENG discloses wherein the at least one processor configured to: cause the discriminator to estimate authenticity of each of a plurality of the anchor discrimination images, and execute normalization relating to an estimation result of the authenticity of each of the plurality of the anchor discrimination images, and to execute the learning of the discriminator based further on an execution result of the normalization (the computing system using the GAN network and a contrastive loss function where x1 and x2 are input image feature vectors you can use Euclidian distance d between normalized embeddings of two input vectors, and i represents the binary label of the pair, where i = 1 if x1 and x2 are positive pair; i = 0 if x1 and x2 are negative pair, m ∈ [0, 2] is the margin that defines the separability in the embedding space, and when m = 0, the loss of negative training pair is not back-propagated in the system, when m > 0, both positive and negative sample pairs are considered, larger m means that the loss of negative training samples has a higher weight in back propagation; page 997, section 3.2.2).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify OJHA to have to execute the learning of the discriminator based further on an execution result of the normalization of DENG reference. The Suggestion/motivation for doing so would have been to provide the ability to preserve the identification information for each translated image as suggested by section 3.2.2 of DENG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine DENG with OJHA to obtain the invention as specified in claim 6.
Conclusion
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
Triplet-Classifier GAN for Finger-Vein Verification
US 2024/0290076 A1
US 2021/0303885 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
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/D J DHOOGE/Examiner, Art Unit 2677
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