DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is in response to an amendment filed on June 8th, 2026. Claims 1-7, 9-16, and 18-20 are pending in the current application, with claims 8 and 17 being canceled and claims 1-4, 7, 10-13, 16, 19, and 20 being currently amended in the current application. The 101 rejections from the previous action have been withdrawn.
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.
Claim(s) 1, 3-10, and 12-19 are rejected under 35 U.S.C. 103 as being unpatentable over Matsui et al. (Herein referred to as Matsui) (Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network) in view of Chengliang Dai et al. (Herein referred to as Dai) (Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling)
Regarding claim 1, Matsui teaches a system comprising at least one data processor and at least one memory storing instructions (Although the processor and memory are not explicitly stated in the disclosure, one would implicitly need those components to run the method of Matsui.) which when executed by the at least one processor result in operations comprising: generating a plurality of counterfactual synthetic vectors for each input vector of a plurality of input vectors wherein the plurality of counterfactual synthetic vectors represent potential counterfactuals associated with the corresponding input vector; (“Generator takes a combination of an image of brain activation and a one-hot label indicating the target class as an input. Generator outputs a counterfactual brain activation that is a minimal transform of the input brain activation toward the target class. Discriminator takes an activation map output by Generator and outputs a one-hot label. Discriminator was co-trained with Generator, as in STAR-GAN.”, pg. 8, Figure 3) (Images are processed by ML models as vectors or embeddings. The output of the generator is a counterfactual synthetic image, which under the broadest reasonable interpretation, is a counterfactual synthetic vector.) training a first machine learning model based on the plurality of input vectors; (“We regarded this generator as CAG. To achieve this, we trained CAG to transform a brain activation x with class-label y (source class) to a perturbation toward yc (target class), such that CAGx , yc → xc.”, pgs. 5 and 6) training a second machine learning model based on the plurality of input vectors and the filtered plurality of counterfactual synthetic vectors; (“Discriminator takes an activation map output by generator and outputs a one-hot label. Discriminator was cotrained with generator, as in StarGAN.”, Figure 3 on pg. 5) and predicting, using the second machine learning model, a classification of at least one input vector of the plurality of input vectors. (“The architecture includes two trainable modules, Discriminator (D) and CAG. Note that DNN-classifier is not trainable. (a) D learns to discriminate between real and fake activations and to classify the real activation to its corresponding domain… Discriminator takes an activation map output by Generator and outputs a one-hot label.”, pg. 25, Figure S1; pg. 8, Figure 3) (The Discriminator is trained on synthetic and real data to learn how differentiate between real and fake data. The label output by the discriminator corresponds to a prediction of a classification of at least one input vector (image).)
However, Matsui does not explicitly teach filtering the plurality of counterfactual synthetic vectors based at least on a comparison between a first score generated by the first machine learning model based on a first input vector of the plurality of input vectors and a second score generated by the first machine learning model based on a first synthetic vector of the plurality of counterfactual synthetic vectors corresponding to the first input vector.
Dai teaches filtering the plurality of counterfactual synthetic vectors based at least on a comparison between a first score generated by the first machine learning model based on a first input vector of the plurality of input vectors and a second score generated by the first machine learning model based on a first synthetic vector of the plurality of counterfactual synthetic vectors corresponding to the first input vector. (“images can be synthesized from z 0 i via the VAE decoder fθ(·) and suggested to the expert for annotation. However, the synthetic image may not be of high quality, which would prevent the expert from producing reliable annotation. To mitigate this issue, we propose to sample in the real image space, searching for existing real images that are most similar to the synthesized image… Fig. 2 illustrates the sampling process in the latent space. The red dots are the latent representation of S that are randomly selected initially. After training the base model with these samples, for any xi from S, the loss gradient is backpropagated and integrated to xi (Eq. 3), then projected to z 0 i in the latent space (Eq. 4 and red cross in Fig. 2). One of two criteria we use to find an existing real image is to find a zj ∈ Z (yellow dot) that has the shortest Euclidean distance to zi’.”, pg. 4-5) (The samples are projected into real space via an autoencoder, which corresponds to the first machine learning model. (Both GAN’s and VAE are generative AI models.) The selection of samples, which corresponds to a filtering step, is done based on the shortest distance to zi’ (a sample, which corresponds to a synthetic vector) from zj ∈ Z (which correspond to an input vector, as it represents a real image). The distance is calculated from “scores” corresponding to a placement in the latent space of both zi’ and zj. With the synthetic vectors of Matsui, configured to work with the autoencoder of Dai, to find a hard sample, and filter samples based on the hard sample, the limitation is fully taught.)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the models, synthetic data, and counterfactuals of Matsui, with the filtering process using a VAE, as described by Dai. One of ordinary skill in the art would have been motivated to combine the two teachings, prior to the application’s filing date, as this leads to produced images for professional annotation, and better training of a network, as disclosed in Dai. (“we select m informative samples and suggest them to the expert for manual annotation. A new training dataset with m more samples 0 = {(x1, y1),(x2, y2), ...,(x2m, y2m)} can be constructed, which consists of initial samples and new samples suggested by the proposed method. The new training set can be used for further training the base network.”, pg. 4, bottom paragraph; pg. 5, above “3 Experiments”)
Regarding claim 10, Matsui teaches a method comprising: generating a plurality of counterfactual synthetic vectors for each input vector of a plurality of input vectors used to train a first machine learning model, wherein the plurality of counterfactual synthetic vectors represent potential counterfactuals associated with the corresponding input vector (“Generator takes a combination of an image of brain activation and a one-hot label indicating the target class as an input. Generator outputs a counterfactual brain activation that is a minimal transform of the input brain activation toward the target class. Discriminator takes an activation map output by Generator and outputs a one-hot label. Discriminator was co-trained with Generator, as in STAR-GAN.”, pg. 8, Figure 3) (Images are processed by ML models as vectors or embeddings. The output of the generator is a counterfactual synthetic image, which under the broadest reasonable interpretation, is a counterfactual synthetic vector.) training a first machine learning model based on the plurality of input vectors; (“We regarded this generator as CAG. To achieve this, we trained CAG to transform a brain activation x with class-label y (source class) to a perturbation toward yc (target class), such that CAGx , yc → xc.”, pgs. 5 and 6) training a second machine learning model based on the plurality of input vectors and the filtered plurality of counterfactual synthetic vectors; (“Discriminator takes an activation map output by generator and outputs a one-hot label. Discriminator was cotrained with generator, as in StarGAN.”, Figure 3 on pg. 5) and predicting, using the second machine learning model, a classification of at least one input vector of the plurality of input vectors. (“Discriminator takes an activation map output by Generator and outputs a one-hot label.”, pg. 8, Figure 3) (The label output by the discriminator corresponds to a prediction of a classification of at least one input vector (image).)
However, Matsui does not explicitly teach filtering the plurality of counterfactual synthetic vectors based at least on a comparison between a first score generated by the first machine learning model based on a first input vector of the plurality of input vectors and a second score generated by the first machine learning model based on a first synthetic vector of the plurality of counterfactual synthetic vectors corresponding to the first input vector.
Dai teaches filtering the plurality of counterfactual synthetic vectors based at least on a comparison between a first score generated by the first machine learning model based on a first input vector of the plurality of input vectors and a second score generated by the first machine learning model based on a first synthetic vector of the plurality of counterfactual synthetic vectors corresponding to the first input vector. (“images can be synthesized from z 0 i via the VAE decoder fθ(·) and suggested to the expert for annotation. However, the synthetic image may not be of high quality, which would prevent the expert from producing reliable annotation. To mitigate this issue, we propose to sample in the real image space, searching for existing real images that are most similar to the synthesized image… Fig. 2 illustrates the sampling process in the latent space. The red dots are the latent representation of S that are randomly selected initially. After training the base model with these samples, for any xi from S, the loss gradient is backpropagated and integrated to xi (Eq. 3), then projected to z 0 i in the latent space (Eq. 4 and red cross in Fig. 2). One of two criteria we use to find an existing real image is to find a zj ∈ Z (yellow dot) that has the shortest Euclidean distance to zi’.”, pg. 4-5) (The samples are projected into real space via an autoencoder, which corresponds to the first machine learning model. (Both GAN’s and VAE are generative AI models.) The selection of samples, which corresponds to a filtering step, is done based on the shortest distance to zi’ (a sample, which corresponds to a synthetic vector) from zj ∈ Z (which correspond to an input vector, as it represent a real image). The distance is calculated from “scores” corresponding to a placement in the latent space of both zi’ and zj. With the synthetic vectors of Matsui, configured to work with the autoencoder of Dai, to find a hard sample, and filter samples based on the hard sample, the limitation is fully taught.)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the models, synthetic data, and counterfactuals of Matsui, with the filtering process using a VAE, as described by Dai. One of ordinary skill in the art would have been motivated to combine the two teachings, prior to the application’s filing date, as this leads to produced images for professional annotation, and better training of a network, as disclosed in Dai. (“we select m informative samples and suggest them to the expert for manual annotation. A new training dataset with m more samples 0 = {(x1, y1),(x2, y2), ...,(x2m, y2m)} can be constructed, which consists of initial samples and new samples suggested by the proposed method. The new training set can be used for further training the base network.”, pg. 4, bottom paragraph; pg. 5, above “3 Experiments”)
Regarding claim 19, Matsui teaches A non-transitory computer-readable medium storing instructions, (While Matsui does not explicitly disclose a non-transitory computer-readable medium, you would implicitly need one to distribute the method of Matsui.) which when executed by at least one data processor, result in operations comprising: generating a plurality of counterfactual synthetic vectors for each input vector of a plurality of input vectors used to train a first machine learning model, wherein the plurality of counterfactual synthetic vectors represent potential counterfactuals associated with the corresponding input vector (“Generator takes a combination of an image of brain activation and a one-hot label indicating the target class as an input. Generator outputs a counterfactual brain activation that is a minimal transform of the input brain activation toward the target class. Discriminator takes an activation map output by Generator and outputs a one-hot label. Discriminator was co-trained with Generator, as in STAR-GAN.”, pg. 8, Figure 3) (Images are processed by ML models as vectors or embeddings. The output of the generator is a counterfactual synthetic image, which under the broadest reasonable interpretation, is a counterfactual synthetic vector.) training a first machine learning model based on the plurality of input vectors; (“We regarded this generator as CAG. To achieve this, we trained CAG to transform a brain activation x with class-label y (source class) to a perturbation toward yc (target class), such that CAGx , yc → xc.”, pgs. 5 and 6) training a second machine learning model based on the plurality of input vectors and the filtered plurality of counterfactual synthetic vectors; (“Discriminator takes an activation map output by generator and outputs a one-hot label. Discriminator was cotrained with generator, as in StarGAN.”, Figure 3 on pg. 5) and predicting, using the second machine learning model, a classification of at least one input vector of the plurality of input vectors. (“Discriminator takes an activation map output by Generator and outputs a one-hot label.”, pg. 8, Figure 3) (The label output by the discriminator corresponds to a prediction of a classification of at least one input vector (image).)
However, Matsui does not explicitly teach filtering the plurality of counterfactual synthetic vectors based at least on a comparison between a first score generated by the first machine learning model based on a first input vector of the plurality of input vectors and a second score generated by the first machine learning model based on a first synthetic vector of the plurality of counterfactual synthetic vectors corresponding to the first input vector.
Dai teaches filtering the plurality of synthetic vectors based at least on a comparison between a first score generated by the first machine learning model based on a first input vector of the plurality of input vectors and a second score generated by the first machine learning model based on a first synthetic vector of the plurality of synthetic vectors corresponding to the first input vector. (“images can be synthesized from z 0 i via the VAE decoder fθ(·) and suggested to the expert for annotation. However, the synthetic image may not be of high quality, which would prevent the expert from producing reliable annotation. To mitigate this issue, we propose to sample in the real image space, searching for existing real images that are most similar to the synthesized image… Fig. 2 illustrates the sampling process in the latent space. The red dots are the latent representation of S that are randomly selected initially. After training the base model with these samples, for any xi from S, the loss gradient is backpropagated and integrated to xi (Eq. 3), then projected to z 0 i in the latent space (Eq. 4 and red cross in Fig. 2). One of two criteria we use to find an existing real image is to find a zj ∈ Z (yellow dot) that has the shortest Euclidean distance to zi’.”, pg. 4-5) (The samples are projected into real space via an autoencoder, which corresponds to the first machine learning model. (Both GAN’s and VAE are generative AI models.) The selection of samples, which corresponds to a filtering step, is done based on the shortest distance to zi’ (a sample, which corresponds to a synthetic vector) from zj ∈ Z (which correspond to an input vector, as it represent a real image). The distance is calculated from “scores” corresponding to a placement in the latent space of both zi’ and zj. With the synthetic vectors of Matsui, configured to work with the autoencoder of Dai, to find a hard sample, and filter samples based on the hard sample, the limitation is fully taught.)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the models, synthetic data, and counterfactuals of Matsui, with the filtering process using a VAE, as described by Dai. One of ordinary skill in the art would have been motivated to combine the two teachings, prior to the application’s filing date, as this leads to produced images for professional annotation, and better training of a network, as disclosed in Dai. (“we select m informative samples and suggest them to the expert for manual annotation. A new training dataset with m more samples 0 = {(x1, y1),(x2, y2), ...,(x2m, y2m)} can be constructed, which consists of initial samples and new samples suggested by the proposed method. The new training set can be used for further training the base network.”, pg. 4, bottom paragraph; pg. 5, above “3 Experiments”)
Regarding claims 3 and 12, Matsui, as modified by Dai, teaches the system and method of claims 1 and 10 respectively, wherein the filtering comprises: generating, by the first machine learning model and based at least on the first input vector, the first score; (The first score corresponds to zj in Dai) generating, by the first machine learning model and based at least on the first synthetic vector, the second score; (The second score corresponds to zi’ in Dai.) determining a difference between the first score and the second score; (The distance between zi and zj corresponds to a difference between the first and second score.) and determining to include the first synthetic vector in the filtered plurality of counterfactual synthetic vectors based at least on the difference between the first score and the second score meeting a threshold difference. (“One of two criteria we use to find an existing real image is to find a zj ∈ Z (yellow dot) that has the shortest Euclidean distance to z 0 i . However, we have found that using the Euclidean distance alone sometimes may fail to find an existing image that is similar to the synthesized image. To mitigate this issue, we introduce an angular condition to limit the search angle (black angle in Fig. 2)… By using the gradient-guided sampling method, one or more samples can be found given each z 0 i . To simplify the process, we only select one sample zj ∈ Z for each z 0 i in our work, which corresponds to image xj ∈ X.”, pg. 5, second and third paragraph (Dai)) (The synthetic vector is included if an existing image is found within the shortest Euclidian distance and given the angular condition and corresponds to xj ∈ X. The determination is based on, in part the difference between the first score and the second score meeting a threshold difference, with the threshold distance being the next shortest.)
Regarding claims 4 and 13, Matsui, as modified by Dai, teaches the system and method of claims 3 and 12 respectively, wherein the filtering further comprises: identifying a synthetic vector of the plurality of counterfactual synthetic vectors having a highest absolute residual value among the plurality of counterfactual synthetic vectors for each input vector, wherein the synthetic vector having the highest absolute residual value indicates a boundary of a data manifold associated with each input vector. (“A VAE is trained on X with the loss function… MSE denotes the mean square error function and DKL denotes the KL-divergence, which regularizes the optimization problem by minimizing the distance between the latent variable distribution and a Gaussian distribution [12]. Once trained, the VAE can be used to obtain the latent representations Z = {z1, z2, ..., zn} given X. It will be used for sampling purpose in the later stage.”, pgs. 3 and 4; Also see Figure 2 on pg. 5 of Dai.) (The dotted circle represents a manifold and its boundary is determined via a trained VAE, (variational autoencoder) which regularizes the optimization problem by minimizing the distance between the latent variable distribution and a Gaussian distribution. The minimum distance between the latent variable distribution and a Gaussian distribution corresponds to a residual with the highest absolute value, teaching the limitation.
Regarding claims 5 and 14, Matsui, as modified by Dai, teaches the system and method of claims 4 and 13 respectively, wherein determining to include the first synthetic vector in the filtered plurality of counterfactual synthetic vectors is further based on an angle between the first synthetic vector and the synthetic vector having the highest absolute residual value meeting a threshold angle. (“The angular condition is similar to the cosine distance widely used in machine learning, which in our case was used to constrain the search in the VAE manifold with respect to similar cases”, pg. 5, second paragraph (Dai)) (The angular condition corresponds to an angle and the hard sample within the angle corresponds to a synthetic vector having the highest absolute residual value meeting a threshold angle.)
Regarding claims 6 and 15, Matsui, as modified by Dai, teaches the system and method of claims 5 and 14 respectively, wherein the angle is a cosine distance, and wherein the threshold angle is a threshold cosine distance. (“The angular condition is similar to the cosine distance widely used in machine learning, which in our case was used to constrain the search in the VAE manifold with respect to similar cases”, pg. 5, second paragraph (Dai))
Regarding claims 7 and 16, Matsui, as modified by Dai, teaches the system and method of claims 1 and 10 respectively, wherein the filtering comprises: iteratively determining to include a synthetic vector of the plurality of counterfactual synthetic vectors for each input vector in the filtered plurality of counterfactual synthetic vectors until a threshold quantity of synthetic vectors is included in the filtered plurality of counterfactual synthetic vectors. (“By using the gradient-guided sampling method, one or more samples can be found given each z 0 i . To simplify the process, we only select one sample zj ∈ Z for each z 0 i in our work, which corresponds to image xj ∈ X. In this way, we select m informative samples and suggest them to the expert for manual annotation.”, pg. 5, third paragraph (Dai)) (One sample is chosen for each “zi’” up to m samples, indicating a threshold quantity.)
Regarding claims 9 and 18, Matsui, as modified by Dai, teaches the system and method of claims 1 and 10 respectively, wherein the first machine learning model is a first neural network, and wherein the second machine learning model is a second neural network. (“Two DNNs, generator (CAG) and discriminator, were simultaneously trained (Fig. 3a; Fig. S1).”, pg. 12, under “CAG generated counterfactual activations were realistic and fooled the classifiers” (Matsui))
Claim(s) 2, 11, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Matsui et al. (Herein referred to as Matsui) (Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network) in view of Chengliang Dai et al. (Herein referred to as Dai) (Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling) and in further view of Hansoo Lee et al. (Herein referred to as Lee) (Gaussian-Based SMOTE Algorithm for Solving Skewed Class Distributions)
Regarding claims 2, 11, and 20, Matsui, as modified by Dai, teaches the system, method, and non-transitory computer-readable medium of claims 1, 10, and 19 respectively but does not explicitly teach the plurality of counterfactual synthetic vectors are generated based on a Gaussian distribution associated with each input vector.
Lee teaches the plurality of counterfactual synthetic vectors are generated based on a Gaussian distribution associated with each input vector. (“By including the Gaussian probability distribution in the process, it is possible to expand the place, where the synthetic sample is generated, from the line between minorities. Also, the Gaussian distribution allows the synthetic data located near the line.”, pg. 3, left column, bottom paragraph)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the teachings of Matsui, as modified by Dai, with the Gaussian distribution and synthetic data of Lee. One of ordinary skill in the art would have been motivated to combine the teachings, prior to the application’s filing date, as the distribution solves for class imbalance, as disclosed in Lee. (“Sufficient amount of learning data is an essential condition to implement a classifier with excellent performance. However, the obtained data usually follow a significantly biased distribution of classes. It is called a class imbalance problem, which is one of the frequently occurred issues in the real-world applications. This problem causes a considerable performance drop because most of the machine learning methods assume given data follow a balanced distribution of classes. The implemented classifier will derive false classification results if the problem is not solved. Therefore, this paper proposes a novel method, named as Gaussian-based SMOTE, to solve the problem by combining Gaussian distribution in a synthetic data generation process. It is confirmed that the proposed method could solve the class imbalance problem by conducting experiments with actual cases.”, pg. 1, Abstract)
Response to Arguments
Applicant's arguments filed on June 8th, 2026 have been fully considered but they are not persuasive. The amendment filed on June 8th, 2026 overcome the 101 rejections from the previous action. As such, the 101 rejections have been withdrawn.
The applicant argues in substance:
Argument 1: Matsui and Dai do not teach a plurality of counterfactual synthetic vectors are generated for each input vector of a plurality of input vectors.
The examiner respectfully disagrees. As explained in this action and the previous action, Matsui’s generator takes brain activation maps as inputs to then output a counterfactual brain activation image based on the inputs. This was done for every activation map in the dataset. (“activation maps were spatially downsampled from 570 by 1,320 pixels to 50 by 140 pixels. Data were split into training data (N = 4,730) and validation data (N = 518)”, pg. 3, right column, bottom paragraph) The maps are interpreted to be, under the broadest reasonable interpretation, a plurality of input vectors, with the Generator outputting a plurality of counterfactual synthetic vectors in turn. Furthermore, the design philosophy of the generator was to create images that could map to seven unique classes (emotion, gambling, language, motor, relational, social, wim) as evidenced by Matsui’s disclosure. (“The training of the CAG was also designed such that the CAG transformed an input activation map to any of the seven classes and fool the DNN classifier.”, pg. 7, right column, first paragraph) The generator of Matsui is capable of generating a mapping for each of the seven classes, teaching a plurality of counterfactual synthetic vectors generated from a single input vector; this process performed for each input vector of a plurality of input vectors, thereby teaching the limitation fully. Therefore the 103 rejections are maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.E.I./Patent Examiner, Art Unit 2122
/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
