Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to application filed on February 12, 2026, in which claims 1-9 and 13-18 are presented for further examination.
Response to Arguments
Applicant's arguments filed February 12, 2026 have been fully considered but they are not persuasive. (See Remarks).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 13-18 are rejected 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception, namely an abstract idea, without significantly more.Claim 1An information processing device comprising:a memory storing one or more instructions; andat least one processor configured to execute the one or more instructions to:acquire a plurality of guide data items classified into a single target class; andgenerate one adversarial sample by using the plurality of guide data items.Step 2A, Prong One – Whether the Claim Recites a Judicial ExceptionClaim 1 recites limitations directed to generating an adversarial sample using classified guide data items. The claimed operations include:• acquiring data items classified into a target class; and• generating an adversarial sample using the acquired data.These limitations describe collecting, analyzing, manipulating, and mathematically processing information to produce a modified data output. Such operations constitute mental processes and mathematical concepts, both of which are recognized categories of abstract ideas under the 2019 Revised Patent Subject Matter Eligibility Guidance.More specifically, the claim merely recites using data associated with a classification and applying computational techniques to generate another data representation (“adversarial sample”). The claim does not recite any specific technological implementation for improving computer functionality, image processing hardware, neural network architecture, memory management, or another technical field. Instead, the claim focuses on the abstract result of generating data based on other data.The claimed “adversarial sample” generation constitutes mathematical manipulation of information and evaluation of relationships between data points. Courts have consistently held that collecting information, analyzing information, and generating modified information are abstract ideas. See, e.g., Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016).Accordingly, claim 1 recites an abstract idea.Step 2A, Prong Two – Whether the Claim Integrates the Exception Into a Practical ApplicationThe claim does not integrate the abstract idea into a practical application.The additional elements beyond the abstract idea are:• “an information processing device”;• “a memory”; and• “at least one processor.”These elements are recited at a high level of generality and merely represent generic computer components performing generic computer functions, namely storing instructions, acquiring data, and executing instructions.The claim does not recite:• any improvement to computer functionality;• any specialized machine;• any specific neural network architecture;• any unconventional training methodology;• any particular adversarial attack technique;• any hardware-level implementation;• any transformation of physical matter; or• any technical solution to a technical problem.The claim merely uses generic computer implementation as a tool to perform the abstract process of manipulating and generating data. Limiting the guide data items to a “single target class” is merely a field-of-use limitation or data classification constraint and does not impose a meaningful limit on the judicial exception.Further, the claim does not specify how the adversarial sample is generated, what algorithm is used, what technical improvement results, or how computer performance is improved. The claim therefore merely automates an abstract data-processing concept on generic computing hardware.As such, the claim fails to integrate the judicial exception into a practical application.Step 2B – Whether the Claim Includes Significantly More Than the Judicial ExceptionThe claim does not include additional elements that amount to significantly more than the abstract idea itself.The recited processor and memory are well-understood, routine, and conventional computer components performing their ordinary functions. Merely requiring generic computer implementation cannot transform an abstract idea into patent-eligible subject matter. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014).Additionally:• acquiring data is a conventional computer activity;• storing instructions in memory is routine;• generating data using other data is conventional mathematical processing; and• no unconventional arrangement of components is recited.The claim lacks any inventive concept because it merely applies the abstract idea using generic computer technology.The claim also fails to recite any particular machine-learning architecture, technical mechanism for perturbation generation, image processing improvement, cybersecurity enhancement, or training optimization that could amount to an inventive concept.Instead, the claim broadly preempts generating adversarial samples using classified guide data regardless of implementation details. Such breadth further indicates that the claim is directed to an ineligible abstract idea.Accordingly, claim 1 does not amount to significantly more than the judicial exception. Claims 8 and 9 are rejected under the same rationale.
Claim 2, is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein the wherein the at least one processor is configured to execute the one or more instructions to acquire a plurality of real data items classified into the target class as the plurality of guide data items.” which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Claims 13-15 are rejected based on the same rationale.
Claim 3, is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein the at least one processor is configured to execute the one or more instructions to acquire convert one or more of the guide data items to generate a new guide data item.” which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 4, is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein the at least one processor is configured to execute the one or more instructions to generate an adversarial sample using an objective function containing a term indicating a similarity between a feature quantity of an adversarial sample candidate and the feature quantities of the guide data items, and a term indicating a norm between the feature quantity of the adversarial sample candidate and feature quantities of the plurality of guide data items.” which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 5, is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim recites the additional limitations of “use an adversarial sample to learn a detection model that detects adversarial samples.”, which amounts to data-gathering steps and which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)), and recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Therefore, does not amount to significantly more than the abstract idea. Claim 16 is rejected based on the same rationale.
Claim 6 is dependent on claim 4 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitation of “to learn a feature quantity extraction model for data classification.”, which amounts to data-gathering steps and which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)), and recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Therefore, does not amount to significantly more than the abstract idea. Claim 17 is rejected based on the same rationale.
Claim 7 is dependent on claim 4 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional limitation of “calculate a similarity between a feature quantity of an adversarial sample and the feature quantities of target class data as an evaluation value of a risk of misidentification by the adversarial sample.”, The additional elements represent the mental process steps or mathematical calculations of computing the score as in the independent claims. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Mathematical Concepts” groupings of abstract ideas. This additional step is considered an abstract idea (mental process step and/or mathematical concept) and does not integrate the judicial exception into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements represent further mental process steps or mathematical concepts. Therefore, these additional limitations are not sufficient to amount to significantly more than the judicial exception.
Claim 18 is rejected based on the same rationale.
Claims 1-8 and 13-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1 and 8 are rejected under 35 U.S.C. 101 because the claims recite "A data processing system", however the claim's limitations do not include any physical structure to perform the steps recited in the claim, furthermore the claim fails to disclose a physical article or object associated with the claimed system. These claims lack the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 USC 101. They are clearly not a series of steps or acts to be a process nor are they a combination of chemical compounds to be a composition of matter. As such, they fail to fall within a statutory category. They are, at best, functional descriptive material per se.
Claims 2-7 and 13-18 are depending on claims 1 and 8, therefore they are rejected under the same rationale.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-9 and 13-18 rejected under 102(a) as being anticipated under Madani et al. (US 2019/0197368)(hereinafter Madani).
As per claims 1, 8 and 9, Madani discloses a guide data acquirer that acquires a plurality of guide data items classified into a single target class [The generator G receives random noise and generates a fake image. The discriminator D is adapted to receive real labeled image data, real unlabeled image data, and generated image data and operates on the data to identify whether the input medical image data is a medical image representing a real normal image, a real abnormal image, or a fake (generated) image, paragraph 52, (The cited reference teaches acquiring guide data items that are classified into a target class. Specifically, paragraph 52 describes the discriminator receiving and processing labeled image data representing “real normal” and “real abnormal” images. These labeled images constitute a plurality of guide data items grouped within respective target classes (e.g., normal or abnormal). Therefore, the reference reasonably discloses acquiring multiple guide data items classified into a single target class as claimed.)]; and an adversarial sample generator that generates one adversarial sample by using the plurality of guide data items [The generator G 210 is a convolutional neural network that transforms the noise vector z 205 input into an unlabeled image. The discriminator D 250 is a convolutional neural network that attempts to discriminate the input image data into one of a plurality of classes, e.g., real image normal, real image abnormal, or generated (fake) image., paragraph 54 (The cited reference teaches generating a sample using guide data items. Paragraph 54 explains that the discriminator processes image data belonging to multiple classes, including real normal and real abnormal images, while the generator produces an image for use within this adversarial framework. Because the generated image is produced and evaluated based on the classified image data used by the discriminator, the reference reasonably teaches an adversarial sample generator that generates an adversarial sample using the plurality of guide data items as claimed.)].
As per claims 2 and 14, Madani discloses wherein the wherein the at least one processor is configured to execute the one or more instructions to acquire a plurality of real data items classified into the single target class as the plurality of guide data items [A new data source for which the trained GAN is to be adapted is identified and the trained GAN is adapted for the new data source. Image data in the new data source is classified by applying the adapted GAN to the data in the new data source, abstract].
As per claims 3 and 15, Madani discloses wherein the at least one processor is configured to execute the one or more instructions to convert one or more of the plurality of guide data items to generate a new guide data item. [an architecture is provided that converts the GAN into a semi-supervised classifier for abnormality detection in medical images, trained on a fairly small size initial annotated medical image dataset, paragraph 33].
As per claim 4, Madani discloses wherein the at least one processor is configured to execute the one or more instructions to generate an adversarial sample using an objective function containing a term indicating a similarity between the feature quantity of an adversarial sample candidate and the feature quantities of the guide data items, and a term indicating the norm between the feature quantity of the adversarial sample candidate and feature quantities of the plurality of guide data items [in FIGS. 2A and 2B, the discriminator D 250 comprises K+1 output nodes for outputting values of the vector 260 indicative of which of K+1 classes the discriminator D 250 has determined the input medical image data to be associated with. That is, there may be K classes in which one of the K classes is a normal medical image, i.e. a medical image in which no abnormality is identified., paragraph 55].
As per claims 5 and 17, Madani discloses use an adversarial sample to learn a detection model that detects adversarial samples [there are separate output nodes for combinations of real/fake normal and abnormal classes. For example, there may be separate classes for real-normal, real-abnormal, fake-normal, and fake-abnormal, where in this case K is 2, i.e. normal and abnormal, but K may be any number of classes depending on the desired implementation., paragraph 56].
As per claim 6, Madani discloses use an adversarial sample to learn a feature quantity extraction model for data classification [The discriminator D 250 is modified, such as with regard to the loss function employed by the neural network of the discriminator D 250 and the number of output nodes in the discriminator D 250 configuration, to receive these three types of input image data and determine a classification of the input image data into one of a plurality of classifications, paragraph 57].
As per claims 7 and 18, Madani discloses calculate a similarity between a feature quantity of an adversarial sample and feature quantities of target class data as an evaluation value of a risk of misidentification by the adversarial sample [The discriminator provides a classification of the image data and a calculated loss based on the configured loss function (step 950). The operation of the GAN, e.g., the weights of nodes in the generator and discriminator, is then modified based on the loss function calculation and training logic that operates to minimize the loss function (step 960). A determination is made as to whether or not the GAN training has converged (step 970). If so, the operation terminates. Otherwise, the operation returns to step 930 with further training based on additional fake image generation, paragraph 99].
As per claim 13, Madani discloses acquire that acquires a plurality of guide data items classified into a single target class and generate one adversarial sample by using the plurality of guide data items [The generator G receives random noise and generates a fake image. The discriminator D is adapted to receive real labeled image data, real unlabeled image data, and generated image data and operates on the data to identify whether the input medical image data is a medical image representing a real normal image, a real abnormal image, or a fake (generated) image, paragraph 52; The generator G 210 is a convolutional neural network that transforms the noise vector z 205 input into an unlabeled image. The discriminator D 250 is a convolutional neural network that attempts to discriminate the input image data into one of a plurality of classes, e.g., real image normal, real image abnormal, or generated (fake) image., paragraph 54].
Remarks
Claims 1-9 and 13-18 maintain rejected under 35 USC 101 as explained above.
Applicant asserted, page 12 of remarks, that Examiner has refereed to reference “Bandla” in the rejection of independent claim 1; however, the claim is rejected under Madani.
Examiner respectfully mentions that this was an inadvertent typing error.
Applicant asserted, page 13, that Madani does not disclose generate one adversarial sample by using the plurality of guide data items.
Examiner respectfully disagrees with this assertion. The cited reference teaches generating a sample using guide data items. Paragraph 54 explains that the discriminator processes image data belonging to multiple classes, including real normal and real abnormal images, while the generator produces an image for use within this adversarial framework. Because the generated image is produced and evaluated based on the classified image data used by the discriminator, the reference reasonably teaches an adversarial sample generator that generates an adversarial sample using the plurality of guide data items as claimed.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Noosha Arjomandi whose telephone number is (571) 272-9784. The examiner can normally be reached on Monday through Friday, 8:30am - 6:00pm. E.S.T..
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached on (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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May 12, 2026
/NOOSHA ARJOMANDI/Primary Examiner, Art Unit 2166
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