DETAILED ACTION
This nonfinal action is in response to the amendment and remarks filed 05/29/2026 for application 17/545,358.
Claims 1, 8, and 15 have been amended.
Claims 1-2, 4-9, 11-16, and 18-20 thereby remain pending in the application. Claims 1, 8, and 15 are independent claims.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/29/2026 has been entered.
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 1-2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Independent Claims (Claim 1, Claim 8, Claim 15):
Step 1: Claim 1 is drawn to a method, claim 8 is drawn to a system/apparatus, and claim 15 is drawn to a product. Therefore, each of these claims falls under one of the four categories of statutory subject matter (process/method, machine/apparatus, manufacture/product, or composition of matter).
Step 2A Prong 1: Claims 1, 8, and 15 each recite a judicially recognized exception of an abstract idea.
Claim 1 recites, inter alia, a method comprising:
generating an explanation as to why the machine learning model classified the first instance of data and the second instance of data differently – This limitation amounts to observing two data objects (e.g., images), and applying processes of reasoning (identifying difference[s]) to identify particular differences that would have contributed to the two portions of text as being classified differently. Therefore, it recites a process of evaluation capable of being performed in the human mind or using pen and paper – as an example, comparing two images and determining that a difference in a certain portion of each image (e.g., depicting different objects or colors) would have contributed to the images being classified differently, is a procedure that is capable of being performed in the human mind.
generating a first encoding associated with the first instance [representing a first set of image pixels]; generating a second encoding associated with the second instance [representing a second set of image pixels] – These limitations amount to performing generic transformations on generic sets of images – e.g., merely summarizing or re-organizing image content. Therefore, they recite processes of evaluation capable of being performed using pen and paper.
learn similarities in a given pair of input objects – This limitation amounts to observing two data objects, and applying reasoning to identify similarities between them (e.g., merely identifying two images as being related to a similar concept). Therefore, it recites a process of evaluation capable of being performed in the human mind or using pen and paper.
based on the first encoding and the second encoding, identifying a difference in features of the first instance and features of the second instance, which contributed to the first instance and the second instance being classified differently by the machine learning model, a feature in the features of the first instance representing pixels in the first set of image pixels, a feature in the features of the second instance representing pixels in the second set of image pixels, wherein the difference represents the difference of the pixels in the first set of image pixels from the pixels in the second set of image pixels – This limitation amounts to observing images, and applying generic transformations (encoding[s]) and processes of reasoning (identifying difference[s]) to identify particular differences that would have contributed to the two images as being classified differently. Therefore, they recite a process of evaluation capable of being performed in the human mind or using pen and paper – as an example, comparing two images and determining that a difference in a certain portion of each image (e.g., depicting different objects or colors) would have contributed to the images being classified differently, is a procedure that is capable of being performed in the human mind.
wherein the identifying the difference in the features of the first instance and the features of the second instance includes computing gradients, with respect to the first instance, of distances between features of the first encoding and features of the second encoding, the distances between the features of the first encoding and the features of the second encoding determined as cross entropy between labels and logits, wherein the features of one of the first encoding and the second encoding are set as the labels and the features of other one of the first encoding and the second encoding are set as the logits, – This limitation expressly recites performing a series of mathematical operations (computing gradients of calculated cross entropy values) to quantify differences between variables (features of the first instance and features of the second instance), and therefore recites mathematical calculation.
and identifying a feature having a largest negative gradient value to identify the difference in the features of the first instance and the features of the second instance, and outputting the identified difference in the features as the explanation – This limitation amounts to merely observing calculated gradient values to identify a largest negative value as significant, and therefore recites a process of evaluation capable of being performed in the human mind or using pen and paper.
Claims 8 and 15 recite substantially similar abstract idea limitations to those recited in claim 1, and therefore the same judicial exception as claim 1.
Step 2A Prong 2: The following additional elements recited in claims 1, 8, and 15 do not integrate the recited judicial exceptions into a practical application.
Claim 1 additionally recites:
receiving a first instance of data and a second instance of data; receiving the [first/second] instance in a [first/second neural network] – These limitations amount to insignificant steps of gathering and transferring data, and are therefore insignificant extra-solution activity.
[first/second instance of data] which have been classified differently by a machine learning model trained to classify given input data – This limitation amounts to an insignificant pre-solution implementation step with regard to the gathering of input data, and is merely a tangential addition to the overall claimed procedure; it therefore recites insignificant extra-solution activity.
the first instance representing a first set of image pixels and the second instance representing a second set of image pixels; – This limitation amounts to no more than generally linking the claimed procedure to the field of use of image processing / computer vision, as it merely limits the reach of the claimed procedure to processing image data without providing anything more.
[receiving] by a neural network architecture model; [generating] by the neural network architecture model; a first neural network, the first neural network [generating]; a second neural network, the second neural network [generating]; wherein the first neural network and the second neural network form the neural network architecture model trained to [learn], the neural network architecture model being a separate model from the machine learning model – These limitations amount to no more than mere instructions to apply an exception, i.e., they merely invoke neural networks as tools to perform existing abstract steps of performing generic transformations on images and identifying similarities/differences between images.
each of the first neural network and the second neural network is structured to include a logit layer prior to a normalization layer, – This limitation amounts to no more than an incidental addition with regard to implementation of the neural networks, which are merely invoked as tools to perform an existing abstract procedure. It therefore recites insignificant extra-solution activity.
the first encoding generated at the logit layer of the first neural network in tandem with the second encoding generated at the logit layer of the second neural network – This limitation amounts to no more than mere instructions to apply an exception. Simply stating that encodings are drawn from the raw outputs (i.e., logits) of the networks does no more than specify the source of data that is subject to further abstract analysis, and therefore further invokes the neural networks as tools to perform an existing abstract step of performing generic transformations on portions of images.
Claim 8 recites substantially similar additional elements to those recited in claim 1, and further recites:
A system comprising: a processor; a memory device coupled with the processor; the processor configured to at least: – This limitation amounts to mere instructions to implement an abstract idea on a computer or computer components.
Claim 15 recites substantially similar additional elements to those recited in claim 1, and further recites:
A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a device to cause the device to: – This limitation amounts to mere instructions to implement an abstract idea on a computer or computer components.
Step 2B: The additional elements recited in claims 1, 8, and 15, viewed individually or as a combination, do not provide an inventive concept or otherwise amount to significantly more than the recited abstract ideas themselves.
Claim 1 additionally recites:
receiving a first instance of data and a second instance of data; receiving the [first/second] instance in a [first/second neural network] – Receiving and transmitting data is well-understood, routine, and conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”) and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
[first/second instance of data] which have been classified differently by a machine learning model trained to classify given input data – Evaluating outputted predictions of a machine learning model to provide classification explanations (e.g., interpretability/attribution methods) is well understood, routine, and conventional activity (see Linardatos et al., “Explainable AI: A Review of Machine Learning Interpretability Methods” [page 6 Interpretability Methods to Explain Deep Learning Models, page 11 Interpretability Methods to Explain any Black-Box Model]) in the field of explainable AI (XAI), and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
the first instance representing a first set of image pixels and the second instance representing a second set of image pixels; – Generally linking a judicial exception to the field of use of image processing / computer vision without providing anything more does not provide an inventive concept or significantly more to the recited abstract idea.
[receiving] by a neural network architecture model; [generating] by the neural network architecture model; a first neural network, the first neural network [generating]; a second neural network, the second neural network [generating]; wherein the first neural network and the second neural network form the neural network architecture model trained to [learn], the neural network architecture model being a separate model from the machine learning model – Merely invoking neural networks as tools to perform existing abstract ideas does not provide an inventive concept or significantly more to the recited abstract idea.
each of the first neural network and the second neural network is structured to include a logit layer prior to a normalization layer – Using softmax activation functions, which normalize raw outputs from the final hidden layer (i.e., logits) to output a vector of probabilities, in neural networks (e.g., for classification tasks), is well-understood, routine, and conventional activity (see Siegel (“What are activation function in neural networks”) [page 9 Softmax]), and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
the first encoding generated at the logit layer of the first neural network in tandem with the second encoding generated at the logit layer of the second neural network – Utilizing output of a machine learning model to provide classification explanations (e.g., interpretability/attribution methods) is well understood, routine, and conventional activity (see Linardatos et al., “Explainable AI: A Review of Machine Learning Interpretability Methods” [page 6 Interpretability Methods to Explain Deep Learning Models, page 11 Interpretability Methods to Explain any Black-Box Model]) in the field of explainable AI (XAI), and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 8 recites substantially similar additional elements to those recited in claim 1, and further recites:
A system comprising: a processor; a memory device coupled with the processor; the processor configured to at least: – Mere instructions to implement an abstract idea on a computer or computer components do not provide an inventive concept or significantly more to the recited abstract idea.
Claim 15 recites substantially similar additional elements to those recited in claim 1, and further recites:
A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a device to cause the device to: – Mere instructions to implement an abstract idea on a computer or computer components do not provide an inventive concept or significantly more to the recited abstract idea.
Even when considered as an ordered combination, the additional elements recited in the claims ultimately do no more than place an abstract procedure of observing and analyzing model outputs to create explanations for classification differences, via mental processes and/or mathematical calculations, in the context of analyzing output of generic neural networks. As such, claims 1, 8, and 15 are not patent eligible.
Dependent Claims (Claims 2 & 4-7, Claims 9 & 11-14, Claims 16 & 18-20):
Dependent claims 2, 4-7, 9, 11-14, 16, and 18-20 narrow the scope of independent claims 1, 8, and 15, and thus merely narrow the recited judicial exceptions. With respect to the independent claims, the recited judicial exceptions are not meaningfully integrated into a practical application, and also do not amount to significantly more than the recited abstract ideas themselves. The dependent claims recite abstract idea limitations similar to those recited within the independent claims, as they also do not provide anything more than mathematical concepts or mental processes that are capable of being performed in the human mind and/or using pen and paper. The dependent claims also do not recite any further additional elements that successfully integrate the recited judicial exceptions into a practical application or amount to significantly more than the recited abstract ideas themselves. Consequently, claims 2, 4-7, 9, 11-14, 16, and 18-20 are also rejected under 35 U.S.C. 101.
Step 1: Claims 2 & 4-7 are drawn to a method, claims 9 & 11-14 are drawn to a system/apparatus, and claims 16 & 18-20 are drawn to a product. Therefore, each of these claims falls under one of the four categories of statutory subject matter (process/method, machine/apparatus, manufacture/product, or composition of matter).
Step 2A Prong 1: Claims 2, 4-7, 9, 11-14, 16, and 18-20 each recite a judicially recognized exception of an abstract idea.
Claim 2 recites the same judicial exception as claim 1.
Claim 4 recites, inter alia:
the generating further including selecting a predefined number of features having largest negative gradient values to identify the difference in the features of the first instance and the features of the second instance – This limitations amounts to merely observing calculated gradients to identify largest negative values, and therefore recites a process of evaluation capable of being performed in the human mind or using pen and paper.
Claim 5 recites, inter alia:
performing a post processing to a gradient of the computed gradients to reduce noise – This limitation amounts to merely performing a generic “processing” onto a mathematically computed value; it can thereby be reasonably interpreted as reciting a process of evaluation capable of being performed in the human mind or using pen and paper, or also as reciting further mathematical calculations.
Claim 6 recites, inter alia:
wherein the post processing includes multiplying the gradient with the first instance of data – This limitation amounts to performing further mathematical calculations (multiplying) with respect to a computed value (gradient).
Claim 7 recites, inter alia:
wherein the identifying a difference in the features of the first instance and the features of the second instance, includes providing an explanation including a ranked list of the features from the first instance and the features of the second instance, wherein the features included in the ranked list contributed to the first instance and the second instance being classified differently – This limitation amounts to providing a reasoning for features (e.g., words) identified as having contributed to the two instances (i.e., portions of text) being classified differently, including evaluating said identified features on a scale of importance. It thereby merely expands upon the same abstract idea recited in the independent claims (see Step 2A Prong 1 analysis of claim 1 above) of identifying differences between images, i.e., further recites processes of evaluation capable of being performed in the human mind or using pen and paper.
Claims 9 and 16 recite the same judicial exception as claims 8 and 15.
Claims 11-14 and 18-20 recite substantially similar abstract idea limitations to those recited in claims 4-7, and therefore recite the same judicial exceptions as claims 4-7.
Step 2A Prong 2: The following additional elements recited in claims 2, 9, and 16 do not integrate the recited judicial exceptions into a practical application.
Claim 2 additionally recites:
wherein the first neural network and the second neural network have identical hyperparameters and weights – This limitation amounts to an incidental addition with regard to implementation of the neural architecture recited in the independent claims. Wherein the independent claims merely invoke neural network architecture as a tool to perform existing abstract ideas (see Step 2A Prong 2 analysis of claim 1), this limitation does no more than further limit said architecture to being implemented as Siamese/twin networks, thereby generally linking a judicial exception to the technological environment of Siamese/twin networks without providing anything more.
Claims 9 and 16 recite substantially similar additional elements to those recited in claim 2, and therefore also do no more than generally link a judicial exception to the technological environment of Siamese/twin networks.
Step 2B: The additional elements recited in claims 2, 9, and 16, viewed individually or as a combination, do not provide an inventive concept or otherwise amount to significantly more than the recited abstract ideas themselves.
Claim 2 additionally recites:
wherein the first neural network and the second neural network have identical hyperparameters and weights – Generally linking a judicial exception to the technological environment of Siamese/twin networks without providing anything more does not provide an inventive concept or significantly more to the recited abstract idea.
Claims 9 and 16 recite substantially similar additional elements to those recited in claim 2, and therefore also do not provide an inventive concept or significantly more to the recited abstract idea.
Even when considered as an ordered combination, the additional elements recited in the claims ultimately do no more than place an abstract procedure of observing and analyzing model outputs to create explanations for classification differences, via mental processes and/or mathematical calculations, in the context of analyzing output of generic Siamese/twin networks. As such, claims 2, 4-7, 9, 11-14, 16, and 18-20 are not patent eligible.
Response to Amendment and Arguments
The amendment filed 05/29/2026 has been entered.
Applicant’s amendment to the claims with respect to resolving indefiniteness rejections under 35 U.S.C. 112(b) has been considered, and the previous rejections are consequently withdrawn.
The remarks filed 05/29/2026 have been fully considered.
Applicant’s remarks traversing the non-eligible subject matter rejections under 35 U.S.C. 101 set forth in the office action mailed 05/29/2026, in view of claims 1-2, 4-7, 9, 11-14, 16, and 18-20 as amended, have been considered but are not persuasive. Arguments of note are further summarized and addressed below.
Applicant alleges that the claim as a whole recites a technological improvement to machine learning, e.g., to a machine learning architecture that is improved to be able to provide specific details pointing to specific encodings as to why a given machine learning model, trained to classify given input data (i.e., image pixels), classified two instances of data into different classes.
The examiner respectfully disagrees. The asserted improvement of “providing specific details pointing to specific encodings as to why a given machine learning classified two instances of data into different classes” resides directly within the recited abstract analysis itself, as identified in the analysis above. The judicial exception alone cannot provide the improvement. The claimed neural network architecture is largely generic in nature, and is only invoked as a mere tool to implement generation of encodings, which are then analyzed via an abstract procedure of determining observable and/or calculable differences. Additionally, the procedure ends with “outputting the identified difference in features as the explanation”, i.e., this output is merely provided as information, and is not actually leveraged to improve the operation of the invoked model in any particular way. The claim therefore does not properly integrate the recited judicial exception into a practical application, and does not adequately reflect improvement to machine learning or the operation of a machine learning model.
Applicant alleges that processing of image pixels via a “neural network architecture model” comprising a first neural network and a second neural network working in tandem is not practically performable in the human mind.
The examiner respectfully disagrees. The processing of image pixels, as it is recited in the claims, amounts to mentally performable and/or mathematical calculation steps. Further, the mere invocation of a Siamese/twin neural network architecture as a computational model capable of performing said abstract steps does not absolve the claim itself from being directed towards an abstract procedure.
Applicant alleges that recited additional element "the features of one of the first encoding and the second encoding are set as the labels and the features of other one of the first encoding and the second encoding are set as the logits” amounts to significantly more than the recited judicial exception under Step 2B analysis.
The examiner respectfully disagrees. These steps do no more than specify the means in which sources of data are being utilized in the abstract analysis of procedure. Additionally, as explained above, utilizing output of a machine learning model to provide classification explanations is well understood, routine, and conventional activity in the field of explainable AI (XAI), and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
Applicant has not presented further arguments with respect to the dependent claims. As such, amended claims 1-2, 4-7, 9, 11-14, 16, and 18-20 stand rejected under 35 U.S.C. 101.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIJAY M BALAKRISHNAN whose telephone number is (571) 272-0455. The examiner can normally be reached 10am-5pm EST Mon-Thurs.
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/V.M.B./
Examiner, Art Unit 2143
/JENNIFER N WELCH/
Supervisory Patent Examiner, Art Unit 2143
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