DETAILED ACTION
The Applicant’s response, received 15 July 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Status of the Claims
Claims 1-22 are pending.
Claims 1-22 are rejected.
Priority
This application claims benefit of U.S. provisional application number 63/242,848, filed 10 September 2021.
Drawings
The objection to the drawings in the Office action mailed 16 April 2026 has been withdrawn in view of the amendment received 15 July 2026.
The replacement drawings received 15 July 2026 have been accepted.
Specification
The objection to the disclosure in the Office action mailed 16 April 2026 has been withdrawn in view of the amendment received 15 July 2026.
The amendment to the specification received 15 July 2026 has been entered.
Claim Rejections - 35 USC § 112
The rejection of claims 1-22 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, in the Office action mailed 16 April 2026 has been withdrawn in view of the amendment received 15 July 2026.
Claim Rejections - 35 USC § 101
The amendment received 15 July 2026 has been fully considered, however after further consideration, the rejection of claims 1-22 under 35 U.S.C. 101 in the Office action mailed 16 April 2026 has been maintained with modification in view of the amendment received 15 July 2026, as noted below.
The rejection has been modified to incorporate the newly amended limitations.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Claim interpretations
Claims 1-3, 13, and 17 recite the limitation “output.” This limitation is interpreted to mean the result of a computational algorithm, e.g., a mathematical calculation, and further interpreted to not mean transmitting data from a processor to output devices, e.g., a monitor (i.e., a screen).
Claims 1, 13, and 17 recite a substantively similar limitation of “generating, by the one or more processors, an aptameric peptide library comprising a plurality of designed peptides output by the generator of the GAN machine learning model, wherein the aptameric peptide library is specific to the target receptor.” This limitation is interpreted to mean generating a library of digital, theoretical sequences (i.e., data) that merely represent molecular structures.
Claims 1, 13, and 17 recite a substantively similar limitation of “generating, by the one or more processors, a generative adversarial network (GAN) machine learning model comprising a generator and a discriminator.” This limitation is interpreted to comprise an active step of training the GAN using the model architectures, loss functions, and hyperparameters that are defined in a configuration phase.
Claim 21 recites the step of “selecting one or more designed peptides from the aptameric library of claim 1,” which is a library of digital, theoretical sequences (i.e., data) that merely represent molecular structures, and is the final step of claim 1. Therefore claim 21 is further interpreted to require, prior to the step of “selecting one or more designed peptides from the aptameric library of claim 1,” performing the active steps of claim 1 that produce the library.
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-12 recite a method (i.e., a process); claims 13-16 recite an apparatus comprising at least one processor and at least one memory (i.e., a machine and/or a manufacture); claims 17-20 recite at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein (i.e., a machine and/or a manufacture); claim 21 recites a method (i.e., a process); and claim 22 recites a method (i.e., a process).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
encoding a plurality of pre-existing peptides from a peptide dataset (i.e., mental processes);
generating a generative adversarial network (GAN) machine learning model comprising a generator and a discriminator, wherein the generator is configured to output a designed peptide similar to the plurality of pre-existing peptides and the discriminator configured to classify a binding ability of the designed peptide with the target receptor according to a plurality of physiochemical parameters, wherein a binary cross-entropy loss of the discriminator is provided to the generator to update generation of designed peptides until a binary cross-entropy loss threshold of the discriminator is satisfied (i.e., mental processes (e.g., the “configured” step comprises defining aspects such as, e.g., structural architectures, loss functions, optimization hyperparameters, and training schedules prior to the “generating” (i.e., training) of the GAN model); and mathematical concepts (i.e., training the GAN model, e.g., training relies on a two-player zero-sum (minimax) game defined by an objective/loss function, wherein the generator and the discriminator optimize their parameters via calculus (such as gradient descent and backpropagation) to minimize divergence (like Jensen-Shannon divergence) between real and fake sequences)); and
generating an aptameric peptide library comprising a plurality of designed peptides output by the generator of the GAN machine learning model, wherein the aptameric peptide library is specific to the target receptor (i.e., mathematical concepts, e.g., a GAN generates a peptide library through a continuous competition between two neural networks—a generator and a discriminator—wherein, e.g., the generator uses a softmax function to output a probability distribution across the 20 natural amino acids for every position in the chain, and then samples sequences from this matrix to form candidate peptides, and, e.g., the discriminator receives a mix of real peptide sequences (from training databases) and synthetic peptides produced by the generator, and acts as a binary classifier learning to distinguish between "real" (naturally occurring or known functional) peptides and "fake" (AI-generated) sequences).
Independent claim 13 recites an apparatus comprising at least one processor and at least one memory for performing the abstract ideas recited by the method of independent claim 1, as noted above.
Independent claim 17 recites at least one non-transitory computer-readable storage medium storing instructions for performing the abstract ideas recited by the method of independent claim 1, as noted above.
Independent claim 21 recites a method for forming a targeted therapeutic exosome comprising a designed peptide comprising steps for performing the abstract ideas recited by the method of independent claim 1, as noted above.
Independent claim 22 recites a method delivering a drug or therapeutic comprising the therapeutic exosome of claim 21, and therefore further comprising steps for performing the abstract ideas recited by the method of independent claim 1, as noted above.
Dependent claims 2-6, 9-11, 14-16, and 18-20 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
the plurality of designed peptides are output based at least in part on an iterative minimization of a classification accuracy of the discriminator (i.e., mental processes and mathematical concepts).
Dependent claim 3 further recites:
an iteration-specific classification accuracy of the discriminator is provided to the generator such that the generator outputs a designed peptide based at least in part on a previously-output designed peptide and the iteration-specific classification accuracy of the discriminator (i.e., mental processes and mathematical concepts).
Dependent claim 4 further recites:
the classification accuracy of the discriminator is determined based at least in part on the binary cross-entropy loss of the discriminator (i.e., mathematical concepts).
Dependent claim 5 further recites:
the plurality of physiochemical parameters are selected based at least in part on a maximation of the classification accuracy of the discriminator (i.e., mental processes and mathematical concepts).
Dependent claim 6 further recites:
the plurality of physiochemical parameters comprise at least one of solvation energy, binding affinity, radius of gyration, molecular force constant, unit polarization, total mass, motif, or residue position (i.e., mental processes).
Dependent claim 9 further recites:
the plurality of pre-existing peptides are encoded according to one of an alphanumeric encoding scheme, a binary encoding scheme, or a one-hot encoding scheme (i.e., mental processes and mathematical concepts).
Dependent claim 10 further recites:
the plurality of designed peptides satisfy a similarity threshold with the plurality of pre-existing peptides (i.e., mental processes and mathematical concepts).
Dependent claim 11 further recites:
the discriminator comprises at least one of a random forest classifier, a clustering classifier, or a multilayer perceptron (i.e., mental processes and mathematical concepts).
Dependent claim 14 further recites:
the plurality of pre-existing peptides are encoded according to one of an alphanumeric encoding scheme, a binary encoding scheme, or a one-hot encoding scheme (i.e., mental processes and mathematical concepts).
Dependent claim 15 further recites:
the plurality of designed peptides satisfy a similarity threshold with the plurality of pre-existing peptides (i.e., mental processes and mathematical concepts).
Dependent claim 16 further recites:
the discriminator comprises at least one of a random forest classifier, a clustering classifier, or a multilayer perceptron (i.e., mental processes and mathematical concepts).
Dependent claim 18 further recites:
the plurality of pre-existing peptides are encoded according to one of an alphanumeric encoding scheme, a binary encoding scheme, or a one-hot encoding scheme (i.e., mental processes and mathematical concepts).
Dependent claim 19 further recites:
the plurality of designed peptides satisfy a similarity threshold with the plurality of pre-existing peptides (i.e., mental processes and mathematical concepts).
Dependent claim 20 further recites:
the discriminator comprises at least one of a random forest classifier, a clustering classifier, or a multilayer perceptron (i.e., mental processes and mathematical concepts).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., encoding a plurality of pre-existing peptides from a peptide dataset), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., generating an aptameric peptide library comprising a plurality of designed peptides output by the generator of the GAN machine learning model) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-22 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
In the instant application, the independent claims provide additional elements ‘one or more processors’ for receiving and processing data, however once the data is received, the subsequent steps only perform calculations using a machine learning model to generate ‘an aptameric peptide library.’ Thus, the claims do not recite any limitations (i.e., additional elements) to which the ‘aptameric peptide library’ (i.e., the result of the machine learning calculations) is practically applied.
Dependent claims 2-6, 9-11, 14-16, and 18-20 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
one or more processors; and
receiving a selection of a target receptor (i.e., receiving data).
The additional elements in independent claim 13 include:
an apparatus comprising at least one processor and at least one memory; and
receive a selection of a target receptor (i.e., receive data).
The additional elements in independent claim 17 include:
at least one non-transitory computer-readable storage medium; and
receive a selection of a target receptor (i.e., receive data).
The additional elements in independent claim 21 include:
one or more processors (of claim 1);
receiving a selection of a target receptor (of claim 1);
synthesizing the one or more designed peptides;
attaching the one or more designed peptides to a surface of an exosome thereby forming the targeted therapeutic exosome, wherein the cellular receptor is the selected target receptor and wherein the exosome comprises or contains a drug or therapeutic.
The additional elements in independent claim 22 include:
contacting the cell or tissue with the therapeutic exosome of claim 21, wherein the cell or tissue presents the target receptor.
The additional elements in dependent claims 7, 8, and 12 include:
synthesizing at least one designed peptide of the aptameric peptide library (claim 7);
experimentally validating that the plurality of designed peptides of the aptameric peptide library binds to the target receptor using an enzyme-linked immunosorbent assay (claim 8);
synthesizing the one or more designed peptides (claim 12); and
attaching one or more of the designed peptides of the aptameric peptide library onto a surface of an exosome to form an augmented exosome, wherein one or more designed peptides target the augmented exosome to a target cell presenting the target receptor (claim 12).
The additional elements of one or more processors (claims 1 and 21); an apparatus comprising at least one processor and at least one memory (claim 13); and at least one non-transitory computer-readable storage medium (claim 17); invoke a computer and/or computer-related components merely as tools for use in the claimed process, and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional element of receiving/receive data (claims 1, 13, 17, and 21) is merely a pre-solution activity of gathering data for use in the claimed process – a nominal addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of synthesizing at least one designed peptide of the aptameric peptide library (claim 7); and synthesizing the one or more designed peptides (claims 12 and 21); does not amount to more than mere instructions to implement an abstract idea (i.e., does not amount to more than a recitation of the words “apply it” (or an equivalent)). The additional element must do more than simply state the judicial exception (e.g., at least one designed peptide) while adding the words equivalent to “apply it” (e.g., synthesizing the designed peptide) (see MPEP 2106.05(f)).
The additional element of experimentally validating that the plurality of designed peptides of the aptameric peptide library binds to the target receptor using an enzyme-linked immunosorbent assay (claim 8); is merely a post-solution activity used in the claimed process – a tangentially related addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional element of attaching one or more of the designed peptides of the aptameric peptide library onto a surface of an exosome to form an augmented exosome, wherein one or more designed peptides target the augmented exosome to a target cell presenting the target receptor (claim 12); is merely the addition of a token post-solution activity that does not alter or affect how the process steps of determining the library of designed aptameric peptides is performed, and therefore does not amount to more than generally linking the use of a judicial exception to a particular technological environment (MPEP 2106.05(h)).
The additional element of attaching the one or more designed peptides to a surface of an exosome thereby forming the targeted therapeutic exosome, wherein the cellular receptor is the selected target receptor and wherein the exosome comprises or contains a drug or therapeutic (claim 21); is merely the addition of a token post-solution activity that does not alter or affect how the process steps of determining the library of designed aptameric peptides is performed, and therefore does not amount to more than generally linking the use of a judicial exception to a particular technological environment (MPEP 2106.05(h)).
The additional element of contacting the cell or tissue with the therapeutic exosome of claim 21, wherein the cell or tissue presents the target receptor (claim 22); is merely the addition of a token post-solution activity that does not alter or affect how the process steps of determining the library of designed aptameric peptides is performed, and therefore does not amount to more than generally linking the use of a judicial exception to a particular technological environment (MPEP 2106.05(h)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and/or a particular technological environment in which to apply a judicial exception; and as such, when all limitations in claims 1-22 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-22 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-6, 9-11, 14-16, and 18-20 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 13, 17, 21, and 22 and dependent claims 7, 8, and 12 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of one or more processors (claims 1 and 21); an apparatus comprising at least one processor and at least one memory (claim 13); at least one non-transitory computer-readable storage medium (claim 17); and receiving data (claims 1, 13, 17, and 21); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional elements of synthesizing at least one designed peptide of the aptameric peptide library (claim 7); and synthesizing the one or more designed peptides (claims 12 and 21); are conventional. Evidence of conventionality is shown by:
Reverdatto et al. (“Peptide Aptamers: Development and Applications.” Current Topics in Medicinal Chemistry, 2015, vol. 15(12): pp. 1082-1101, as cited in the Office action mailed 16 April 2026).
Reverdatto et al. reviews the development and application of peptide aptamers (Abstract) and shows that peptide aptamers can be synthesized in large quantities through chemical synthesis or bacterial expression (page 3, paras. 2-3; and throughout).
The additional element of experimentally validating that the plurality of designed peptides of the aptameric peptide library binds to the target receptor using an enzyme-linked immunosorbent assay (claim 8); is conventional. Evidence of conventionality is shown by:
Thibaut et al. (“A novel assay for the detection of anti-human platelet antigen antibodies (HPA-1a) based on peptide aptamer technology.” Haematologica, 2012, vol. 97(5), pp. 696-704, as cited in the Office action mailed 16 April 2026);
Straw et al. (“Proof of concept study to identify candidate biomarkers of fibrosis using high throughput peptide aptamer microarray and validate by enzyme linked immunosorbent assay.” Journal of Biomedical Science and Engineering, 2013, vol. 6, pp. 32-42, as cited in the Office action mailed 16 April 2026); and
Xu et al. (“Engineering a peptide aptamer to target calmodulin for the inhibition of Magnaporthe oryzae.” Fungal Biology, 2019, vol. 123, pp. 489-496, as cited in the Office action mailed 16 April 2026).
Thibaut et al. shows using a peptide aptamer to detect and identify HPA-1a-specific antibodies in human serum that do not require human platelets (Abstract: Design and Methods); a peptide aptamer library was screened using an anti-HPA-1a human monoclonal antibody as a bait to isolate an aptamer that mimics the human platelet antigen HPA-1a (Ibid.); and that the result of the study methodology is the first report in platelet immunology of the use of a peptide aptamer for diagnostic purposes (Abstract: Result). Thibaut et al. further shows the characterization of peptide aptamer (hereinafter named Trx-HPA-1a) interaction with HPA-1a-specific antibodies (page 699, col. 2, para. 4) and an ELISA-based (i.e., enzyme-linked immunosorbent assay) detection assay for anti-HPA-1a antibodies using the recombinant Trx-HOA-1a protein (page 700, col. 2, para. 1).
Straw et al. shows investigating nine proteins that were identified as putative profibrotic biomarkers in systemic sclerosis (SSc) and an unrelated fibrotic disease in a previously published proteomic study, in order to determine whether binding peptide aptamers of the Stefin A quadruple mutant-Tracy variant (referred to as “affimers”) could be validated by enzyme-linked immunosorbent assay (ELISA) to allow the quantification of these candidate biomarkers in the sera of SSc patients (Abstract; and throughout).
Xu et al. shows identifying and engineering a peptide aptamer (SNP-D4) to target calmodulin (CaM) for the inhibition of the spore development in the pathogen Magnaporthe oryzae (Title; and Abstract); and using an ELISA (enzyme-linked immunosorbent assay) to measure the affinity between SNP-D4 and CaM (page 494, col. 1, para. 2).
The additional elements of attaching one or more of the designed peptides of the aptameric peptide library onto a surface of an exosome to form an augmented exosome, wherein one or more designed peptides target the augmented exosome to a target cell presenting the target receptor (claim 12); attaching the one or more designed peptides to a surface of an exosome thereby forming the targeted therapeutic exosome, wherein the cellular receptor is the selected target receptor and wherein the exosome comprises or contains a drug or therapeutic (claim 21); and contacting the cell or tissue with the therapeutic exosome of claim 21, wherein the cell or tissue presents the target receptor (claim 22); are conventional. Evidence of conventionality is shown by:
Tran et al. (“Aptamer-guided extracellular vesicle theranostics in oncology.” Theranostics, 2020, vol. 10(9): pp. 3849-3866, as cited in the Office action mailed 16 April 2026).
Tran et al. reviews aptamer-guided extracellular vesicle (EV) theranostics (i.e., therapeutics and diagnostics) in oncology (Title) and shows that the study of exosomes (i.e., nanosized vesicles released into the extracellular space via the fusion of multivesicular bodies with the plasma membrane) has burgeoned with impressive achievements in theranostics applications, and that exosomes possess intrinsic properties – a nanosized structure and unique “homing effects” – that make them outstanding drug delivery vehicles (Abstract). Tran et al. further shows a typical manufacturing cycle of the EV-based engineering of exosomes or microvesicles for targeted therapeutics (Figure 5), which describes two types of EV therapy (i.e., allogeneic EV therapy and autologous EV therapy) that comprise surface functionalization of an exosome with an aptamer and in vitro manipulation to load a therapeutic cargo into the exosome, which allow for aptamer-guided exosomal drug delivery.
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-22 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-22 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Response to Arguments
The Applicant’s arguments/remarks received 15 July 2026 have been fully considered, but are not persuasive.
Regarding Step 2A Prong One, the Applicant states on page 9 (as filed) of the Remarks that the claims recite a particularized technique, not merely abstract ideas, and further states that the Office action has oversimplified the claims by characterizing them as mere mathematical concepts and mental processes, and further states that the Federal Circuit has repeatedly cautioned against such oversimplification. The Applicant points to Ollnova Technologies Ltd. v. ecobee Technologies ULC, and states that the court rejected the defendant’s characterization of claims as directed to the abstract steps required to collect, analyze, and selectively communicate data, holding that this oversimplifies the claims by ignoring their specific operational limitations. The Applicant further states on page 10 that the Office action characterizes the limitations of claims 1, 13, and 17 as amended – generating a GAN machine learning model comprising a generator and a discriminator, the discriminator configured to classify a binding ability of the designed peptide with the target receptor according to a plurality of physiochemical parameters – as generic mathematical concepts and mental processes, and further states that this characterization ignores the specific technical requirements of the claims, just as the characterization rejected in Ollnova ignored the specific operational limitations of the claims at issue there. The Applicant further states that the present claims do not merely recite generic data processing, rather, they recite a specific GAN architecture in which the discriminator is configured to perform a particular technical function – classifying binding ability of a designed peptide with a target receptor according to physiochemical parameters – and in which a binary cross-entropy loss of the discriminator is provided to the generator to update generation of designed peptides until a binary cross-entropy loss threshold is satisfied, and further states that this is a particularized technique for computational peptide design, not a generalized data-handling concept. The Applicant further states that moreover, the Office action’s assertion that generating a GAN machine learning model is a mental process that can be performed in the human mind with the aid of a pen and paper is untenable, and further states that a GAN machine learning model comprising a generator and a discriminator engaged in adversarial training – where the binary cross-entropy loss of the discriminator is iteratively fed back to the generator – cannot practically be performed in the human mind. The Applicant further states that the Office action’s own acknowledgement that one of skill in the art would recognize that there is a configuration phase prior to a training phase to manage the inherent instability of the adversarial learning process underscores the computational complexity of this process.
These arguments/remarks are not persuasive, at least because first, regarding the Applicant’s argument that the Federal Circuit cautioned against oversimplification of the claims by ignoring their specific operational limitations in Ollnova, it is noted here that the fact pattern of the claims in Ollnova (e.g., automation for a wireless communication system in a building automation system (BAS)) differ from the fact pattern of the instant claims (e.g., training a Generative Adversarial Network (GAN) and using the trained GAN to generate (i.e., compute) a result) not least in that the only additional elements provided by instant independent claims 1, 13, and 17 comprise one or more processors (claim 1); an apparatus comprising at least one processor and at least one memory (claim 13); at least one non-transitory computer-readable storage medium (claim 17); and receiving data (claims 1, 13, and 17). Because a GAN is entirely a collection of mathematical functions specifically composed of deep neural networks that parameterize multi-dimensional probability distributions, it is not an oversimplification to identify the recitation of training and using a GAN machine learning model as a mathematical concept at Step 2A Prong One. Second, regarding the Applicant’s argument that the Office action asserts “that generating a GAN machine learning model is a mental process that can be performed in the human mind with the aid of a pen and paper,” it is noted that the Office action does not identify generating a GAN machine learning model as a mental process, but rather, identifies the “configured” limitation of claim 1 as a step that comprises defining aspects such as, e.g., structural architectures, loss functions, optimization hyperparameters, and training schedules prior to the “generating” (i.e., training) of the GAN model as a mental process, whereas the steps of training and using the GAN model are identified as mathematical concepts (and not mental processes). Third, regarding the Applicant’s argument that “the Office action’s own acknowledgement that one of skill in the art would recognize that there is a configuration phase prior to a training phase to manage the inherent instability of the adversarial learning process underscores the computational complexity of this process,” it is noted that this statement was provided under 35 U.S.C. 112(b) in the Office action mailed 16 April 2026 solely with regard to a lack of clarity as to whether the limitation “configuring” included or excluded a step of training the GAN machine learning model.
Regarding Step 2A Prong Two, the Applicant states on page 10 (as filed) of the Remarks that the claims integrate any judicial exception not a practical application, and further states that even if the claims were found to recite a judicial exception at Step 2A Prong One, the Applicant submits that the claims as a whole integrate any such exception into a practical application as an improvement to technology at Step 2A Prong Two. The Applicant points to Ex Parte Desjardins on page 11 (para. 1) and further states that the Appeals Review Panel (ARP) held that claims directed to a method of training a machine learning model were patent-eligible because the specification identified improvements as to how the machine learning model itself operates, and the claims reflected those improvements. The Applicant further states that the ARP credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes as technological improvements, and further states that the ARP further held that such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation, and further states that the ARP cautioned that examiners and panels should not evaluate claims at such a high level of generality that potentially meaningful technical limitations are dismissed without adequate explanation. The Applicant further states on page 11 (para. 2) that the present application is analogous to Desjardins, and points to paragraphs [0048] & [0049] of the as-filed specification as providing descriptions of concrete improvements to the technology of peptide design and epitope-receptor selection. The Applicant further states (para. 3) that the claims as amended reflect these disclosed improvements, and further states on page 12 (para. 1) that these limitations are not generic recitations of mathematical concepts but rather the specific components and steps that provide the improvement described in the specification – a particularized computational technique for designing aptameric peptides that replaces labor-intensive bench methods with a more precise, resource-conserving, and scalable approach.
These arguments/remarks are not persuasive, at least because, first, with regard to the Applicant’s attempt to analogize the instant claims to the claims in Desjardins, it is noted that the fact patterns differ between the claims at issue in Desjardins and the instant claims, not least in that the “ARP” in Desjardins notes that the Federal Circuit held that the eligibility determination should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea” (citing Enfish), prior to the “ARP” finding that the improvement to how the machine learning model operates allows artificial intelligence (AI) systems to use less of their storage capacity and enables reduced system complexity, as supported by the Specification – i.e., the improvement to the model provided an improvement to computer functionality itself. This fact pattern contrasts with the fact pattern of the training steps recited in the instant claims, which comprise steps of optimizing a GAN machine learning model for the purpose of generating a library of aptameric peptides, i.e., generate new, synthetic data that closely mimics a real-world training dataset. Therefore, claims should be interpreted based on the fact patterns set forth in a particular claim, as other claims with different fact patterns may have different eligibility outcomes, as evidenced in the rejection of the instant claims above. Second, as noted in the above rejection, the eligibility analysis of instant claims does not result in the integration of the recited judicial exceptions into a practical application, because the claims do not recite any additional elements that apply, rely on, and/or use the judicial exceptions in a manner that imposes a meaningful limit on the judicial exceptions, and therefore, any purported improvement is in the abstract idea itself (i.e., generating an aptameric peptide library), and not an improvement in the functioning of a computer, or an improvement to any other technology or technical field.
Regarding Step 2B, the Applicant states on page 12 (as filed) of the Remarks that the ordered combination provides an inventive concept, and that even if the claims were found to be directed to a judicial exception at Step 2A, the claims as a whole amount to significantly more than the alleged abstract idea at Step 2B. The Applicant further states that the specific ordered combination of (1) generating a GAN machine learning model with a discriminator that classifies binding ability according to physiochemical parameters, wherein binary cross-entropy loss is fed back to the generator until a threshold is satisfied; (2) generating an aptameric peptide library specific to a target receptor; and, for claims 12, 21, and 22, (3) synthesizing the designed peptides and attaching them to exosomes for targeted drug delivery, is not routine or conventional. The Applicant further states that the ordered combination of these elements transforms the claims into patent-eligible subject matter.
These arguments/remarks are not persuasive, at least because first, as noted in the MPEP at 2106.05, eligibility analysis at Step 2B comprises evaluating the additional elements that were identified at Step 2A Prong Two, i.e., a conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, which comprises steps of: (1) carrying over the identification of any additional element(s) in the claim from Step 2A Prong Two; (2) carrying over the conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h); (3) re-evaluating any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and (4) evaluating whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d). As noted in the rejection above, when all additional elements in claims 1-22 have been evaluated individually and as an ordered combination at Eligibility Step 2B, they are deemed to not contribute an inventive concept, i.e., do not amount to significantly more than the judicial exceptions (MPEP 2106.05(II)). Second, it is noted that the limitations identified in the foregoing argument at (1) and (2) comprise the judicial exceptions identified at Step 2A Prong One, and therefore are not evaluated at Step 2B. Third, regarding the limitations identified in the foregoing argument at (3), these limitations are shown to be conventional additional elements at Step 2B as noted and discussed in the above rejection.
Claim Rejections - 35 USC § 103
The Applicant’s amendment received 15 July 2026 has been fully considered, however after further consideration, the rejections under 35 U.S.C. 103 in the Office action mailed 16 April 2026 have been maintained with modification in view of the amendment, as noted below.
The rejection of claims 1-7, 9-11, and 13-20 under 35 U.S.C. 103 as being unpatentable over Rossetto et al. in view of Reverdatto et al. has been modified to incorporate the newly amended limitations.
The rejection of claim 8 under 35 U.S.C. 103 as being unpatentable over Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, and further in view of Xu et al. has been modified to incorporate the newly amended limitations.
The rejection of claim 12 under 35 U.S.C. 103 as being unpatentable over Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, and further in view of Tran et al. has been modified to incorporate the newly amended limitations.
The rejection of claims 21 and 22 under 35 U.S.C. 103 as being unpatentable over Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, and further in view of Tran et al. has been modified to incorporate the newly amended limitations.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 9-11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rossetto et al. ("Gandalf: Peptide generation for drug design using sequential and structural generative adversarial networks." Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2020. pp. 1-10) in view of Reverdatto et al. (“Peptide Aptamers: Development and Applications.” Current Topics in Medicinal Chemistry, 2015, vol. 15(12): pp. 1082-1101, as cited above and as cited in the Office action mailed 16 April 2026).
Independent claims 1, 13, and 17 broadly encompass a computer system, a computer storage medium, and a program for a method of using a generative adversarial network (GAN) to generate designed peptides, and subsequently using the designed peptides to generate an in silico aptameric library of peptide aptamers that target a specific receptor.
Dependent claims 2-6, 9-11, 14-16, and 18-20 further define the machine learning framework of the generative adversarial network; and dependent claim 7 further defines a step of synthesizing at least one designed peptide.
Rossetto et al. teaches a peptide design system for protein targets based on a generative adversarial network (GAN).
Reverdatto et al. teaches the development and applications of peptide aptamers.
Regarding independent claims 1, 13, and 17, Rossetto et al. shows a generative adversarial network used to generate a new peptide sequence and structure (Abstract); generating multiple peptides for three targets of interest (i.e., PD-1, PDL-1, and CTLA-4) (Abstract); curated data for peptide drugs and targets (Section 2.1; and Table 1); a tensor (i.e., multidimensional data array) representation to represent the structural information of the proteins and drugs in addition to a “properties” feature vector (Section 2.3, paras. 3-6); and predicting the interaction (e.g., binding affinity) between a generated peptide and a target (Section 2.5, paras. 1-3).
Regarding independent claims 1, 13, and 17, Rossetto et al. does not show wherein a binary cross-entropy loss of the discriminator is provided to the generator to update generation of designed peptides until a binary cross-entropy loss threshold of the discriminator is satisfied; nor generating an aptameric peptide library comprising a plurality of designed peptides output by the generator of the GAN machine learning model, wherein the aptameric peptide library is specific to the target receptor.
However, regarding independent claims 1, 13, and 17, Rossetto et al. does show Generative Adversarial Networks (GANs) were originally developed as a framework for generating continuous data by simultaneously training a generative model to capture the data distribution and a discriminate model to estimate the probability that a piece of data is not from the generator model, and that this framework is an important type of unsupervised learning and has many useful applications (page 1, col. 2, para. 2); and further shows the need to train a good discriminator to ensure the generator is working well (page 3, col. 1, Section 2.3, para. 5).
Regarding independent claims 1, 13, and 17, Reverdatto et al. shows a series of steps in creating a peptide aptamer library, beginning with the first step of selecting a proper scaffold (page 4, para. 2; and Section 2, pages 4-11).
Regarding dependent claims 2-6, 9-11, 14-16, and 18-20, Rossetto et al. further shows steps that configure the GAN so that it generates a peptide that conforms to rules of what a peptide should be (Section 2.4).
Regarding dependent claim 7, Rossetto et al. does not show synthesizing at least one designed peptide of the aptameric peptide library.
Regarding dependent claim 7, Reverdatto et al. further shows that aptamers can be engineered and produced completely in a test tube, and can be readily modified during and after chemical synthesis to increase the stability and variability of the library (page 3, para. 1).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Rossetto et al. by incorporating methods for creating a peptide aptamer library, as shown by Reverdatto et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Rossetto et al. with the methods of Reverdatto et al., because Reverdatto et al. shows methods for creating a peptide aptamer library. This modification would have had a reasonable expectation of success given that both Rossetto et al. and Reverdatto et al. disclose methods for the development and application of peptides. Furthermore, one of skill in the art would have recognized that binary cross-entropy (BCE) had traditionally been used to train Generative Adversarial Network (GAN) models.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, and further in view of Xu et al. (“Engineering a peptide aptamer to target calmodulin for the inhibition of Magnaporthe oryzae.” Fungal Biology, 2019, vol. 123, pp. 489-496, as cited above and as cited in the Office action mailed 16 April 2026).
Dependent claim 8 further defines using the synthesized at least one designed peptide by validating the binding affinity of the designed peptide to the target receptor.
Regarding dependent claim 8, Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, does not show experimentally validating that the plurality of designed peptides of the aptameric peptide library binds to the target receptor using an enzyme-linked immunosorbent assay.
Xu et al. teaches the engineering of a peptide aptamer to target a regulatory protein (calmodulin) in order to inhibit spore development in a pathogen.
Regarding dependent claim 8, Xu et al. shows identifying and engineering a peptide aptamer (SNP-D4) to target calmodulin (CaM) for the inhibition of the spore development in the pathogen Magnaporthe oryzae (Title; and Abstract); and using an ELISA (enzyme-linked immunosorbent assay) to measure the affinity between SNP-D4 and CaM (page 494, col. 1, para. 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, by incorporating methods for measuring the binding affinity between a designed molecule and the receptor of the molecule being targeted, as shown by Xu et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, with the methods of Xu et al., because Xu et al. shows methods for using an ELISA (enzyme-linked immunosorbent assay) to measure the affinity between a peptide aptamer and a target molecule. This modification would have had a reasonable expectation of success given that both Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, and Xu et al. disclose methods for developing peptide aptamers that bind to specific sites on their target molecules.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, and further in view of Tran et al. (“Aptamer-guided extracellular vesicle theranostics in oncology.” Theranostics, 2020, vol. 10(9): pp. 3849-3866, as cited above and as cited in the Office action mailed 16 April 2026).
Dependent claim 12 further defines using the synthesized at least one designed peptide by attaching one or more of the designed peptides to the surface of an exosome or extracellular vesicle.
Regarding dependent claim 12, Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, does not show attaching one or more of the designed peptides of the aptameric peptide library onto a surface of an exosome to form an augmented exosome, wherein one or more designed peptides target the augmented exosome to a target cell presenting the target receptor.
Tran et al. teaches aptamer-guided extracellular theranostics in oncology.
Regarding dependent claim 12, Reverdatto et al. further shows that aptamers can be engineered and produced completely in a test tube, and can be readily modified during and after chemical synthesis to increase the stability and variability of the library (page 3, para. 1).
Regarding dependent claim 12, Tran et al. shows using peptide aptamers to capture exosomes, and that by selecting an aptamer against an over-expressed protein on exosomes, an aptamer-guided exosome-capturing nanoplatform system can be developed (page 3857, col. 2, paras. 3-5). Tran et al. further shows a manufacturing cycle for engineering aptamer-functionalized exosomes for targeted therapeutics (Figure 5).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, by incorporating methods for aptamer-guided extracellular vesicle theranostics, as shown by Tran et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, with the methods of Tran et al., because Tran et al. shows methods for functionalizing an exosome with an aptamer of a designed peptide. This modification would have had a reasonable expectation of success given that both Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, and Tran et al. disclose methods for the application of peptide aptamers.
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, and further in view of Tran et al. (“Aptamer-guided extracellular vesicle theranostics in oncology.” Theranostics, 2020, vol. 10(9): pp. 3849-3866, as cited above and as cited in the Office action mailed 16 April 2026).
Independent claim 21 encompasses a method for forming a targeted therapeutic exosome or EV comprising a designed peptide having affinity for a cellular receptor.
Independent claim 22 v a method of delivering a drug or therapeutic to a target cell or tissue.
Regarding independent claims 21 and 22, Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, does not show forming a targeted therapeutic exosome comprising a designed peptide having affinity for a cellular receptor comprising: selecting one or more designed peptides from the aptameric peptide library of claim 1 and attaching the one or more designed peptides to a surface of an exosome thereby forming the targeted therapeutic exosome, wherein the cellular receptor is the selected target receptor and wherein the exosome comprises or contains a drug or therapeutic (claim 21); or delivering a drug or therapeutic to a target cell or tissue comprising contacting the cell or tissue with the therapeutic exosome of claim 21, wherein the cell or tissue presents the target receptor (claim 22).
Tran et al. teaches aptamer-guided extracellular theranostics in oncology.
Regarding independent claim 21, Reverdatto et al. further shows that aptamers can be engineered and produced completely in a test tube, and can be readily modified during and after chemical synthesis to increase the stability and variability of the library (page 3, para. 1).
Regarding independent claim 21, Tran et al. shows a manufacturing cycle for engineering an aptamer-functionalized exosome for targeted therapeutics (Figure 5).
Regarding independent claim 22, Tran et al. shows an application of an aptamer-guided exosomal drug delivery (Figure 5).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, by incorporating methods for aptamer-guided extracellular vesicle theranostics, as shown by Tran et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, with the methods of Tran et al., because Tran et al. shows methods for functionalizing an exosome with an aptamer of a designed peptide. This modification would have had a reasonable expectation of success given that both Rossetto et al. in view of Reverdatto et al. as applied to claims 1-6, 9-11, and 13-20 above, and Tran et al. disclose methods for the application of peptide aptamers.
Response to Arguments
The Applicant’s arguments/remarks received 15 July 2026 have been fully considered, but are not persuasive.
The Applicant states on page 13 (para. 1) (as filed) of the Remarks that the Applicant traverses the rejections at least in view of the amendments and the reasons set forth in the arguments/remarks. The Applicant further states (para. 2) that claims 1, 13, and 17 as amended recite “the discriminator configured to classify a binding ability of the designed peptide with the target receptor according to a plurality of physiochemical parameters,” and that Rossetto provides no such teaching or suggestion. The Applicant further states that Rossetto describes GANs as “simultaneously training a generative model to capture the data distribution and a discriminate model to estimate the probability that a piece of data is not from the generator model,” whereas in contrast, claims 1, 13, and 17 as amended recite “the discriminator configured to classify a binding ability of the designed peptide with the target receptor according to a plurality of physiochemical parameters” – not “estimate the probability that a piece of data is not from the generator model” as described by Rossetto. The Applicant further states (para. 3) that in Rossetto, binding affinity is assessed separately from the GAN, and that Rossetto states: “we also verify the binding affinity and the docking conformation using PyDockWEB” and Rossetto further states that PyDockWEB “uses the 3D structures of two proteins and returns the best rigid-body docking orientations and the binding affinity.” The Applicant further states that in contrast, claims 1, 13, and 17 as amended recite “the discriminator configured to classify a binding ability of the designed peptide with the target receptor according to a plurality of physiochemical parameters, wherein a binary cross-entropy loss of the discriminator is provided to the generator to update generation of designed peptides until a binary cross-entropy loss threshold is satisfied.” The Applicant further states (para. 4) that Reverdatto does not cure this deficiency, and further states that Reverdatto describes identifying and isolating high affinity ligands from combinatorial libraries through methods such as phage display, yeast two hybrid selection, and in vitro display, however Reverdatto does not disclose GAN machine learning models, let alone “the discriminator configured to classify a binding ability of the designed peptide with the target receptor according to a plurality of physiochemical parameters” as recited by claims 1, 13, and 17.
These arguments/remarks are not persuasive, because first, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Second, regarding the Applicant’s argument that Rossetto describes GANs as “simultaneously training a generative model to capture the data distribution and a discriminate model to estimate the probability that a piece of data is not from the generator model,” whereas in contrast, claims 1, 13, and 17 as amended recite “the discriminator configured to classify a binding ability of the designed peptide with the target receptor according to a plurality of physiochemical parameters” – not “estimate the probability that a piece of data is not from the generator model” as described by Rossetto, it is noted that the primary purpose of a Generative Adversarial Network (GAN) is to generate new, synthetic data that closely mimics a real-world training dataset. A GAN achieves this through an adversarial competition between two neural networks, wherein a generator takes input and creates fake data, whereas the discriminator evaluate that data to decide whether it is real (from the actual dataset) of fake (produced by the generator). Both networks train together in a feedback loop where the generator tries to fool the discriminator, and the discriminator gets better at spotting fakes, and ideally, the generator reaches a point where it creates data so realistic that the discriminator can no longer tell the difference. Thus, the combination of references used in the rejection of independent claims 1, 13, and 17 show that it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Rossetto et al. by incorporating methods for creating a peptide aptamer library, as shown by Reverdatto et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Rossetto et al. with the methods of Reverdatto et al., because Reverdatto et al. shows methods for creating a peptide aptamer library. This modification would have had a reasonable expectation of success given that both Rossetto et al. and Reverdatto et al. disclose methods for the development and application of peptides. Furthermore, one of skill in the art would have recognized that binary cross-entropy (BCE) had traditionally been used to train Generative Adversarial Network (GAN) models.
The Applicant states on page 13 (para. 5) of the Remarks that claims 1, 13, and 17 recite “generating, by the one or more processors, an aptameric peptide library comprising a plurality of designed peptides output by the generator of the GAN machine learning model, the aptameric peptide library being specific to the target receptor.” The Applicant further states that Rossetto describes “a peptide design system for protein targets” based on a GAN. The Applicant further states on page 14 (para. 1) that the Office action acknowledges that Rossetto does not teach generating an aptameric peptide library and relies on Reverdatto to fill this gap, however, Reverdatto describes creating peptide aptamer libraries through “efficient directional cloning of randomized oligonucleotides with the context of the E. coli thioredoxin gene” followed by “yeast two hybrid selection,” and further states that Reverdatto further describes “screening large libraries of single chain antibodies and designs based on non-antibody scaffolds, i.e., peptide aptamers.” The Applicant further states that none of these methods involves “generating, by the one or more processors, an aptameric peptide library comprising a plurality of designed peptides output by the generator of the GAN machine learning model” as recited by claims 1, 13, and 17. The Applicant further states (para. 2) that for at least these reasons that claims 1, 13, and 17 as amended are not rendered obvious by the combination of Rossetto and Reverdatto, and that claims 2-10, 14-15, and 18-19, which depend from claims 1, 13, and 17 respectively, are likewise patentable for at least the same reasons.
These arguments/remarks are not persuasive, at least for the reasons provided in the foregoing response to arguments, i.e., because first, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Second, regarding the Applicant’s argument that claims 1, 13, and 17 recite “generating, by the one or more processors, an aptameric peptide library comprising a plurality of designed peptides output by the generator of the GAN machine learning model, the aptameric peptide library being specific to the target receptor,” and none of the references show this limitation, it is noted that the primary purpose of a Generative Adversarial Network (GAN) is to generate new, synthetic data that closely mimics a real-world training dataset. A GAN achieves this through an adversarial competition between two neural networks, wherein a generator takes input and creates fake data, whereas the discriminator evaluate that data to decide whether it is real (from the actual dataset) of fake (produced by the generator). Both networks train together in a feedback loop where the generator tries to fool the discriminator, and the discriminator gets better at spotting fakes, and ideally, the generator reaches a point where it creates data so realistic that the discriminator can no longer tell the difference. Thus, the combination of references used in the rejection of independent claims 1, 13, and 17 show that it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Rossetto et al. by incorporating methods for creating a peptide aptamer library, as shown by Reverdatto et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Rossetto et al. with the methods of Reverdatto et al., because Reverdatto et al. shows methods for creating a peptide aptamer library. This modification would have had a reasonable expectation of success given that both Rossetto et al. and Reverdatto et al. disclose methods for the development and application of peptides.
Conclusion
No claims are allowed.
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 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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/S.W.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/ Supervisory Patent Examiner, Art Unit 1687
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