DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Claim Status
Claims 1-9 are pending.
Claims 1-9 are under examination.
Claims 1-9 are rejected.
Priority
The instant Application claims domestic benefit as a CON of US application 17711310, filed 04/01/2022. Accordingly, each of claims 1-9 are afforded the effective filing date of the 04/01/2022.
Information Disclosure Statement
No IDS was filed with this application.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Fig. 5 reference 500, Fig. 6 reference 614, and Fig. 7 reference 714.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 613 and 713.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
Generating peptide-based vaccines as recited in claim 8 is interpretated as being an in-silico activity because the trained Wasserstein Generative Adversarial Network (WGAN) is used to generate the peptide-based vaccine.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to a computer-implemented method (Claim 1) i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claim 1:
training, by a processor device, a Generative Adversarial Network GAN having a generator and a discriminator only on a set of binding peptide sequences given training data comprising the set of binding peptide sequences and a set of non-binding peptide sequences, wherein a GAN training objective comprises the discriminator being iteratively updated to distinguish generated peptide sequences from sampled binding peptide sequences as fake or real and the generator being iteratively updated to fool the discriminator
optimizing the GAN training objective while learning two projection vectors for a binding class with two cross-entropy losses, a first of the two cross-entropy losses discriminating binding peptide sequences in the training data from non-binding peptide sequences in the training data, and a second of the two cross- entropy losses discriminating generated binding peptide sequences from non-binding peptide sequences in the training data, in the training at a beginning, an initial penalty coefficient is set for entropy regularization, and in the training at a timing later than the beginning, a penalty coefficient larger than the initial penalty coefficient is used for the entropy regularization.
Dependent claim 3:
updating the generator with tempering Softmax units to minimize the two cross-entropy losses
Dependent claim 5:
a deep neural network
Dependent claim 8:
using the trained GAN
generating peptide-based vaccines with user-specified properties using the trained GAN.
Dependent claim 9:
the peptide-based vaccines are output from the generator as Softmax output units, and wherein the generator comprises a fully-connected layer for receiving an input random noise vector and another fully-connected layer for outputting the Softmax output units (37-50).
Dependent claims 2 and 4-7recite further steps that limit the judicial exceptions in independent claim 1, as such, also are directed to those abstract ideas. For example, claim 2 further limits the GAN of claim 1, claim 4 further limits the generator of claim 1, claim 5 further limits the generator of claim 1, claim 6 further limits GAN of claim 1, and claim 7 further limits the two cross entropy losses of claim 1.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually train a GAN. Without further detail as to the methodology involved in “training”, “optimizing”, “updating”, “run” and “generating” under the BRI, one may simply, for example, use pen and paper to generate a peptide-base vaccine prediction. Some of these steps and those recited in the dependent claims require mathematical techniques as the only supported embodiments, as is disclosed in the specification at: A non-negative scalar weight It(x) may be learned for each data point x associated with the two cross-entropy losses, balancing the discriminator loss. A penalty term of -0.5 log(l(x)) may be added to penalize large values of I(x). Data-label pairs may be denoted as {xXx drawn from a join , where x is apeptide sequence and y is a label. The generator 302 is trained to transform samples z-Pz from a canonical distribution conditioned on labels to match the real data distributions (0041), amortized homoscedastic weights may be learned for each data point. The term 2(x) ;> 0 would then be a function of x producing per-sample weights (0047), A convex combination of the latent codes of the m peptides may be calculated with randomly sampled coefficients, where 2 < m < K and K is a user-specified hyperparameter. A convex combination may be a positive-weighted linear combination with the sum of the weights equal to (0051), and all of (0046-0047). Softmax may be used in the last output layer of the generator, with entropy regularization being used to implicitly control the temperature in the tempering softmax units (0048). The discriminator is a deep neural network with convolutional layers and fully-connected layers between the input representation layer and the output layer that outputs a scalar as in standard WGAN (0020]. The training includes optimizing the GAN training objective while learning two projection vectors for a binding class with two cross-entropy losses (0003).
Therefore, claim 1and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
The claims also include non-abstract computing elements.
Independent claim 1: A processor device
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to additional non-abstract elements of “processor device”, (i.e., a processor), using the trained GAN and “deep neural network having a convolutional layer and a fully- connected layer” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to claim 1 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0023-0025]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Conclusion
It is noted that claims 1-9 are free from the prior because the prior art does not teach nor fairly suggest “optimizing the GAN training objective while learning two projection vectors for a binding class with two cross-entropy losses, a first of the two cross-entropy losses discriminating binding peptide sequences in the training data from non-binding peptide sequences in the training data, and a second of the two cross-entropy losses discriminating generated binding peptide sequences from non-binding peptide sequences in the training data, in the training at a beginning, an initial penalty coefficient is set for entropy regularization, and in the training at a timing later than the beginning, a penalty coefficient larger than the initial penalty coefficient is used for the entropy regularization”. The closest prior art is Li et al. (Li, Guangyuan, et al. "DeepImmuno: deep learning-empowered prediction and generation of." Improving cancer immunotherapy through the lens of single-cell genomics and neoantigen discovery 513 (2019), newly cited) in view of Kuksa et al (Kuksa, Pavel P., et al. "High-order neural networks and kernel methods for peptide-MHC binding prediction." Bioinformatics 31.22 (2015): 3600-3607, cited on IDS filed on 04/01/2022). Li discloses deep learning-empowered prediction and generation of immunogenic peptides (title). Li further discloses collecting all immunogenic peptides known to bind to HLA-A∗0201 for training the deep GAN model (p. 6, col. 2, par. 3). Li also discloses initial training and validation, we analyzed >9000 tested immunogenicity molecular assays from the Immune Epitope Database, IEDB database (p. 3, col. 1, par. 1) that is split into 10 rotating subsets—9 for training and 1 for validation(p. 3, col. 1, par. 2) which reads on a training dataset. Li further discloses that the model is composed of a generator and a discriminator (p. 4, Fig. 1). Li also discloses that this learning generator produces pseudo-sequences to artificially convince the discriminator that the immunogenic sequences are real, while the discriminator uses real peptide sequences along with generated pseudo-sequences to distinguish the difference (p. 4, Fig. 1) which reads on a iteratively updated discriminator and generator. Li is silent on binding and non-binding peptide sequence datasets.
However, Kuksa discloses computational methods for peptide-protein binding prediction for clinical peptide vaccine search and design (abstract). Kuksa also teaches using a dataset of MHC-I of binding and nonbinding peptides. (p. 4, col. 2, tbl 1) which reads on a dataset of positive and negative binding peptide sequences. However, Li and Kuska are silent to optimizing the GAN training objective while learning two projection vectors for a binding class with two cross-entropy losses, a first of the two cross-entropy losses discriminating binding peptide sequences in the training data from non-binding peptide sequences in the training data, and a second of the two cross-entropy losses discriminating generated binding peptide sequences from non-binding peptide sequences in the training data, in the training at a beginning, an initial penalty coefficient is set for entropy regularization, and in the training at a timing later than the beginning, a penalty coefficient larger than the initial penalty coefficient is used for the entropy regularization
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Dawn M. Bickham whose telephone number is (703)756-1817. The examiner can normally be reached M-Th 7:30 - 4:30.
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/D.M.B./Examiner, Art Unit 1685
/Soren Harward/Primary Examiner, TC 1600
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