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 .
Priority
Application is a continuation of application No.:17/898,662 filed on August 30, 2022
which claims the benefit of PCT Application No. PCT/EP2020/078968, filed on October 14,
2020.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, 8, and 15 all recite employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex (MHC) allele sequences and peptide sequences; training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences of variable lengths into continuous embedding vectors of a fixed size: running a Monte Carlo Tree Search (MCTS) to generate a first set of positive peptide vaccine candidates; running a Bayesian Optimization search with the trained VAE (BO-VAE) and a Backpropagation search with the trained VAE (BP-VAE) to generate a second set of positive peptide vaccine candidates; using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates; screening and merging the first, second, and third sets of positive peptide vaccine candidates; and outputting qualified peptides for immunotherapy from the screened and merged first, second, and third sets of positive peptide vaccine candidates. The claims fail to particularly point out how the claimed invention functions. The claims specify running a Monte Carlo Tree Search, a Bayesian Optimization search, A Backpropagation search, and using a sampling from a Position Weight Matrix without specifying the input to these algorithms and models. It is unclear as to what data is used for the models and algorithms claimed and where that data is obtained. The claims are indefinite due to the lack of clarity of what data is used as an input to these models and algorithms. Claims 2-7, 9-14, and 16-20 are rejected being dependent on claims 1, 8, and 15.
Further if the rejection stated above were to be overcome the claims would be rejected further as follows, claims 4, 11, and 18, recite wherein the BP-VAE is employed to learn presentation scores from the deep neural network and generate a portion of the first set of positive peptide vaccine candidates by optimizing the continuous embedding vectors with a gradient ascent. The claims recite that the BP-VAE is generating a portion of the first set of the positive peptide vaccine candidates while claim 1. 8, and 15 which these claims depend on all recite the BP-VAE generating the second set of positive peptides vaccine candidates. It is unclear as to if the BP-VAE is generating a part of the first set of positive peptide vaccine candidates or the second set. The claims are indefinite due to the lack of clarity of the claim language in relation to the independent claims.
Further if the rejection stated above were to be overcome the claims would be rejected further as follows, claims 5, 12, and 19, recite wherein the sPWM generates the second set of positive peptide vaccine candidates having a length ℓ by sampling from amino acid distributions of all ℓ positions calculated from the second set of positive peptide vaccine candidates. The claims recite the sPWM generating the second set of positive peptide vaccine candidates while claim 1. 8, and 15 which these claims depend on all recite the sPWM generating the third set of positive peptide vaccine candidates. Further the claim recites doing a calculation using the second set of positive peptide vaccine candidates to generate that same second set of positive peptide vaccine candidates which makes it unclear as to what data is used to do the calculation. This is indefinite due to the lack of clarity on of both the lack of clarity of the claim language in relation to the independent claims and the lack of clarity of the use of the second set to generate the second set.
Further if the rejection stated above were to be overcome the claims would be rejected further as follows, Claim 7 recites the limitation " the extracted library of peptides and corresponding mutations with sequence similarities to unmutated peptides " in line 1-2. There is insufficient antecedent basis for this limitation in the claim.
Further if the rejection stated above were to be overcome the claims would be rejected further as follows, Claim 14 recites the limitation " the extracted library of peptides and corresponding mutations with sequence similarities to unmutated peptides " in line 1-2. There is insufficient antecedent basis for this limitation in the claim.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 1 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Pub No. 20230083313 A1 in view of Vien et al. “Bayesian Functional Optimization”
Pub No. 20230083313 A1
Current Application
A method for searching for binding peptides for immunotherapy, the method comprising:
A method for searching for binding peptides for immunotherapy, the method comprising:
training a deep neural network to predict a peptide presentation given Major Histocompatibility Complex (MHC) allele sequences and peptide sequences;
employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex (MHC) allele sequences and peptide sequences;
converting the peptide sequences with variable lengths into peptide embeddings having continuous embedding vectors of a fixed size; training a Variational Autoencoder (VAE) to reconstruct peptides from peptide embeddings by converting the peptide sequences of variable lengths into continuous embedding
training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences of variable lengths into continuous embedding vectors of a fixed size;
generating a first set of positive peptide vaccine candidates from the peptide embeddings based on a Monte Carlo Tree Search (MCTS);
running a Monte Carlo Tree Search (MCTS) to generate a first set of positive peptide vaccine candidates;
generating a second set of positive peptide vaccine candidates from the peptide embeddings based on a Bayesian Optimization search with the trained VAE (BO-VAE) and a backpropagation search with the trained VAE (BP-VAE);
running a Bayesian Optimization search with the trained VAE (BO-VAE) and a Backpropagation search with the trained VAE (BP-VAE) to generate a second set of positive peptide vaccine candidates;
generating a third set of positive peptide vaccine candidates from the peptide embeddings with a sampling from a Position Weight Matrix (sPWM);
using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates;
screening and merging the first, second, and third sets of positive peptide vaccine candidates into qualified peptides using the deep neural network;
screening and merging the first, second, and third sets of positive peptide vaccine candidates;
generating the qualified peptides for immunotherapy that targets the targeted tumor by leveraging immune reactions triggered by peptide-MHC complexes from the screened and merged first, second, and third sets of positive peptide vaccine candidates.
outputting qualified peptides for immunotherapy from the screened and merged first, second, and third sets of positive peptide vaccine candidates;
Vien et al. “Bayesian Functional Optimization”
Current Application
Vien, page 4174, teaches “We are now computing the functional gradients of those acquisition functionals. Specifically, here we are stating the functional gradients for the iGP-UCB acquisition functional and RBF kernel K(h,h′)=exp(−∥h′ − h∥2 Hk /2σ2) 1 We use the notion of the Fr´ echet derivative which is a derivative on Banach spaces. Let V and W be Banach spaces, and U ∈ V be an open subset of V, then a function f : U →W is called Fr´ echet differentiable at h ∈ U if there exists a lim bounded linear operator Df|h : V→Wsuch that ∥f(h +g)−f(h)−Df|h(g)∥W ∥g∥V g→0 =0 Assumption 1 Assume that each functional kernel K(ht,h) has a Fr´echet derivative Dht : Hk →ℜ According to (Chae 1985), when W is a real (or complex) space, the Fr´ echet derivative becomes a function in Hk i.e. Df|h ∈Hk and Df|h(g)=⟨Df|h,g⟩Hk . Lemma1 The Fr´echet derivative at h ∈Hk of the RBF kernel function K(ht,h)=exp(−∥ht − h∥2 Hk /2σ2) is Dht|h : g→K(ht,h) σ2 ht−h ,g Hk As we can see the Fr´ echet derivative Dht|h at h of the RBFkernel is a function in Hk which support points are the combined set of suport points from h and ht. Specifically, assuming that h and ht have representation N1 h = i=1 then Dht|h is written as Dht|h(x)=K(ht,h) N2 αik(xi,·), h t = σ2 − K(ht,h) σ2 i=1 βiK(x′ i,·) i=1 N2 i=1 N1 βiK(x′ i,x) αik(xi,x) where x,xi,x′ i ∈ℜn”
wherein the BO-VAE is used the Bayesian optimization algorithm with Radial Basis Function (RBF) kernel;
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Pub No. 20230083313 A1 teaching of A method for searching for binding peptides with Viens’s teaching of using RBF kernels in Bayesian optimization. The motivation to do so would be to accelerate the functioning of the machine leaning model.
Claims 8 and 15 are rejected on the same grounds as Claim 1.
Claim 2 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of U.S. Pub No. 20230083313 A1 in view of Vien et al. “Bayesian Functional Optimization”
Pub No. 20230083313 A1
Current Application
The method of claim 1, further comprising training a Multi-Layer Perceptron (MLP) is trained to predict presentation scores of the peptides from the peptide embeddings.
The method of claim 1, wherein a Multi-Layer Perceptron (MLP) is trained to predict presentation scores of the peptides from the continuous embedding vectors.
Claims 9 and 16 are rejected on the same grounds as Claim 2.
Claim 3 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 3 of U.S. Pub No. 20230083313 A1 in view of Vien et al. “Bayesian Functional Optimization”
Pub No. 20230083313 A1
Current Application
The method of claim 2, wherein the BO-VAE and the BP-VAE are employed to maximize the presentation scores of the peptides from the peptide embeddings.
The method of claim 2, wherein the BO-VAE and the BP-VAE are employed to maximize the presentation scores of the peptides from the continuous embedding vectors.
Claims 10 and 17 are rejected on the same grounds as Claim 3.
Claim 4 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4 of U.S. Pub No. 20230083313 A1 in view of Vien et al. “Bayesian Functional Optimization”
Pub No. 20230083313 A1
Current Application
The method of claim 1, wherein generating a second set of positive peptide vaccine candidates further comprises learning presentation scores from the deep neural network with the BP-VAE and generate a portion of the second set of positive peptide vaccine candidates by optimizing the peptide embeddings with a gradient ascent.
The method of claim 1, wherein the BP-VAE is employed to learn presentation scores from the deep neural network and generate a portion of the first set of positive peptide vaccine candidates by optimizing the continuous embedding vectors with a gradient ascent.
Claims 11 and 18 are rejected on the same grounds as Claim 4.
Claim 5 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of U.S. Pub No. 20230083313 A1 in view of Vien et al. “Bayesian Functional Optimization”
Pub No. 20230083313 A1
Current Application
The method of claim 1, wherein generating a third set of positive peptide vaccine candidates further comprises the sPWM generates the second set of positive
peptide vaccine candidates having a length l by sampling from amino acid distributions of all l positions calculated from the second set of positive peptide embeddings.
The method of claim 1, wherein the sPWM generates the second set of positive peptide vaccine candidates having a length by sampling from amino acid distributions of all positions calculated from the second set of positive peptide vaccine candidates.
Claims 12 and 19 are rejected on the same grounds as Claim 5.
Claim 6 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 6 of U.S. Pub No. 20230083313 A1 in view of Vien et al. “Bayesian Functional Optimization”
Pub No. 20230083313 A1
Current Application
The method of claim 1, wherein the peptides are extracted from a library of peptides of a target virus.
The method of claim 1, wherein the peptides are extracted from a library of peptides of a target virus or tumor cells.
Claims 13 and 20 are rejected on the same grounds as Claim 6.
Claim 7 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of U.S. Pub No. 20230083313 A1 in view of Vien et al. “Bayesian Functional Optimization”
Pub No. 20230083313 A1
Current Application
The method of claim 6, wherein the extracted library of peptides and corresponding mutations with sequence similarities to unmutated peptides are employed as starting points for the MCTS, the BO-VAE, the BP-VAE, and the sPWM.
The method of claim 6, wherein the extracted library of peptides and
corresponding mutations with sequence similarities to unmutated peptides are employed as starting
points for the MCTS, the BO-VAE, the BP-VAE, and the sPWM.
Claims 14 are rejected on the same grounds as Claim 7.
ConclusionAny inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 6:40am-4:40pm; F: Out of Office.
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/THOMAS BERNARD LANE/Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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