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 .
Status of Claims
In the preliminary amendment filed August 5, 2024, Applicant canceled claims 1-117 and added claims 118-145. Claims 118-145 are pending in the current application.
Information Disclosure Statement
The information disclosure statement (IDS) received on June 17, 2024 has been considered by examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 118-130 and 132-145 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shaver et al. (US 2023/0253067 A1).
Regarding claim 118, Shaver discloses a computer-implemented method for generating a set of candidate variant amino acid sequences of an antibody, a nanobody, or a fragment thereof, having binding ability to protein targets, comprising:
(a) obtaining a set of seed amino acid sequences (Paragraph [0021]: First protein sequence data 104 can include a number of amino acid sequences that can be used in the training of the generative machine learning architecture); and
(b) processing the set of seed amino acid sequences using a first trained machine learning algorithm to generate the set of novel candidate variant amino acid sequences (Paragraph [0024]: After the generative machine learning architecture 102 has undergone a training process, one or more first trained models 106 can be generated that can produce amino acid sequences of proteins),
wherein the first trained machine learning algorithm is trained with the first training data comprising a set of training amino acid sequences for the protein targets (Paragraph [0024]: The one or more first trained models 106 can include one or more components of the generative machine learning architecture 102 after a training process using the first protein sequence data),
wherein the first trained machine learning algorithm is further trained through a transfer learning method using a second trained machine learning algorithm, wherein the second trained machine learning algorithm is trained with second training data comprising a set of training amino acid sequences for a second protein target, wherein the second protein target is different from the first protein targets (Paragraph [0028]: The one or more first trained models 106 can undergo transfer learning at 108 based on second protein sequence data 110. The transfer learning that is performed at 108 can modify the one or more first trained models 106 based on the amino acid sequences included in the second protein sequence data 110. The transfer learning that is performed at 108 can produce one or more second trained models 112 that are modified versions of the one or more first trained models).
Regarding claim 119, Shaver discloses the method of claim 118, wherein the set of seed amino acid sequences comprises antibody variable regions (Fvs) (Paragraph [0063]).
Regarding claim 120, Shaver discloses the method of claim 119, wherein the Fvs comprise complementarity determining regions (CDRs) (Paragraph [0063]).
Regarding claim 121, Shaver discloses the method of claim 119, wherein the Fvs comprise heavy chains (VH) or light chains (VL) (Paragraph [0063]).
Regarding claim 122, Shaver discloses the method of claim 119, wherein the Fvs comprise frameworks (FWRs) (Paragraph [0065]).
Regarding claim 123, Shaver discloses the method of claim 118, wherein the protein targets comprise at least a portion of a target antigen (Paragraph [0064]).
Regarding claim 124, Shaver discloses the method of claim 118, wherein the set of training amino acid sequences for the second protein target has a smaller number of training data than the set of training amino acid sequences for the first protein targets (Paragraph [0049]).
Regarding claim 125, Shaver discloses the method of claim 118, further comprises, prior to (b), pre-processing the set of seed amino acid sequences (i) to have the same sequence length or (ii) at least in part by generating a numerical representation of the set of seed amino acid sequences (Paragraph [0044]).
Regarding claim 126, Shaver discloses the method of claim 125, wherein the numerical representation comprises a matrix, wherein the matrix comprises a one-hot encoding and/or an embedding pre-processing of (i) the set of amino acid sequences or (ii) amino acid bio-physicochemical values (Paragraph [0047]).
Regarding claim 127, Shaver discloses the method of claim 126, wherein the amino acid bio-physicochemical values are selected from isoelectric point, volume, hydrophobicity, solubility, stability, charge, solvent- accessible surface area (SASA), Immunogenicity, and humanness score (Paragraph [0055]).
Regarding claim 128, Shaver discloses the method of claim 118, wherein the first trained machine learning algorithm or the second trained machine learning algorithm comprises a deep learning model (Paragraph [0056]).
Regarding claim 129, Shaver discloses the method of claim 128, wherein the deep learning model comprises a deep generative Al model, wherein the deep generative Al model comprises at least one of a generative adversarial network (GAN), an autoencoder (AE), a variational autoencoder (VAE), and a residual neural network (Resnet), and a Reinforcement Learning (RL) (Paragraph [0056]).
Regarding claim 130, Shaver discloses the method of claim 118, wherein the first trained machine learning algorithm or the second trained machine learning algorithm comprises an encoder and a decoder (Paragraph [0023]).
Regarding claim 132, Shaver discloses the method of claim 131, further comprising extracting a set of features associated with the set of seed amino acid sequences generated by the encoder, and reconstructing the set of features using a decoder thereby generating new candidate variant amino acid sequences (Paragraph [0034]).
Regarding claim 133, Shaver discloses the method of claim 131, further comprising clustering features of the set of seed amino acid sequences based on sequence similarity or sequence diversity (Paragraph [0043]).
Regarding claim 134, Shaver discloses the method of claim 133, wherein the clustering comprises generating a two-dimensional plot indicative of single specificity or multi-specificity of the set of seed amino acid sequences (Fig. 5; Paragraph [0094]).
Regarding claim 135, Shaver discloses the method of claim 118, wherein the first trained machine learning algorithm and the second trained machine learning algorithm are trained without the use of structural data (Paragraph [0016]).
Regarding claim 136, Shaver discloses the method of claim 118, further comprising processing the set of generated candidate variant amino acid sequences to determine a therapeutic property selected from single specificity, multiple specificity, single cross-reactivity, and multiple cross-reactivity (Paragraph [0012]).
Regarding claim 137, Shaver discloses the method of claim 118, further comprising processing the set of generated candidate variant amino acid sequences to determine molecular characteristics of the set of generated candidate variant amino acid sequences (Paragraph [0087]).
Regarding claim 138, Shaver discloses the method of claim 118, wherein the molecular characteristics comprise at least one of amino acid distribution, amino acid frequency, amino acid length distribution, sequence similarity, sequence diversity, total charge distribution, hydrophobicity, net charge, stability, solubility, isoelectric point, solvent accessible surface area (SASA), binding affinity, affinity maturation, epitope-paratrope interaction, epitope prediction, humaneness, humanization, developability, manufacturability, half-life, pharmacokinetic, yield, aggregation, function, clearance, viscosity, immunogenicity, single-target specificity, multi-target specificity, single- target cross-reactivity, and multi-target cross-reactivity (Paragraph [0099]).
Regarding claim 139, Shaver discloses the method of claim 138, further comprising determining sequence similarity between the set of generated candidate variant amino acid sequences and the set of seed amino acid sequences (Paragraph [0043]).
Regarding claim 140, Shaver discloses the method of claim 138, further comprising determining sequence diversity between the set of generated candidate variant amino acid sequences and the set of seed amino acid sequences (Paragraph [0043]).
Regarding claim 141, Shaver discloses the method of claim 138, further comprising determining specificity or binding affinity of the set of generated candidate variant amino acid sequences to the protein target (Paragraph [0099]).
Regarding claim 142, Shaver discloses the method of claim 138, further comprising (i) selecting or ranking at least one of the set of generated candidate variant amino acid sequences based at least in part on having desired molecular characteristics, or (ii) filtering out at least one of the set of generated candidate variant amino acid sequences having undesired molecular characteristics (Paragraph [0079]).
Regarding claim 143, Shaver discloses the method of claim 118, wherein the set of generated candidate variant amino acid sequences correspond to an antibody, a nanobody, or a fragment thereof (Paragraph [0022]).
Regarding claim 144, Shaver discloses the method of claim 118, further comprising predicting a humanized antibody, nanobody, or fragment variable region based at least in part on the set of generated candidate variant amino acid sequences (Paragraph [0038]).
Regarding claim 145, Shaver discloses a computer-implemented method for generating a set of candidate variant amino acid sequences of an antibody, a nanobody, or a fragment thereof, having binding ability to a protein target, comprising:
(a) obtaining a set of seed amino acid sequences (Paragraph [0021]: First protein sequence data 104 can include a number of amino acid sequences that can be used in the training of the generative machine learning architecture); and
(b) processing the set of seed amino acid sequences using a trained machine learning algorithm to generate the set of candidate variant amino acid sequences, wherein the trained machine learning algorithm comprises at least one of a generative adversarial network (GAN) and Reinforcement Learning (RL) (Paragraph [0024]: After the generative machine learning architecture 102 has undergone a training process, one or more first trained models 106 can be generated that can produce amino acid sequences of proteins).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 131 is rejected under 35 U.S.C. 103 as being unpatentable over Shaver et al. (US 2023/0253067 A1) in view of Feala et al. (US 2022/0122692 A1).
Shaver does not explicitly disclose:
performing, using the encoder, a dimensionality reduction of the set of seed amino acid sequences.
Feala teaches:
performing, using the encoder, a dimensionality reduction of the set of seed amino acid sequences (Paragraph [0057]: the machine learning method is selected from the group including… dimensionality reduction and ensemble selection methods).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Shaver to disclose performing, using the encoder, a dimensionality reduction of the set of seed amino acid sequences as taught by Feala. Shaver discloses amino acid sequences of proteins being produced using one or more generative machine learning architectures Shaver Abstract). Using the machine learning guided polypeptide analysis of Feala for dimensionality reduction would enhance accuracy for identifying associations between amino acid sequences and protein functions or properties (Feala Abstract).
Conclusion
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/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685