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 .
Information Disclosure Statement
IDS filed 4/10/2023 is acknowledged.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d) to EP2020140.8 filed 10/15/2020.
Status of Claims
Claims 1-17 are under examination.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-11 and 14-17 are drawn to a method, so a process.
Claims 12 and 13 are drawn to a computer system and computer program product. The claims are drawn to a computer program or software per se which reads on a transitory signal such as a carrier wave, or a program, per se, which is not a statutory category of invention, as set forth in In re Nuijten. A review of the specification does not show a definition that excludes an embodiment that is information in a signal. As such, an embodiment of the claims read on non-statutory subject matter (In re Nuijten 84 USPQ2d 1495 (2007)). The applicants may overcome the rejection by 1) amendment of the claims to be limited to physical forms of computer readable storage media described in the specification or 2) by amending the claimed subject matter to be limited to “non-transitory”, see the notice regarding Computer Readable Media (1351 OG 212 (23 February 2010)).
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s):
1. obtaining an amino acid sequence of a peptide wherein the peptide comprises proteinogenic and/or non-proteinogenic amino acids.
This step reads on a process of selecting amino acid data which can be performed by the human mind. The step is therefore an abstract idea.
2. obtaining for each proteinogenic amino acid a plurality of numeric indices each related to a physiochemical property.
This step reads on evaluating amino acids to determine values associated with a physiochemical property. The step can be performed by the human mind or with math and is therefore an abstract idea.
3. Obtaining training data set comprising a positive data set and a negative data set wherein the positive set comprises numerical data related to a plurality of amino acid sequences of immunogenic peptides, wherein the negative set comprises numerical data related to a plurality of amino acid sequences of non-immunogenic peptides.
This step reads on a process of evaluating immunogenic peptides to determine numeric amino acid characteristics and evaluating non-immunogenic peptides to determine numeric amino acid characteristics. The step reads on a process that can be performed by the human mind and is therefore an abstract idea.
4. training a mathematical classification model on the training data set.
This step reads on organizing information which can be performed as a mental process or by math. The step is therefore an abstract idea.
5. determining a likelihood of said peptide eliciting an immune response by means of trained classification model.
This step reads on a process of determining a likelihood which is a mathematical calculation. A likelihood calculation can also be performed by the human mind. The step is therefore an abstract idea.
6. performing a principal component analysis on the numeric indices of each of the proteinogenic amino acids to obtain principal components for each analyzed proteinogenic amino acid.
This step reads on calculating math which is principal component analysis (PCA) on amino acids represented by numerical data. The step is therefore an abstract idea.
7. obtaining a feature vector for the amino acid sequences of the training data set and the amino acid sequence of said peptide, wherein the feature vector for the amino acid sequence is obtained by replacing each amino acid of said amino acid sequence by one or more corresponding principal components, wherein the trained classification model is trained on the feature vectors of the training data set.
The process of replacing each amino acid of said amino acid sequence by principal components is a process that can be performed by the human mind or with mathematical correlations. The step is therefore an abstract idea.
8. wherein the likelihood of said peptide being immunogenic is determined for the feature vector of said peptide.
Determining a likelihood from feature vector data is a mathematical process and is therefore an abstract idea.
Step 2A Prong Two: Consideration of Practical Application
The claimed process results in obtaining a feature vector for the amino acid sequences of the training data and amino acid sequence of a peptide. This is as step of obtaining organized information and not an additional element that integrates the abstract idea into a practical application. The claims do not recite any additional elements that integrate the abstract idea into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method does not recite "additional elements" that are not limitations drawn to an abstract idea. The “computer system” and “computer product” of claims 12-13 are read on a program per se or signal.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112-2nd paragraph
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.
Claims 1-17 are 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 pre-AIA the applicant regards as the invention.
Claim 1 recites “comprise (numerical) data,” in lines 9-10. It is unclear what is intended by placing parenthesis around “numerical.” It is suggested that the parenthesis be deleted.
Claim 14 recites “Use of” the method of claim 1 “for determining treatment of a subject by determining immunogenicity of a neoantigens presented by a tumor cell of the subject.” The process of claim 1 does not recite any steps for determining immunogenicity of a neoantigens presented by a tumor cell of a subject. Attempts to claim a process without setting forth any steps involved in the process generally raises an issue of indefiniteness. See MPEP 2173.05(q). It is unclear how the process of claim 1 is intended to be used to achieve the recited limitations of determining treatment of a subject.
Claim 14 recites “the computer-implemented method according to claim 1.” There is lack of antecedent basis for this limitation in the claim. It is unclear what “the computer-implemented method” is referring to because claim 1 is already a computer-implemented method. It is suggested claim 14 be amended to simply recite “the method according to claim 1.”
Claim 15 recites “Use of” the method of claim 1 “for screening of a virus or bacterium on a vaccination target by determining immunogenicity of epitope from viral or bacterial proteins.” The process of claim 1 does not recite any steps drawn to screening of a virus or bacterium on a vaccination target or step of determining immunogenicity of epitope from viral or bacterial proteins. Attempts to claim a process without setting forth any steps involved in the process generally raises an issue of indefiniteness. See MPEP 2173.05(q). It is unclear how the process of claim 1 is intended to be used to achieve the recited limitations of determining immunogenicity of epitope from viral or bacterial proteins.
Claim 15 recites “the computer-implemented method according to claim 1.” There is lack of antecedent basis for this limitation in the claim. It is unclear what “the computer-implemented method” is referring to because claim 1 is already a computer-implemented method. It is suggested claim 15 be amended to simply recite “the method according to claim 1.”
Claim 16 recites “Use of” the method of claim 1 “for characterizing autoimmunity reactions of a subject by determining immunogenicity of self-antigens presented by a cell of the subject.” The process of claim 1 does not recite any steps drawn to characterizing autoimmunity or determining immunogenicity of self-antigens. Attempts to claim a process without setting forth any steps involved in the process generally raises an issue of indefiniteness. See MPEP 2173.05(q). It is unclear how the process of claim 1 is intended to be used to achieve the recited limitations of determining immunogenicity of self-antigens.
Claim 16 recites “the computer-implemented method according to claim 1.” There is lack of antecedent basis for this limitation in the claim. It is unclear what “the computer-implemented method” is referring to because claim 1 is already a computer-implemented method. It is suggested claim 16 be amended to simply recite “the method according to claim 1.”
Claim 17 recites “Use of” the method of claim 1 “for assessing immunogenicity changes of a peptide upon altering one or more amino acids or the amino acid sequence of said peptide.” The process of claim 1 does not recite any steps drawn to altering one or more amino acids or the amino acid sequence of said peptide and therefore it is unclear how claim 1 is to be used for assessing immunogenicity changes of a peptide as intended in claim 17. Attempts to claim a process without setting forth any steps involved in the process generally raises an issue of indefiniteness. See MPEP 2173.05(q). It is unclear how the process of claim 1 is intended to be used to achieve the recited limitations of assessing immunogenicity changes of a peptide.
Claim 17 recites “the computer-implemented method according to claim 1.” There is lack of antecedent basis for this limitation in the claim. It is unclear what “the computer-implemented method” is referring to because claim 1 is already a computer-implemented method. It is suggested claim 16 be amended to simply recite “the method according to claim 1.”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 1, 2 and 4-13 are rejected under 35 U.S.C. 103(a) as being unpatentable over Bremel et al. (US 2013/330335; IDS filed 4/10/2023).
Bremel et al. teach (par. 0014) in-putting an amino acid sequence from a target source into a computer; and analyzing more than one physical parameter of subsets of amino acids in the sequence via a computer processor to identify amino acid subsets that interact or bind to a binding partner such as a B cell receptor, antibody or MHC-I or MHC-II binding region (i.e. obtaining an amino acid sequence of a peptide wherein the peptide comprises proteinogenic and/or non-proteinogenic amino acids), as in claim 1.
Bremel et al. teach (par. 0016) the analyzing physical parameters of subsets of amino acids comprises replacing alphabetical coding of individual amino acids in the subset with mathematical expression properties. In some embodiments, the physical parameters properties are represented by one or more principal components (i.e. obtaining for each proteinogenic amino acid a plurality of numeric indices each related to a physiochemical property), as in claim 1.
Bremel et al. teach training sets of peptides for binding to an MHC region (par. 0019 and 0198)(i.e. obtaining training data set comprising a positive data set) where the peptide training set whose binding affinity for various MHC alleles has been determined experimentally (par. 0191)(i.e. positive set related to amino acid sequences of immunogenic peptides); Bremel et al. also teach peptides with amino acid length that could not be accommodated by an MHC-I pocket and therefore could not bind (par. 0196)(i.e. negative set comprising data related to a plurality of amino acid sequences of non-immunogenic peptides), as in claim 1.
Bremel et al. teach deriving a principal components analysis with partial least square regression (par. 0191) and neural networks predications (par. 0196-0198) for binding (i.e. training a mathematical classification model on the training data set), as in claim 1.
Bremel et al. teach (par. 0194) artificial neural network regression that predicts peptide binding to MHC or HLA binding regions or alleles (i.e. determining a likelihood of said peptide eliciting an immune response by means of trained classification model), as in claim 1.
Bremel et al. teach comprises replacing alphabetical coding of individual amino acids in the subset with mathematical expression properties and then transformed by principal component analysis (par. 0016)(i.e. performing a principal component analysis on the numeric indices of each of the proteinogenic amino acids); Bremel et al. teach (par. 0016) principal components are weighted and ranked proxies for the physical properties of the amino acids (i.e. to obtain principal components for each analyzed proteinogenic amino acid), as in claim 1.
Bremel et al. teach that in the principal components analysis, the amino acids are represented by a vector comprising the physical properties of each amino acid (par. 0050)
7. obtaining a feature vector for the amino acid sequences of the training data set and the amino acid sequence of said peptide, wherein the feature vector for the amino acid sequence is obtained by replacing each amino acid of said amino acid sequence by one or more corresponding principal components); Bremel et al. then teach that a matrix is created by multiplication of the vectors of two amino acid subsets; utilizing the diagonal elements in the matrix as a measure of the Euclidian distance of physical properties between the two amino acid subsets; weighting the diagonal by the variable importance projection of amino acid positions in a MHC molecule; and identifying amino acid subset pairs with a low distance score for physical properties and a high binding affinity for one or more MHC molecules (i.e. wherein the trained classification model is trained on the feature vectors of the training data set), as in claim 1.
Regarding the limitation of claim 1 reciting “wherein the likelihood of said peptide being immunogenic is determined for the feature vector of said peptide,” Bremel et al. teach a neural network that predicts binding affinity of a peptide to an MHC-1 molecule (par. 0055 and Figure 3) which would include the feature vector of the peptide. However, Bremel et al. do not specifically teach determining likelihood of immunogenicity. However, Bremel teach providing polypeptides that provide immunogens for a target cell (par. 0264). Bremel et al. therefore suggest that the peptides predicted as binders are immunogenic.
It would be obvious to one of ordinary skill to combing the teachings of Bremel et al. for a neural network that predicts peptides that bind to MHC-I with the teachings of Bremel et al. for providing polypeptides that provide immunogens (i.e. are immunogenic peptide). Such is a combination of known elements that would yield a predictable result of determining a likelihood that a peptide is immunogenic for the feature vector of said peptide, when the peptide is determined as binding to MHC-1.
Bremel et al. teach training sets for MHC-II binding (0198) and peptides with known amino acid sequence and binding affinities have been determined (par. 0197)(i.e. which suggests housekeeping proteins, wherein it would be obvious to one of ordinary skill to compare sequences of binders to house keeping proteins to determine a negative set that would not bind to another MHC type (i.e. a negative set), as in claim 2.
Bremel et al. teach a sequence listing (par. 0002) and sequences of binding peptides (par. 0013), the combination of which would make obvious that the sequences are linear, as in claim 4.
Bremel et al. teach peptides within a proteome (par. 0013) and T-cell epitopes and T-cell stimulatory regions as sources of peptides (par. 0065)(i.e. which suggests the proteome peptides and T-cell response inducing peptides); Bremel et al. teach assigning proteins into sets based on size and amino acid sequence (par. 0226) which suggests comparing. It would be obvious to one of ordinary skill to compare sequences of proteome and T-cell response inducing peptides to determine a positive set, as in claim 5.
Bremel et al. teach assigning proteins into sets based on size and amino acid sequence (par. 0226) which suggests comparing and determining differences in sequences; BLAST is taught which is well known for determining mismatches (par. 0226)(i.e. sequences closes related but not identical having more amino acid mismatches compared to proteins in the proteome), as in claim 6.
Bremel et al. teach a sequence listing (par. 0002) which suggests sequences that are linear, as in claim 7.
Claim 8 is drawn to negative data comprising more records compared to the positive data set, which is a well known technique for creating a training set that mimics real-world data where non-target events are more likely than target events.
Bremel et al. teach using at least three principal components (par. 0198), as in claim 9.
Bremel et al. teach determining principal components (par. 0016) which would inherently include determining z-values or z-scores. It is well known that the z-values or z-scores are computed as the very first preprocessing step before finding principal components, as in claim 10.
Regarding claim 11, Bremel et al. do not specifically teach classification model that is a supervised classification machine learning algorithm, as in claim 11. However, Bremel teaches that a neural network is a classifier used in machine learning (par. 0136); Bremel et al. teach comparing classifiers (par. 0192) which suggest that classifiers are interchangeable. Claim 11 lists well known supervised classification machine learning algorithms including Gaussian Naïve Bayes classifier, LSVM, Kernel Support Vector Machine Learning, K-Nearest Neighbor and Random Forest. It would be obvious to one of ordinary skill to substitute a neural network classifier for any of the models of claim 11. Such would be an obvious substitution of one known element for another to yield a predictable result.
Bremel et al. teach a computer for performing the claimed method (par. 0014), as in claims 12-13.
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anna Skibinsky whose telephone number is (571) 272-4373. The examiner can normally be reached on 12 pm - 8:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ram Shukla can be reached on (571) 272-7035. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Anna Skibinsky/
Primary Examiner, AU 1635
