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 .
Claims Status
Claims 1-23 are pending.
Claims 1-23 are examined.
Priority
The instant application is a national stage application of PCT/EP2020/068109, filed 06/26/2020, which claims priority to EPO Application 20170475.6, filed 04/20/2020. Therefore, the Effective Filing Date (EFD) assigned to each of the claims 1-23 is the European filing date of Application 20170475.6, filed 04/20/2020.
Information Disclosure Statement
The Information Disclosure Statements filed 09/19/2022, 08/07/2024, 06/18/2025, and 12/19/2025 are in compliance with the provisions of 37 CFR 1.97 and have therefore been considered. Signed copies of the IDS documents are included with this Office Action.
Drawings
The drawings filed 06/23/2022 are accepted.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
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 is 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.
With respect to claim 1, the claim recites the limitation of “selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequence”. The claim is indefinite because it is unclear if predicting the immunogenic candidate amino acid is an active step that is included within the confines of the claims or not.
With further respect to claim 1, the claim recites the limitation of “identifying an immune profile response value for each candidate amino acid sequence”. The claim is indefinite because it is unclear if the candidate amino acid is the same as the “predicted immunogenic candidate amino acid sequences” or if it is a different set of amino acid sequences as there is no prediction being performed.
With further respect to claim 1, the claim recites the limitation of “wherein the representative immune profiles overlap with the sample components of the immune profiles”. The claim is indefinite because it is unclear if the representative immune profiles overlap with the sample components of the “a plurality of immune profiles for a population” or “the sample components of the immune profile” from step of identifying an immune profile response value.
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 1-20, 22, and 23 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to an abstract idea of mental steps, mathematic concepts, or a natural law without significantly more.
Claim 21 is eligible under 35 USC 101 because any judicial exceptions are integrated into a practical application through the step of synthesizing the one or more amino acid sequences, encoding the one or more amino acid sequences into a DNA or RNA sequence, and/or incorporating the DNA or RNA sequence into a genome of a delivery system to create a vaccine.
The MPEP at MPEP 2106.03 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of
matter?
(2A)(1) Are the claims directed to a judicially recognized exception, i.e. a law of nature,
a natural phenomenon, or an abstract idea?
(2A)(2) If the claims are directed to a judicial exception under Prong One, then is the
judicial exception integrated into a practical application?
(2B) If the claims are directed to a judicial exception and do not integrate the judicial
exception, do the claims provide an inventive concept?
With respect to step (1): Yes, the claims are directed to a method, system, and non-transitory computer-readable medium.
With respect to step (2A)(1): The claims recite abstract ideas of mental processes and mathematical concepts.
“Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection” (MPEP 2106.04). Abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (procedures for observing, evaluating, analyzing/judging and organizing information (MPEP 2106.04(a)(2)). Laws of nature or natural phenomena include naturally occurring principles/relations that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP 2106(b)).
Mental processes recited in claim 1:
identifying an immune profile response value for each candidate amino acid sequence with respect to each one of a plurality of sample components of an immune profile, wherein the immune profile response value represents whether the respective candidate amino acid sequence results in an immune response for the sample components of the immune profile
generating a plurality of representative immune profiles for the population, wherein the representative immune profiles overlap with the sample components of the immune profiles
selecting the one or more amino acid sequences for inclusion in the vaccine that minimizes a likelihood of no immune response for each representative immune profile, based on the immune profile response values
Dependent claims 2-20 recite additional steps that either are directed to abstract ideas or further limit the judicial exceptions in independent claim 1, and as such, are further directed to abstract ideas. Hence, the claims explicitly recite numerous elements that individually and in combination constitute abstract ideas. The relevant recitations are:
Claim 2: “wherein the step of generating the plurality of representative immune profiles comprises: (i) creating a first distribution over the plurality of immune profiles; (ii) sampling the first distribution to create the plurality of representative immune profiles”
Claim 3: “wherein the first distribution is a distribution over the plurality of immune profiles for each region of the population”
Claim 4: “wherein the first distribution is a posterior distribution over genotypes in each region of the population based on a prior distribution and observed genotypes from the plurality of immune profiles in each region of the population”
Claim 5: “wherein the first distribution is a symmetric Dirichlet distribution, wherein the method further comprises the step of collecting all genotypes observed at least once across all regions of the population, and wherein the step of sampling the first distribution comprises sampling a desired number of genotypes from each region of the population based on count counts of each genotype in the sample”
Claim 6: “simulating a digital population based on the retrieved plurality of immune profiles for the population, wherein the step of creating the first distribution is based on the simulated population such that the step of sampling is performed on the distribution of the simulated population”
Claim 7: “wherein the step of simulating a digital population comprises: defining a population size; and creating a second distribution over regions of the population”
Claim 8: “wherein the second distribution is a Dirichlet distribution”
Claim 9: “wherein the representative immune profiles are generated such that the representative immune profiles maximise coverage of combinations of immune profiles in the population”
Claim 10: “wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine comprises applying a mathematical optimization algorithm to minimize a maximum likelihood of no immune response for each of the representative immune profiles”
Claim 11: “wherein the immune profile comprises a set of HLA alleles and the sample component of an immune profile comprise sample HLA alleles, and wherein the variables of the mathematical optimization algorithm comprise…”
Claim 12: “wherein the mathematical optimization algorithm is a mixed integer linear program”
Claim 13: assigning a cost to each candidate amino acid sequence, wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is constrained based on the cost assigned to each candidate amino acid sequence, such that the selected one or more amino acid sequences have a total cost below a predetermined threshold budget”
Claim 14: “wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is constrained based on a maximum amount of amino acid sequences allowed in a vaccine delivery platform”
Claim 15: “creating a tripartite graph, wherein…”
Claim 16: “wherein the immune response value is in each case a log likelihood value based on amino acid sub-sequences of the respective candidate amino acid sequence”
Claim 17: “wherein the step of identifying the immune profile response value for each candidate amino acid sequence comprises selecting a best likelihood value as the immune response value from a likelihood for each amino acid sub-sequence”
Claim 18: “wherein the one or more candidate amino acid sequences are comprised in one or more proteins of a coronavirus”
Claim 19: “wherein the representative immune profiles comprise one or more of a set of human leukocyte antigen (HLA) alleles; presence of tumor infiltrating lymphocytes; presence of immune checkpoint markers; presence of hypoxia markers; presence of chemokine receptors; and/or previous infection by human papillomavirus”
Claim 20: “wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is further based on a correspondence between the sample components of the immune profile and the representative immune profiles”
The abstract ideas in the claims are evaluated under Broadest Reasonable Interpretation (BRI) and determined herein to each cover mental processes and mathematic concepts because the claims recite no more than using mathematical concepts to analyze and simulate data to select amino acid sequences.
With respect to step (2A)(2): The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone or in combination to determine if the judicial exception 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 judicial exception, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
Claim 1 recites the following additional elements that are not abstract ideas:
computer-implemented method
retrieving a plurality of immune profiles for a population
The step of retrieving a plurality of immune profiles is directed to a data gathering step as the step retrieves the data on which the judicial exceptions are performed. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)). The element of the method being computer-implemented is interpreted as the method being applied to a generic computer. he 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 (see MPEP 2106.05(f)). Thus, applying the judicial exceptions to a generic computer is not sufficient to integrate the judicial exceptions into a practical application.
Dependent claims 22 and 23 are directed to further generic computer elements.
None of these dependent claims recite additional elements, alone or in combination, which would integrate a judicial exception into a practical application.
Lastly, the claims have been evaluated with respect to step (2B): Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims lack a specific inventive concept. Under said analysis, Applicant is reminded that 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 provide significantly more than the judicial exception (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements described above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP at 2106.05(d)(I), determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either 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: The additional element of a computer-implemented method and retrieving a plurality of immune profiles for a population does not rise to the level of significantly more than the judicial exception. As recited in the MPEP at 2106.05(d) with respect to Mayo, 566 U.S. at 79, 101 USPQ2d at 1968, determining the level of a biomarker in blood by any means is a well-understood, routine, and conventional activity. As exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the device constitutes 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 abstract idea (see MPEP 2105(b)I-III). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 22: The additional element of a system comprising at least one processor in communication with at least one memory device, the at least one memory device having stored thereon instructions does not rise to the level of significantly more than the judicial exception. As exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the device constitutes 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 abstract idea (see MPEP 2105(b)I-III). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 23: The additional element of a tangible, non-transitory computer-readable medium having instructions stored thereon does not rise to the level of significantly more than the judicial exception. As exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the device constitutes 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 abstract idea (see MPEP 2105(b)I-III). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. Individually, the limitations of the claims and the claims as a whole have been found lacking.
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 1, 2, 10, 11, 16, 17, 19-23 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rose et al. (US 2022/0208301 A1, effectively filed 05/17/2019).
Regarding claim 1, Rose et al. teaches a method of selecting one or more amino acid sequences for inclusion in a vaccine, the method comprising:
identifying an immune profile response value for each candidate amino acid sequence with respect to each one of a plurality of sample components of an immune profile, wherein the immune profile response value represents whether the respective candidate amino acid sequence results in an immune response for the sample components of the immune profile (paragraphs [0031]; [0034]; [0080]);
retrieving a plurality of immune profiles for a population (paragraphs [0071]; [0081; [0153]]);
generating a plurality of representative immune profiles for the population, wherein the representative immune profiles overlap with the sample components of the immune profiles (paragraphs [0074]; [0081]; [0101]; [0102]; [0103]; [0105]); and
selecting the one or more amino acid sequences for inclusion in the vaccine that minimizes a likelihood of no immune response for each representative immune profile, based on the immune profile response values (paragraph [0037]).
Furthermore, Rose et al. teaches a computer-implemented method (Abstract).
Regarding claim 2, the claim is directed to the step of generating the plurality of representative immune profiles comprising: (i) creating a first distribution over the plurality of immune profiles; and (ii) sampling the first distribution to create the plurality of representative immune profiles. Rose et al. teaches the method of claim 1. Rose et al. also teaches creating a distribution over the plurality of the reference binder-target pairs (paragraphs [0125]; [0136]), and teaches approximating samples from a joint posterior distribution (paragraphs [0130]; [0143]).
Regarding claim 10, the claim is directed to the step of selecting the one or more amino acid sequences for inclusion in the vaccine comprising applying a mathematical optimization algorithm to minimize a maximum likelihood of no immune response for each of the representative. Rose et al. teaches the method of claim 1. Rose et al. also teaches using a mathematical optimization algorithm to select the amino acid sequences for inclusion in the vaccine that minimize a maximum likelihood of no immune response by increasing an accurate probability of binding affinity (paragraph [0020]).
Regarding claim 11, Rose et al. teaches the method of claim 10. Rose et al. teaches the immune profiles comprising HLA alleles and the sample components of the immune profiles comprising amino acid sequences that make up HLA alleles (paragraphs [0031]; [0057]; [0070]). Rose et al. also teaches a binary indicator variable that indicates whether the candidate amino acid is included (paragraph [0017]), a continuous variable for a profile that characterizes a log likelihood of no immune response, a continuous variable for each sample component of a profile that characterizes a log likelihood of no response, a continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequence, and the log likelihood function minimizes the maximum log likelihood that there is no response (paragraph [0135]).
Regarding claim 16, the claim is directed to the immune response value being in each case a log likelihood value based on amino acid sub-sequences of the respective candidate amino acid sequence. Rose et al. teaches the method of claim 1. Rose et al. also teaches the immune response value being a log likelihood value based on the amino acid sub-sequences of the candidate amino acid sequences (paragraphs [0131]; [0135]).
Regarding claim 17, the claim is directed to the step of identifying the immune profile response value for each candidate amino acid sequence comprising selecting a best likelihood value as the immune response value from a likelihood value for each amino acid sub-sequence. Rose et al. teaches the method of claim 1. Rose et al. also teaches identifying immune profile response values for a candidate sequence by selecting a likelihood value above a threshold (paragraph [0020]) and a maximum likelihood (paragraph [0024]).
Regarding claim 19, the claim is directed to the representative immune profiles comprising one or more of a set of human leukocyte antigen (HLA) alleles; presence of tumor infiltrating lymphocytes; presence of immune checkpoint markers; presence of hypoxia markers; presence of chemokine receptors; and/or previous infection by human papillomavirus. Rose et al. teaches the method of claim 1. Rose et al. also teaches the representative immune profiles comprising HLA protein sequences (paragraphs [0031]; [0070]).
Regarding claim 20 the claim is directed to the step of selecting the one or more amino acid sequences for inclusion in the vaccine being further based on a correspondence between the sample components of the immune profile and the representative immune profiles. Rose et al. teaches the method of claim 1. Rose et al. also teaches that the selection is based on the correlation of a tumor profile and in silico generated mutated peptides (paragraph [0153]). Rose et al. teaches using associated mutated peptides, predicting and selecting a neoantigen, and then identifying a target epitope for vaccine design (paragraph [0154]).
Regarding claim 21, the claim is directed to selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences according to the computer-implemented method of claim 1; and synthesizing the one or more amino acid sequences or encoding the one or more amino acid sequences into a corresponding deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sequence and/or incorporating the DNA or RNA sequence into a genome of a bacterial or viral delivery system to create the vaccine. Rose et al. teaches the method of selecting candidate amino acid sequences for inclusion in a vaccine of claim 1. Rose et al. also teaches synthesizing the one or more candidate peptides (paragraph [0036]) and incorporating the candidate peptides into a corresponding DNA or RNA sequencing, or incorporating the sequence into a genome of a bacterial or viral delivery system to create a vaccine (paragraph [0037]).
Regarding claim 22, the claim is directed to a system for selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequence, the system comprising at least one processor in communication with at least one memory device, the at least one memory device having stored thereon instructions for causing the at least one processor to perform the computer-implemented method according to claim 1. Rose et al. teaches the method of claim 1. Rose et al. also teaches a system comprising at least one processor in communication with at least one memory device, and the memory device having stored thereon instructions for causing the at least one processor to perform the method (paragraph [0039]).
Regarding claim 23, the claim is directed to a tangible, non-transitory computer-readable medium having instructions stored thereon, which, upon being executed by one or more processors, provides for implementing the method of claim 1. Rose et al. teaches the method of claim 1. Rose et al. also teaches a non-transitory computer-readable having instructions that when executed perform the method steps (paragraph [0041]).
Claim Rejections - 35 USC § 103
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 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.
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 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Rose et al., as applied to claims 1, 2, 10, 11, 16, 17, 19-23 in the 102 rejection above, in view of Liu et al. (“Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies”, 2019).
Regarding claim 3, the claim is directed to the first distribution being a distribution over the plurality of immune profiles for each region of the population. Rose et al. teaches the method of claim 2.
Rose et al. does not teach the claim element of the first distribution being a distribution over the plurality of immune profiles for each region of the population.
However, Liu et al. teaches Bayesian analysis of complex mutations in HBV, HCV, and HIV studies, and teaches using these statistical modeling methods to find virus sequence mutations and the difference in two or three different groups of patients (page 148, column 1, paragraph 1). Liu et al. teaches generating a distribution of the datasets, wherein data from each data group is sampled (page 148, Section 3.1).
Regarding claim 4, the claim is directed to the first distribution being a posterior distribution over genotypes in each region of the population based on a prior distribution and observed genotypes from the plurality of immune profiles in each region of the population. Rose et al. teaches the method of claim 3 in view of Liu et al. Rose et al. also teaches the distribution being a posterior distribution (claim 15) wherein the posterior likelihood is calculated based on a prior probability (paragraphs [0131]; [0138]).
Regarding claim 5, the claim is directed to the first distribution being a symmetric Dirichlet distribution, wherein the method further comprises the step of collecting all genotypes observed at least once across all regions of the population, and wherein the step of sampling the first distribution comprises sampling a desired number of genotypes from each region of the population based on counts of each genotype in the sample. Rose et al. teaches the method of claim 4 in view of Liu et al. Rose et al. also teaches predicting pan-allele binding affinity for MHC class I and II (paragraph [0011]) and that an allele name is included with each reference data (paragraph [0061]).
Rose et al. does not teach the claim elements of a symmetric Dirichlet distribution.
However, Liu et al. teaches the distribution being a symmetric Dirichlet distributions (page 148, column 2), and teaches sampling at least once from each group (page 150, column 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 incorporated the distribution of Liu et al. to the method of Rose et al. because both Rose et al. and Liu et al. are directed to the statistical modeling of genetic material with applications toward disease studies (see Rose et al. paragraph [0001], See Liu et al. Abstract). Rose et al. teaches choosing candidate peptides for use in a vaccine based on an analyzed binding affinity value to a target molecules (paragraph [0002]), and Liu et al. is specifically directed to analyzing the viral mutations of HBV, HCV, and HIV (Abstract). Thus, one of ordinary skill in the art would have a reasonable expectation of analyzing candidate amino acids for a virus vaccine by combining the prior art references and would be motivated to do so in order to attempt to treat HBV, HCV, or HIV.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Rose et al., as applied to claim 1, 2, 10, 11, 16, 17, 19-23 in the 102 rejection above, in view of Zaykin (WO 02/35442 A2, published 05/02/2002).
Regarding claim 6, the claim is directed to simulating a digital population based on the retrieved plurality of immune profiles for the population, wherein the step of creating the first distribution is based on the simulated population such that the step of sampling is performed on the distribution of the simulated population. Rose et al. teaches the method of claim 2.
Rose et al. does not teach the claim elements of simulating a digital population.
However, Zaykin teaches evaluating haplotype frequencies for a plurality of individuals and the association with continuous traits (Abstract). Zaykin teaches simulating digital populations and simulating samples sampled from a symmetric Dirichlet distribution (page 23, line 22).
Regarding claim 7, the claim is directed to the step of simulating a digital population comprising, defining a population size, and creating a second distributions over regions of the population. Rose et al. teaches the method of claim 6 in view of Zaykin.
Rose et al. does not teach the claim elements of simulating a digital population.
However, Zaykin teaches simulating a digital population by defining a sample size of 50 and sampling a distribution over different marker traits (page 23, line 27).
Regarding claim 8, the claim is directed to the second distribution being a Dirichlet distribution. Rose et al. teaches the method of claim 7 in view of Zaykin.
Rose et al. does not teach the claim element of the second distribution being a Dirichlet distribution.
However, Zaykin teaches the second distribution being a symmetric Dirichlet distribution (page 23, line 24).
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 incorporated the simulation of Zaykin to the method of Rose et al. because Rose et al. is directed to statistical modeling to determine binding affinity of amino acids (Abstract) for the identification of immunogenic antigens and characterization of biological processes in diseased tissues (paragraph [0001]). Zaykin teaches haplotypes can be useful for fine mapping of disease susceptibility genes (page 1, line 20) and classification of individuals in continuous traits (page 2, line32), such as epitope traits. Thus, one of ordinary skill in the art would have a reasonable expectation of success of using a simulated population to sample a distribution of amino acid sequences by combining the prior art references and would be motivated to do so in order to finely map the disease susceptibility traits.
Claims 9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Rose et al., as applied to claims 1, 2, 10, 11, 16, 17, 19-23 in the 102 rejection above, in view of Saraf et al. (“IPRO: An Iterative Computational Protein Library Redesign and Optimization Procedure”, 2006).
Regarding claim 9, the claim is directed to the representative immune profiles being generated such that the representative immune profiles maximizing coverage of combinations of immune profiles in the population. Rose et al. teaches the method of claim 1.
Rose et al. does not teach the claim elements of the representative immune profiles being generated such that the representative immune profiles maximizing coverage of combinations of immune profiles in the population.
However, Saraf et al. is directed to modeling approached to optimize protein structure (Abstract). Saraf et al. teaches generating proteins to maximized library coverage (page 4174, column 1, Section Application Example).
Regarding claim 12, the claim is directed to the mathematical optimization algorithm being a mixed integer linear program. Rose et al. teaches the method of claim 10.
Rose et al. does not teach the claim element of the mathematical optimization algorithm being a mixed integer linear program.
However, Saraf et al. teaches using a mixed-integer linear program for design of an entire combinatorial library and for optimization over a local perturbation region (page 4168, column 2, paragraph 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art to have incorporated the coverage and optimization of the epitope selection for a vaccine of Saraf et al. to the method of Rose et al. because both Rose et al. and Saraf et al. are directed to selection of amino acids for vaccine development (see Rose et al. paragraph [0019] and Saraf et al. Abstract and page 4168, column 1, paragraph 2). Saraf et al. teaches the computation approach improving binding energy scores (Abstract). Thus, one of ordinary skill in the art would have a reasonable expectation of success of optimizing protein binding affinity for use in a vaccine using mixed integer linear programming and would be motivated to do so in order to improve the immunogenicity of the vaccine.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Rose et al., as applied to claims 1, 2, 10, 11, 16, 17, 19-23 in the 102 rejection above, in view of Nicosia et al. (US 2021/0379170 A1, PCT filed 11/15/2019).
The claim is directed to the step of selecting the one or more amino acid sequences for inclusion in the vaccine being constrained based on a maximum amount of amino acid sequences allowed in a vaccine delivery platform. Rose et al. teaches the method of claim 1.
Rose et al. does not teach the claim element of selecting the one or more amino acid sequences for inclusion in the vaccine being constrained based on a maximum amount of amino acid sequences allowed in a vaccine delivery platform.
However, Nicosia et al. teaches selection of cancer mutations for generation of a personalized cancer vaccine (Abstract). Nicosia et al. teaches a maximum number of amino acids that can be accommodated in an adenoviral personalized vaccine vector (paragraph [0244]) and teaches prioritizing selection based on having to accommodate the maximal insert size for amino acids in the vector (paragraph [0237]).
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 incorporated the constraint of Nicosia et al. to the method of Rose et al. because both Rose et al. and Nicosia et al. are directed to selection of amino acids for personalized cancer vaccines (see Rose et al. paragraph [0056]), see Nicosia et al. Abstract). Rose et al. teaches incorporating the selected sequence into the genome of a viral delivery system as a vaccine (paragraph [0156]) and Nicosia et al. teaches the number constraint for amino acids in an adenoviral personalized vaccine vector (paragraph [0244]). Thus, one of ordinary skill in the art would find it obvious to use an adenoviral vaccine as a personalized cancer vaccine by combining the prior art references and would be motivated to include the number constraint in order to have the maximal insert size for the vector.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Rose et al., as applied to claims 1, 2, 10, 11, 16, 17, 19-23 in the 102 rejection above, in view of Oany et al. (“Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach”, 2014).
The claim is directed to the one or more candidate amino acid sequences being comprised in one or more proteins of a coronavirus. Rose et al. teaches the method of claim 1.
Rose et al. does not teach the claim element of the one or more candidate amino acid sequence being comprised in one or more proteins of a coronavirus.
However, Oany et al. teaches design of an epitope-based peptide vaccine against spike proteins of human coronavirus (Abstract). Oany et al. teaches use of an algorithm to select optimal epitopes based on binding (page 1140, column 2, T Cell epitope identification).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date in the art to have used candidate amino acid sequences comprises in a coronavirus because both Rose et al. and Oany et al. are directed to the selection of proteins for a vaccine formulation (see Rose et al. Abstract and paragraph [0001], see Oany et al. Abstract). Thus, it would be obvious to one of ordinary skill in the art to use the method of Rose et al. to select amino acids to formulate a vaccine for a coronavirus and would have had a reasonable expectation of success by combining these prior art references both directed to vaccine development.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-23 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of copending Application No. 18/420,953. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious over the Specification of Application ‘953.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Claims
Application ‘953
Claims
Limitations
Claims
Limitations
1
A computer-implemented method of selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences, the method comprising: identifying an immune profile response value for each candidate amino acid sequence with respect to each one of a plurality of sample components of an immune profile, wherein the immune profile response value represents whether the respective candidate amino acid sequence results in an immune response for the sample components of the immune profile; retrieving a plurality of immune profiles for a population; generating a plurality of representative immune profiles for the population, wherein the representative immune profiles overlap with the sample components of the immune profiles; and selecting the one or more amino acid sequences for inclusion in the vaccine that minimises a likelihood of no immune response for each representative immune profile, based on the immune profile response values.
1
A computational intelligence-implemented method of selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences, the method comprising: identifying an immune profile response value for each candidate amino acid sequence in respect of each one of sample components of an immune profile, wherein the immune profile response value represents whether the candidate amino acid sequence results in an immune response for the sample component of the immune profile; retrieving a plurality of immune profiles for a population; generating a plurality of representative immune profiles for the population, wherein the representative immune profiles overlap with the sample components of the immune profiles; and, selecting the one or more amino acid sequences for inclusion in the vaccine such that the likelihood that every member of a population has a positive response to the vaccine is maximized, based on the immune profile response values
2
wherein the step of generating the plurality of representative immune profiles comprises: (i) creating a first distribution over the plurality of immune profiles; and (ii) sampling the first distribution to create the plurality of representative immune profiles
2
wherein the method comprises: creating a first distribution over the plurality of immune profiles; and sampling the first distribution to create the plurality of representative immune profiles
3
wherein the first distribution is a distribution over the plurality of immune profiles for each region of the population
3
wherein the first distribution is a distribution over the plurality of immune profiles for each region of the population
4
wherein the first distribution is a posterior distribution over genotypes in each region of the population based on a prior distribution and observed genotypes from the plurality of immune profiles in each region of the population
4
wherein the first distribution is a posterior distribution over genotypes in each region based on a prior distribution and observed genotypes from the plurality of immune profiles in each region of the population
5
wherein the first distribution is a symmetric Dirichlet distribution, wherein the method further comprises the step of collecting all genotypes observed at least once across all regions of the population, and wherein the step of sampling the first distribution comprises sampling a desired number of genotypes from each region of the population based on counts of each genotype in the sample
5
wherein the first distribution is a symmetric Dirichlet distribution, wherein the method comprises: collecting all genotypes observed at least once across all regions; and sampling a desired number of genotypes from each region based on counts of each genotype in a sample
6
simulating a digital population based on the retrieved plurality of immune profiles for the population, wherein the step of creating the first distribution is based on the simulated population such that the step of sampling is performed on the distribution of the simulated population
6
wherein the method comprises: simulating a digital population based on the retrieved plurality of immune profiles for the population; and creating a first distribution based on the simulated population such that the sampling is performed on the distribution of the simulated population
7
wherein the step of simulating a digital population comprises: defining a population size; and creating a second distribution over regions of the population
7
wherein the method comprises: defining a population size; and creating a second distribution over the regions
8
wherein the second distribution is a Dirichlet distribution
8
wherein the second distribution is a Dirichlet distribution
9
wherein the representative immune profiles are generated such that the representative immune profiles maximise coverage of combinations of immune profiles in the population
9
wherein the representative immune profiles are generated such the representative immune profiles maximize coverage of combinations of immune profiles in the population
10
wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine comprises applying a mathematical optimisation algorithm to minimise a maximum likelihood of no immune response for each of the representative immune profiles
10
wherein the method comprises: applying a mathematical optimization algorithm to minimize a maximum likelihood of no immune response for each of the representative immune profiles
11
wherein the immune profile comprises a set of human leukocyte antigen (HLA) alleles and the sample components of the immune profile comprise sample HLA alleles, and wherein the variables of the mathematical optimisation algorithm comprise: (a) a binary indicator variable for each candidate amino acid sequence which indicates whether the candidate amino acid is included in a vaccine; (b) a continuous variable for each representative immune profile which gives a log likelihood of no immune response; (c) a continuous variable for each sample component of the immune profile which gives a log likelihood of no response; and (d) a continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences, wherein the mathematical optimisation algorithm minimises the continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences
11
wherein the immune profile comprises a set of HLA alleles, and wherein variables of the mathematical optimization algorithm comprise: (a) a binary indicator variable for each amino acid sequence which indicates whether the candidate amino acid is included in a vaccine; (b) a continuous variable for each representative immune profile which gives a log likelihood of no immune response; (c) a continuous variable for each sample component of an immune profile which gives a log likelihood of no response; and (d) a continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences, wherein the mathematical optimization algorithm minimizes the continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences
12
wherein the mathematical optimisation algorithm is a mixed integer linear program
12
wherein the mathematical optimization algorithm is a mixed integer linear program
13
further comprising: assigning a cost to each candidate amino acid sequence, wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is constrained based on the cost assigned to each candidate amino acid sequence, such that the selected one or more amino acid sequences have a total cost below a predetermined threshold budget
13
wherein the method comprises: assigning a cost to each amino acid sequence, and wherein selecting is constrained based on the cost assigned to each amino acid sequence, such that the selected one or more amino acid sequences have a total cost below a predetermined threshold budget
14
wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is constrained based on a maximum amount of amino acid sequences allowed in a vaccine delivery platform
14
wherein selecting is constrained based on a maximum amount of amino acid sequences allowed in a vaccine delivery platform
15
creating a tripartite graph, wherein: a first set of nodes corresponds to the candidate amino acid sequences; a second set of nodes corresponds to the sample components of an immune profile; a third set of nodes corresponds to the representative immune profiles for the population, weights of edges between the first set of nodes and the second set of nodes are the immune response values; and weights of edges between the second set of nodes and the third set of nodes represent correspondence between the sample components of an immune profile and each representative immune profile
15
wherein the method comprises: creating a tripartite graph, wherein; a first set of nodes corresponds to the amino acid sequences, wherein a second set of nodes corresponds to the sample components of an immune profile; and a third set of nodes corresponds to the representative immune profiles for the population, and wherein: weights of edges between the first set of nodes and the second set of nodes are the immune response values; and weights of edges between the second set of nodes and the third set of nodes represent correspondence between the sample components of the immune profile and each representative immune profile
16
wherein the immune response value is in each case a log likelihood value based on amino acid sub-sequences of the respective candidate amino acid sequence
16
wherein the immune response value is a log likelihood value based on amino acid subsequences of the candidate amino acid sequence
17
wherein the step of identifying the immune profile response value for each candidate amino acid sequence comprises selecting a best likelihood value as the immune response value from a likelihood value for each amino acid sub-sequence
17
wherein the method comprises: selecting a best likelihood value as the immune response value from a likelihood value for each amino-acid subsequence
18
wherein the one or more candidate amino acid sequences are comprised in one or more proteins of a coronavirus
18
wherein the one or more amino acid sequences are comprised in one or more proteins of a coronavirus, preferably the SARS-CoV-2 virus
19
wherein the representative immune profiles comprise one or more of a set of human leukocyte antigen (HLA) alleles; presence of tumor infiltrating lymphocytes; presence of immune checkpoint markers; presence of hypoxia markers; presence of chemokine receptors; and/or previous infection by human papillomavirus
19
wherein the representative immune profile comprises one or more selected from a group comprising: a set of HLA alleles; presence of tumor infiltrating lymphocytes; presence of immune checkpoint markers; presence of hypoxia markers; presence of chemokine receptors; and previous infection by human papillomavirus
20
wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is further based on a correspondence between the sample components of the immune profile and the representative immune profiles
20
wherein selecting the one or more amino acid sequences for inclusion in the vaccine is further based on a correspondence between the sample components of an immune profile and the representative immune profiles
23
A tangible, non-transitory computer- readable medium having instructions stored thereon, which, upon being executed by one or more processors, provides for implementing the method of claim 1
21
A non-transitory computer readable medium having computer executable instructions stored thereon for implementing a method of selecting a set of candidate amino acid sequences for inclusion in a vaccine, the method comprising: identifying an immune profile response value for each candidate amino acid sequence in respect of each one of sample components of an immune profile, wherein the immune profile response value represents whether the candidate amino acid sequence results in an immune response for the sample component of the immune profile; retrieving a plurality of immune profiles for a population; generating a plurality of representative immune profiles for the population, wherein the representative immune profiles overlap with the sample components of the immune profiles; and selecting the one or more amino acid sequences for inclusion in the vaccine such that the likelihood that every member of a population has a positive response to the vaccine is maximized, based on the immune profile response values.
Regarding claim 21, although the claims of application ‘953 are silent in regard to synthesizing, the Specification at paragraphs [0061] and [0063] discloses synthesizing the selected candidate amino acids.
Regarding claim 22, although the claims of application ‘953 are silent in regard to a system, the Specification at paragraph [0002] discloses that the method is directed to a system.
Claims 1-23 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/422,250. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious over the Specification of Application ‘250.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant claims
Application ‘250
Claims
Limitations
Claims
Limitations
1
A computer-implemented method of selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences, the method comprising: identifying an immune profile response value for each candidate amino acid sequence with respect to each one of a plurality of sample components of an immune profile, wherein the immune profile response value represents whether the respective candidate amino acid sequence results in an immune response for the sample components of the immune profile; retrieving a plurality of immune profiles for a population; generating a plurality of representative immune profiles for the population, wherein the representative immune profiles overlap with the sample components of the immune profiles; and selecting the one or more amino acid sequences for inclusion in the vaccine that minimises a likelihood of no immune response for each representative immune profile, based on the immune profile response values.
1
A computer-implemented method of selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences, the method comprising: identifying an immune profile response value for each candidate amino acid sequence in respect of each one of a plurality of sample components of an immune profile, wherein the immune profile response value represents whether the candidate amino acid sequence results in an immune response for the sample component of an immune profile; retrieving a plurality of immune profiles for a population; generating a plurality of representative immune profiles for the population, wherein the representative immune profiles overlap with the sample components of an immune profiles; and selecting the one or more amino acid sequences for inclusion in the vaccine by simulating a population of digital twin citizens, wherein the digital twin comprises human leukocyte antigen (HLA) profile of a citizen, based on the immune profile response values
2
wherein the step of generating the plurality of representative immune profiles comprises: (i) creating a first distribution over the plurality of immune profiles; and (ii) sampling the first distribution to create the plurality of representative immune profiles
2
wherein the method comprises: creating a first distribution over the plurality of immune profiles; and sampling the first distribution to create the plurality of representative immune profiles
3
wherein the first distribution is a distribution over the plurality of immune profiles for each region of the population
3
wherein the first distribution is a distribution over the plurality of immune profiles for each region of the population
4
wherein the first distribution is a posterior distribution over genotypes in each region of the population based on a prior distribution and observed genotypes from the plurality of immune profiles in each region of the population
4
wherein the first distribution is a posterior distribution over genotypes in each region based on a prior distribution and observed genotypes from the plurality of immune profiles in each region of the population
5
wherein the first distribution is a symmetric Dirichlet distribution, wherein the method further comprises the step of collecting all genotypes observed at least once across all regions of the population, and wherein the step of sampling the first distribution comprises sampling a desired number of genotypes from each region of the population based on counts of each genotype in the sample
5
wherein the first distribution is a symmetric Dirichlet distribution, and wherein the method comprises: collecting all genotypes observed at least once across all regions; and sampling a desired number of genotypes from each region based on counts of each genotype in a sample
6
simulating a digital population based on the retrieved plurality of immune profiles for the population, wherein the step of creating the first distribution is based on the simulated population such that the step of sampling is performed on the distribution of the simulated population
6
wherein the method comprises: simulating a digital population based on the retrieved plurality of immune profiles for the population; and creating a first distribution based on the simulated population such that the sampling is performed on the distribution of the simulated population
7
wherein the step of simulating a digital population comprises: defining a population size; and creating a second distribution over regions of the population
7
wherein the method comprises: defining a population size; and creating a second distribution over the regions
8
wherein the second distribution is a Dirichlet distribution
8
wherein the second distribution is a Dirichlet distribution
9
wherein the representative immune profiles are generated such that the representative immune profiles maximise coverage of combinations of immune profiles in the population
9
wherein the representative immune profiles are generated such that the representative immune profiles maximize coverage of combinations of immune profiles in the population
10
wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine comprises applying a mathematical optimisation algorithm to minimise a maximum likelihood of no immune response for each of the representative immune profiles
10
wherein the method comprises: applying a mathematical optimization algorithm to minimize a maximum likelihood of no immune response for each of the representative immune profiles
11
wherein the immune profile comprises a set of human leukocyte antigen (HLA) alleles and the sample components of the immune profile comprise sample HLA alleles, and wherein the variables of the mathematical optimisation algorithm comprise: (a) a binary indicator variable for each candidate amino acid sequence which indicates whether the candidate amino acid is included in a vaccine; (b) a continuous variable for each representative immune profile which gives a log likelihood of no immune response; (c) a continuous variable for each sample component of the immune profile which gives a log likelihood of no response; and (d) a continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences, wherein the mathematical optimisation algorithm minimises the continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences
11
wherein the immune profile comprises a set of HLA alleles, and wherein variables of the mathematical optimization algorithm comprise: (a) a binary indicator variable for each amino acid sequence which indicates whether the candidate amino acid is included in a vaccine; (b) a continuous variable for each representative immune profile which gives a log likelihood of no immune response; (c) a continuous variable for each sample component of an immune profile which gives a log likelihood of no response; and (d) a continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences, wherein the mathematical optimization algorithm minimizes the continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences
12
wherein the mathematical optimisation algorithm is a mixed integer linear program
12
wherein the mathematical optimization algorithm is a mixed integer linear program
13
further comprising: assigning a cost to each candidate amino acid sequence, wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is constrained based on the cost assigned to each candidate amino acid sequence, such that the selected one or more amino acid sequences have a total cost below a predetermined threshold budget
13
wherein the method comprises: assigning a cost to each amino acid sequence, and wherein selecting is constrained based on the cost assigned to each amino acid sequence, such that the selected one or more amino acid sequences have a total cost below a predetermined threshold budget
14
wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is constrained based on a maximum amount of amino acid sequences allowed in a vaccine delivery platform
14
wherein selecting is constrained based on a maximum amount of amino acid sequences allowed in a vaccine delivery platform
15
creating a tripartite graph, wherein: a first set of nodes corresponds to the candidate amino acid sequences; a second set of nodes corresponds to the sample components of an immune profile; a third set of nodes corresponds to the representative immune profiles for the population, weights of edges between the first set of nodes and the second set of nodes are the immune response values; and weights of edges between the second set of nodes and the third set of nodes represent correspondence between the sample components of an immune profile and each representative immune profile
15
creating a tripartite graph, wherein: a first set of nodes corresponds to the amino acid sequences; a second set of nodes corresponds to the sample components of an immune profile; and a third set of nodes corresponds to the representative immune profiles for the population, and wherein: weights of edges between the first set of nodes and the second set of nodes are the immune response values; and weights of edges between the second set of nodes and the third set of nodes represent correspondence between the sample components of the immune profile and each representative immune profile
16
wherein the immune response value is in each case a log likelihood value based on amino acid sub-sequences of the respective candidate amino acid sequence
16
wherein the immune response value is a log likelihood value based on amino acid subsequences of the candidate amino acid sequence
17
wherein the step of identifying the immune profile response value for each candidate amino acid sequence comprises selecting a best likelihood value as the immune response value from a likelihood value for each amino acid sub-sequence
17
wherein the method comprises: selecting a best likelihood value as the immune response value from a likelihood value for each amino-acid subsequence
18
wherein the one or more candidate amino acid sequences are comprised in one or more proteins of a coronavirus
18
wherein the one or more amino acid sequences are comprised in one or more proteins of a coronavirus, preferably the SARS-CoV-2 virus
19
wherein the representative immune profiles comprise one or more of a set of human leukocyte antigen (HLA) alleles; presence of tumor infiltrating lymphocytes; presence of immune checkpoint markers; presence of hypoxia markers; presence of chemokine receptors; and/or previous infection by human papillomavirus
19
wherein the representative immune profile may comprise one or more selected from a group comprising: a set of HLA alleles; presence of tumor infiltrating lymphocytes; presence of immune checkpoint markers; presence of hypoxia markers; presence of chemokine receptors; and, previous infection by human papillomavirus
23
A tangible, non-transitory computer- readable medium having instructions stored thereon, which, upon being executed by one or more processors, provides for implementing the method of claim 1
20
A non-transitory computer readable medium having computer executable instructions stored thereon for implementing a method of selecting a set of candidate amino acid sequences for inclusion in a vaccine, the method comprising: identifying an immune profile response value for each candidate amino acid sequence in respect of each one of sample components of an immune profile, wherein the immune profile response value represents whether the candidate amino acid sequence results in an immune response for the sample component of the immune profile; retrieving a plurality of immune profiles for a population; generating a plurality of representative immune profiles for the population, wherein the representative immune profiles overlap with the sample components of the immune profiles; and selecting the one or more amino acid sequences for inclusion in the vaccine by simulating a population of digital twin citizens; wherein the digital twin comprises human leukocyte antigen (HLA) profile of a citizen, based on the immune profile response values.
Regarding claim 20, although the claims of application ‘250 are silent the amino acid sequences regarding selecting based on correspondence between the sample components of the immune profile and the representative immune profiles, the Specification at [0025] discloses selecting the one or more amino acid sequences based on a correspondence between the sample components of an immune profile and the components of the immune profile present in the respective representative immune profiles.
Regarding claim 21, although the claims of application ‘250 are silent in regard to synthesizing, the Specification at paragraphs [0061] and [0063] discloses synthesizing the selected candidate amino acids.
Regarding claim 22, although the claims of application ‘250 are silent in regard to a system, the Specification at paragraph [0002] discloses that the method is directed to a system.
Claims 1-23 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of copending Application No. 18/424,042. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious over the Specification of Application ‘042.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant claims
Application ‘042
Claims
Limitations
Claims
Limitations
1
A computer-implemented method of selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences, the method comprising: identifying an immune profile response value for each candidate amino acid sequence with respect to each one of a plurality of sample components of an immune profile, wherein the immune profile response value represents whether the respective candidate amino acid sequence results in an immune response for the sample components of the immune profile; retrieving a plurality of immune profiles for a population; generating a plurality of representative immune profiles for the population, wherein the representative immune profiles overlap with the sample components of the immune profiles; and selecting the one or more amino acid sequences for inclusion in the vaccine that minimises a likelihood of no immune response for each representative immune profile, based on the immune profile response values.
1
A computational intelligence-implemented method of selecting a set of candidate vaccine elements for inclusion in a vaccine, the method comprising: creating a set of digital twin citizens for a population of interest, where a digital twin is a set of human leukocyte antigen (HLA) alleles or an immune profile; creating a tripartite graph in which nodes correspond to the vaccine elements, the HLA alleles, and the citizens; and selecting (i) a first set of vaccine elements such that a likelihood that each citizen has a positive response is maximized or (ii) a second set of vaccine elements such that a likelihood of no response for each citizen is minimized.
2
wherein the method comprises: identifying an immune profile response value for each vaccine element with respect to each one of sample components of an immune profile, wherein the immune profile response value represents whether the respective vaccine element results in an immune response for the sample components of the immune profile; retrieving a plurality of immune profiles for the population; and generating a plurality of representative immune profiles for the population, wherein each of the plurality of immune profiles overlaps with the sample components of the immune profiles.
2
wherein the step of generating the plurality of representative immune profiles comprises: (i) creating a first distribution over the plurality of immune profiles; and (ii) sampling the first distribution to create the plurality of representative immune profiles
3
wherein the method comprises: creating a first distribution over the plurality of immune profiles; and sampling the first distribution to create the plurality of representative immune profiles
3
wherein the first distribution is a distribution over the plurality of immune profiles for each region of the population
4
wherein the first distribution is a distribution over the plurality of immune profiles for each region of the population
4
wherein the first distribution is a posterior distribution over genotypes in each region of the population based on a prior distribution and observed genotypes from the plurality of immune profiles in each region of the population
5
wherein the first distribution is a posterior distribution over genotypes in each region based on a prior distribution and observed genotypes from the plurality of immune profiles in each region of the population
5
wherein the first distribution is a symmetric Dirichlet distribution, wherein the method further comprises the step of collecting all genotypes observed at least once across all regions of the population, and wherein the step of sampling the first distribution comprises sampling a desired number of genotypes from each region of the population based on counts of each genotype in the sample
6
wherein the first distribution is a symmetric Dirichlet distribution, wherein the method comprises: collecting all genotypes observed at least once across all regions; and sampling a desired number of genotypes from each region based on counts of each genotype in a sample
6
simulating a digital population based on the retrieved plurality of immune profiles for the population, wherein the step of creating the first distribution is based on the simulated population such that the step of sampling is performed on the distribution of the simulated population
7
wherein the method comprises: simulating a digital population based on the retrieved plurality of immune profiles for the population; and creating a first distribution based on the simulated population such that the sampling is performed on the distribution of the simulated population
7
wherein the step of simulating a digital population comprises: defining a population size; and creating a second distribution over regions of the population
8
wherein the method comprises: defining a population size; and creating a second distribution over the regions
8
wherein the second distribution is a Dirichlet distribution
9
wherein the second distribution is a Dirichlet distribution
9
wherein the representative immune profiles are generated such that the representative immune profiles maximise coverage of combinations of immune profiles in the population
10
wherein the representative immune profiles are generated such the representative immune profiles maximize coverage of combinations of immune profiles in the population
10
wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine comprises applying a mathematical optimisation algorithm to minimise a maximum likelihood of no immune response for each of the representative immune profiles
11
wherein the method comprises: applying a mathematical optimization algorithm to minimize a maximum likelihood of no immune response for each of the representative immune profiles
11
wherein the immune profile comprises a set of human leukocyte antigen (HLA) alleles and the sample components of the immune profile comprise sample HLA alleles, and wherein the variables of the mathematical optimisation algorithm comprise: (a) a binary indicator variable for each candidate amino acid sequence which indicates whether the candidate amino acid is included in a vaccine; (b) a continuous variable for each representative immune profile which gives a log likelihood of no immune response; (c) a continuous variable for each sample component of the immune profile which gives a log likelihood of no response; and (d) a continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences, wherein the mathematical optimisation algorithm minimises the continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more amino acid sequences
12
wherein the immune profile comprises a set of HLA alleles, and wherein variables of the mathematical optimization algorithm comprise: (a) a binary indicator variable for each vaccine element which indicates whether the vaccine element is included in a vaccine; (b) a continuous variable for each representative immune profile which gives a log likelihood of no immune response; (c) a continuous variable for each sample component of an immune profile which gives a log likelihood of no response; and (d) a continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more vaccine elements, wherein the mathematical optimization algorithm minimizes the continuous variable which gives a maximum log likelihood that any representative immune profile does not respond to the selected one or more vaccine elements
12
wherein the mathematical optimisation algorithm is a mixed integer linear program
13
wherein the mathematical optimization algorithm is a mixed integer linear program
13
further comprising: assigning a cost to each candidate amino acid sequence, wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is constrained based on the cost assigned to each candidate amino acid sequence, such that the selected one or more amino acid sequences have a total cost below a predetermined threshold budget
14
wherein the method comprises: assigning a cost to each vaccine element; and selecting is constrained based on the cost assigned to each vaccine element, such that the selected one or more vaccine elements have a total cost below a predetermined threshold budget
14
wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is constrained based on a maximum amount of amino acid sequences allowed in a vaccine delivery platform
15
wherein the method comprises: selecting is constrained based on a maximum amount of vaccine elements allowed in a vaccine delivery platform
15
creating a tripartite graph, wherein: a first set of nodes corresponds to the candidate amino acid sequences; a second set of nodes corresponds to the sample components of an immune profile; a third set of nodes corresponds to the representative immune profiles for the population, weights of edges between the first set of nodes and the second set of nodes are the immune response values; and weights of edges between the second set of nodes and the third set of nodes represent correspondence between the sample components of an immune profile and each representative immune profile
16
wherein the method comprises: creating a tripartite graph, wherein: a first set of nodes corresponds to the vaccine elements; a second set of nodes corresponds to the sample components of an immune profile; and a third set of nodes corresponds to the representative immune profiles for the population, and wherein: weights of edges between the first set of nodes and the second set of nodes are the immune response values; and weights of edges between the second set of nodes and the third set of nodes represent correspondence between the sample components of an immune profile and each representative immune profile
16
wherein the immune response value is in each case a log likelihood value based on amino acid sub-sequences of the respective candidate amino acid sequence
17
wherein the immune response value is a log likelihood value based on amino acid subsequences of the candidate vaccine element
17
wherein the step of identifying the immune profile response value for each candidate amino acid sequence comprises selecting a best likelihood value as the immune response value from a likelihood value for each amino acid sub-sequence
18
wherein the method comprises: selecting a best likelihood value as the immune response value from a likelihood value for each amino-acid subsequence
18
wherein the one or more candidate amino acid sequences are comprised in one or more proteins of a coronavirus
19
wherein the one or more vaccine elements are comprised in one or more proteins of a coronavirus, preferably the SARS-CoV-2 virus
19
wherein the representative immune profiles comprise one or more of a set of human leukocyte antigen (HLA) alleles; presence of tumor infiltrating lymphocytes; presence of immune checkpoint markers; presence of hypoxia markers; presence of chemokine receptors; and/or previous infection by human papillomavirus
20
wherein the representative immune profile comprises one or more selected from a group comprising: a set of HLA alleles; presence of tumor infiltrating lymphocytes; presence of immune checkpoint markers; presence of hypoxia markers; presence of chemokine receptors; and previous infection by human papillomavirus.
20
wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is further based on a correspondence between the sample components of the immune profile and the representative immune profiles
21
wherein the method comprises: selecting the one or more vaccine elements for inclusion in the vaccine based on a correspondence between the sample components of an immune profile and the representative immune profiles
23
A tangible, non-transitory computer- readable medium having instructions stored thereon, which, upon being executed by one or more processors, provides for implementing the method of claim 1
22
A non-transitory computer readable medium having computer executable instructions stored thereon for implementing a method of selecting a set of candidate vaccine elements for inclusion in a vaccine, the method comprising: creating a set of digital twin citizens for a population of interest, where a digital twin is a set of human leukocyte antigen (HLA) alleles or an immune profile; creating a tripartite graph in which nodes correspond to the vaccine elements, the HLA alleles, and the citizens; and selecting (i) a first set of vaccine elements such that a likelihood that each citizen has a positive response is maximized or (ii) a second set of vaccine elements such that a likelihood of no response for each citizen is minimized
Regarding claim 21, although the claims of application ‘250 are silent in regard to synthesizing, the Specification at paragraphs [0061] and [0063] discloses synthesizing the selected candidate amino acids.
Regarding claim 22, although the claims of application ‘250 are silent in regard to a system, the Specification at paragraph [0002] discloses that the method is directed to a system.
Conclusion
No claims are allowed.
Claim 21 is eligible under 35 USC 101 because any judicial exceptions are integrated into a practical application through the step of synthesizing the one or more amino acid sequences, encoding the one or more amino acid sequences into a DNA or RNA sequence, and/or incorporating the DNA or RNA sequence into a genome of a delivery system to create a vaccine.
Claims 13 and 15 appear to be free from the art because the art does not seem to teach or fairly suggest the limitations of “assigning a cost to each candidate amino acid sequence, wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine is constrained based on the cost assigned to each candidate amino acid sequence, such that the selected one or more amino acid sequences have a total cost below a predetermined threshold budget”, and “creating a tripartite graph, wherein: a first set of nodes corresponds to the candidate amino acid sequences; a second set of nodes corresponds to the sample components of an immune profile…”.
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/E.A.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686