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 .
Applicant’s Response
Applicant’s response, filed 06/09/2026, and Applicant’s Supplemental Response, filed 06/12/2026, have been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Claims Status
Claims 10, 11, and 21 are canceled.
Claims 1-9, 12-20, 22, and 23 are pending.
Claims 1-9, 12-20, 22, and 23 are examined.
Withdrawn Objections/Rejections
The objection to the Specification is withdrawn in view of the amendments submitted
The rejection of claims 1-9, 12-20, 22, and 23 under 35 USC 112(b) from the Office Action mailed 03/09/2026is withdrawn in view of the amendments submitted
The rejection of claims 1, 2, 16, 17, 19, 20, 22, and 23 under 35 USC 102(a)(2) over Rose et al. is withdrawn in view of the amendments submitted. Furthermore, arguments with regard to Rose et al. and the limitations of previous claims 10 and 11, which are integrated into claim 1, are persuasive.
The rejection of claims 3-5 under 35 USC 103 over Rose et al. in view of Liu et al. is withdrawn in view of the amendments and arguments submitted.
The rejection of claims 6-8 under 35 USC 103 over Rose et al. in view of Zaykin is withdrawn in view of the amendments and arguments submitted.
The rejection of claims 9 and 12 under 35 USC 103 over Rose et al. in view of Saraf et al. is withdrawn in view of the amendments and arguments submitted.
The rejection of claim 14 under 35 USC 103 over Rose et al. in view of Nicosia et al. is withdrawn in view of the amendments and arguments submitted.
The rejection of claim 18 under 35 USC 103 over Rose et al. in view of Oany et al. is withdrawn in view of the amendments and arguments submitted.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 22 and 23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The Specification provides support for a system for vaccine design (paragraph [0002]), “a system for selecting one or more amino acid sequences for inclusion in a vaccine” (paragraph [0065]), “a computer readable medium having computer executable instruction stored thereon for implementing the method of any of the above aspects” (paragraph [0066]), “synthesizing one or more selected amino acid sequences” (paragraph [0061]), etc. However, the Specification does not provide support for a system comprising a processor in communication with at least one memory device for causing the at least one processor to perform the method and a non-transitory computer-readable medium to synthesize the selected amino acid sequences or incorporate the DNA or RNA sequence into a vaccine, and it is not well known how a processor or non-transitory computer readable medium would be able to synthesize amino acids or generate a vaccine. This is a new ground of rejection as necessitated by claim amendments.
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 1-9, 12-20, 22, and 23 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. This is a new ground of rejection as necessitated by claim amendments.
With respect to claim 1, the claim recites the limitation of “wherein the representative immune profiles overlap with the sample components of the plurality of immune profiles retrieved for the population”. The claim is indefinite because it is unclear which sample components overlap. The claim recites “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”, and then recites “retrieving a plurality of immune profiles for a population”. However, there is no antecedent basis for the sample components of the plurality of immune profiles, and it is unclear if these are the same sample component as recited in the first step for an immune profile.
With respect to claim 12, the claim recites the limitation of “The computer-implemented method of claim 10”. The claim is indefinite because “the computer-implemented method of claim 10” lacks antecedent basis because claim 10 has been canceled. Thus, it is unclear what method claim 12 is limiting.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9, 12-20, 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. Any newly recited portion is necessitated by claim amendments
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 plurality of immune profiles retrieved for the population
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
encoding the one or more selected amino acid sequences into a corresponding deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sequence
Mathematical concepts recited in claim 1:
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
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
Dependent claims 2-9, and 12-20 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 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
synthesizing the one or more selected amino acid sequences and/or incorporating the corresponding DNA or RNA sequence into a genome of a bacterial or viral delivery system to create the vaccine
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 step of synthesizing the selected amino acid sequences or incorporating the corresponding DNA or RNA sequence to create the vaccine does not integrate the judicial exceptions into a practical application because the step is not necessarily required by the claim, because the last step gives the alternative of encoding the one or more selected amino acid sequences into a DNA or RNA sequence. Thus, the claim does not require the physical synthesis of amino acid sequences or a vaccine. 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.
Response to Arguments
In Applicant’s Response filed 06/12/2026, Applicant states that claim 1 is amended to incorporate limitations of claim 21, which was previously indicated to be eligible under 35 USC 101.
It is respectfully submitted that incorporating the limitations of claim 21 does not render claim 1 eligible under 35 USC 101. The claim does not necessarily require physical synthesis of the amino acids of creating of a vaccine, as the claims also includes encoding the one or more selected amino acid sequences into a corresponding DNA or RNA sequence. Under broadest reasonable interpretation, this limitation can be performed mentally, to encode the DNA or RNA sequences of amino acids. Thus, the judicial exceptions are not integrated into a practical application. Previous claim 21 was directed to a method of creating a vaccine, and thus under broadest reasonable interpretation the encoding of the DNA or RNA was as a vaccine. Claim 1 is not directed to a method of creating a vaccine and thus this step does not provide a practical application. Therefore, the rejection under 35 USC 101 is maintained.
In Applicant’s Response filed 06/09/2026, Applicant states that “the claims are directed to selecting amino acid sequences for inclusion in a vaccine based on immune response modelling for use in a concrete biomedical application.” Applicant states that the present claims can be compared to claim 1 of Example 40 in the Subject Matter Eligibility Examples and states that the Office determined that for Example 40 although each of the collecting steps analyzed individually could be viewed as mere pre- or post-solution activity, the claims as a whole is directed to a particular improvement in collecting traffic data. Applicant states that “Claim 1 recites the steps of identifying an immune profile response value for each candidate amino acid sequence, retrieving a plurality of immune profiles for a population, generating a plurality of representative immune profiles for the population and selecting the one or more amino acid sequences for inclusion in the vaccine. Further, 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 comprising steps (a)-(d). These steps cannot be performed in the human mind and represent and improvement of a specific technological process of vaccine design and development. Therefore, each claim taken as a whole integrates any asserted abstract idea into a practical application”.
It is respectfully submitted that this is not persuasive. The claim does not require a massive amount of data that cannot be processed mentally, and one can mentally select amino acids. Furthermore, as recited in the claim itself, the optimization is a mathematical concept. Thus, the claims recite abstract ideas. Furthermore, it is the additional elements of the claims that are analyzed to determine whether the claims are integrated into a practical application (MPEP 2106.04(d).I; MPEP 2106.05(a-h)). Thus, the improvement to integrate the claims into a practical application cannot be from the abstract ideas themselves. The additional elements in claim 1 comprise a computer-implemented method and a step of 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. With respect to Applicant’s comparison to Example 40, Example 40 is deemed to be eligible because the step of collecting addition data when it is deemed that the network traffic data is greater than a predefined threshold integrates the judicial exception of comparing the data into a practical application. Thus, the additional elements are using the judicial exceptions in a manner that provides an improvement to collecting traffic data. The instant claims do not comprise any additional steps that are not abstract and are required by the claims that use the abstract steps in a manner than provides an improvement or uses the abstract ideas beyond using the abstract steps to perform more abstract steps. Therefore, the rejection under 35 USC 101 is maintained.
Furthermore, in Applicant’s Response filed 06/09/2026, Applicant states that “The claims recite optimization steps (a)-(d) and the min-max objective function that are not well-understood routine or conventional. Accordingly, the claims recite significantly more than any alleged abstract idea and are patent eligible at Step 2B of the patent eligibility analysis.”
It is respectfully submitted that this is not persuasive. It is the additional elements that are examined to determine if there is an inventive concept (MPEP 2106.05.A i-vi). The optimization steps and min-max objective function are mathematical concepts and thus a part of the judicial exceptions, and thus cannot provide the inventive concept. The additional elements of claim 1 comprise a computer-implemented method, and retrieving a plurality of immune profiles for a population. 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). Therefore, the rejection under 35 USC 101 is maintained.
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 1-9, 12-20, 22, and 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 prior art Toussaint et al. (“A mathematical Framework for the Selection of an Optimal Set of Peptides for Epitope-Based Vaccines”, IDS reference). Any newly recited portion is necessitated by claim amendments.
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 candidate amino acid sequences predicted to be immunogenic, 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 plurality of immune profiles retrieved for the population; 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 wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine comprises applying a mathematical optimization algorithm to minimise a maximum likelihood of no immune response for each of the representative immune profiles, 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 optimization 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 response 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, and synthesizing the one or more selected amino acid sequences, encoding the one or more selected amino acid sequences into corresponding deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sequence, and/or incorporating the corresponding DNA or RNA sequence into a genome of a bacterial or viral delivery system to create the vaccine.
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
10
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 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
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
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.
Although the claims of application ‘953 are silent in regard to synthesizing, the prior art Toussaint et al. teaches that there are numerous options for constructing a vaccine once a set of potential antigens is known, they can be administered as RNA or DNA cording for the antigen, used an intact proteins, or the epitopes can be used for vaccines. Toussaint et al. teaches that the use of epitope-based vaccines brings about manifold advantages for evoking immune response (page 1, column 1). The claims of application ‘953 are directed to selecting amino acid sequences for inclusion in a vaccine. Thus, it would be obvious to include the selected amino acids in a vaccine because for generating an immune response with a personalized vaccine.
Regarding claim 22, although the claims of application ‘953 are silent in regard to a system, claim 1 of application ‘953 is computer-implemented and thus inherently requires a computer system to perform the method.
Claims 1-9, 12-20, 22, and 23 are 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 prior art Toussaint et al. Any newly recited portion is necessitated by claim amendments.
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 candidate amino acid sequences predicted to be immunogenic, 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 plurality of immune profiles retrieved for the population; 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 wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine comprises applying a mathematical optimization algorithm to minimise a maximum likelihood of no immune response for each of the representative immune profiles, 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 optimization 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 response 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, and synthesizing the one or more selected amino acid sequences, encoding the one or more selected amino acid sequences into corresponding deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sequence, and/or incorporating the corresponding DNA or RNA sequence into a genome of a bacterial or viral delivery system to create the vaccine.
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
10
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 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
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
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
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
15
wherein: weights of edges between the first set of nodes and the second set of nodes are the immune response values; and wights 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
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.
Although the claims of application ‘250 are silent in regard to synthesizing, the prior art Toussaint et al. teaches that there are numerous options for constructing a vaccine once a set of potential antigens is known, they can be administered as RNA or DNA cording for the antigen, used an intact proteins, or the epitopes can be used for vaccines. Toussaint et al. teaches that the use of epitope-based vaccines brings about manifold advantages for evoking immune response (page 1, column 1). The claims of application ‘250 are directed to selecting amino acid sequences for inclusion in a vaccine. Thus, it would be obvious to include the selected amino acids in a vaccine because for generating an immune response with a personalized vaccine.
Regarding claim 22, although the claims of application ‘250 are silent in regard to a system, claim 1 of application ‘250 is computer-implemented and thus inherently requires a computer system.
Claims 1-9, 12-20, 22, and 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 prior art Toussaint et al. Any newly recited portion is necessitated by claim amendments.
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 candidate amino acid sequences predicted to be immunogenic, 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 plurality of immune profiles retrieved for the population; 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 wherein the step of selecting the one or more amino acid sequences for inclusion in the vaccine comprises applying a mathematical optimization algorithm to minimise a maximum likelihood of no immune response for each of the representative immune profiles, 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 optimization 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 response 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, and synthesizing the one or more selected amino acid sequences, encoding the one or more selected amino acid sequences into corresponding deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sequence, and/or incorporating the corresponding DNA or RNA sequence into a genome of a bacterial or viral delivery system to create the vaccine.
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.
11
applying a mathematical optimization algorithm to minimize a maximum likelihood of no immune response for each of the representative immune profiles
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
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
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
Although the claims of application ‘042 are silent in regard to synthesizing, the prior art Toussaint et al. teaches that there are numerous options for constructing a vaccine once a set of potential antigens is known, they can be administered as RNA or DNA cording for the antigen, used an intact proteins, or the epitopes can be used for vaccines. Toussaint et al. teaches that the use of epitope-based vaccines brings about manifold advantages for evoking immune response (page 1, column 1). The claims of application ‘042 are directed to selecting amino acid sequences for inclusion in a vaccine. Thus, it would be obvious to include the selected amino acids in a vaccine because for generating an immune response with a personalized vaccine.
Regarding claim 22, although the claims of application ‘042 are silent in regard to a system, the method of claim 1 of application ‘042 is computer-implemented and thus inherently requires a computer system.
Response to Arguments
Applicant requests that the provisional double patenting rejections be held in abeyance.
It is respectfully submitted that the provisional double patenting rejection may not be held in abeyance, as a filing of a terminal disclaimer or a filing that the claims are patentably distinct from the reference application’s claims is necessary for further consideration of the rejection of the claims. Only compliance with an objection or requirement as to form not necessary for further consideration of the claims may be held in abeyance until allowable subject matter is indicated. See MPEP 804.I.B.1.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emilie A Smith whose telephone number is (571)272-7543. The examiner can normally be reached 9am - 5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571)270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.A.S./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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