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 .
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.
Claims Status
Claims 1-20 are pending. Claims 1, 12 and 20 are independent claims. Claims 1-20 are examined below.
Priority
As detailed on the 05/23/2023 filing receipt, this application claims priority to as early as 07/06/2020. This application is a CON of 17/772,976 filed 04/28/2022, PAT 11,657,894 which is a 371 of PCT/IB21/56049 filed 07/06/2021 which claims benefit of 63/048,414 filed 07/06/2020.
Information Disclosure Statement
The Information Disclosure Statements filed 04/28/2023 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The drawings filed 04/28/2023 are accepted.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are: …a quantum computing system comprising a plurality of qubits, a qubit control device, and a measurement unit configured to search the search space using a quantum computing algorithm based on the scoring function… in claim 12.
In the present case, the nonce term, "qubit control device, and a measurement unit", as presently claimed, is coupled with the functional language "configured to search the search space…" The claim and the specification fail to provide or describe any particular structure as the "qubit control device, and a measurement unit" configured to perform or achieve the claimed function. The specification fails to provide sufficient structure for performing the claimed functions. See this limitation further addressed below under 35 U.S.C. 112(b).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 8 and 12-19 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.
Claims 8 and 18 recite “wherein the protein sequence for optimization includes an amino acid or a DNA sequence.” It is unclear what is meant by protein sequence for optimization includes an amino acid or a DNA sequence because protein sequences and DNA sequences are different entities and protein sequences are made up of amino acids. Therefore, the claims are indefinite.
The limitation in claim 12 of “a quantum computing system comprising a plurality of qubits, a qubit control device, and a measurement unit configured to search the search space using a quantum computing algorithm based on the scoring function, and to provide an output of the quantum computing algorithm, the output indicative of an optimized protein…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Paragraphs [0096] to [0098] of the instant specification mentions the qubit control unit and the measurement unit, but does not provide sufficient structure for performing the claimed function of searching the search space. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Dependent claims 13-19 are rejected for depending on rejected claim 12.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis of claims in Step 1.
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Independent claim 1 is directed to a 101 process, here a "computer-implemented method," with process steps such as "providing…, defining…"
Independent claim 12 is directed to a 101 machine or manufacture, here a "non-transitory computer-readable medium and a processor."
Independent claim 20 is directed to a 101 machine or manufacture, here a "non-transitory computer-readable medium."
[Step 1: claims 1-20: YES]
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Mental processes recited include:
Claim 1 recites: "…defining a scoring function based on the protein property for optimization…determining, by the processor, a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or rotamer from the rotamer library; searching the search space using a quantum computing algorithm based on the scoring function, the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence…" Determining defining and identifying are acts of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 3 recites testing the optimized protein for the protein property for optimization… applying an artificial intelligence method to the experimental data to learn an association between a configuration of the optimized protein sequence and the protein property for optimization
Claim 7 recites: "…determining the protein sequence for optimization based on the protein property for optimization via an artificial intelligence or machine learning algorithm." Determining is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 11 recites: "…ranking, by the processor, each optimized protein of the plurality of optimized proteins; determining, by the processor, a subset of the ranked plurality of optimized proteins; and further performing an optimization of a protein of the subset of the ranked plurality of optimized proteins." Ranking and determining are an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 12 recites: "...determine a protein having a protein sequence for optimization… define a scoring function based on the protein property for optimization; determine at least one of: a position in the protein sequence to be subjected to modification, a replacement amino acid to be substituted for, an amino acid occurring at the position, an amino acid deletion at the position, an amino acid insertion at the position, or a rotamer library to be searched for a target rotamer to be applied to the protein sequence; and determine a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or the target rotamer from the rotamer library to be applied to the protein sequence…" Determining is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 13 recites: "... wherein to determine a protein having a protein sequence for optimization..." Determining is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 20 recites: "…determine a protein having a protein sequence for optimization…define a scoring function based on the protein property for optimization; determine at least one of: a position in the protein sequence to be subjected to modification, a replacement amino acid to be substituted for, an amino acid occurring at the position, an amino acid deletion at the position, an amino acid insertion at the position, or a rotamer library to be searched for a target rotamer to be applied to the protein sequence and determine a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or the target rotamer from the rotamer library to be applied to the protein sequence; search the search space using a quantum computing algorithm based on the scoring function, the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence" Determining defining and identifying are acts of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Mathematical concepts recited include:
Claim 1 recites: " searching the search space using a quantum computing algorithm based on the scoring function... and providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization." Using a quantum computing algorithm and scoring function are mathematical concepts and/or formulas.
Claim 2 recites: "applying a computer-based model."
Claim 3 recites applying an artificial intelligence method to the experimental data to learn an association between a configuration of the optimized protein sequence and the protein property for optimization
Claim 5 recites: "wherein the quantum computing algorithm is a quantum annealing algorithm configured to search the search space for the optimized protein based on a target Hamiltonian determined from the scoring function..." Quantum computing algorithm and quantum annealing algorithm are mathematical concepts and/or formulas.
Claim 6 recites: "wherein the quantum computing algorithm is one of a quantum- inspired algorithm, digital annealing algorithm, quantum annealing algorithm, gate-based quantum algorithm, quantum simulation algorithm, or a quantum-inspired optimization." Quantum computing algorithm and quantum annealing algorithm are mathematical concepts and/or formulas.
Claim 12 recites: "using a quantum computing algorithm based on the scoring function, and to provide an output of the quantum computing algorithm"
Claim 16 recites: "wherein the quantum computing algorithm is a quantum annealing algorithm configured to search the search space for the optimized protein based on a target Hamiltonian determined from the scoring function"
Claim 17 recites: "wherein the quantum computing algorithm is one of a quantum- inspired algorithm, digital annealing algorithm, quantum annealing algorithm, gate-based quantum algorithm, quantum simulation algorithm, or a quantum-inspired optimization"
Claim 20 recites: "… using a quantum computing algorithm based on the scoring function "
As indicated above, claim 1, 3, 7, 11-13 and 20 recite limitations that are mental processes. For instance, claim 1 recites defining a scoring function and determining a search space converting, claim 3 recites testing the optimized protenant and learning an association, claims 7 recites determining the protein sequence and claim 11 recites ranking the optimized protein and determining a subset of the ranked plurality of optimized proteins. These claim limitations are mental processes because they are acts of evaluating, analyzing, observing, organizing and judging data as indicated above. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2) subsection III). Although, claims 1, 12 and 20 recite performing the method as part of a method executed on a computer, there are no additional limitations to indicate that anything other than a generic computer is required. However, merely requiring that the steps are carried out with a generic computer does not negate the mental nature of these steps and equates rather to merely using a computer as a tool to perform the mental process. Therefore, under the broadest reasonable interpretation, the indicated claims above can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "Mental processes" grouping of abstract ideas.
Claims 1-3, 5-6, 12, 16-17 and 20 recite mathematical concepts and formulas as indicated above. For instance, using a quantum computing algorithm and the process of generating testing models in claims 1, 12 and 20 are mathematical concepts and/or formulas that falls under the “mathematical concepts” grouping of abstract ideas.
As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional elements that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claim 1 recites "a computer-implemented method; providing, to a processor, a protein sequence for optimization; providing, to the processor, a protein structure having the protein sequence; providing, to the processor, a protein property for optimization; providing, to the processor, at least one of: a position in the protein sequence to be subjected to modification, or an amino acid to be substituted for the amino acid occurring at the position, inserted at the position, or deleted from the position, or a rotamer library to be searched for a target rotamer to be applied to one or more positions in the protein sequence; and providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization. " These limitations include inputting and outputting data.
Claim 2 recites "…providing, to the processor, a predefined binding partner of interest, and wherein providing the protein having the protein sequence for optimization comprises applying a computer-based model based on a structure or amino acid sequence of the binding partner of interest, or optimizing a binding partner for the selected protein during the searching. "
Claim 3 recites "…generating experimental data from the testing…"
Claim 11 recites "wherein the output state of the quantum computing algorithm is indicative of a plurality of optimized proteins, and further comprising: ranking, by the processor, each optimized protein of the plurality of optimized proteins; determining, by the processor, a subset of the ranked plurality of optimized proteins; and further performing an optimization of a protein of the subset of the ranked plurality of optimized proteins. "
Claim 12 recites "a non-transitory computer-readable storage medium and a processor," "…receive a protein property for optimization…to provide an output of the quantum computing algorithm, the output indicative of an optimized protein, the optimized protein being optimized according to the scoring function based on the protein property for optimization"
Claim 20 recites: "non-transitory computer-readable medium storing instructions" and "…receive a protein property for optimization…and provide an output of the quantum computing algorithm, the output indicative of an optimized protein, the optimized protein being optimized according to the scoring function based on the protein property for optimization"
The elements of claims 1-3, 11-12 and 20 as indicated above equate to insignificant extra solutional activities of data gathering and outputting. Data gathering serves as input to the recited judicial exception in the claims. Claim 1 recites a computer-implemented method, claim 12 recites a non-transitory computer-readable medium and processors and claim 20 recites a non-transitory computer-readable medium. These elements of claims 1, 12 and 20 equate to generic computer components. Claims 1, 12 and 20 invoke the computer components merely as tools to execute the abstract idea. The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. (see MPEP 2106.05(f)). Additionally, the listed additional elements are mere instructions to apply an exception because they recite no more than an idea of a solution or outcome and does not recite a technological solution to a technological problem. (See MPEP 2106.05(f)(1)). As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claim 1 recites "a computer-implemented method; providing, to a processor, a protein sequence for optimization; providing, to the processor, a protein structure having the protein sequence; providing, to the processor, a protein property for optimization; providing, to the processor, at least one of: a position in the protein sequence to be subjected to modification, or an amino acid to be substituted for the amino acid occurring at the position, inserted at the position, or deleted from the position, or a rotamer library to be searched for a target rotamer to be applied to one or more positions in the protein sequence; and providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization. " These limitations include inputting and outputting data.
Claim 2 recites "…providing, to the processor, a predefined binding partner of interest, and wherein providing the protein having the protein sequence for optimization comprises applying a computer-based model based on a structure or amino acid sequence of the binding partner of interest, or optimizing a binding partner for the selected protein during the searching. "
Claim 3 recites "…generating experimental data from the testing…"
Claim 11 recites "wherein the output state of the quantum computing algorithm is indicative of a plurality of optimized proteins, and further comprising: ranking, by the processor, each optimized protein of the plurality of optimized proteins; determining, by the processor, a subset of the ranked plurality of optimized proteins; and further performing an optimization of a protein of the subset of the ranked plurality of optimized proteins. "
Claim 12 recites "a non-transitory computer-readable storage medium and a processor," "…receive a protein property for optimization…to provide an output of the quantum computing algorithm, the output indicative of an optimized protein, the optimized protein being optimized according to the scoring function based on the protein property for optimization"
Claim 20 recites: "non-transitory computer-readable medium storing instructions" and "…receive a protein property for optimization…and provide an output of the quantum computing algorithm, the output indicative of an optimized protein, the optimized protein being optimized according to the scoring function based on the protein property for optimization"
The additional elements indicated above do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. The limitations equate to mere data gathering and outputting activities, which are insignificant extra solutional activities. The courts have recognized that techniques for determining the level of a biomarker in blood by any means; analyzing DNA to provide sequence information or detect allelic variants; amplifying and sequencing nucleic acid sequences and detecting DNA or enzymes in a sample as well-understood, routine, conventional activities in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. (See MPEP 2106.05(d)). As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. (see MPEP 2106.05(g)). Also, limitations that equate to mere data gathering and outputting via generic computer components, such as receiving data at a computer or outputting data, amount to insignificant extra-solution activity as set forth by the courts in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and OIP Techs., Inc, v, Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Also, the additional elements include storing and retrieving information in memory. Storing and retrieving information in memory were identified by the courts as well-understood, routine and conventional in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Also, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more as identified by the courts in Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Additionally, gathering protein data, utilizing computers and applying quantum computing for protein optimization is a known method as taught by Cao ("Potential of quantum computing for drug discovery." IBM Journal of Research and Development 62.6 (2018): 6-1.; cited on the 04/28/2023 IDS Document). Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cao ("Potential of quantum computing for drug discovery." IBM Journal of Research and Development 62.6 (2018): 6-1.; cited on the 04/28/2023 IDS Document).
Regarding independent claim 1, Cao teaches a computer-implemented method with “In this paradigm, quantum computers are used as coprocessors in tandem with classical computers for accomplishing simulation tasks (see Figure 2). One of the main appeals of this paradigm is that it combines the advantages of both quantum and classical computers. The quantum computer handles only the state preparation and measurement, whereas the classical computer is tasked with optimizing the parameters with which the quantum computer uses for the state preparation.” (page 6:9, col. 1, para. 2)
Cao teaches providing, to a processor, a protein sequence for optimization; providing, to the processor, a protein structure having the protein sequence with “…building the 3-D structures of a protein by comparing its sequence with those of proteins whose 3-D structures have been experimentally characterized (i.e., 40% similarity). A typical prediction with comparative modeling implies the identification of related proteins that can serve as templates from a database of known structures, usually the Protein Data Bank. The sequences of unknown proteins and the templates are aligned and compared, and the geometries of the regions with good alignment are copied.” (page 6:5, col. 1, para. 1); “Predicting the protein structure from the knowledge of the amino acid sequence requires simulating the protein folding process, which is so far out of reach except for small peptides and fast folders; however, in the absence of experimental structures, it is still possible to approximate the 3-D structure of an unknown target protein by comparing its sequence with related known proteins, a process known as comparative modeling” (page 6:3, col. 1, para. 1); “In the case of targets without known experimental tertiary structures, computational approaches such as threading, comparative modeling, and ab initio methods can be used to predict 3-D structures from the knowledge of the protein amino acid sequence.” (page 6:4, col. 2, para. 4); “CADD approaches employed on the stages of hit search, lead discovery, and lead optimization are generally classified into two main categories: structured-based and ligand-based [see Figure 1(b)]. Structured-based CADD relies on knowledge of the target protein three-dimensional (3-D) structure to predict the ability of a candidate to bind to the target, whereas ligand-based CADD employs information of known active and inactive molecules to predict the activity of new candidates. Structure-based CADD is preferred over ligand based if the structural information of the biological target is available.” (page 6:3, col. 1, para. 1) and Figure 1 (page 6:3).
Cao teaches providing, to the processor, a protein property for optimization with “Typical descriptors employed in ligand-based CADD encode a variety of chemical information, including molecular weight, geometry, volume, surface areas, ring content, 3-D geometrical information, atom types, electronegativities, polarizabilities, molecular symmetry, atom distribution, topological charge indices, functional group composition, aromaticity indices, and solvation properties, among others. Both QSAR models and structure comparison require experimental information for the set of molecules employed as either reference for the comparison or training set for QSAR. Consequently, the models employed in ligand-based CADD are often restricted to libraries of candidates that share sufficient similarities with the set of molecules employed as reference” (page 6:4, col. 1, para. 1); “The purpose of this second phase of screening is to optimize the druglike properties of the hit compounds, which includes not only the biological activity, but also the ADMET profile and other pharmacokinetic properties. The general assumption behind this process is that small changes on the chemical structure will produce incremental changes of the druglike properties; therefore, the optimization involves the synthesis of the drug-candidates along with testing of their biological activities accompanied by CADD.” (page 6:7, col. 2, para. 3) and “The usual drug discovery pipeline requires the identification and characterization of a suitable biological target, which can be effectively proved to intervene in the mechanism of disease. This step often requires intense experimentation as well as extensive statistical analysis of the collected data. Once a biological target is in place, the next step is the search for hits, which usually involves extensive biological and virtual screening over libraries of molecules, or the generation of completely new compounds (de novo design), which must be synthesized and tested. The group of hits collected on this stage undergoes further optimization of the pharmacokinetics and ADMET properties, involving a combination of biological and in silico tests, to generate the final group of leads. These stages, going from target identification to lead optimization, benefit the most from CADD techniques. The subsequent steps in the drug discovery pipeline, which involve clinical studies in animals and humans prior to the Federal Drug.” (page 6:2, col. 2, para. 3).
Cao teaches defining a scoring function based on the protein property for optimization with “…most CADD approaches require the following: first, determining the pose or conformation of the ligand that fits best the binding site of the target, and second, assigning a numerical score that expresses the strength of the interaction of the ligand-target complex. The process of finding the best conformation is generally called docking and the process of computing the affinity is referred as scoring. These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes. Extensive sampling of conformations is often required in structured-based CADD approaches to account for the mobility of protein and ligands in biological conditions (aqueous solutions at room temperature).” (page 6:3, col. 2, para. 1)
Cao teaches providing, to the processor, at least one of: a position in the protein sequence to be subjected to modification, or an amino acid to be substituted for the amino acid occurring at the position, inserted at the position, or deleted from the position, or a rotamer library to be searched for a target rotamer to be applied to one or more positions in the protein sequence; determining, by the processor, a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or rotamer from the rotamer library with “In cases where suitable templates are not available for the target of interest, free modeling approaches are employed. This term groups knowledge-based approaches for structure prediction and physically motivated methods. Knowledge-based approaches usually assemble a protein structure by using the geometries of small protein fragments extracted from known 3-D structures. Purely, first principles prediction has been mainly limited to the use of MD simulations with appropriate solvent models to optimize the structure of the protein, an approach that is much more computationally demanding than comparative modeling or knowledge-based free methods. The advantage of physical-based methods is that they reveal the pathway of protein folding for the unknown structure, but their main bottlenecks are their need for extensive conformational sampling and accurate force-field potentials. In this area, quantum simulation and quantum optimization approaches could become powerful tools by addressing problems such as protein folding, as described in Section 4. (page 6:5, col. 1, para. 2).
Cao teaches searching the search space using a quantum computing algorithm based on the scoring function with “The purpose of this perspective is to initiate a mutually beneficial cross-disciplinary discussion and collaboration between the fields of CADD and quantum computing. For the quantum computing community, such dialogue will help to outline the practically useful regimes where quantum computers may have an advantage over classical counterparts.” (page 6:2, col. 2, para. 2); “The process of hit search generally involves HTS of a database of candidate compounds. Traditionally, this process has required the synthesis and experimental determination of the activity of the compounds, which is extremely expensive and slow. Nowadays, the process is accelerated using virtual HTS (vHTS). Different score functions are employed to rank the activity of the candidates depending on whether a structured-based or ligand-based approach is used. Some ligand-based approaches score the candidates based on their similarity with a set of known active compounds. Another option is QSAR, which constructs a statistical model based on experimental information of the activities and chemical information of the ligands. In both approaches, the chemical information is expressed with molecular descriptors that encode physicochemical and structural information of the molecules in a digital format, suitable for comparison. Molecular descriptors can be generated by knowledge-based, graph-theoretical, molecular mechanical, or quantum-mechanical methods. Arguably, the most popular descriptors are molecular fingerprints, which encode various molecular properties as predefined bit settings. Other descriptors are computed solely from the 2-D or 3-D topology of the molecule based on graph-theoretical methods.” (page 6:5, col. 2, para. 1); and “…most CADD approaches require the following: first, determining the pose or conformation of the ligand that fits best the binding site of the target, and second, assigning a numerical score that expresses the strength of the interaction of the ligand-target complex. The process of finding the best conformation is generally called docking and the process of computing the affinity is referred as scoring. These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes. Extensive sampling of conformations is often required in structured-based CADD approaches to account for the mobility of protein and ligands in biological conditions (aqueous solutions at room temperature)” (page 6:3, col. 2, para. 1)
Cao teaches the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence with “By identifying populations that are more susceptible to exposition to an active compound and looking at their mutations, it is possible to infer which proteins are associated to the activity. Another technique called gene profiling combines gene expression analysis (e.g., message RNA profiles) with chemical studies to identify targets. This approach is based on the assumption that deleting the genes that codify the target protein should produce the same inhibitory effect of the active compounds. Consequently, the target can be identified by comparing the expression profiles (information of which proteins are synthesized or expressed) of the population of mutants with the profiles of populations exposed to the active compound. A similar idea can be applied to identify targets by examining message RNA/protein levels to determine whether they correlate with the manifestation of the disease.” (page 6:4, col. 2, para. 2)
Cao teaches providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization with “Once hit compounds have been identified, they enter an optimization phase to produce a smaller set of better candidates, called leads. The set of leads undergoes further optimization in a process that iterates between CADD development and in vitro and animal experiments. The purpose of this second phase of screening is to optimize the druglike properties of the hit compounds, which includes not only the biological activity, but also the ADMET profile and other pharmacokinetic properties. The general assumption behind this process is that small changes on the chemical structure will produce incremental changes of the druglike properties; therefore, the optimization involves the synthesis of the drug-candidates along with testing of their biological activities accompanied by CADD. In this stage, QSAR models for smaller datasets play a major role in the optimization, allowing for quickly judging whether certain modifications improve drug-likeness or not, especially when no target information is available.” (page 6:7, col. 1, para. 3 to page 6:7, col. 2, para. 2).
Regarding claim 2, Cao teaches providing, to the processor, a predefined binding partner of interest, and wherein providing the protein having the protein sequence for optimization comprises applying a computer-based model based on a structure or amino acid sequence of the binding partner of interest, or optimizing a binding partner for the selected protein during the searching with “…most CADD approaches require the following: first, determining the pose or conformation of the ligand that fits best the binding site of the target, and second, assigning a numerical score that expresses the strength of the interaction of the ligand-target complex. The process of finding the best conformation is generally called docking and the process of computing the affinity is referred as scoring. These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes. Extensive sampling of conformations is often required in structured-based CADD approaches to account for the mobility of protein and ligands in biological conditions (aqueous solutions at room temperature) (page 6:3, col. 2, para. 1); Figure 1 (page 6:3); “An alternative approach to vHTS for hit search is de novo design of ligands. These methods apply a strategy to build a completely new compound that can bind to the protein, generally by ligand growing or ligand linking methods. In the first approach, a known ligand is docked onto the binding pocket, and additional groups are added or replaced on the initial structure to improve binding. In the second approach, a group of ligands is simultaneously docked onto the binding site and subsequently linked to generate a candidate.” (page 6:7, col. 1, para. 2) and “Structured-based CADD relies on knowledge of the target protein three-dimensional (3-D) structure to predict the ability of a candidate to bind to the target, whereas ligand-based CADD employs information of known active and inactive molecules to predict the activity of new candidates. Structure-based CADD is preferred over ligand based if the structural information of the biological target is available.” (page 6:3, col. 1, para. 1). The recited “computer-based model” corresponds to “CADD” as taught by Cao.
Regarding claim 3, Cao teaches testing the optimized protein for the protein property for optimization; generating experimental data from the testing with “Once hit compounds have been identified, they enter an optimization phase to produce a smaller set of better candidates, called leads. The set of leads undergoes further optimization in a process that iterates between CADD development and in vitro and animal experiments. The purpose of this second phase of screening is to optimize the druglike properties of the hit compounds, which includes not only the biological activity, but also the ADMET profile and other pharmacokinetic properties. The general assumption behind this process is that small changes on the chemical structure will produce incremental changes of the druglike properties; therefore, the optimization involves the synthesis of the drug-candidates along with testing of their biological activities accompanied by CADD. In this stage, QSAR models for smaller datasets play a major role in the optimization, allowing for quickly judging whether certain modifications improve drug-likeness or not, especially when no target information is available” (page 6:7, col. 1, para. 3 to page 6:7, col. 2, para. 2); “Once a biological target is in place, the next step is the search for hits, which usually involves extensive biological and virtual screening over libraries of molecules, or the generation of completely new compounds (de novo design), which must be synthesized and tested. The group of hits collected on this stage undergoes further optimization of the pharmacokinetics and ADMET properties, involving a combination of biological and in silico tests, to generate the final group of leads. These stages, going from target identification to lead optimization, benefit the most from CADD techniques.” (page 6:2, col. 2, para. 3).
Cao teaches applying an artificial intelligence method to the experimental data to learn an association between a configuration of the optimized protein sequence and the protein property for optimization, the configuration comprising a two- or three- dimensional protein structure, an amino acid sequence, a DNA sequence that encodes the protein, or parts of a two- or three- dimensional protein structure, in particular a catalytic domain, or one or more domains of a protein with “This paper is organized as follows: First, we describe the general pipeline of CADD and some of the methodologies employed in the industry and their challenges. Second, we outline some of the latest quantum computing algorithms that we consider relevant for CADD, namely quantum simulation and quantum machine learning. Techniques.” (page 6:2, col. 2, para. 1); “…however, in the absence of experimental structures, it is still possible to approximate the 3-D structure of an unknown target protein by comparing its sequence with related known proteins, a process known as comparative modeling. Along with the structure, it is necessary to characterize the target by identifying the binding (active) sites that are responsible for the biological activity and where the potential drug candidate (ligand) is expected to bind.” (page 6:3, col. 1, para. 1) and “Typical descriptors employed in ligand-based CADD encode a variety of chemical information, including molecular weight, geometry, volume, surface areas, ring content, 3-D geometrical information, atom types, electronegativities, polarizabilities, molecular symmetry, atom distribution, topological charge indices, functional group composition, aromaticity indices, and solvation properties, among others.” (page 6:4, col. 1, para. 1) and Table 1 (page 6:11) titled Examples of techniques for using quantum computers for machine learning tasks.
Regarding claim 4, Cao teaches providing, based on the learned association, a protein sequence, a position, an amino acid substitution, an amino acid deletion, an amino acid insertion, or a rotamer library for consideration for the search space with “In cases where suitable templates are not available for the target of interest, free modeling approaches are employed. This term groups knowledge-based approaches for structure prediction and physically motivated methods. Knowledge-based approaches usually assemble a protein structure by using the geometries of small protein fragments extracted from known 3-D structures. Purely, first principles prediction has been mainly limited to the use of MD simulations with appropriate solvent models to optimize the structure of the protein, an approach that is much more computationally demanding than comparative modeling or knowledge-based free methods. The advantage of physical-based methods is that they reveal the pathway of protein folding for the unknown structure, but their main bottlenecks are their need for extensive conformational sampling and accurate force-field potentials. In this area, quantum simulation and quantum optimization approaches could become powerful tools by addressing problems such as protein folding, as described in Section 4. (page 6:5, col. 1, para. 2). The recited “rotamer library” corresponds to “conformational sampling” as taught by Cao.
Regarding claim 5, Cao teaches wherein the quantum computing algorithm is a quantum annealing algorithm configured to search the search space for the optimized protein based on a target Hamiltonian determined from the scoring function with “In structure-based drug discovery [see Figure 1(b)], an important part of the input concerns the structure of the target protein. Some progress has been made in the past decade on quantum techniques for protein folding based on the amino acid sequence. In particular, the quantum computing community has considered two simple models: the hydrophobic-polar model and the Miyazawa–Jernigan model, both of which model the protein as a self-avoided walk on a lattice. Solutions in both quantum annealers and gate-model quantum devices4 have been explored.” (page 6:13, col. 1, para. 3). “The unique features of quantum simulation have deep consequences for quantum chemistry. When doing quantum chemical calculations on classical computers, one would almost strictly avoid maintaining the explicit wave function of the physical system or propagating the full wave function unitarily under some quantum Hamiltonian due to the prohibitive costs of either method, whereas on a quantum computer, state preparation and time evolution can often be done efficiently. This distinction makes for rather different design patterns in quantum algorithms versus their classical counterparts. Building on previous results for quantum simulation, it was shown that using the ability to generate quantum states with sufficiently large overlap with the ground state and the ability to efficiently time evolve a state under a molecular Hamiltonian, one could obtain the ground state energy of the molecular Hamiltonian with accuracy comparable to full configuration interaction (FCI) (corresponding to exact diagonalization). While FCI suffers from exponential growth in computational cost, the cost of the quantum algorithm in only scales polynomially with respect to the system size— an exponential improvement over classical algorithms. (6:10, col. 1, para. 1) and “As a heuristic quantum algorithm for finding the ground state energy of a Hamiltonian, VQE operates by tuning the parameters of the quantum circuit to minimize the energy expectation of the output state with respect to the Hamiltonian.” (page 6:10, col. 2, para. 1)
Regarding claim 6, Cao teaches wherein the quantum computing algorithm is one of a quantum- inspired algorithm, digital annealing algorithm, quantum annealing algorithm, gate-based quantum algorithm, quantum simulation algorithm, or a quantum-inspired optimization with “In this scenario, the development of quantum algorithms that can tackle quantum simulation efficiently and accurately can contribute to extending the applicability of FES strategies to drug discovery, as quantum computers achieve size and precision that enables the simulation of large molecules.” (page 6:8, col. 2, para. 1) and “In this section, we focus on two large (and rapidly expanding) categories of quantum algorithms that are relevant to drug discovery, namely quantum algorithms for simulating molecular electronic structure in computational chemistry, and quantum-enhanced machine learning.” (page 6:9, col. 1, para. 3).
Regarding claim 7, Cao teaches determining the protein sequence for optimization based on the protein property for optimization via an artificial intelligence or machine learning algorithm with “In this section, we focus on two large (and rapidly expanding) categories of quantum algorithms that are relevant to drug discovery, namely quantum algorithms for simulating molecular electronic structure in computational chemistry, and quantum-enhanced machine learning.” (page 6:9, col. 1, para. 3) and Figure 1 (page 6:3).
Regarding claim 8, Cao teaches wherein the protein sequence for optimization includes an amino acid or a DNA sequence with “In the case of targets without known experimental tertiary structures, computational approaches such as threading, comparative modeling, and ab initio methods can be used to predict 3-D structures from the knowledge of the protein amino acid sequence.” (page 6:4, col. 2, para. 4).
Regarding claim 9, Cao teaches providing, to the processor, multiple target rotamers to be applied to a plurality of positions in the protein sequence with “…most CADD approaches require the following: first, determining the pose or conformation of the ligand that fits best the binding site of the target, and second, assigning a numerical score that expresses the strength of the interaction of the ligand-target complex. The process of finding the best conformation is generally called docking and the process of computing the affinity is referred as scoring. These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes. Extensive sampling of conformations is often required in structured-based CADD approaches to account for the mobility of protein and ligands in biological conditions (aqueous solutions at room temperature).” (page 6:3, col. 2, para. 1) and “In cases where suitable templates are not available for the target of interest, free modeling approaches are employed. This term groups knowledge-based approaches for structure prediction and physically motivated methods. Knowledge-based approaches usually assemble a protein structure by using the geometries of small protein fragments extracted from known 3-D structures. Purely, first principles prediction has been mainly limited to the use of MD simulations with appropriate solvent models to optimize the structure of the protein, an approach that is much more computationally demanding than comparative modeling or knowledge-based free methods. The advantage of physical-based methods is that they reveal the pathway of protein folding for the unknown structure, but their main bottlenecks are their need for extensive conformational sampling and accurate force-field potentials. In this area, quantum simulation and quantum optimization approaches could become powerful tools by addressing problems such as protein folding, as described in Section 4.” (page 6:5, col. 1, para. 2).
Regarding claim 10, Cao teaches providing, to the processor, a plurality of positions in the protein sequence to be subjected to modification, or a plurality of amino acids to be substituted for the amino acids occurring at the positions, inserted at the positions, or deleted from the positions with “By identifying populations that are more susceptible to exposition to an active compound and looking at their mutations, it is possible to infer which proteins are associated to the activity. Another technique called gene profiling combines gene expression analysis (e.g., message RNA profiles) with chemical studies to identify targets. This approach is based on the assumption that deleting the genes that codify the target protein should produce the same inhibitory effect of the active compounds. Consequently, the target can be identified by comparing the expression profiles (information of which proteins are synthesized or expressed) of the population of mutants with the profiles of populations exposed to the active compound. A similar idea can be applied to identify targets by examining message RNA/protein levels to determine whether they correlate with the manifestation of the disease.” (page 6:4, col. 2, para. 2)
Regarding claim 11, Cao teaches wherein the output state of the quantum computing algorithm is indicative of a plurality of optimized proteins, and further comprising: ranking, by the processor, each optimized protein of the plurality of optimized proteins; determining, by the processor, a subset of the ranked plurality of optimized proteins; and further performing an optimization of a protein of the subset of the ranked plurality of optimized proteins with “These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes.” (page 6:3, col. 2, para. 1); “Different score functions are employed to rank the activity of the candidates depending on whether a structured-based or ligand-based approach is used.” (page 6:5, col. 2, para. 1)
Regarding independent claim 12, Cao teaches a classical computing system comprising a non-transitory computer-readable medium and a processor with “In this paradigm, quantum computers are used as coprocessors in tandem with classical computers for accomplishing simulation tasks (see Figure 2). One of the main appeals of this paradigm is that it combines the advantages of both quantum and classical computers. The quantum computer handles only the state preparation and measurement, whereas the classical computer is tasked with optimizing the parameters with which the quantum computer uses for the state preparation.” (page 6:9, col. 1, para. 2)
Cao teaches determine a protein having a protein sequence for optimization with “…building the 3-D structures of a protein by comparing its sequence with those of proteins whose 3-D structures have been experimentally characterized (i.e., 40% similarity). A typical prediction with comparative modeling implies the identification of related proteins that can serve as templates from a database of known structures, usually the Protein Data Bank. The sequences of unknown proteins and the templates are aligned and compared, and the geometries of the regions with good alignment are copied.” (page 6:5, col. 1, para. 1); “Predicting the protein structure from the knowledge of the amino acid sequence requires simulating the protein folding process, which is so far out of reach except for small peptides and fast folders; however, in the absence of experimental structures, it is still possible to approximate the 3-D structure of an unknown target protein by comparing its sequence with related known proteins, a process known as comparative modeling” (page 6:3, col. 1, para. 1); “In the case of targets without known experimental tertiary structures, computational approaches such as threading, comparative modeling, and ab initio methods can be used to predict 3-D structures from the knowledge of the protein amino acid sequence.” (page 6:4, col. 2, para. 4) and Figure 1 (page 6:3).
Cao teaches receive a protein property for optimization with “Typical descriptors employed in ligand-based CADD encode a variety of chemical information, including molecular weight, geometry, volume, surface areas, ring content, 3-D geometrical information, atom types, electronegativities, polarizabilities, molecular symmetry, atom distribution, topological charge indices, functional group composition, aromaticity indices, and solvation properties, among others. Both QSAR models and structure comparison require experimental information for the set of molecules employed as either reference for the comparison or training set for QSAR. Consequently, the models employed in ligand-based CADD are often restricted to libraries of candidates that share sufficient similarities with the set of molecules employed as reference” (page 6:4, col. 1, para. 1); “The purpose of this second phase of screening is to optimize the druglike properties of the hit compounds, which includes not only the biological activity, but also the ADMET profile and other pharmacokinetic properties. The general assumption behind this process is that small changes on the chemical structure will produce incremental changes of the druglike properties; therefore, the optimization involves the synthesis of the drug-candidates along with testing of their biological activities accompanied by CADD.” (page 6:7, col. 2, para. 3) and “The usual drug discovery pipeline requires the identification and characterization of a suitable biological target, which can be effectively proved to intervene in the mechanism of disease. This step often requires intense experimentation as well as extensive statistical analysis of the collected data. Once a biological target is in place, the next step is the search for hits, which usually involves extensive biological and virtual screening over libraries of molecules, or the generation of completely new compounds (de novo design), which must be synthesized and tested. The group of hits collected on this stage undergoes further optimization of the pharmacokinetics and ADMET properties, involving a combination of biological and in silico tests, to generate the final group of leads. These stages, going from target identification to lead optimization, benefit the most from CADD techniques. The subsequent steps in the drug discovery pipeline, which involve clinical studies in animals and humans prior to the Federal Drug.” (page 6:2, col. 2, para. 3).
Cao teaches define a scoring function based on the protein property for optimization with “…most CADD approaches require the following: first, determining the pose or conformation of the ligand that fits best the binding site of the target, and second, assigning a numerical score that expresses the strength of the interaction of the ligand-target complex. The process of finding the best conformation is generally called docking and the process of computing the affinity is referred as scoring. These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes. Extensive sampling of conformations is often required in structured-based CADD approaches to account for the mobility of protein and ligands in biological conditions (aqueous solutions at room temperature).” (page 6:3, col. 2, para. 1)
Cao teaches determine at least one of: a position in the protein sequence to be subjected to modification, a replacement amino acid to be substituted for, an amino acid occurring at the position, an amino acid deletion at the position, an amino acid insertion at the position, or a rotamer library to be searched for a target rotamer to be applied to the protein sequence and determine a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or the target rotamer from the rotamer library to be applied to the protein sequence with “In cases where suitable templates are not available for the target of interest, free modeling approaches are employed. This term groups knowledge-based approaches for structure prediction and physically motivated methods. Knowledge-based approaches usually assemble a protein structure by using the geometries of small protein fragments extracted from known 3-D structures. Purely, first principles prediction has been mainly limited to the use of MD simulations with appropriate solvent models to optimize the structure of the protein, an approach that is much more computationally demanding than comparative modeling or knowledge-based free methods. The advantage of physical-based methods is that they reveal the pathway of protein folding for the unknown structure, but their main bottlenecks are their need for extensive conformational sampling and accurate force-field potentials. In this area, quantum simulation and quantum optimization approaches could become powerful tools by addressing problems such as protein folding, as described in Section 4. (page 6:5, col. 1, para. 2).
Cao teaches a quantum computing system comprising a plurality of qubits, a qubit control device, and a measurement unit configured to search the search space using a quantum computing algorithm based on the scoring function with “Most digital devices use bits as the building blocks for information processing. Each bit expresses a discrete, “classical” state of 0 or 1. Devices that perform computation by manipulating bits are referred to as classical computers. Quantum computers manipulate quantum states of matter for performing computation. A standard choice for constructing those quantum states is to combine two-level quantum systems called qubits. By manipulating the qubit states and taking advantage of uniquely quantum-mechanical phenomena, such as superposition and entanglement, quantum computers can perform computational tasks in ways that are beyond what is possible on their classical counterparts. A predefined way of manipulating quantum states to solve a computational problem is referred to as a quantum algorithm.” (page 6:8, col. 2, para. 2) and “In this paradigm, quantum computers are used as coprocessors in tandem with classical computers for accomplishing simulation tasks (see Figure 2). One of the main appeals of this paradigm is that it combines the advantages of both quantum and classical computers. The quantum computer handles only the state preparation and measurement, whereas the classical computer is tasked with optimizing the parameters with which the quantum computer uses for the state preparation.” (page 6:9, col. 1, para. 2)
Cao teaches to provide an output of the quantum computing algorithm, the output indicative of an optimized protein, the optimized protein being optimized according to the scoring function based on the protein property for optimization with “Once hit compounds have been identified, they enter an optimization phase to produce a smaller set of better candidates, called leads. The set of leads undergoes further optimization in a process that iterates between CADD development and in vitro and animal experiments. The purpose of this second phase of screening is to optimize the druglike properties of the hit compounds, which includes not only the biological activity, but also the ADMET profile and other pharmacokinetic properties. The general assumption behind this process is that small changes on the chemical structure will produce incremental changes of the druglike properties; therefore, the optimization involves the synthesis of the drug-candidates along with testing of their biological activities accompanied by CADD. In this stage, QSAR models for smaller datasets play a major role in the optimization, allowing for quickly judging whether certain modifications improve drug-likeness or not, especially when no target information is available.” (page 6:7, col. 1, para. 3 to page 6:7, col. 2, para. 2).
Regarding claim 13, Cao teaches receive a predefined binding partner of interest, and wherein to determine a protein having a protein sequence for optimization the classical computing system is further configured to apply a computer-based model based on a structure or amino acid sequence of the binding partner of interest, or optimize a binding partner for the selected protein during the searching with “…most CADD approaches require the following: first, determining the pose or conformation of the ligand that fits best the binding site of the target, and second, assigning a numerical score that expresses the strength of the interaction of the ligand-target complex. The process of finding the best conformation is generally called docking and the process of computing the affinity is referred as scoring. These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes. Extensive sampling of conformations is often required in structured-based CADD approaches to account for the mobility of protein and ligands in biological conditions (aqueous solutions at room temperature) (page 6:3, col. 2, para. 1); Figure 1 (page 6:3); “An alternative approach to vHTS for hit search is de novo design of ligands. These methods apply a strategy to build a completely new compound that can bind to the protein, generally by ligand growing or ligand linking methods. In the first approach, a known ligand is docked onto the binding pocket, and additional groups are added or replaced on the initial structure to improve binding. In the second approach, a group of ligands is simultaneously docked onto the binding site and subsequently linked to generate a candidate.” (page 6:7, col. 1, para. 2) and “Structured-based CADD relies on knowledge of the target protein three-dimensional (3-D) structure to predict the ability of a candidate to bind to the target, whereas ligand-based CADD employs information of known active and inactive molecules to predict the activity of new candidates. Structure-based CADD is preferred over ligand based if the structural information of the biological target is available.” (page 6:3, col. 1, para. 1). The recited “computer-based model” corresponds to “CADD” as taught by Cao.
Regarding claim 14, Cao teaches wherein the system is further configured to: receive experiment data from testing the optimized protein for the protein property for optimization with “Once hit compounds have been identified, they enter an optimization phase to produce a smaller set of better candidates, called leads. The set of leads undergoes further optimization in a process that iterates between CADD development and in vitro and animal experiments. The purpose of this second phase of screening is to optimize the druglike properties of the hit compounds, which includes not only the biological activity, but also the ADMET profile and other pharmacokinetic properties. The general assumption behind this process is that small changes on the chemical structure will produce incremental changes of the druglike properties; therefore, the optimization involves the synthesis of the drug-candidates along with testing of their biological activities accompanied by CADD. In this stage, QSAR models for smaller datasets play a major role in the optimization, allowing for quickly judging whether certain modifications improve drug-likeness or not, especially when no target information is available” (page 6:7, col. 1, para. 3 to page 6:7, col. 2, para. 2); “Once a biological target is in place, the next step is the search for hits, which usually involves extensive biological and virtual screening over libraries of molecules, or the generation of completely new compounds (de novo design), which must be synthesized and tested. The group of hits collected on this stage undergoes further optimization of the pharmacokinetics and ADMET properties, involving a combination of biological and in silico tests, to generate the final group of leads. These stages, going from target identification to lead optimization, benefit the most from CADD techniques.” (page 6:2, col. 2, para. 3).
Cao teaches apply an artificial intelligence method to the experimental data to learn an association between a configuration of the optimized protein sequence and the protein property for optimization, the configuration comprising a two- or three- dimensional protein structure, an amino acid sequence, a DNA sequence that encodes the protein, or parts of a two- or three- dimensional protein structure, in particular a catalytic domain, or one or more domains of a protein with “This paper is organized as follows: First, we describe the general pipeline of CADD and some of the methodologies employed in the industry and their challenges. Second, we outline some of the latest quantum computing algorithms that we consider relevant for CADD, namely quantum simulation and quantum machine learning. Techniques.” (page 6:2, col. 2, para. 1); “…however, in the absence of experimental structures, it is still possible to approximate the 3-D structure of an unknown target protein by comparing its sequence with related known proteins, a process known as comparative modeling. Along with the structure, it is necessary to characterize the target by identifying the binding (active) sites that are responsible for the biological activity and where the potential drug candidate (ligand) is expected to bind.” (page 6:3, col. 1, para. 1) and “Typical descriptors employed in ligand-based CADD encode a variety of chemical information, including molecular weight, geometry, volume, surface areas, ring content, 3-D geometrical information, atom types, electronegativities, polarizabilities, molecular symmetry, atom distribution, topological charge indices, functional group composition, aromaticity indices, and solvation properties, among others.” (page 6:4, col. 1, para. 1); Figure 1 (page 6:3) and Table 1 (page 6:11) titled Examples of techniques for using quantum computers for machine learning tasks.
Regarding claim 15, Cao teaches wherein the system is further configured to provide, based on the learned association, a protein sequence, a position, an amino acid substitution, an amino acid deletion, an amino acid insertion, or a rotamer library for consideration for the search space with “In cases where suitable templates are not available for the target of interest, free modeling approaches are employed. This term groups knowledge-based approaches for structure prediction and physically motivated methods. Knowledge-based approaches usually assemble a protein structure by using the geometries of small protein fragments extracted from known 3-D structures. Purely, first principles prediction has been mainly limited to the use of MD simulations with appropriate solvent models to optimize the structure of the protein, an approach that is much more computationally demanding than comparative modeling or knowledge-based free methods. The advantage of physical-based methods is that they reveal the pathway of protein folding for the unknown structure, but their main bottlenecks are their need for extensive conformational sampling and accurate force-field potentials. In this area, quantum simulation and quantum optimization approaches could become powerful tools by addressing problems such as protein folding, as described in Section 4. (page 6:5, col. 1, para. 2). The recited “rotamer library” corresponds to “conformational sampling” as taught by Cao.
Regarding claim 16, Cao teaches wherein the quantum computing algorithm is a quantum annealing algorithm configured to search the search space for the optimized protein based on a target Hamiltonian determined from the scoring function with “In structure-based drug discovery [see Figure 1(b)], an important part of the input concerns the structure of the target protein. Some progress has been made in the past decade on quantum techniques for protein folding based on the amino acid sequence. In particular, the quantum computing community has considered two simple models: the hydrophobic-polar model and the Miyazawa–Jernigan model, both of which model the protein as a self-avoided walk on a lattice. Solutions in both quantum annealers and gate-model quantum devices4 have been explored.” (page 6:13, col. 1, para. 3). “The unique features of quantum simulation have deep consequences for quantum chemistry. When doing quantum chemical calculations on classical computers, one would almost strictly avoid maintaining the explicit wave function of the physical system or propagating the full wave function unitarily under some quantum Hamiltonian due to the prohibitive costs of either method, whereas on a quantum computer, state preparation and time evolution can often be done efficiently. This distinction makes for rather different design patterns in quantum algorithms versus their classical counterparts. Building on previous results for quantum simulation, it was shown that using the ability to generate quantum states with sufficiently large overlap with the ground state and the ability to efficiently time evolve a state under a molecular Hamiltonian, one could obtain the ground state energy of the molecular Hamiltonian with accuracy comparable to full configuration interaction (FCI) (corresponding to exact diagonalization). While FCI suffers from exponential growth in computational cost, the cost of the quantum algorithm in only scales polynomially with respect to the system size— an exponential improvement over classical algorithms. (6:10, col. 1, para. 1) and “As a heuristic quantum algorithm for finding the ground state energy of a Hamiltonian, VQE operates by tuning the parameters of the quantum circuit to minimize the energy expectation of the output state with respect to the Hamiltonian.” (page 6:10, col. 2, para. 1)
Regarding claim 17, Cao teaches wherein the quantum computing algorithm is one of a quantum- inspired algorithm, digital annealing algorithm, quantum annealing algorithm, gate-based quantum algorithm, quantum simulation algorithm, or a quantum-inspired optimization with “In this scenario, the development of quantum algorithms that can tackle quantum simulation efficiently and accurately can contribute to extending the applicability of FES strategies to drug discovery, as quantum computers achieve size and precision that enables the simulation of large molecules.” (page 6:8, col. 2, para. 1) and “In this section, we focus on two large (and rapidly expanding) categories of quantum algorithms that are relevant to drug discovery, namely quantum algorithms for simulating molecular electronic structure in computational chemistry, and quantum-enhanced machine learning.” (page 6:9, col. 1, para. 3).
Regarding claim 18, Cao teaches wherein the protein sequence for optimization includes an amino acid or a DNA sequence with “In the case of targets without known experimental tertiary structures, computational approaches such as threading, comparative modeling, and ab initio methods can be used to predict 3-D structures from the knowledge of the protein amino acid sequence.” (page 6:4, col. 2, para. 4).
Regarding claim 19, Cao teaches wherein the output of the quantum computing algorithm is indicative of a plurality of optimized proteins, and wherein the system is further configured to: rank, by the processor, each optimized protein of the plurality of optimized proteins; determine, by the processor, a subset of the ranked plurality of optimized proteins; and further perform an optimization of a protein of the subset of the ranked plurality of optimized proteins with “These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes.” (page 6:3, col. 2, para. 1); “Different score functions are employed to rank the activity of the candidates depending on whether a structured-based or ligand-based approach is used.” (page 6:5, col. 2, para. 1)
Regarding independent claim 20, Cao teaches A non-transitory computer-readable medium storing instructions with “In this paradigm, quantum computers are used as coprocessors in tandem with classical computers for accomplishing simulation tasks (see Figure 2). One of the main appeals of this paradigm is that it combines the advantages of both quantum and classical computers. The quantum computer handles only the state preparation and measurement, whereas the classical computer is tasked with optimizing the parameters with which the quantum computer uses for the state preparation.” (page 6:9, col. 1, para. 2)
Cao teaches determine a protein having a protein sequence for optimization with “…building the 3-D structures of a protein by comparing its sequence with those of proteins whose 3-D structures have been experimentally characterized (i.e., 40% similarity). A typical prediction with comparative modeling implies the identification of related proteins that can serve as templates from a database of known structures, usually the Protein Data Bank. The sequences of unknown proteins and the templates are aligned and compared, and the geometries of the regions with good alignment are copied.” (page 6:5, col. 1, para. 1); “Predicting the protein structure from the knowledge of the amino acid sequence requires simulating the protein folding process, which is so far out of reach except for small peptides and fast folders ; however, in the absence of experimental structures, it is still possible to approximate the 3-D structure of an unknown target protein by comparing its sequence with related known proteins, a process known as comparative modeling” (page 6:3, col. 1, para. 1); “In the case of targets without known experimental tertiary structures, computational approaches such as threading, comparative modeling, and ab initio methods can be used to predict 3-D structures from the knowledge of the protein amino acid sequence.” (page 6:4, col. 2, para. 4) and Figure 1 (page 6:3).
Cao teaches receive a protein property for optimization with “Typical descriptors employed in ligand-based CADD encode a variety of chemical information, including molecular weight, geometry, volume, surface areas, ring content, 3-D geometrical information, atom types, electronegativities, polarizabilities, molecular symmetry, atom distribution, topological charge indices, functional group composition, aromaticity indices, and solvation properties, among others. Both QSAR models and structure comparison require experimental information for the set of molecules employed as either reference for the comparison or training set for QSAR. Consequently, the models employed in ligand-based CADD are often restricted to libraries of candidates that share sufficient similarities with the set of molecules employed as reference” (page 6:4, col. 1, para. 1); “The purpose of this second phase of screening is to optimize the druglike properties of the hit compounds, which includes not only the biological activity, but also the ADMET profile and other pharmacokinetic properties. The general assumption behind this process is that small changes on the chemical structure will produce incremental changes of the druglike properties; therefore, the optimization involves the synthesis of the drug-candidates along with testing of their biological activities accompanied by CADD.” (page 6:7, col. 2, para. 3) and “The usual drug discovery pipeline requires the identification and characterization of a suitable biological target, which can be effectively proved to intervene in the mechanism of disease. This step often requires intense experimentation as well as extensive statistical analysis of the collected data. Once a biological target is in place, the next step is the search for hits, which usually involves extensive biological and virtual screening over libraries of molecules, or the generation of completely new compounds (de novo design), which must be synthesized and tested. The group of hits collected on this stage undergoes further optimization of the pharmacokinetics and ADMET properties, involving a combination of biological and in silico tests, to generate the final group of leads. These stages, going from target identification to lead optimization, benefit the most from CADD techniques. The subsequent steps in the drug discovery pipeline, which involve clinical studies in animals and humans prior to the Federal Drug.” (page 6:2, col. 2, para. 3).
Cao teaches define a scoring function based on the protein property for optimization with “…most CADD approaches require the following: first, determining the pose or conformation of the ligand that fits best the binding site of the target, and second, assigning a numerical score that expresses the strength of the interaction of the ligand-target complex. The process of finding the best conformation is generally called docking and the process of computing the affinity is referred as scoring. These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes. Extensive sampling of conformations is often required in structured-based CADD approaches to account for the mobility of protein and ligands in biological conditions (aqueous solutions at room temperature).” (page 6:3, col. 2, para. 1)
Cao teaches determine at least one of: a position in the protein sequence to be subjected to modification, a replacement amino acid to be substituted for, an amino acid occurring at the position, an amino acid deletion at the position, an amino acid insertion at the position, or a rotamer library to be searched for a target rotamer to be applied to the protein sequence with “In cases where suitable templates are not available for the target of interest, free modeling approaches are employed. This term groups knowledge-based approaches for structure prediction and physically motivated methods. Knowledge-based approaches usually assemble a protein structure by using the geometries of small protein fragments extracted from known 3-D structures. Purely, first principles prediction has been mainly limited to the use of MD simulations with appropriate solvent models to optimize the structure of the protein, an approach that is much more computationally demanding than comparative modeling or knowledge-based free methods. The advantage of physical-based methods is that they reveal the pathway of protein folding for the unknown structure, but their main bottlenecks are their need for extensive conformational sampling and accurate force-field potentials. In this area, quantum simulation and quantum optimization approaches could become powerful tools by addressing problems such as protein folding, as described in Section 4. (page 6:5, col. 1, para. 2).
Cao teaches determine a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or the target rotamer from the rotamer library to be applied to the protein sequence with “In cases where suitable templates are not available for the target of interest, free modeling approaches are employed. This term groups knowledge-based approaches for structure prediction and physically motivated methods. Knowledge-based approaches usually assemble a protein structure by using the geometries of small protein fragments extracted from known 3-D structures. Purely, first principles prediction has been mainly limited to the use of MD simulations with appropriate solvent models to optimize the structure of the protein, an approach that is much more computationally demanding than comparative modeling or knowledge-based free methods. The advantage of physical-based methods is that they reveal the pathway of protein folding for the unknown structure, but their main bottlenecks are their need for extensive conformational sampling and accurate force-field potentials. In this area, quantum simulation and quantum optimization approaches could become powerful tools by addressing problems such as protein folding, as described in Section 4. (page 6:5, col. 1, para. 2).
Cao teaches search the search space using a quantum computing algorithm based on the scoring function with “The purpose of this perspective is to initiate a mutually beneficial cross-disciplinary discussion and collaboration between the fields of CADD and quantum computing. For the quantum computing community, such dialogue will help to outline the practically useful regimes where quantum computers may have an advantage over classical counterparts.” (page 6:2, col. 2, para. 2); “The process of hit search generally involves HTS of a database of candidate compounds. Traditionally, this process has required the synthesis and experimental determination of the activity of the compounds, which is extremely expensive and slow. Nowadays, the process is accelerated using virtual HTS (vHTS). Different score functions are employed to rank the activity of the candidates depending on whether a structured-based or ligand-based approach is used. Some ligand-based approaches score the candidates based on their similarity with a set of known active compounds. Another option is QSAR, which constructs a statistical model based on experimental information of the activities and chemical information of the ligands. In both approaches, the chemical information is expressed with molecular descriptors that encode physicochemical and structural information of the molecules in a digital format, suitable for comparison. Molecular descriptors can be generated by knowledge-based, graph-theoretical, molecular mechanical, or quantum-mechanical methods. Arguably, the most popular descriptors are molecular fingerprints, which encode various molecular properties as predefined bit settings. Other descriptors are computed solely from the 2-D or 3-D topology of the molecule based on graph-theoretical methods.” (page 6:5, col. 2, para. 1); and “…most CADD approaches require the following: first, determining the pose or conformation of the ligand that fits best the binding site of the target, and second, assigning a numerical score that expresses the strength of the interaction of the ligand-target complex. The process of finding the best conformation is generally called docking and the process of computing the affinity is referred as scoring. These procedures are generally intertwined since docking requires a score function that ranks different conformations according to their ability to form bound ligand-protein complexes. Extensive sampling of conformations is often required in structured-based CADD approaches to account for the mobility of protein and ligands in biological conditions (aqueous solutions at room temperature)” (page 6:3, col. 2, para. 1)
Cao teaches searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence with “By identifying populations that are more susceptible to exposition to an active compound and looking at their mutations, it is possible to infer which proteins are associated to the activity. Another technique called gene profiling combines gene expression analysis (e.g., message RNA profiles) with chemical studies to identify targets. This approach is based on the assumption that deleting the genes that codify the target protein should produce the same inhibitory effect of the active compounds. Consequently, the target can be identified by comparing the expression profiles (information of which proteins are synthesized or expressed) of the population of mutants with the profiles of populations exposed to the active compound. A similar idea can be applied to identify targets by examining message RNA/protein levels to determine whether they correlate with the manifestation of the disease.” (page 6:4, col. 2, para. 2)
Cao teaches provide an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization with “Once hit compounds have been identified, they enter an optimization phase to produce a smaller set of better candidates, called leads. The set of leads undergoes further optimization in a process that iterates between CADD development and in vitro and animal experiments. The purpose of this second phase of screening is to optimize the druglike properties of the hit compounds, which includes not only the biological activity, but also the ADMET profile and other pharmacokinetic properties. The general assumption behind this process is that small changes on the chemical structure will produce incremental changes of the druglike properties; therefore, the optimization involves the synthesis of the drug-candidates along with testing of their biological activities accompanied by CADD. In this stage, QSAR models for smaller datasets play a major role in the optimization, allowing for quickly judging whether certain modifications improve drug-likeness or not, especially when no target information is available.” (page 6:7, col. 1, para. 3 to page 6:7, col. 2, para. 2).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 and 18 of Application No. 17772976, US Patent #11657894 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims recite a method for protein optimization using quantum computing algorithm. Overall, the difference is that the claims of the instant application are broader in scope than the claims of the reference application and thus the instant claims are anticipated by the reference application (see MPEP 804.II.B.2). See table below for a mapping of the claims of the reference application that anticipate the claims of the instant application.
Application No. 18141199, 4/28/2023 (Instant Application)
Application No. 17772976, US Patent #11657894, 8/11/2022 (reference application)
1. A computer-implemented method comprising: providing, to a processor, a protein sequence for optimization; providing, to the processor, a protein structure having the protein sequence; providing, to the processor, a protein property for optimization; defining a scoring function based on the protein property for optimization; providing, to the processor, at least one of: a position in the protein sequence to be subjected to modification, or an amino acid to be substituted for the amino acid occurring at the position, inserted at the position, or deleted from the position, or a rotamer library to be searched for a target rotamer to be applied to one or more positions in the protein sequence; determining, by the processor, a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or rotamer from the rotamer library; searching the search space using a quantum computing algorithm based on the scoring function, the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence; and providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization.
1. (Currently Amended) A computer-implemented method comprising: determining, by a processor, a protein sequence for optimization based on a protein property for optimization via an artificial intelligence or machine learning algorithm; providing, to [[a]] the processor, [[a]] the protein sequence for optimization; providing, to the processor, a protein structure having the protein sequence for optimization; providing, to the processor, [[a]] the protein property for optimization; defining a scoring function based on the protein property for optimization; providing, to the processor, at least one of: a position in the protein sequence to be subjected to modification, or an amino acid to be substituted for the amino acid occurring at the position, inserted at the position, or deleted from the position, or a rotamer library to be searched for a target rotamer to be applied to one or more positions in the protein sequence; determining, by the processor, a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or rotamer from the rotamer library; searching the search space using a quantum computing algorithm based on the scoring function, the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence; [[and]] providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization. experimentally testing the optimized protein to generate experimental data, the experimental data measuring one or more properties of the optimized protein, the one or more properties including at least one of: the stability of the protein in terms of thermostability, pH stability, solvent stability, stability to other excipients, and/or stability in application; expressibility; solubility; efficacy; charge distribution; protein folding; activity; specificity in terms of bond, group, substrate, stereospecificity, and/or co- factor; reversibility; enzyme kinetics; substrate inhibition; product inhibition; resistance to protease degradation; gain-of-new function; the affinity of the protein to the binding agent; or the specificity of the protein binding to similar binding partners, and providing the experimental data to the artificial intelligence or machine learning algorithm to train the artificial intelligence or machine learning algorithm to recognize a relationship between a sequence or structure of the optimized protein and the one or more properties measured by the experimental data.
15. (Currently Amended) The method of claim 1, further comprising providing, to the processor, a plurality of positions in the protein sequence to be subjected to modification, or a plurality of amino acids to be substituted for the amino acids occurring at the positions, inserted at the positions, or deleted from the positions.
2. The method of claim 1, further comprising: providing, to the processor, a predefined binding partner of interest, and wherein providing the protein having the protein sequence for optimization comprises applying a computer-based model based on a structure or amino acid sequence of the binding partner of interest, or optimizing a binding partner for the selected protein during the searching.
2. (Original) The method of claim 1, further comprising: providing, to the processor, a predefined binding partner of interest, and wherein providing the protein having the protein sequence for optimization comprises applying a computer-based model based on a structure or amino acid sequence of the binding partner of interest, or optimizing a binding partner for the selected protein during the searching.
3. The method of claim 1, further comprising: testing the optimized protein for the protein property for optimization; generating experimental data from the testing; and applying an artificial intelligence method to the experimental data to learn an association between a configuration of the optimized protein sequence and the protein property for optimization, the configuration comprising a two- or three- dimensional protein structure, an amino acid sequence, a DNA sequence that encodes the protein, or parts of a two- or three- dimensional protein structure, in particular a catalytic domain, or one or more domains of a protein.
3. (Currently Amended) The method of claim 1, further comprising: testing the optimized protein for the protein property for optimization; generating experimental data from the testing; and applying an artificial intelligence method to the experimental data to learn an association between a configuration of the optimized protein sequence and the protein property for optimization, the configuration comprising a two- or three- dimensional protein structure, an amino acid sequence, a DNA sequence that encodes the protein, or parts of a two- or three- dimensional protein structure, in particular a catalytic domain, or one or more domains of a protein.
4. The method of claim 3, further comprising providing, based on the learned association, a protein sequence, a position, an amino acid substitution, an amino acid deletion, an amino acid insertion, or a rotamer library for consideration for the search space.
5. (Original) The method of claim 3, further comprising providing, based on the learned association, a protein sequence, a position, an amino acid substitution, an amino acid deletion, an amino acid insertion, or a rotamer library for consideration for the search space.
6. (Currently Amended) The method of claim 1, further comprising excluding a position, an amino acid substitution, an amino acid deletion, an amino acid insertion, or a rotamer library from the search space based on available quantum computing hardware.
12. (Currently Amended) The method of claim 1, wherein providing the protein comprises identifying a starting protein based on the protein property for optimization via computer-based modeling of the starting protein.
15. (Currently Amended) The method of claim 1, further comprising providing, to the processor, a plurality of positions in the protein sequence to be subjected to modification, or a plurality of amino acids to be substituted for the amino acids occurring at the positions, inserted at the positions, or deleted from the positions.
5. The method of claim 1, wherein the quantum computing algorithm is a quantum annealing algorithm configured to search the search space for the optimized protein based on a target Hamiltonian determined from the scoring function.
7. (Currently Amended) The method of claim 1, wherein the quantum computing algorithm is a quantum annealing algorithm configured to search the search space for the optimized protein based on a target Hamiltonian determined from the scoring function.
6. The method of claim 1, wherein the quantum computing algorithm is one of a quantum- inspired algorithm, digital annealing algorithm, quantum annealing algorithm, gate-based quantum algorithm, quantum simulation algorithm, or a quantum-inspired optimization.
8. (Currently Amended) The method of
9. (Currently Amended) The method of claim 1, wherein the quantum computing algorithm comprises one of a Quantum Approximate Optimization Algorithm, Grover Adaptive Search, adiabatic quantum computing, a quantum least squares fitting, quantum semidefinite programming, a quantum combinatorial optimization, a quantum-inspired stochastic regressions, a quantum- inspired evolutionary algorithm, quantum Monte Carlo quantum annealing, a simulated quantum annealing, quantum simulated annealing, or a quantum simulation with a variational quantum eigensolver.
7. The method of claim 1, further comprising determining the protein sequence for optimization based on the protein property for optimization via an artificial intelligence or machine learning algorithm.
4. (Original) The method of claim 3, wherein the artificial intelligence method comprises a machine learning method.
8. The method of claim 1, wherein the protein sequence for optimization includes an amino acid or a DNA sequence.
13. (Currently Amended) The method of claim 1, wherein the protein sequence for optimization includes an amino acid or a DNA sequence.
9. The method of claim 1, further comprising providing, to the processor, multiple target rotamers to be applied to a plurality of positions in the protein sequence.
14. (Currently Amended) The method of claim 1, further comprising providing, to the processor, multiple target rotamers to be applied to a plurality of positions in the protein sequence.
10. The method of claim 1, further comprising providing, to the processor, a plurality of positions in the protein sequence to be subjected to modification, or a plurality of amino acids to be substituted for the amino acids occurring at the positions, inserted at the positions, or deleted from the positions. 11. The method of claim 1, wherein the output state of the quantum computing algorithm is indicative of a plurality of optimized proteins, and further comprising: ranking, by the processor, each optimized protein of the plurality of optimized proteins; determining, by the processor, a subset of the ranked plurality of optimized proteins; and further performing an optimization of a protein of the subset of the ranked plurality of optimized proteins.
15. (Currently Amended) The method of claim 1, further comprising providing, to the processor, a plurality of positions in the protein sequence to be subjected to modification, or a plurality of amino acids to be substituted for the amino acids occurring at the positions, inserted at the positions, or deleted from the positions.
16. (Currently Amended) The method of claim 1, wherein the output state of the quantum computing algorithm is indicative of a plurality of optimized proteins, and further comprising: ranking, by the processor, each optimized protein of the plurality of optimized proteins; determining, by the processor, a subset of the ranked plurality of optimized proteins; and further performing an optimization of a protein of the subset of the ranked plurality of optimized proteins.
12. A system comprising: a classical computing system comprising a non-transitory computer-readable medium and a processor configured to: determine a protein having a protein sequence for optimization; receive a protein property for optimization; define a scoring function based on the protein property for optimization; determine at least one of: a position in the protein sequence to be subjected to modification, a replacement amino acid to be substituted for, an amino acid occurring at the position, an amino acid deletion at the position, an amino acid insertion at the position, or a rotamer library to be searched for a target rotamer to be applied to the protein sequence; and determine a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or the target rotamer from the rotamer library to be applied to the protein sequence; and a quantum computing system comprising a plurality of qubits, a qubit control device, and a measurement unit configured to search the search space using a quantum computing algorithm based on the scoring function, and to provide an output of the quantum computing algorithm, the output indicative of an optimized protein, the optimized protein being optimized according to the scoring function based on the protein property for optimization.
1. (Currently Amended) A computer-implemented method comprising: determining, by a processor, a protein sequence for optimization based on a protein property for optimization via an artificial intelligence or machine learning algorithm; providing, to [[a]] the processor, [[a]] the protein sequence for optimization; providing, to the processor, a protein structure having the protein sequence for optimization; providing, to the processor, [[a]] the protein property for optimization; defining a scoring function based on the protein property for optimization; providing, to the processor, at least one of: a position in the protein sequence to be subjected to modification, or an amino acid to be substituted for the amino acid occurring at the position, inserted at the position, or deleted from the position, or a rotamer library to be searched for a target rotamer to be applied to one or more positions in the protein sequence; determining, by the processor, a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or rotamer from the rotamer library; searching the search space using a quantum computing algorithm based on the scoring function, the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence; [[and]] providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization. experimentally testing the optimized protein to generate experimental data, the experimental data measuring one or more properties of the optimized protein, the one or more properties including at least one of: the stability of the protein in terms of thermostability, pH stability, solvent stability, stability to other excipients, and/or stability in…
17. (Original) The method of claim 16, wherein further performing an optimization of the protein comprises performing an optimization according to a quantum computing paradigm.
18. (Original) The method of claim 16, wherein further performing an optimization of the protein comprises performing an optimization by a classical computing system.
13. The system of claim 12, wherein the system is further configured to: receive a predefined binding partner of interest, and wherein to determine a protein having a protein sequence for optimization the classical computing system is further configured to apply a computer-based model based on a structure or amino acid sequence of the binding partner of interest, or optimize a binding partner for the selected protein during the searching.
2. (Original) The method of claim 1, further comprising: providing, to the processor, a predefined binding partner of interest, and wherein providing the protein having the protein sequence for optimization comprises applying a computer-based model based on a structure or amino acid sequence of the binding partner of interest, or optimizing a binding partner for the selected protein during the searching.
14. The system of claim 12, wherein the system is further configured to: receive experiment data from testing the optimized protein for the protein property for optimization; and apply an artificial intelligence method to the experimental data to learn an association between a configuration of the optimized protein sequence and the protein property for optimization, the configuration comprising a two- or three- dimensional protein structure, an amino acid sequence, a DNA sequence that encodes the protein, or parts of a two- or three- dimensional protein structure, in particular a catalytic domain, or one or more domains of a protein.
1. (Currently Amended) A computer-implemented method comprising: determining, by a processor, a protein sequence for optimization based on a protein property for optimization via an artificial intelligence or machine learning algorithm; providing, to [[a]] the processor, [[a]] the protein sequence for optimization; providing, to the processor, a protein structure having the protein sequence for optimization; providing, to the processor, [[a]] the protein property for optimization; defining a scoring function based on the protein property for optimization; providing, to the processor, at least one of: a position in the protein sequence to be subjected to modification, or an amino acid to be substituted for the amino acid occurring at the position, inserted at the position, or deleted from the position, or a rotamer library to be searched for a target rotamer to be applied to one or more positions in the protein sequence; determining, by the processor, a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or rotamer from the rotamer library; searching the search space using a quantum computing algorithm based on the scoring function, the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence; [[and]] providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization. experimentally testing the optimized protein to generate experimental data, the experimental data measuring one or more properties of the optimized protein, the one or more properties including at least one of: the stability of the protein in terms of thermostability, pH stability, solvent stability, stability to other excipients, and/or stability in
15. The system of claim 14, wherein the system is further configured to provide, based on the learned association, a protein sequence, a position, an amino acid substitution, an amino acid deletion, an amino acid insertion, or a rotamer library for consideration for the search space.
14. (Currently Amended) The method of claim 1, further comprising providing, to the processor, multiple target rotamers to be applied to a plurality of positions in the protein sequence.
15. (Currently Amended) The method of claim 1, further comprising providing, to the processor, a plurality of positions in the protein sequence to be subjected to modification, or a plurality of amino acids to be substituted for the amino acids occurring at the positions, inserted at the positions, or deleted from the positions.
16. The system of claim 12, wherein the quantum computing algorithm is a quantum annealing algorithm configured to search the search space for the optimized protein based on a target Hamiltonian determined from the scoring function.
7. (Currently Amended) The method of claim 1, wherein the quantum computing algorithm is a quantum annealing algorithm configured to search the search space for the optimized protein based on a target Hamiltonian determined from the scoring function.
17. The system of claim 12, wherein the quantum computing algorithm is one of a quantum- inspired algorithm, digital annealing algorithm, quantum annealing algorithm, gate-based quantum algorithm, quantum simulation algorithm, or a quantum-inspired optimization.
16. (Currently Amended) The method of claim 1, wherein the output state of the quantum computing algorithm is indicative of a plurality of optimized proteins, and further comprising: ranking, by the processor, each optimized protein of the plurality of optimized proteins; determining, by the processor, a subset of the ranked plurality of optimized proteins; and further performing an optimization of a protein of the subset of the ranked plurality of optimized proteins.
8. The system of claim 12, wherein the protein sequence for optimization includes an amino acid or a DNA sequence.
13. (Currently Amended) The method of claim 1, wherein the protein sequence for optimization includes an amino acid or a DNA sequence.
19. The system of claim 12, wherein the output of the quantum computing algorithm is indicative of a plurality of optimized proteins, and wherein the system is further configured to: rank, by the processor, each optimized protein of the plurality of optimized proteins; determine, by the processor, a subset of the ranked plurality of optimized proteins; and further perform an optimization of a protein of the subset of the ranked plurality of optimized proteins.
20. A non-transitory computer-readable medium storing instructions, that when executed, cause a system to: determine a protein having a protein sequence for optimization; receive a protein property for optimization; define a scoring function based on the protein property for optimization; determine at least one of: a position in the protein sequence to be subjected to modification, a replacement amino acid to be substituted for, an amino acid occurring at the position, an amino acid deletion at the position, an amino acid insertion at the position, or a rotamer library to be searched for a target rotamer to be applied to the protein sequence; and determine a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or the target rotamer from the rotamer library to be applied to the protein sequence; search the search space using a quantum computing algorithm based on the scoring function, the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence; and provide an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization.
16. (Currently Amended) The method of claim 1, wherein the output state of the quantum computing algorithm is indicative of a plurality of optimized proteins, and further comprising: ranking, by the processor, each optimized protein of the plurality of optimized proteins; determining, by the processor, a subset of the ranked plurality of optimized proteins; and further performing an optimization of a protein of the subset of the ranked plurality of optimized proteins.
1. (Currently Amended) A computer-implemented method comprising: determining, by a processor, a protein sequence for optimization based on a protein property for optimization via an artificial intelligence or machine learning algorithm; providing, to [[a]] the processor, [[a]] the protein sequence for optimization; providing, to the processor, a protein structure having the protein sequence for optimization; providing, to the processor, [[a]] the protein property for optimization; defining a scoring function based on the protein property for optimization; providing, to the processor, at least one of: a position in the protein sequence to be subjected to modification, or an amino acid to be substituted for the amino acid occurring at the position, inserted at the position, or deleted from the position, or a rotamer library to be searched for a target rotamer to be applied to one or more positions in the protein sequence; determining, by the processor, a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or rotamer from the rotamer library; searching the search space using a quantum computing algorithm based on the scoring function, the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence; [[and]] providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization. experimentally testing the optimized protein to generate experimental data, the experimental data measuring one or more properties of the optimized protein, the one or more properties including at least one of: the stability of the protein in terms of thermostability, pH stability, solvent stability, stability to other excipients, and/or stability in…
Conclusion
No claims are allowed.
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/K.K./Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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