CTNF 17/898,755 CTNF 97328 DETAILED ACTION The Applicant’s filing, received 30 August 2022, has been fully considered. The following rejections and/or objections constitute the complete set presently being applied to the instant application. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Status of the Claims Claims 1-14 are pending. Claims 1-14 are rejected. Claims 4, 5 and 12 are objected to. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. There are no domestic applications for which benefit is claimed. Claims 1-14 are given benefit of foreign applications: REPUBLIC OF KOREA 10-2022-0062120, filed 20 May 2022; and REPUBLIC OF KOREA 10-2021-0123488, filed 15 September 2021. Therefore, the effective filing date of the claimed invention is 15 September 2021. Drawings The drawings were received 30 August 2022. These drawings are objected to for the reasons noted below. The drawings are objected to under 37 C.F.R. 1.84(t) because the sheets of drawings are not numbered. 37 C.F.R. 1.84(t) reads as follows: The sheets of drawings should be numbered in consecutive Arabic numerals, starting with 1, within the sight as defined in paragraph (g) of this section. These numbers, if present, must be placed in the middle of the top of the sheet, but not in the margin. The numbers can be placed on the right-hand side if the drawing extends too close to the middle of the top edge of the usable surface. The drawing sheet numbering must be clear and larger than the numbers used as reference characters to avoid confusion. The number of each sheet should be shown by two Arabic numerals placed on either side of an oblique line, with the first being the sheet number and the second being the total number of sheets of drawings, with no other marking. The drawings are objected to under 37 C.F.R. 1.84(u) because the views are not numbered correctly. 37 C.F.R. 1.84(u) reads as follows: (1) The different views must be numbered in consecutive Arabic numerals, starting with 1, independent of the numbering of the sheets and, if possible, in the order in which they appear on the drawing sheet(s). Partial views intended to form one complete view, on one or several sheets, must be identified by the same number followed by a capital letter . View numbers must be preceded by the abbreviation "FIG." Where only a single view is used in an application to illustrate the claimed invention, it must not be numbered and the abbreviation "FIG." must not appear. (2) Numbers and letters identifying the views must be simple and clear and must not be used in association with brackets , circles, or inverted commas. The view numbers must be larger than the numbers used for reference characters. 06-22 Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Information Disclosure Statement An information disclosure statement has not been filed, and therefore the foreign reference documents received 30 August 2022 (five documents) have been placed in the application file, but the reference documents have not been considered as to the merits. Applicant is advised that the date of any submission or re-submission of any item of information or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a). 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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. 07-30-05 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 limitations are: a simulation device, in claims 1, 7, 8, and 14; a drug response screening device, in claims 1, 8, and 14; a cell image capturing device, in claims 1 and 8; and a computing device, in claims 2, 4, 5, 7, 9, 11, 12, and 14. Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The written description discloses a corresponding structure for the non-structural generic placeholder: a simulation device , in claims 1, 7, 8, and 14, at paragraphs [00205] & [00208] in the Specification (e.g., computing device 810 ); a drug response screening device , in claims 1, 8, and 14, at paragraph [00206] in the Specification (i.e., a computing device, a drug bank, a drug combination device, a micropipette, a well-matrix dish, and a cell image capturing device); and a computing device , in claims 2, 4, 5, 7, 9, 11, 12, and 14, at paragraphs [00174], [00190] & [00207] in the Specification (i.e., a computing device (e.g., 810, 710 , 610 , respectively) may include an input/output (I/O interface unit, a memory, and a central processing unit (CPU)). The written description does not disclose a corresponding structure for the non-structural generic placeholder: a cell image capturing device , in claims 1 and 8. If applicant does not intend to have 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 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 them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Objections Claim 4 is objected to because of the following informalities: The claim recites “a vector z” (i.e., lower case “z”) in line four of the claim, but also recites “the vector Z” (i.e., an upper case “Z”) in line nine of the claim. 07-29-01 AIA Claim s 5 and 12 are objected to because of the following informalities: The claims recite the symbol “p g ” as a letter followed by a subscript letter, however the specification only shows the symbol as “pg” (i.e., without a subscript letter) . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-01 AIA The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. 07-31-01 Claims 1-6 and 8-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1 and 8 recite the limitation “a cell image capturing device” however the disclosure does not clearly link any structure for the “cell image capturing device” as required by MPEP 2181. For example, the disclosure describes a function of the real-time image analysis command process instructing the cell image capturing device to capture images of the first cancer cell line in the M wells and return the resulting image to the real-time cell image analysis command process (e.g., at para. [00221]), but does not link any structure to the functions. Claims 2-6 and 9-13 are rejected for depending from either of claims 1 or 8 and failing to remedy the failure of either of claims 1 or 8 to comply with the written description requirement. 07-30-02 AIA 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. 07-34-01 Claims 1-14 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 2 and 9 are indefinite for reciting the terms “=episode” and “=p k ” because they are surrounded by parentheses, and thus the metes and bounds of the limitations are unclear as to whether the limitations within the parentheses are intended to have a meaning outside of the meaning that one skilled in the art would attribute to these limitations, and further indefinite because the metes and bounds of the limitations are unclear as to the intended meaning of the limitations within the parentheses with respect to the preceding limitations, i.e., “a process” and “mutation information for N,” respectively. Claims 3-6 and 10-13 are indefinite for depending from either of claims 2 or 9 and for failing to remedy the indefiniteness of either of claims 2 or 9. Claims 5 and 12 are indefinite for reciting the term “=episode” because the term is surrounded by parentheses, and thus the metes and bounds of the limitation is unclear as to whether the limitation within the parentheses is intended to have a meaning outside of the meaning that one skilled in the art would attribute to the limitation, and further indefinite because the metes and bounds of the limitation is unclear as to the intended meaning of the limitation within the parentheses with respect to the preceding limitation, i.e., “process.” Claims 6 and 13 are indefinite for depending from either of claims 5 or 12 and for failing to remedy the indefiniteness of either of claims 5 or 12. Claim 7 is indefinite for reciting the terms “=episode” and “=p k ” because they are surrounded by parentheses, and thus the metes and bounds of the limitations are unclear as to whether the limitations within the parentheses are intended to have a meaning outside of the meaning that one skilled in the art would attribute to these limitations, and further indefinite because the metes and bounds of the limitations are unclear as to the intended meaning of the limitations within the parentheses with respect to the preceding limitations, i.e., “a process” and “mutation information for N,” respectively. Claim 1 is indefinite for reciting “capturing, by the drug response screening device, images of the first cancer cell line in the plurality of wells using a cell image capturing device” because it is not clear as to whether the images are captured by the drug response screening device or by the cell image capturing device. Claims 2-6 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1. Claim 1 is further indefinite for reciting “using a cell image capturing device to analyze the captured images” because it is not clear as to whether the cell image capturing device only performs the function of capturing images, and the analysis of the captured images is separately performed by a computing processor configured to execute software for performing the analysis, or if the cell image capturing device performs both the function of capturing images and also the function of analyzing the captured images. Claims 2-6 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1. Claim 1 recites the limitation "the plurality of determined candidate drugs" in line nine of the claim. There is insufficient antecedent basis for this limitation in the claim, because the claim only recites “a plurality of candidate drugs” in line six of the claim. Claims 2-6 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1. Claim 1 recites the limitation "the analysis result" in line eighteen of the claim. There is insufficient antecedent basis for this limitation in the claim, because the claim only recites “a result of an in vitro test” in line seventeen of the claim, however it is not clear as to whether this result is the same result generated from using the cell image capturing device to analyze the captured images. Claims 2-6 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1. 07-34-05 AIA Claim 2 recites the limitation " the links " in line sixteen of the claim . There is insufficient antecedent basis for this limitation in the claim. Claims 3-6 are indefinite for depending from claim 2 and for failing to remedy the indefiniteness of claim 2. Claim 2 is further indefinite for reciting “observing…each reward provided to the agent at each learning step” in lines seventeen and eighteen, because the word “each” is a distributive word used to highlight individual items in a group, however it is not clear as to what comprises a group of rewards or what distinguishes each reward from another. Claims 3-6 are indefinite for depending from claim 2 and for failing to remedy the indefiniteness of claim 2. Claim 8 is indefinite for reciting “the drug response screening device is configured to: … capture images of the first cancer cell line in the plurality of wells using a cell image capturing device” because it is not clear as to whether the images are captured by the drug response screening device or by the cell image capturing device. Claims 9-13 are indefinite for depending from claim 8 and for failing to remedy the indefiniteness of claim 8. Claim 8 is further indefinite for reciting “using a cell image capturing device to analyze the captured images” because it is not clear as to whether the cell image capturing device only performs the function of capturing images, and the analysis of the captured images is separately performed by a computing processor configured to execute software for performing the analysis, or if the cell image capturing device performs both the function of capturing images and also the function of analyzing the captured images. Claims 9-13 are indefinite for depending from claim 8 and for failing to remedy the indefiniteness of claim 8. Claim 8 is further indefinite for reciting “using a cell image capturing device to analyze the captured images” because the claim recites a system as well as a method step of using the system (MPEP 2173.05(p) II.). Claims 9-13 are indefinite for depending from claim 8 and for failing to remedy the indefiniteness of claim 8. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts , (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes , i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion). 07-30-03-h AIA Claim Interpretations Claims 2, 7, 9, and 14 recite the limitation “an agent that has been trained by reinforcement learning is used.” This limitation is interpreted as a product-by-process limitation with the product being the trained agent, and further interpreted as not requiring a process of performing active steps to produce the product. Subject matter eligibility evaluation in accordance with MPEP 2106. Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter? Claims 1-6 recite a method for determining a cancer treatment candidate drug (i.e., a process); claim 7 recites a method for determining a cancer treatment candidate drug (i.e., a process); claims 8-13 recite a system comprising a computing device (i.e., a machine or manufacture) for determining a cancer treatment candidate drug; and claim 14 recites a system comprising a computing device (i.e., a machine or manufacture) for determining a cancer treatment candidate drug. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1. [Step 1: YES] Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception. Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: generating a plurality of specific perturbation networks by applying mutation information for a first cancer cell line to each of a plurality of drug responsive networks for a plurality of drugs (i.e., mental processes and mathematical concepts); selecting a plurality of candidate drugs from among the plurality of drugs based on a plurality of cell death probabilities for the first cancer cell line output by the plurality of specific perturbation networks (i.e., mental processes and mathematical concepts); and analyze the captured images (i.e., mental processes). Independent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: performing a process of determining weights of a k-th drug responsive network responding to a k-th drug among a plurality of drug responsive networks for a plurality of drugs (i.e., mental processes and mathematical concepts); generating a plurality of specific perturbation networks by applying mutation information for a first cancer cell line to each of the plurality of drug responsive networks (i.e., mental processes and mathematical concepts); and selecting a plurality of candidate drugs from among the plurality of drugs based on a plurality of cell death probabilities for the first cancer cell line output by the plurality of specific perturbation networks (i.e., mental processes and mathematical concepts), wherein in the performing of the process, an agent that has been trained by reinforcement learning is used (i.e., mental processes and mathematical concepts), and the performing of the process comprises: generating N specific perturbation networks by applying the N pieces of mutation information to the k-th drug responsive network responding to the k-th drug (i.e., mental processes and mathematical concepts); repeatedly performing a learning step a plurality of times by using the agent, the learning step being provided for updating the weights of the links of the k-th drug responsive network (i.e., mental processes and mathematical concepts); selecting the learning step when a reward provided to the agent has a largest value among the plurality of times of the learning step (i.e., mental processes and mathematical concepts); and deciding that the link weights output by the agent in the selected learning step are weights of the links of the k-th drug responsive network (i.e., mental processes and mathematical concepts). Independent claim 8 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: generate a plurality of specific perturbation networks by applying mutation information for a first cancer cell line to each of a plurality of drug responsive networks for a plurality of drugs (i.e., mental processes and mathematical concepts); select a plurality of candidate drugs from among the plurality of drugs based on a plurality of cell death probabilities for the first cancer cell line output by the plurality of specific perturbation networks (i.e., mental processes and mathematical concepts); and analyze the captured images (i.e., mental processes). Independent claim 14 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: perform a process of determining weights of a k-th drug responsive network responding to a k-th drug among a plurality of drug responsive networks (i.e., mental processes and mathematical concepts); generate a plurality of specific perturbation networks by applying mutation information for a first cancer cell line to each of a plurality of drug responsive networks for a plurality of drugs (i.e., mental processes and mathematical concepts); and select a plurality of candidate drugs from among the plurality of drugs based on a plurality of cell death probabilities for the first cancer cell line output by the plurality of specific perturbation networks (i.e., mental processes and mathematical concepts), in the performing of the process, an agent that has been trained by reinforcement learning is used (i.e., mental processes and mathematical concepts), and the performing of the process comprises: generating N specific perturbation networks by applying the N pieces of mutation information to the k-th drug responsive network responding to the k-th drug (i.e., mental processes and mathematical concepts); repeatedly performing a learning step a plurality of times by using the agent, the learning step being provided for updating the weights of the links of the k-th drug responsive network (i.e., mental processes and mathematical concepts); selecting the learning step when a reward provided to the agent has a largest value among the plurality of times of the learning step (i.e., mental processes and mathematical concepts); and deciding that the link weights output by the agent in the selected learning step are weights of the links of the k-th drug responsive network (i.e., mental processes and mathematical concepts). Dependent claims 2-6 and 9-13 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below. Dependent claim 2 further recites: prior to the generating, performing a process of determining weights of a k-th drug responsive network responding to a k-th drug among the plurality of drug responsive networks (i.e., mental processes and mathematical concepts), wherein in the performing of the process, an agent that has been trained by reinforcement learning is used (i.e., mental processes and mathematical concepts), and the performing of the process comprises: generating N specific perturbation networks by applying the N pieces of mutation information to the k-th drug responsive network responding to the k-th drug (i.e., mental processes and mathematical concepts); repeatedly performing a learning step a plurality of times by using the agent, the learning step being provided for updating the weights of the links of the k-th drug responsive network (i.e., mental processes and mathematical concepts); observing each reward provided to the agent at each learning step (i.e., mental processes); selecting a learning step corresponding to a reward with a largest value among the rewards observed at the observing step (i.e., mental processes and mathematical concepts); and deciding that the link weights output by the agent in the selected learning step are weights of the links of the k-th drug responsive network (i.e., mental processes and mathematical concepts). Dependent claim 3 further recites: the agent is configured to determine weights of the links of the k-th drug responsive network in a next learning step, based on the reward and the weights of the links of the k-th drug responsive network in a current learning step (i.e., mental processes and mathematical concepts). Dependent claim 4 further recites: preparing a vector Y composed of N cell death probabilities output by the N specific perturbation networks and a vector z composed of N values related to a percentage cell death of the first cancer cell line observed by the in vitro test in which the k-th drug is administered to the first cancer cell line, in the current learning step in the plurality of times of the learning step (i.e., mental processes and mathematical concepts); calculating a first value inversely proportional to a distance between the vector Y and the vector Z (i.e., mental processes and mathematical concepts); and calculating the reward based on a difference value between the first value and a second value (i.e., mental processes and mathematical concepts), and the second value is a value inversely proportional to a distance between the vector Y and the vector Z prepared in the learning step immediately before the current learning step (i.e., mental processes and mathematical concepts). Dependent claim 5 further recites: training the agent before the performing of the process (=episode) of determining the weights of the k-th drug responsive network, wherein in the training of the agent, a process of training the agent is repeatedly performed for different G drugs (i.e., mathematical concepts), and the process of training the agent that is performed for a g-th drug comprises: generating p g specific perturbation networks by applying the p g pieces of mutation information to a p-th drug responsive network responding to a p-th drug (i.e., mathematical concepts); repeatedly performing a learning step a plurality of times by using the agent, the learning step being provided for updating the weights of the links of the g-th drug responsive network (i.e., mathematical concepts); and training the agent by using the rewards provided to the agent during the plurality of learning steps and the weights obtained in a process of repeatedly performing the learning step a plurality of times (i.e., mathematical concepts). Dependent claim 6 further recites: the agent is configured to determine weights of the links of the g-th drug responsive network in the next learning step, based on the reward and the weights of the links of the g-th drug responsive network in the current learning step (i.e., mental processes and mathematical concepts). Dependent claim 9 further recites: perform a process of determining weights of a k-th drug responsive network responding to a k-th drug among the plurality of drug responsive networks before the simulation generates the plurality of specific perturbation networks (i.e., mental processes and mathematical concepts), in performing the process, an agent that has been trained by reinforcement learning is used (i.e., mental processes and mathematical concepts), and the performing of the process comprises: generating N specific perturbation networks by applying the N pieces of mutation information to the k-th drug responsive network responding to the k-th drug (i.e., mental processes and mathematical concepts); repeatedly performing a learning step a plurality of times by using the agent, the learning step being provided for updating the weights of the links of the k-th drug responsive network (i.e., mental processes and mathematical concepts); selecting the learning step when a reward provided to the agent has a largest value among the plurality of times of the learning step (i.e., mental processes and mathematical concepts); and deciding that the link weights output by the agent in the selected learning step are weights of the links of the k-th drug responsive network (i.e., mental processes and mathematical concepts). Dependent claim 10 further recites: the agent is configured to determine weights of the links of the k-th drug responsive network in a next learning step, based on the reward and the weights of the links of the k-th drug responsive network in a current learning step (i.e., mental processes and mathematical concepts). Dependent claim 11 further recites: a process of determining the reward comprises: preparing a vector Y composed of N cell death probabilities output by the N specific perturbation networks and a vector z composed of N values related to a percentage cell death of the first cancer cell line observed by the in vitro test in which the k-th drug is administered to the first cancer cell line, in the current learning step in the plurality of times of the learning step (i.e., mental processes and mathematical concepts); calculating a first value inversely proportional to a distance between the vector Y and the vector Z (i.e., mental processes and mathematical concepts); and calculating the reward based on a difference value between the first value and a second value (i.e., mental processes and mathematical concepts), and the second value is a value inversely proportional to a distance between the vector Y and the vector Z prepared in the learning step immediately before the current learning step (i.e., mental processes and mathematical concepts). Dependent claim 13 further recites: the agent is configured to determine weights of the links of the g-th drug responsive network in the next learning step, based on the reward and the weights of the links of the g-th drug responsive network in a current learning step (i.e., mental processes and mathematical concepts). The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., selecting a plurality of candidate drugs from among the plurality of drugs based on a plurality of cell death probabilities for the first cancer cell line output by the plurality of specific perturbation networks), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., determine weights of the links of the k-th drug responsive network in a next learning step, based on the reward and the weights of the links of the k-th drug responsive network in a current learning step) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Therefore, claims 1-14 recite an abstract idea . [Step 2A Prong One: YES] Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)). The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below. Dependent claims 3, 6, 10, and 13 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception. The additional elements in independent claim 1 include: a simulation device (i.e., a computing device); providing information on the plurality of determined candidate drugs to a drug response screening device (i.e., inputting data); performing, by the drug response screening device, an in vitro test in which the plurality of candidate drugs are administered to a plurality of wells in which the first cancer cell line is stored; capturing, by the drug response screening device, images of the first cancer cell line in the plurality of wells using a cell image capturing device; and outputting, by the drug response screening device, a result of an in vitro test for at least some of the plurality of candidate drugs based on the analysis result (i.e., outputting data). The additional elements in independent claim 7 include: a computing device; a simulation device (i.e., a computing device); and obtaining mutation information for N (=p k ) cell lines in which information on responsiveness by the in vitro test using the k-th drug among the plurality of drugs exists (i.e., data gathering). The additional elements in independent claim 8 include: a simulation device (i.e., a computing device); a drug response screening device; provide information on the plurality of determined candidate drugs to a drug response screening device (i.e., inputting data); perform an in vitro test in which the plurality of candidate drugs are administered to a plurality of wells in which the first cancer cell line is stored; capture images of the first cancer cell line in the plurality of wells using a cell image capturing device; and output a result of an in vitro test for at least some of the plurality of candidate drugs based on the analysis result (i.e., outputting data). The additional elements in independent claim 14 include: a simulation device (i.e., a computing device); a drug response screening device; a computing device; and obtaining mutation information for N (=pk) cell lines in which information on responsiveness by the in vitro test using the k-th drug among the plurality of drugs exists (i.e., data gathering). The additional elements in dependent claims 2, 4, 5, 9, 11, and 12 include: a computing device (claims 2, 4, 5, 9, 11, and 12); obtaining mutation information for N (=p k ) cell lines in which information on responsiveness by the in vitro test using the k-th drug among the plurality of drugs exists (i.e., data gathering) (claims 2 and 9); and obtaining p g pieces of mutation information for cell lines in which information on responsiveness by the in vitro test using the g-th drug is present (i.e., data gathering) (claims 5 and 12). The additional elements of a computing device (claims 2, 4, 5, 7, 9, 11, 12, and 14); and a simulation device (i.e., a computing device) (claims 1, 7, 8, and 14); invoke a computer and/or computer-related components merely as tools for use in the claimed process, and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)). The additional element of providing information on the plurality of determined candidate drugs to a drug response screening device (i.e., inputting data) (claims 1 and 8); is merely a pre-solution activity that is part of the gathering of data for use in the claimed process – a nominal addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)). The additional elements of obtaining mutation information for N (=p k ) cell lines in which information on responsiveness by the in vitro test using the k-th drug among the plurality of drugs exists (i.e., data gathering) (claims 2, 7, 9, and 14); and obtaining p g pieces of mutation information for cell lines in which information on responsiveness by the in vitro test using the g-th drug is present (i.e., data gathering) (claims 5 and 12); are merely pre-solution activities that are part of the gathering of data for use in the claimed process – nominal additions to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)). The additional elements of a drug response screening device (claim 14); performing, by the drug response screening device, an in vitro test in which the plurality of candidate drugs are administered to a plurality of wells in which the first cancer cell line is stored (claims 1 and 8); capturing, by the drug response screening device, images of the first cancer cell line in the plurality of wells using a cell image capturing device (claims 1 and 8); are merely part of the pre-solution activities of gathering data for use in the claimed process – nominal additions to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)). The additional element of outputting, by the drug response screening device, a result of an in vitro test for at least some of the plurality of candidate drugs based on the analysis result (i.e., outputting data) (claims 1 and 8); is merely a post-solution activity used in the claimed process – a nominal addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)). Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and as such, when all limitations in claims 1-14 have been considered as a whole , the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-14 are directed to an abstract idea (MPEP 2106.04(d)). [Step 2A Prong Two: NO] Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi). The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below. Dependent claims 3, 6, 10, and 13 do not recite any elements in addition to the judicial exception(s). The additional elements recited in independent claims 1, 7, 8, and 14 and dependent claims 2, 4, 5, 9, 11, and 12 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d). The additional elements of a computing device (claims 2, 4, 5, 7, 9, 11, 12, and 14); a simulation device (i.e., a computing device) (claims 1, 7, 8, and 14); data gathering (claims 2, 5, 7, 9, 12, and 14); inputting data (claims 1 and 8); and outputting data (claims 1 and 8); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes). The additional elements of a drug response screening device (claim 14); performing, by the drug response screening device, an in vitro test in which the plurality of candidate drugs are administered to a plurality of wells in which the first cancer cell line is stored (claims 1 and 8); capturing, by the drug response screening device, images of the first cancer cell line in the plurality of wells using a cell image capturing device (claims 1 and 8); are conventional. Evidence of conventionality is shown by: Isherwood et al. (“Live Cell in Vitro and in Vivo Imaging Applications: Accelerating Drug Discovery.” Pharmaceutics, 2011, Vol. 3, pp. 141-170); Gordon et al. (“Cell-Based Methods for Determination of Efficacy for Candidate Therapeutics in the Clinical Management of Cancer.” Diseases , 2018, Vol. 6, No. 85, pp. 1-13); Lang et al. (“Cellular imaging in drug discovery.” Nature Reviews: Drug Discovery , 2006, Vol. 5, pp. 343-356). Isherwood et al. reviews recent advances in microscopic imaging platform technology combined with the development of novel optical biosensors and image analysis solutions that have increase the scope of live cell imaging applications in drug discover, and provides examples of how temporal profiling of phenotypic response signatures using imaging platforms can increase the value of in vitro high-content screening (Abstract); and further shows kinetic phenotypic profiling of drug response following treatment with different concentrations (Figure 3); and further shows that high resolution in vitro imaging applications are heralding a new era in drug response profiling because profiling drug candidates across dynamic in vitro imaging assays can provide unique insights into therapeutic mode-of-action and robust quantification of transient responses (page 163, Section 7.). Gordon et al. reviews cell-based methods for determination of efficacy for candidate therapeutics in the clinical management of cancer (Title) wherein candidate therapeutics are evaluated using cell-based in vitro methods to assess their anti-cancer potential (Abstract). Gordon et al. further describes the utility and limitations of evaluating therapeutic efficacy using human tumor-derived cell lines, wherein indicators for therapeutic efficacy using tumor-derived cell lines included cell viability, cell proliferation, colony formation, cytotoxicity, cytostasis, induction of apoptosis, and cell cycle arrest (Abstract). Gordon et al. further reviews cytotoxicity assays that are used to assess both live and dead cells after treatment with a therapeutic, wherein staining techniques and imaging techniques such as fluorescence microscopy are used to quickly visualize the presence of live and dead cells (page 5, Section 2.6.1.). Lang et al. reviews cellular imaging in drug discovery (Title) and the quantitative analysis of cellular events and visualization of relevant cellular phenotypes using cellular imaging system (Abstract). Lang et al. further shows widely used automated cellular imaging platforms used in industry (page 349, Table 2); and the use of cellular imaging technology at various stages of drug research and development (page 353, Table 3). Therefore, when taken alone, all additional elements in claims 1-14 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-14 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)). [Step 2B: NO] Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Cho et al. (“An optimized anti-cancer drug identification platform for personalized therapy.” KR 2018-0114733) in view of Kitano et al. (“Evaluation system, learning device, prediction device, evaluation method, and program.” JP 6810294 (06 January 2021)) . Independent claims 1 and 8 are broadly directed to a method and system, respectively, for determining a cancer treatment candidate drug, comprising steps of using a computational model (i.e., a perturbation network) to map how chemical agents and genetic interventions alter the behavior of a biological system to induce or inhibit cell death, and by analyzing the probability of cell death in response to these interventions, the network identifies and prioritizes potential candidate drugs, which are then screened using in vitro assays, with the assay results captured as an image and subsequently analyzed to determine the effect that a drug had on a cell. Cho et al. is directed to a method for determining a reference network corresponding to a cancer related to a patient or a cell line among a plurality of cancer networks related to a plurality of cancers; determining one or more target nodes targeted by each of one or more drugs given among nodes included in the reference network; converting information on a change in a molecular level into one or more specific model parameters to reflect the information on the change in a molecular level, which is generated in a gene of a patient or a cell line, to the reference network; generating simulation input information in which the reference network, one or more target nodes, and one or more specific model parameters are integrated; and allowing a simulation module to receive the simulation input information to output information on one or more among a single drug sensitivity, a multi-drug sensitivity, an optimal drug, and an optimal drug combination. Kitano et al. is directed to an evaluation system capable of evaluating the efficacy of an anticancer drug in consideration of not only cell omics information but also information on a patient’s medical condition; administering an anticancer drug to cells collected from the subject to generate learning data including state information indicating a cancer state and an effect of the anticancer drug; using a learning information acquisition unit that acquires teacher data, which is information about; a learning unit that generates a prediction model by having the learning model learn the correspondence between the learning data and the teacher data with supervision, and a prediction model; the learning information acquisition unit includes a prediction unit that predicts treatment using an anticancer drug, and a learning information acquisition unit that includes cancer cells collected from a subject and cells that constitute the stroma; and information on the anti-cancer effect obtained by administering the anti-cancer drug to the three-dimensional cell structure is acquired as teacher data. Regarding independent claims 1 and 8 , Cho et al. shows a simulation module that may be a machine learning analysis module (page 2, para. 3) generating a first specific network by mapping gene mutation information of a first cancer cell to a nominal network and applying a first perturbation corresponding to a first drug to the first specific network to create a first specific perturbation network and calculating a score for the utility of the first drug (page 6, bottom paragraph and page 7, top paragraph); different perturbations representing different drugs may be applied to each specific network to obtain information about the reaction results of different cancer cells for each drug (page 14, para. 12; and page 17, para. 4); calculating a probability value at which the first cancer cells are to be killed (page 7, para. 5); and the simulation module may simulate the state of drug administration (page 12, para. 1) and output drug sensitivities wherein the sensitivity can be defined as the degree of response of the cell to the dose of the drug (page 12, paras. 4-5). Regarding independent claims 1 and 8 , Cho et al. does not show providing information on the plurality of determined candidate drugs to a drug response screening device; performing, by the drug response screening device, an in vitro test in which the plurality of candidate drugs are administered to a plurality of wells in which the first cancer cell line is stored; or capturing, by the drug response screening device, images of the first cancer cell line in the plurality of wells using a cell image capturing device to analyze the captured images. Regarding independent claims 1 and 8 , Kitano et al. shows an evaluation method for evaluating an anticancer effect, wherein a learning information acquisition unit uses learning data which is information on cancer in an unspecified subject and cells collected from the subject; acquiring teacher data which is information on the effect of the anticancer drug obtained by administering the anticancer drug, the learning unit corresponds to the learning data acquired by the learning information acquisition unit and the teacher data, and by letting the learning model learn the relationship, a prediction model for predicting treatment using an anticancer drug is generated (page 3, para. 4). Kitano et al. further shows the anticancer effect of an anticancer agent acting on the cancer cells of a subject is evaluated ex vivo ( in vitro ), and cancer cells are subjected to an anticancer drug, immune cells and an anticancer drug, or a combination of a plurality of anticancer drugs, and by culturing, the anticancer effect may be evaluated (page 7, bottom); and an imaging device that is a camera that images a three-dimensional cell structure as a target for evaluating the anticancer effect of an anticancer agent, and transmits the image data to a determination device (page 11, para. 2) wherein the determination device is a computer device that evaluates the anticancer effect of the anticancer agent using the image (page 11, para. 3). Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Cho et al. by incorporating methods for in vitro assaying of the effects of anticancer agents on cancer cells, and subsequent image-based analyses those effects, as shown by Kitano et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Cho et al. with the methods of Kitano et al., because Kitano et al. shows methods for testing the effects of anticancer agents on tumor cells using in vitro assaying methods. This modification would have had a reasonable expectation of success given that both Cho et al. and Kitano et al. disclose methods for evaluating the efficacy of an anticancer drug for personalized treatment . 07-22-aia AIA Claim s 2-6 and 9-13 are rejected under 35 U.S.C. 103 as being unpatentable over Cho et al. in view of Kitano et al . as applied to claim s 1 and 8 above, and further in view of Eckardt et al. (“Reinforcement Learning for Precision Oncology.” Cancers , 15 September 2021, Vol. 13, No. 4624, pp.1-15) . Dependent claims 2-6 and 9-13 further define the steps of the claimed process that utilize reinforcement learning techniques to optimize an AI agent to perform a role in determining weights assigned to the links in a simulated biological network composed of nodes and links, for the purpose of selecting a drug suitable for the treatment of a cancer patient. Eckardt et al. is directed to a review of reinforcement learning-based decision support systems for precision oncology, in particular reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes; and discusses how the accelerating merger of information technology and cancer research heralds the advent of novel methods and models for clinical decision making in oncology, and specifically, how reinforcement learning – as one of the major subspecialties in machine learning – holds the potential for the development of high-performance decision support tools. Regarding dependent claims 2-6 and 9-13 , Cho et al. in view of Kitano et al. as applied to claims 1 and 8 above, do not show using reinforcement learning to train and use an AI agent for determining weights assigned to the links in the simulated biological network. Regarding dependent claims 2-6 and 9-13 , Eckardt et al. provides an overview of reinforcement learning (RL), and shows that in RL, an agent interacts with its environment over time by selecting actions depending on the observed states of the environment while following a policy in order to maximize a cumulative reward (page 3, para. 4; and Figure 2); and further shows an iterative workflow of a reinforcement learning approach to precision oncology (Figure 3) where for the individual patient, multimodal data, e.g., from genetic assays, laboratory tests, radiographic images and electronic health records, serve as input to a reinforcement learning (RL) framework, here depicted as a deep neural network, which selects an action such as a treatment decision according to its policy, where this treatment decision will affect tumor response and toxicity simultaneously and thus, ultimately, affect long-term patient outcome, and that this is translated into a reward signal for the RL agent which results in a policy update, while at the same time, the state of the patient changes, and this update to the state initiates a new cycle where the updated inputs to the RL framework lead to a new treatment decision according to the updated policy (Figure 3). For example (page 4, bottom, and page 5, top) an RL agent could be presented with multimodal patient data, e.g., demographics, laboratory values, tumor burden and therapy-associated toxicities, that represent the environment, and for every iteration, the agent then selects an action, e.g., a dose adjustment on a linear scale from 0 to 100%, given the state of the environment, and this action will subsequently result in an alteration of the environment, i.e., of the patient’s condition and the data associated with it, resulting in a reward or penalty for the agent based on whether or not the chosen action led to a favorable outcome for the patient, and in that sense, the agent can abstract a policy either from rewards or state-action pairs that drives action selection, e.g., the agent may learn that increasing doses of chemotherapy are associated with an increased anti-tumor effect, but also increase toxicity. Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Cho et al. in view of Kitano et al. as applied to claims 1 and 8 above, by incorporating methods for using reinforcement learning to train and use an AI agent to iteratively explore the environment (multimodal patient data) to find candidate drugs (action) that optimize drug efficacy (reward), as shown by Eckardt et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Cho et al. in view of Kitano et al. as applied to claims 1 and 8 above with the methods of Eckardt et al., because Eckardt et al. shows how reinforcement learning with AI agents is being used for treatment decisions in precision oncology. This modification would have had a reasonable expectation of success given that both Cho et al. in view of Kitano et al. as applied to claims 1 and 8 above and Eckardt et al. disclose methods for evaluating treatment decisions based on identifying an optimized anti-cancer drug . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Cho et al. (as cited above) in view of Eckardt et al. (as cited above) . Independent claim 7 is broadly directed to a method for determining a cancer treatment candidate drug, comprising steps of using a computational model (i.e., a perturbation network) to map how chemical agents and genetic interventions alter the behavior of a biological system to induce or inhibit cell death, and by incorporating reinforcement learning techniques to optimize an AI agent to perform a role in determining weights assigned to the links in the simulated biological network composed of nodes and links, for the purpose of selecting an optimal candidate drug. Cho et al. is directed to a method for determining a reference network corresponding to a cancer related to a patient or a cell line among a plurality of cancer networks related to a plurality of cancers; determining one or more target nodes targeted by each of one or more drugs given among nodes included in the reference network; converting information on a change in a molecular level into one or more specific model parameters to reflect the information on the change in a molecular level, which is generated in a gene of a patient or a cell line, to the reference network; generating simulation input information in which the reference network, one or more target nodes, and one or more specific model parameters are integrated; and allowing a simulation module to receive the simulation input information to output information on one or more among a single drug sensitivity, a multi-drug sensitivity, an optimal drug, and an optimal drug combination. Eckardt et al. is directed to a review of reinforcement learning-based decision support systems for precision oncology, in particular reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes; and discusses how the accelerating merger of information technology and cancer research heralds the advent of novel methods and models for clinical decision making in oncology, and specifically, how reinforcement learning – as one of the major subspecialties in machine learning – holds the potential for the development of high-performance decision support tools. Regarding independent claim 7 , Cho et al. shows a simulation module that may be a machine learning analysis module (page 2, para. 3) generating a first specific network by mapping gene mutation information of a first cancer cell to a nominal network and applying a first perturbation corresponding to a first drug to the first specific network to create a first specific perturbation network and calculating a score for the utility of the first drug (page 6, bottom paragraph and page 7, top paragraph); different perturbations representing different drugs may be applied to each specific network to obtain information about the reaction results of different cancer cells for each drug (page 14, para. 12; and page 17, para. 4); calculating a probability value at which the first cancer cells are to be killed (page 7, para. 5); and the simulation module may simulate the state of drug administration (page 12, para. 1) and output drug sensitivities wherein the sensitivity can be defined as the degree of response of the cell to the dose of the drug (page 12, paras. 4-5). Regarding independent claim 7 , Cho et al. does not show using reinforcement learning to train and use an AI agent for determining weights assigned to the links in the simulated biological network. Regarding independent claim 7 , Eckardt et al. provides an overview of reinforcement learning (RL), and shows that in RL, an agent interacts with its environment over time by selecting actions depending on the observed states of the environment while following a policy in order to maximize a cumulative reward (page 3, para. 4; and Figure 2); and further shows an iterative workflow of a reinforcement learning approach to precision oncology (Figure 3) where for the individual patient, multimodal data, e.g., from genetic assays, laboratory tests, radiographic images and electronic health records, serve as input to a reinforcement learning (RL) framework, here depicted as a deep neural network, which selects an action such as a treatment decision according to its policy, where this treatment decision will affect tumor response and toxicity simultaneously and thus, ultimately, affect long-term patient outcome, and that this is translated into a reward signal for the RL agent which results in a policy update, while at the same time, the state of the patient changes, and this update to the state initiates a new cycle where the updated inputs to the RL framework lead to a new treatment decision according to the updated policy (Figure 3). For example (page 4, bottom, and page 5, top) an RL agent could be presented with multimodal patient data, e.g., demographics, laboratory values, tumor burden and therapy-associated toxicities, that represent the environment, and for every iteration, the agent then selects an action, e.g., a dose adjustment on a linear scale from 0 to 100%, given the state of the environment, and this action will subsequently result in an alteration of the environment, i.e., of the patient’s condition and the data associated with it, resulting in a reward or penalty for the agent based on whether or not the chosen action led to a favorable outcome for the patient, and in that sense, the agent can abstract a policy either from rewards or state-action pairs that drives action selection, e.g., the agent may learn that increasing doses of chemotherapy are associated with an increased anti-tumor effect, but also increase toxicity. Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Cho et al. by incorporating methods for using reinforcement learning to train and use an AI agent to iteratively explore the environment (multimodal patient data) to find candidate drugs (action) that optimize drug efficacy (reward), as shown by Eckardt et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Cho et al. with the methods of Eckardt et al. because Eckardt et al. shows how reinforcement learning with AI agents is being used for treatment decisions in precision oncology. This modification would have had a reasonable expectation of success given that both Cho et al. and Eckardt et al. disclose methods for evaluating treatment decisions based on identifying an optimized anti-cancer drug . 07-21-aia AIA Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Cho et al. (as cited above) in view of Kitano et al. (as cited above) in view of Eckardt et al. (as cited above) . Independent claim 14 is broadly directed to a system for determining a cancer treatment candidate drug, comprising steps of using a computational model (i.e., a perturbation network) to map how chemical agents and genetic interventions alter the behavior of a biological system to induce or inhibit cell death, and by incorporating reinforcement learning techniques to optimize an AI agent to perform a role in determining weights assigned to the links in the simulated biological network composed of nodes and links, for the purpose of selecting an optimal candidate drug. Cho et al. is directed to a method for determining a reference network corresponding to a cancer related to a patient or a cell line among a plurality of cancer networks related to a plurality of cancers; determining one or more target nodes targeted by each of one or more drugs given among nodes included in the reference network; converting information on a change in a molecular level into one or more specific model parameters to reflect the information on the change in a molecular level, which is generated in a gene of a patient or a cell line, to the reference network; generating simulation input information in which the reference network, one or more target nodes, and one or more specific model parameters are integrated; and allowing a simulation module to receive the simulation input information to output information on one or more among a single drug sensitivity, a multi-drug sensitivity, an optimal drug, and an optimal drug combination. Kitano et al. is directed to an evaluation system capable of evaluating the efficacy of an anticancer drug in consideration of not only cell omics information but also information on a patient’s medical condition; administering an anticancer drug to cells collected from the subject to generate learning data including state information indicating a cancer state and an effect of the anticancer drug; using a learning information acquisition unit that acquires teacher data, which is information about; a learning unit that generates a prediction model by having the learning model learn the correspondence between the learning data and the teacher data with supervision, and a prediction model; the learning information acquisition unit includes a prediction unit that predicts treatment using an anticancer drug, and a learning information acquisition unit that includes cancer cells collected from a subject and cells that constitute the stroma; and information on the anti-cancer effect obtained by administering the anti-cancer drug to the three-dimensional cell structure is acquired as teacher data. Eckardt et al. is directed to a review of reinforcement learning-based decision support systems for precision oncology, in particular reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes; and discusses how the accelerating merger of information technology and cancer research heralds the advent of novel methods and models for clinical decision making in oncology, and specifically, how reinforcement learning – as one of the major subspecialties in machine learning – holds the potential for the development of high-performance decision support tools. Regarding independent claim 14 , Cho et al. shows a simulation module that may be a machine learning analysis module (page 2, para. 3) generating a first specific network by mapping gene mutation information of a first cancer cell to a nominal network and applying a first perturbation corresponding to a first drug to the first specific network to create a first specific perturbation network and calculating a score for the utility of the first drug (page 6, bottom paragraph and page 7, top paragraph); different perturbations representing different drugs may be applied to each specific network to obtain information about the reaction results of different cancer cells for each drug (page 14, para. 12; and page 17, para. 4); calculating a probability value at which the first cancer cells are to be killed (page 7, para. 5); and the simulation module may simulate the state of drug administration (page 12, para. 1) and output drug sensitivities wherein the sensitivity can be defined as the degree of response of the cell to the dose of the drug (page 12, paras. 4-5). Regarding independent claim 14 , Cho et al. does not show using reinforcement learning to train and use an AI agent for determining weights assigned to the links in the simulated biological network; or a drug response screening device. Regarding independent claim 14 , Kitano et al. shows a cell culture vessel for screening anticancer drugs that act on cancer cells (page 9, para. 3). Regarding independent claim 14 , Kitano et al. does not show using reinforcement learning to train and use an AI agent for determining weights assigned to the links in the simulated biological network; or a drug response screening device. Regarding independent claim 14 , Eckardt et al. provides an overview of reinforcement learning (RL), and shows that in RL, an agent interacts with its environment over time by selecting actions depending on the observed states of the environment while following a policy in order to maximize a cumulative reward (page 3, para. 4; and Figure 2); and further shows an iterative workflow of a reinforcement learning approach to precision oncology (Figure 3) where for the individual patient, multimodal data, e.g., from genetic assays, laboratory tests, radiographic images and electronic health records, serve as input to a reinforcement learning (RL) framework, here depicted as a deep neural network, which selects an action such as a treatment decision according to its policy, where this treatment decision will affect tumor response and toxicity simultaneously and thus, ultimately, affect long-term patient outcome, and that this is translated into a reward signal for the RL agent which results in a policy update, while at the same time, the state of the patient changes, and this update to the state initiates a new cycle where the updated inputs to the RL framework lead to a new treatment decision according to the updated policy (Figure 3). For example (page 4, bottom, and page 5, top) an RL agent could be presented with multimodal patient data, e.g., demographics, laboratory values, tumor burden and therapy-associated toxicities, that represent the environment, and for every iteration, the agent then selects an action, e.g., a dose adjustment on a linear scale from 0 to 100%, given the state of the environment, and this action will subsequently result in an alteration of the environment, i.e., of the patient’s condition and the data associated with it, resulting in a reward or penalty for the agent based on whether or not the chosen action led to a favorable outcome for the patient, and in that sense, the agent can abstract a policy either from rewards or state-action pairs that drives action selection, e.g., the agent may learn that increasing doses of chemotherapy are associated with an increased anti-tumor effect, but also increase toxicity. Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Cho et al. by incorporating a cell culturing vessel for screening potential anticancer drugs, as shown by Kitano et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Cho et al. with the methods of Kitano et al., because Kitano et al. shows methods for testing the effects of anticancer agents on tumor cells using in vitro assaying methods. This modification would have had a reasonable expectation of success given that both Cho et al. and Kitano et al. disclose methods for evaluating the efficacy of an anticancer drug for personalized treatment. It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Cho et al. and Kitano et al. by incorporating methods for using reinforcement learning to train and use an AI agent to iteratively explore the environment (multimodal patient data) to find candidate drugs (action) that optimize drug efficacy (reward), as shown by Eckardt et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Cho et al. and Kitano et al. with the methods of Eckardt et al. because Eckardt et al. shows how reinforcement learning with AI agents is being used for treatment decisions in precision oncology. This modification would have had a reasonable expectation of success given that both Cho et al. and Kitano et al. and Eckardt et al. disclose methods for evaluating treatment decisions based on identifying an optimized anti-cancer drug. Conclusion No claims are allowed. This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application. Inquiries Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN W. BAILEY whose telephone number is (571)272-8170. The examiner can normally be reached Mon - Fri. 1000 - 1800. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KARLHEINZ SKOWRONEK can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /S.W.B./Examiner, Art Unit 1687 /Joseph Woitach/Primary Examiner, Art Unit 1687 Application/Control Number: 17/898,755 Page 2 Art Unit: 1687 Application/Control Number: 17/898,755 Page 3 Art Unit: 1687 Application/Control Number: 17/898,755 Page 4 Art Unit: 1687 Application/Control Number: 17/898,755 Page 5 Art Unit: 1687 Application/Control Number: 17/898,755 Page 6 Art Unit: 1687 Application/Control Number: 17/898,755 Page 7 Art Unit: 1687 Application/Control Number: 17/898,755 Page 8 Art Unit: 1687 Application/Control Number: 17/898,755 Page 9 Art Unit: 1687 Application/Control Number: 17/898,755 Page 10 Art Unit: 1687 Application/Control Number: 17/898,755 Page 11 Art Unit: 1687 Application/Control Number: 17/898,755 Page 12 Art Unit: 1687 Application/Control Number: 17/898,755 Page 13 Art Unit: 1687 Application/Control Number: 17/898,755 Page 14 Art Unit: 1687 Application/Control Number: 17/898,755 Page 15 Art Unit: 1687 Application/Control Number: 17/898,755 Page 16 Art Unit: 1687 Application/Control Number: 17/898,755 Page 17 Art Unit: 1687 Application/Control Number: 17/898,755 Page 18 Art Unit: 1687 Application/Control Number: 17/898,755 Page 19 Art Unit: 1687 Application/Control Number: 17/898,755 Page 20 Art Unit: 1687 Application/Control Number: 17/898,755 Page 21 Art Unit: 1687 Application/Control Number: 17/898,755 Page 22 Art Unit: 1687 Application/Control Number: 17/898,755 Page 23 Art Unit: 1687 Application/Control Number: 17/898,755 Page 24 Art Unit: 1687 Application/Control Number: 17/898,755 Page 25 Art Unit: 1687 Application/Control Number: 17/898,755 Page 26 Art Unit: 1687 Application/Control Number: 17/898,755 Page 27 Art Unit: 1687 Application/Control Number: 17/898,755 Page 28 Art Unit: 1687 Application/Control Number: 17/898,755 Page 29 Art Unit: 1687 Application/Control Number: 17/898,755 Page 30 Art Unit: 1687 Application/Control Number: 17/898,755 Page 31 Art Unit: 1687 Application/Control Number: 17/898,755 Page 32 Art Unit: 1687 Application/Control Number: 17/898,755 Page 33 Art Unit: 1687 Application/Control Number: 17/898,755 Page 34 Art Unit: 1687 Application/Control Number: 17/898,755 Page 35 Art Unit: 1687 Application/Control Number: 17/898,755 Page 36 Art Unit: 1687 Application/Control Number: 17/898,755 Page 37 Art Unit: 1687 Application/Control Number: 17/898,755 Page 38 Art Unit: 1687 Application/Control Number: 17/898,755 Page 39 Art Unit: 1687 Application/Control Number: 17/898,755 Page 40 Art Unit: 1687 Application/Control Number: 17/898,755 Page 41 Art Unit: 1687 Application/Control Number: 17/898,755 Page 42 Art Unit: 1687 Application/Control Number: 17/898,755 Page 43 Art Unit: 1687 Application/Control Number: 17/898,755 Page 44 Art Unit: 1687 Application/Control Number: 17/898,755 Page 45 Art Unit: 1687 Application/Control Number: 17/898,755 Page 46 Art Unit: 1687 Application/Control Number: 17/898,755 Page 47 Art Unit: 1687 Application/Control Number: 17/898,755 Page 48 Art Unit: 1687 Application/Control Number: 17/898,755 Page 49 Art Unit: 1687
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