DETAILED ACTION
The Applicant’s response, received 06 July 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Status of the Claims
Claims 1-14 are pending.
Claims 1-14 are rejected.
Priority
There are no domestic applications for which benefit is claimed.
This application claims 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.
Drawings
The objection to the drawings in the Office action mailed 07 April 2026 has been withdrawn in view of the amendment received 06 July 2026.
The replacement drawings received 06 July 2026 have been accepted.
Information Disclosure Statement
The information disclosure statement (IDS) received 06 July 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Interpretation
The claim interpretations under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, in the Office action mailed 07 April 2026 have been maintained with modification in view of the amendment received 06 July 2026, as noted below.
The claim interpretations have been modified to incorporate the newly amended claim limitations (e.g., amended claim 14 newly recites “capture, by a cell image capturing device …”).
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 limitations are:
“generating, by a simulation device, a plurality of …” in claims 1 and 7;
“the simulation device is configured to: generate a plurality of …” in claims 8 and 14;
“performing, by the drug response screening device, an in vitro test …” in claim 1;
“the drug response screening device is configured to: automatically control …” in claims 8 and 14;
“capturing, by a cell image capturing device of the drug response screening device, images of …”, in claim 1;
“capture, by a cell image capturing device of the drug response screening device, images of …”, in claims 8 and 14; 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, 8, and 14.
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.
Response to Arguments
The Applicant’s arguments/remarks received 06 July 2026 have been fully considered, but are not persuasive.
The Applicant states on page 13 of the Remarks that because the amended claims recite clear structural boundaries, well-recognized physical nouns, and specialized technical acts, invocation of § 112(f) is no longer appropriate.
These arguments/remarks are not persuasive, because the claims recite limitations that use 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, as noted and explained in the foregoing claim interpretations section.
Claim Objections
The objections to claims 4, 5, and 12 in the Office action mailed 07 April 2026 have been withdrawn in view of the amendment received 06 July 2026.
Claim Rejections - 35 USC § 112
The rejection of claims 1-6 and 8-13 under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, in the Office action mailed 07 April 2026 has been maintained with modification in view of the amendment received 06 July 2026, as noted below.
The rejection has been modified to incorporate the newly recited limitation “capture, by a cell image capturing device …” in claim 14.
Additionally, the amendment received 06 July 2026 has been fully considered, however after further consideration, new grounds of rejection are raised under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, in view of the amendment.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-14 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.
The following rejection is maintained with modification, as noted above.
Claims 1, 8, and 14 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.
The following rejection is newly raised in view of the amendment, as noted above.
Claims 1 and 7 recite “automatically controlling … a drug combination device …” and “… automatically extract and inject …” and claims 8 and 14 recite “automatically control … a drug combination device …” and “… automatically extract and inject …” however the specification does not contain the terms “automatic,” “automatically,” “automatically controlling,” or “automatically extract and inject.” Therefore, these limitations appear to be a new scope not contemplated nor supported by the present specification.
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.
Response to Arguments
The Applicant’s arguments/remarks received 06 July 2026 have been fully considered, but are not persuasive.
The Applicant states on page 15 (para. 1) of the Remarks that the claims have been amended to structurally integrate the ‘cell image capturing device’ into the physical hardware loop, and further states that the amended claims explicitly recite that the device is positioned to capture images from the physical well of a well-matrix dish under the coordination of a central process unit (CPU), perfectly matching the hardware layout disclosed in the specification (e.g., Fig. 6).
These arguments/remarks are not persuasive, because the disclosure does not clearly link any structure for the “cell image capturing device” as required by MPEP 2181.
The rejection of claims 1-14 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, in the Office action mailed 07 April 2026 has been withdrawn in view of the amendment received 06 July 2026.
The amendment received 06 July 2026 has been fully considered, however after further consideration, new grounds of rejection are raised under 35 U.S.C. 112(b) in view of the amendment.
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 2-6, 7, and 9-13 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 “further comprising … a computing device …” because the claims from which they depend (i.e., 1 and 8, respectively) recite “a computing device” and therefore it is not clear as to whether dependent claims 2 and 9 are introducing a computing device that is in addition to the computing device introduced in claims 1 and 8.
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.
Claim 7 recites the limitation "the in vitro test" in line twenty. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
The rejection of claims 1-14 under 35 U.S.C. 101 in the Office action mailed 07 April 2026 has been maintained with modification in view of the amendment received 06 July 2026, as noted below.
The rejection has been modified to incorporate the amended claim limitations.
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).
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 (i.e., training the agent).
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 and/or a manufacture) for determining a cancer treatment candidate drug; and claim 14 recites a system comprising a computing device (i.e., a machine and/or a 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, wherein each of the plurality of drug responsive networks comprises a plurality of nodes and a plurality of links (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
analyzing the captured images a cell image analysis process to generate an analysis result (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 an episode 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 episode, an agent that has been trained by reinforcement learning is used (i.e., mental processes and mathematical concepts), and
the performing of the episode 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, wherein each of the plurality of drug responsive networks comprises a plurality of nodes and a plurality of links (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 by a cell image analysis process to generate an analysis result (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 an episode of determining weights of a k-th drug responsive network responding to a k-th drug among a plurality of drug responsive networks, each of the plurality of drug responsive networks comprising a plurality of nodes and a plurality of links (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);
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),
analyze the captured images by a cell image analysis process to generate an analysis result,
in the performing of the episode, an agent that has been trained by reinforcement learning is used (i.e., mental processes and mathematical concepts), and
the performing of the episode 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 an episode 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 episode, an agent that has been trained by reinforcement learning is used (i.e., mental processes and mathematical concepts), and
the performing of the episode 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 episode of determining the weights of the k-th drug responsive network, wherein in the training of the agent, an episode of training the agent is repeatedly performed for different G drugs (i.e., mathematical concepts), and
the episode of training the agent that is performed for a g-th drug comprises:
generating pg specific perturbation networks by applying the pg 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 an episode 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 episode, an agent that has been trained by reinforcement learning is used (i.e., mental processes and mathematical concepts), and
the performing of the episode 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 12 further recites:
train the agent before performing the episode of determining the weights of the k-th drug responsive network (i.e., mathematical concepts),
in training the agent, an episode of training the agent is repeatedly performed for different G drugs (i.e., mathematical concepts), and
the episode of training the agent that is performed for a g-th drug comprises:
generating pg specific perturbation networks by applying the pg 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 reward values and the weights obtained in a process of repeatedly performing the learning step a plurality of times (i.e., 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.
In the instant application, the independent claims provide additional elements, e.g., to provide candidate drug information to a drug response screening device, using the drug response screening device to perform an in vitro test of candidate drugs on a cell line, and then capture an image of the cell line, however once the image data is captured, the subsequent steps only perform analysis of the image to generate an analysis result and output a result based on the analysis result. Thus, the claims do not recite any limitations (i.e., additional elements) to which the ‘result’ is practically applied.
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 candidate drugs to a drug response screening device (i.e., inputting data);
automatically controlling, based on the provided information, a drug combination device and a micropipette of the drug response screening device to automatically extract and inject the candidate drugs into a well-matrix dish storing the first cancer cell line;
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 a cell image capturing device of the drug response screening device, images of the first cancer cell line in the plurality of wells;
a central processing unit of a computing device configured to execute real-time instructions; 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);
automatically controlling, by a computing device based on the selected plurality of candidate drugs, a drug combination device and a micropipette of a drug response screening device to automatically extract and inject the candidate drugs into a well-matrix dish storing the first cancer cell line; and
obtaining mutation information for N 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);
automatically control an internal drug bank, a drug combination device, and a micropipette based on the provided information to automatically extract and inject the plurality of candidate drugs into a plurality of wells in which the first cancer cell line is stored;
capture, by a cell image capturing device of the drug response screening device, images of the first cancer cell line in the plurality of wells;
a central processing unit of a computing device configured to execute real-time instructions; 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;
provide information on the plurality of candidate drugs to the drug response screening device (i.e., input data);
the drug response screening device is configured to: automatically control an internal drug bank, a drug combination device, and a micropipette based on the provided information to automatically extract and inject the plurality of candidate drugs into a plurality of wells in which the first cancer cell line is stored, thereby performing an in vitro test;
capture, by a cell image capturing device of the drug response screening device, images of the first cancer cell line in the plurality of wells;
a central processing unit of a computing device configured to execute real-time instructions; and
obtaining mutation information for N 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 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 pg 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 central processing unit of a computing device configured to execute real-time instructions (claims 1, 8, and 14); 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/provide information on the plurality of candidate drugs to a drug response screening device (i.e., inputting data) (claims 1, 8, and 14); 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 a drug response screening device (claims 1, 7, 8, and 14); automatically controlling, based on the provided information, a drug combination device and a micropipette of the drug response screening device to automatically extract and inject the candidate drugs into a well-matrix dish storing the first cancer cell line (claim 1); 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 (claim 1); automatically controlling, by a computing device based on the selected plurality of candidate drugs, a drug combination device and a micropipette of a drug response screening device to automatically extract and inject the candidate drugs into a well-matrix dish storing the first cancer cell line (claim 7); automatically control an internal drug bank, a drug combination device, and a micropipette based on the provided information to automatically extract and inject the plurality of candidate drugs into a plurality of wells in which the first cancer cell line is stored (claim 8); and automatically control an internal drug bank, a drug combination device, and a micropipette based on the provided information to automatically extract and inject the plurality of candidate drugs into a plurality of wells in which the first cancer cell line is stored, thereby performing an in vitro test (claim 14); 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 obtaining mutation information for N 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 pg 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 element of capturing/capture, by a cell image capturing device of the drug response screening device, images of the first cancer cell line in the plurality of wells (claims 1, 8, and 14); is 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/output, 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, 8, and 14); 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 (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), 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 central processing unit of a computing device configured to execute real-time instructions (claims 1, 8, and 14); 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, 8, and 14); and outputting data (claims 1, 8, and 14); 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 (claims 1, 7, 8, and 14); automatically controlling, based on the provided information, a drug combination device and a micropipette of the drug response screening device to automatically extract and inject the candidate drugs into a well-matrix dish storing the first cancer cell line (claim 1); 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 (claim 1); automatically controlling, by a computing device based on the selected plurality of candidate drugs, a drug combination device and a micropipette of a drug response screening device to automatically extract and inject the candidate drugs into a well-matrix dish storing the first cancer cell line (claim 7); automatically control an internal drug bank, a drug combination device, and a micropipette based on the provided information to automatically extract and inject the plurality of candidate drugs into a plurality of wells in which the first cancer cell line is stored (claim 8); and automatically control an internal drug bank, a drug combination device, and a micropipette based on the provided information to automatically extract and inject the plurality of candidate drugs into a plurality of wells in which the first cancer cell line is stored, thereby performing an in vitro test (claim 14); and capturing/capture, by a cell image capturing device of the drug response screening device, images of the first cancer cell line in the plurality of wells (claims 1, 8, and 14); 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, as cited in the Office action mailed 07 April 2026);
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, as cited in the Office action mailed 07 April 2026);
Lang et al. (“Cellular imaging in drug discovery.” Nature Reviews: Drug Discovery, 2006, Vol. 5, pp. 343-356, as cited in the Office action mailed 07 April 2026).
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 (i.e., individually), all additional elements in claims 1-14 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered 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]
Response to Arguments
The Applicant’s arguments/remarks received 06 July 2026 have been fully considered, but are not persuasive.
The Applicant states on page 16 (as filed) of the Remarks that the Examiner has characterized the algorithmic and computational modeling steps as ineligible abstract ideas, and further states that the Applicant traverses this rejection, and further states that the amended claims explicitly tie the computational optimization steps to the real-time physical control of laboratory automation hardware, establishing clear eligibility under the USPTO guidance.
These arguments/remarks are not persuasive, because first, the MPEP at 2106.04(II)(A) states that Step 2A is a two-prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. Together, these prongs represent the first part of the Alice/Mayo test, which determines whether a claim is directed to a judicial exception. The MPEP further states at 2106.04(a) that examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiners believes recites an abstract idea, and (2) determining whether the identified limitation(s) fall within at last one of the groupings of abstract ideas. If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One. As noted in the rejection above at eligibility Step 2A Prong One, claim limitations are identified that recite abstract ideas from one or both of the judicial exception groupings of mathematical concepts and mental processes. Second, the MPEP states at 2106.04(d) that after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. 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. Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I at 2106.04(d) of the MPEP, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h). Regarding the Applicant’s assertion that “the amended claims explicitly tie the computational optimization steps to the real-time physical control of laboratory automation hardware, establishing clear eligibility under the USPTO guidance” it is noted that merely tying a judicial exception to hardware does not necessarily establish clear eligibility under the USPTO guidance. As noted in the rejection above at eligibility Step 2A Prong Two, the independent claims provide additional elements, e.g., to provide candidate drug information to a drug response screening device, using the drug response screening device to perform an in vitro test of candidate drugs on a cell line, and then capture an image of the cell line, however once the image data is captured, the subsequent steps only perform analysis of the image to generate an analysis result and output a result based on the analysis result. Thus, the claims do not recite any limitations (i.e., additional elements) to which the ‘result’ is practically applied.
Regarding Step 2A, the Applicant states on page 16 of the Remarks that the claims as a whole integrate the abstract idea into a practical application, and further states that under USPTO eligibility guidelines, a claim is patent-eligible if it integrates any recited judicial exception into a practical application by applying or using the exception to improve another technology. The Applicant further states that the Examiner has dissected individual clauses to label network weight updates and probability calculations as abstract concepts, further asserting that the hardware merely serves as a generic tool. The Applicant further states that evaluating the amended claims as a whole completely overturns this characterization, and that the amended claims now explicitly require “… automatically controlling, based on the provided information, a drug combination device and a micropipette of the drug response screening device to automatically extract and inject the candidate drugs into a well-matrix dish…”. The Applicant further states that, accordingly, the output of the trained reinforcement learning agent is not a mere display of data or a generic computer task, but rather, it serves as an active, machine-control signal that dictates the microscopic physical fluid handling of dedicated screening hardware, and further states that a human mind utilizing pen and paper cannot mechanically govern a physical micropipette or automate a cellular assay loop in real time. The Applicant further states on page 17 (para. 1) that by directly linking the mathematical optimization core to the physical actuation of an internal drug bank, drug combination device, and micropipette, the claims transform a virtual simulation into a high-precision cyber-physical system, and that this integration limits the abstract idea to a concrete technological improvement in biochemical screening – specifically minimizing human pipetting errors, achieving extreme fluidic precision, and drastically scaling screening throughput. Finally, the Applicant states that therefore, the claims successfully integrate the judicial exceptions into a practical application under Step 2A Prong Two.
These arguments/remarks are not persuasive, because first, regarding the Applicant’s assertion that “under USPTO eligibility guidelines, a claim is patent-eligible if it integrates any (emphasis added) recited judicial exception into a practical application by applying or using the exception to improve another technology” it is noted that the MPEP at 2106.04(d) states “after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. 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 …” and as noted in the above rejection and in the foregoing response to arguments, when all limitations in claims 1-14 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Second, regarding the Applicant’s argument that “the Examiner has dissected individual clauses to label network weight updates and probability calculations as abstract concepts, further asserting that the hardware merely serves as a generic tool” it is noted that the MPEP at 2106.04(II)(A) states that Step 2A is a two-prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception, as noted in the above rejection and the foregoing response to arguments. Furthermore, and as noted in the above rejection, the recited hardware limitations are used as tools to gather the image data that is subsequently used to generate an analysis result, which as previously noted, is a judicial exception that is not integrated into a practical application, because the claims do not recite any limitations (i.e., additional elements) to which the ‘result’ is practically applied. Third, regarding the Applicant’s assertion that “the amended claims now explicitly require ‘… automatically controlling, based on the provided information, a drug combination device and a micropipette of the drug response screening device to automatically extract and inject the candidate drugs into a well-matrix dish…’” it is noted that this step is a pre-solution step in the process of gathering the data for use in the claimed process, i.e., the automated in vitro testing processing is used to expose a cell line to a candidate drug, and then an image is captured of the cell line in the wells, which is also a step in the process of gathering data. Thereafter, the image (i.e., the gathered data) is analyzed to generate an analysis result, however, as noted in the above rejection and in the foregoing response to arguments, the claims do not recite any limitations (i.e., additional elements) to which the ‘result’ is practically applied. Fourth, regarding the Applicant’s assertion that “the output of the trained reinforcement learning agent is not a mere display of data or a generic computer task, but rather, it serves as an active, machine-control signal that dictates the microscopic physical fluid handling of dedicated screening hardware” it is noted again that the claims do not recite any limitations (i.e., additional elements) to which the ‘result’ is practically applied. Fifth, regarding the Applicant’s argument that “a human mind utilizing pen and paper cannot mechanically govern a physical micropipette or automate a cellular assay loop in real time” it is noted that this limitation has not been identified as a judicial exception the rejection of record or in the above rejection. Sixth, regarding the Applicant’s argument that “by directly linking the mathematical optimization core to the physical actuation of an internal drug bank, drug combination device, and micropipette, the claims transform a virtual simulation into a high-precision cyber-physical system, and that this integration limits the abstract idea to a concrete technological improvement in biochemical screening – specifically minimizing human pipetting errors, achieving extreme fluidic precision, and drastically scaling screening throughput” it is noted that merely tying a judicial exception to hardware does not necessarily establish clear eligibility under the USPTO guidance. As noted in the rejection above at eligibility Step 2A Prong Two, the independent claims provide additional elements, e.g., to provide candidate drug information to a drug response screening device, using the drug response screening device to perform an in vitro test of candidate drugs on a cell line, and then capture an image of the cell line, however once the image data is captured, the subsequent steps only perform analysis of the image to generate an analysis result and output a result based on the analysis result. Thus, the claims do not recite any limitations (i.e., additional elements) to which the ‘result’ is practically applied.
Regarding Step 2B, the Applicant states on page 17 of the Remarks that even if evaluated under Step 2B, the combination of elements provides significantly more than routine computer operations, and further states that while the Examiner cited generic literature to argue that high-content screening is conventional, none of these references disclose or suggest controlling the real-time mechanical extraction and injection mechanisms of a physical biochemical assay through the dynamic learning state of an active machine learning model, and further states that this non-conventional technological integration constitutes a clear inventive concept.
These arguments/remarks are not persuasive, because first, a conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, which comprises steps of: (1) carrying over the identification of any additional element(s) in the claim from Step 2A Prong Two; (2) carrying over the conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h); (3) re-evaluating any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), 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 (4) evaluating whether any additional element 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). As noted in the rejection above, when taken alone (i.e., individually), all additional elements in claims 1-14 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered 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)). Second, regarding the Applicant’s argument that none of the Step 2B references “disclose or suggest controlling the real-time mechanical extraction and injection mechanisms of a physical biochemical assay through the dynamic learning state of an active machine learning model,” it is noted that only the additional elements that were identified at Step 2A Prong Two are carried over to Step 2B for further evaluation, and therefore, the limitations that were identified as judicial exceptions at Step 2A Prong One (e.g., repeatedly performing a learning step a plurality of times by using an agent, the learning step being provided for updating the weights of the links) are not considered at Step 2B in the search for an inventive concept.
Claim Rejections - 35 USC § 103
The rejection of claims 1 and 8 under 35 U.S.C. 103 as being unpatentable over Cho et al. in view of Kitano et al. in the Office action mailed 07 April 2026 has been withdrawn in view of the amendment received 06 July 2026.
The rejection of claims 2-6 and 9-13 under 35 U.S.C. 103 as being unpatentable over Cho et al. in view of Kitano et al. as applied to claims 1 and 8 above, and further in view of Eckardt et al. in the Office action mailed 07 April 2026 has been withdrawn in view of the amendment received 06 July 2026.
The rejection of claim 7 under 35 U.S.C. 103 as being unpatentable over Cho et al. (as cited above) in view of Eckardt et al. in the Office action mailed 07 April 2026 has been withdrawn in view of the amendment received 06 July 2026.
The rejection of claim 14 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. in the Office action mailed 07 April 2026 has been withdrawn in view of the amendment received 06 July 2026.
The amendment received 07 April 2026 has been fully considered, however after further consideration, new grounds of rejection are raised under 35 U.S.C. 103 in view of the amendment.
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.
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.
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.
Claims 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, as cited in the Office action mailed 07 April 2026) in view of Kitano et al. (“Evaluation system, learning device, prediction device, evaluation method, and program.” JP 6810294 (06 January 2021), as cited in the Office action mailed 07 April 2026) in view of Ly et al. (“Automated Reagent-Dispensing System for Microfluidic Cell Biology Assays.” Journal of Laboratory Automation, 2013, vol. 18(6), pp. 530-541, newly cited).
Independent claims 1 and 8 encompass 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. teaches 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. teaches 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.
Ly et al. teaches an automated reagent-dispensing system for microfluidic cell biology assays.
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 candidate drugs to a drug response screening device; a drug response screening device configured to automatically control, based on the provided information, an internal drug bank, a drug combination device, and a micropipette based on the provided information to automatically extract and inject the plurality of candidate drugs into a plurality of wells in which the first cancer cell line is stored; 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 a cell image capturing device of the drug response screening device, images of the first cancer cell line in the plurality of wells.
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).
Regarding independent claims 1 and 8, Ly et al. shows a fluid-delivery platform to interface with low-cost microfluidic chips containing arrays of microchambers, and further shows that by using multiple pairs of needles to aspirate and dispense reagents, the platform enables automated coating of chambers, loading of cells, and treatment with growth media or other agents, e.g., drugs, fixative, membrane permeabilizers, washes, stains, etc. (Abstract), and that is automated via software programming (page 532, col. 1, para. 3). Ly et al. further shows an automated platform that performs flexible sequences of solution loading via automated pipetting and incubation steps for incubation steps for individually addressable cell chambers in an array-format microfluidic chip (page 531, col. 2, para. 3); and further shows a pipette-based approach to minimize sample use and number of cells required while providing flexibility for diverse assays and a wide selection of cell treatment and readout conditions in each microfluidic chamber, such that the resulting platform enables parallel cell culture and quantification without human intervention on up to three microfluidic chips, each containing 24 chambers, for a total of 72 individually addressable chambers (page 537, col. 2, para. 2); and further shows performing cell viability assays (page 536, col. 2, paras. 2-3) and a quantitative target inhibition assay (page 536, col. 2, para. 4 through age 537, col. 1, para. 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have 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.
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. by incorporating methods for using an automated reagent-dispensing system for microfluidic cell biology assays, as shown by Ly 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 Ly et al., because Ly et al. shows that automation of fluid and cell handling increases repeatability, eliminates human error, and enables increased throughput, especially for sophisticated, multistep assays (Abstract). This modification would have had a reasonable expectation of success given that Cho et al. discloses analyzing information corresponding to a cancer related to a patient or a cell line and outputting information on one or more among a single drug sensitivity, a multi-drug sensitivity, an optimal drug, and an optimal drug combination, and Ly et al. discloses an automated system for performing cell viability assays.
Claims 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. in view of Ly et al. as applied to claims 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, as cited in the Office action mailed 07 April 2026).
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. reviews 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. in view of Ly 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. in view of Ly 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. in view of Ly 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. in view of Ly 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.
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) in view of Ly et al. (as cited above).
Independent claim 7 encompasses 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. teaches 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. reviews 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.
Ly et al. teaches an automated reagent-dispensing system for microfluidic cell biology assays.
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; nor automatically controlling, by a computing device based on the selected plurality of candidate drugs, a drug combination device and a micropipette of a drug response screening device to automatically extract and inject the candidate drugs into a well-matrix dish storing the first cancer cell line.
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.
Regarding independent claim 7, Ly et al. shows a fluid-delivery platform to interface with low-cost microfluidic chips containing arrays of microchambers, and further shows that by using multiple pairs of needles to aspirate and dispense reagents, the platform enables automated coating of chambers, loading of cells, and treatment with growth media or other agents, e.g., drugs, fixative, membrane permeabilizers, washes, stains, etc. (Abstract), and that is automated via software programming (page 532, col. 1, para. 3). Ly et al. further shows an automated platform that performs flexible sequences of solution loading via automated pipetting and incubation steps for incubation steps for individually addressable cell chambers in an array-format microfluidic chip (page 531, col. 2, para. 3); and further shows a pipette-based approach to minimize sample use and number of cells required while providing flexibility for diverse assays and a wide selection of cell treatment and readout conditions in each microfluidic chamber, such that the resulting platform enables parallel cell culture and quantification without human intervention on up to three microfluidic chips, each containing 24 chambers, for a total of 72 individually addressable chambers (page 537, col. 2, para. 2); and further shows performing cell viability assays (page 536, col. 2, paras. 2-3) and a quantitative target inhibition assay (page 536, col. 2, para. 4 through age 537, col. 1, para. 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have 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.
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. by incorporating methods for using an automated reagent-dispensing system for microfluidic cell biology assays, as shown by Ly 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 Ly et al., because Ly et al. shows that automation of fluid and cell handling increases repeatability, eliminates human error, and enables increased throughput, especially for sophisticated, multistep assays (Abstract). This modification would have had a reasonable expectation of success given that Cho et al. discloses analyzing information corresponding to a cancer related to a patient or a cell line and outputting information on one or more among a single drug sensitivity, a multi-drug sensitivity, an optimal drug, and an optimal drug combination, and Ly et al. discloses an automated system for performing cell viability assays.
Claim 14 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) in view of Kitano et al. (as cited above) in view of Ly et al. (as cited above).
Independent claim 14 encompasses 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. teaches 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. reviews 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.
Kitano et al. teaches 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.
Ly et al. teaches an automated reagent-dispensing system for microfluidic cell biology assays.
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; nor a drug response screening device; nor automatically control an internal drug bank, a drug combination device, and a micropipette based on the provided information to automatically extract and inject the plurality of candidate drugs into a plurality of wells in which the first cancer cell line is stored, thereby performing an in vitro test.
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.
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, Ly et al. shows a fluid-delivery platform to interface with low-cost microfluidic chips containing arrays of microchambers, and further shows that by using multiple pairs of needles to aspirate and dispense reagents, the platform enables automated coating of chambers, loading of cells, and treatment with growth media or other agents, e.g., drugs, fixative, membrane permeabilizers, washes, stains, etc. (Abstract), and that is automated via software programming (page 532, col. 1, para. 3). Ly et al. further shows an automated platform that performs flexible sequences of solution loading via automated pipetting and incubation steps for incubation steps for individually addressable cell chambers in an array-format microfluidic chip (page 531, col. 2, para. 3); and further shows a pipette-based approach to minimize sample use and number of cells required while providing flexibility for diverse assays and a wide selection of cell treatment and readout conditions in each microfluidic chamber, such that the resulting platform enables parallel cell culture and quantification without human intervention on up to three microfluidic chips, each containing 24 chambers, for a total of 72 individually addressable chambers (page 537, col. 2, para. 2); and further shows performing cell viability assays (page 536, col. 2, paras. 2-3) and a quantitative target inhibition assay (page 536, col. 2, para. 4 through age 537, col. 1, para. 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have 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.
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. 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. by incorporating methods for using an automated reagent-dispensing system for microfluidic cell biology assays, as shown by Ly 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 Ly et al., because Ly et al. shows that automation of fluid and cell handling increases repeatability, eliminates human error, and enables increased throughput, especially for sophisticated, multistep assays (Abstract). This modification would have had a reasonable expectation of success given that Cho et al. discloses analyzing information corresponding to a cancer related to a patient or a cell line and outputting information on one or more among a single drug sensitivity, a multi-drug sensitivity, an optimal drug, and an optimal drug combination, and Ly et al. discloses an automated system for performing cell viability assays.
Response to Arguments
The Applicant’s arguments/remarks received 06 July 2026 have been fully considered, but are not persuasive.
The Applicant states on pages 18-19 of the Remarks that the rejections are traversed because of the following reasons: (1) Structural gaps in the cited prior art (no hindsight combination) and that the prior art references are technically disparate and fail to disclose the structural and functional limitations of the amended independent claims; (2) Rebuttal of the motivation to combine and the “missing link” and that there is no technical bridge or motivation in the prior art to transform numerical algorithmic outputs (e.g., link weights) into active, automated machine-control signals that physically control an internal drug bank, a drug combination device, and a micropipette in a closed loop; and (3) Unexpected and synergistic technical results, i.e., integrating the AI learning loop with the physical micropipette and combination hardware minimizes human pipetting errors, eliminates cross-contamination, achieves extreme volumetric precision, and drastically scales high-content screening throughput, and these technological advantages cannot be achieved by a mere mechanical gathering of the prior art.
These arguments/remarks are not persuasive, because first, the Applicant’s amendment necessitated a new grounds of rejection utilizing a new reference to show the newly amended structural and functional limitations, e.g., automatically controlling a drug screening device. Second, the new grounds of rejection has provided a new motivation statement for combining the references in view of the new limitations. Third, the new grounds of rejection in view of the amendment has provided a new reference showing that it would have been obvious to use a computer to automate a device for performing cell biology assays.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.W.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
