DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on October 29, 2025 and May 25, 2026 are in compliance with the provisions of 37 CFR 1.97, and have been considered by the examiner.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 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 1, 4, and 7 recite “predicting, by using a first machine learning model, a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line”. The claims currently require that the two data combinations include at least one common item. However, this claim language includes situations where the all of the data in the two data combinations is the same. In that situation, there would not be a predicted difference in drug efficacies if the data is the same. Therefore, it is generally unclear how to generate a predicted difference in drug efficacy in one or more of two data combinations if all of the data in the two data combinations is the same. Instead, the Examiner suggests that Applicant amend the claim language to recite “each of the two data combinations including at least one different item among items of a drug and a cell line”, or make some other appropriate correction of course. Appropriate correction is required.
Additionally, the “one or more of two data combinations” claim language also includes the situation where only one data combination is considered. Therefore, it seems like there would not be a predicted difference in drug efficacy if only one data combination is applied to the first machine learning model, because only one drug efficacy would be generated for the one data combination and there would not be another predicted drug efficacy to compare the first predicted drug efficacy to. Instead, the Examiner suggests that Applicant amend the claim language to recite “predicting, by using a first machine learning model, a difference in drug efficacy between at least two of two data combination sets under a same experimental system […]”, or make some other appropriate correction of course. Appropriate correction is required.
Claims 2, 3, 5, 6, 8, and 9 are also rejected under 35 U.S.C. § 112(b) or 35 U.S.C. § 112 (pre-AIA ), second paragraph, as being indefinite, for similar reasons as described above in the analysis of the § 112(b) rejections applied to claims 1, 4, and 7 (due to their individual dependencies on claims 1, 4, and 7).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP § 2106 (hereinafter referred to as the “2019 Revised PEG”).
Step 1 of the Alice/Mayo Test
Following Step 1 of the 2019 Revised PEG, claims 1-9 are directed to a non-transitory computer-readable recording medium having stored therein a drug efficacy prediction program, a computer-implemented method for predicting drug efficacy, or a drug efficacy prediction device, which are within one of the four statutory categories of invention (i.e., a manufacture, process, or machine or apparatus). See MPEP § 2106.03.
Step 2A of the 2019 Revised PEG - Prong One
Following Prong One of Step 2A of the 2019 PEG, the claim limitations are to be analyzed to determine whether they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. See MPEP §2106.04. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: (1) Mathematical Concepts; (2) Certain Methods of Organizing Human Activity, and (3) Mental Processes. See MPEP § 2106.04(a).
Claims 1-9 are rejected under 35 U.S.C. § 101, because the claimed invention is directed to an abstract idea without significantly more. Representative independent claims 1, 4, and 7 include limitations that recite an abstract idea. Note that independent claim 7 is directed to a device, while claim 1 covers the matching non-transitory computer-readable recording medium and claim 4 covers the matching method. Specifically, independent claim 7 recites the following limitations:
A drug efficacy predicting device comprising:
a memory;
an input unit; and
a processor being coupled to the memory and configured to:
predict, by using a first machine learning model, a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line; and
predict, by using a second machine learning model, one or more drug efficacies of two or more data using the predicted difference.
However, the Examiner submits that the foregoing underlined limitations constitute a process that, under its broadest reasonable interpretation, falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. The Mental Processes category covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, or opinion) (i.e., a method for predicting drug efficacy comprising: predicting a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line; and predicting one or more drug efficacies of two or more data using the predicted difference). See MPEP § 2106.04(a)(2)(III). That is, other than reciting some computer components and functions (the foregoing limitations in claim 7 which are not underlined), the context of claims 1, 4, and 7 encompass concepts that are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, and/or opinion) (i.e., a method for predicting drug efficacy comprising: predicting a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line; and predicting one or more drug efficacies of two or more data using the predicted difference).
The aforementioned claim limitations described in claims 1, 4, and 7 are analogous to claim limitations directed toward concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, because they merely recite limitations which encompass a person mentally and/or manually: (1) predicting a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally make a prediction about a difference in drug efficacy between at least one of two data combinations, where the common data item is a drug or cell line); and (2) predicting one or more drug efficacies of two or more data using the predicted difference (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally make a prediction about one or more drug efficacies of two or more data using the predicted difference from the previous step).
Further, Applicant’s claims are similar to claims which have been held to recite an abstract mental process. For example, the Federal Circuit held that a claim directed to “collecting information, analyzing it, and displaying certain results of the collection and analysis”, where the data analysis steps are recited at a high level of generality, amounted to steps that could practically be performed in the human mind. See MPEP § 2106.04(a)(2)(III)(A) (citing Electric Power Group g. Alstom, S.A.). Similarly, Applicant’s claims recite merely recite steps for analyzing the data (i.e., in this case, prediction steps), at a high level of generality, because the claims do not recite any specificity for how the prediction is made. Therefore, the aforementioned underlined claim limitations may reasonably be interpreted as mental/manual observations, evaluations, judgments, and/or opinions made by a person, such as a healthcare professional. If a claim limitation, under its broadest reasonable interpretation, covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. Accordingly, claims 1, 4, and 7 recite an abstract idea that falls within the Mental Processes category.
Furthermore, Examiner notes that dependent claims 2, 3, 5, 6, 8, and 9 include limitations that are deemed to be additional elements, and require further analysis under Prong Two of Step 2A.
Step 2A of the 2019 Revised PEG – Prong Two
Regarding Prong Two of Step 2A of the 2019 Revised PEG, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted in the 2019 Revised PEG, it must be determined whether any additional elements in the claims are indicative of integrating the abstract idea into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” See MPEP §§ 2106.05 (f)-(h).
In the present case, for independent claim 7, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A drug efficacy predicting device (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) comprising:
a memory (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f));
an input unit (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)); and
a processor being coupled to the memory and configured to (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)):
predict, by using a first machine learning model (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and generally linking the abstract idea to a particular field of use, as noted below, see MPEP § 2106.05(h)), a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line; and
predict, by using a second machine learning model (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and generally linking the abstract idea to a particular field of use, as noted below, see MPEP § 2106.05(h)), one or more drug efficacies of two or more data using the predicted difference; and
a non-transitory computer-readable recording medium having stored therein a drug efficacy predicting program for causing a computer to execute a process (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) (as described in claim 1).
However, the recitation of these generic computer components and functions in claims 1, 4, and 7 are recited at a high-level of generality (i.e., using generic a computer device, non-transitory computer-readable recording medium, and a first machine learning model and a second machine learning model to perform the abstract mental process of: a method for predicting drug efficacy comprising: predicting a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line; and predicting one or more drug efficacies of two or more data using the predicted difference), such that it amounts to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f)-(h). For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
- The following are examples of court decisions that demonstrate merely applying instructions by reciting the computer structure as a tool to implement the claimed limitations (e.g., see MPEP § 2106.05(f)):
- Invoking computers or other machinery merely as a tool to perform an existing process, e.g. see, Affinity Labs v. DirecTV – similarly, the current invention invokes computers (i.e., the drug efficacy predicting device, comprising a memory and processor; non-transitory computer-readable recording medium; and computer) and other machinery to perform the existing process of: (1) predicting a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line; and (2) predicting one or more drug efficacies of two or more data using the predicted difference.
- Requiring the use of software to tailor information and provide it to the user on a generic computer, e.g. see, Intellectual Ventures I LLC v. Capital One Bank – similarly, the current invention merely requires the drug efficacy program stored on the non-transitory computer-readable recording medium, first machine learning model, and second machine learning model, to ultimately perform the abstract mental process of: (1) predicting a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line; and (2) predicting one or more drug efficacies of two or more data using the predicted difference, as described in claim 1, 4, and 7.
- The following represent examples that courts have identified as generally linking the abstract idea to a particular technological environment (e.g., see MPEP § 2106.05(h)):
- (1) Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, FairWarning v. Iatric Sys.; (2) Specifying that the abstract idea of using advertising as currency is used on the Internet, because this narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment, Ultramercial, Inc. v. Hulu; and (3) Requiring that the abstract idea of creating a contractual relationship that guarantees performance of a transaction (a) be performed using a computer that receives and sends information over a network, or (b) be limited to guaranteeing online transactions, because these limitations simply attempted to limit the use of the abstract idea to computer environments, buySAFE Inc. v. Google, Inc. - similarly, describing that the abstract idea is performed using “a first machine learning model” and “a second machine learning model”, as described in claims 1, 4, and 7, merely limits the claims to the field of machine learning technologies, and does not provide any meaningful limits to the abstract mental process described in the claims.
Thus, the additional elements in independent claims 1, 4, and 7 are not indicative of integrating the judicial exception into a practical application. Examiner notes that dependent claims 2, 3, 5, 6, 8, and 9 recite the following additional elements identified in bold font below (with limitations deemed to be part of the above identified abstract idea identified in underlined font):
wherein the first machine learning model is trained, using feature vectors of each of the two data combinations as an explanatory variable and using a difference in measured value of the drug efficacy between the two data combinations as a response variable (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and generally linking the abstract idea to a particular field of use, as noted below, see MPEP § 2106.05(h)) (as described in claims 2, 5, and 8); and
wherein the second machine learning model is trained, using the difference in the measured value of the drug efficacy and the feature vectors of each of the two or more data combinations as explanatory variables and using the measured value of the drug efficacy as a response variable (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and generally linking the abstract idea to a particular field of use, as noted below, see MPEP § 2106.05(h)) (as described in claims 3, 6, and 9).
As such, the additional elements in claims 1-9 are not indicative of integrating the judicial exception into a practical application. Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, unlike the claims that have been held as a whole to be directed to an improvement or otherwise directed to something more than the abstract idea, claims 1-9: (1) are not directed to improvements to the functioning of a computer, or to any other technology or technical field similar to the Enfish, LLC v. Microsoft Corp. case (see MPEP § 2106.05(a)); (2) do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see MPEP § 2106.04(d)(2)); (3) do not apply the judicial exception with, or by use of, a particular machine (see MPEP § 2106.05(b)); (4) do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)); nor do they (5) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as whole is more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05(e) and MPEP § 2106.04(d)(2)). For these reasons, claims 1-9 do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B of the 2019 Revised PEG
Regarding Step 2B of the 2019 Revised PEG, claims 1-9 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of abstract idea into a practical application, the additional elements of claims 1-9 amount to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.05(f)-(h). Further the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than limitations consistent with what the courts recognize, or those having ordinary skill in the art would recognize, to be well-understood, routine, and conventional computer components. See MPEP § 2106.05 (d).
Specifically, the Examiner submits that the additional elements of claims 1-9, as recited, the non-transitory computer-readable recording medium having stored therein a drug efficacy predicting program; computer; drug efficacy predicting device; memory; processor being coupled to the memory; first machine learning model; second machine learning model; and the steps directed to: “the first machine learning model is trained, using feature vectors of each of the two data combinations as an explanatory variable, and using a difference in measured value of drug efficacy between the two data combinations as a response variable”; and “the second machine learning model is trained, using the difference in the measured value of the drug efficacy and the feature vectors of each of the two or more data combinations as explanatory variables and using the measured value of the drug efficacy as a response variable”, are well-understood, routine, and conventional functions. See MPEP § 2106.05(d)(II).
- In regard to the aforementioned additional elements, these additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than well-understood, routine, and conventional activities previously known to the industry, because:
- Applicant’s disclosure supports this assertion. For example, Applicant discloses that the non-transitory computer-readable recording medium includes a magnetic/optical disk, and a flash memory, where the magnetic/optical disk includes “a flexible disk, a Compact Disc (CD), a Digital Versatile Disc (DVD), a Blu-ray disk, and a Holographic Versatile Disc (HVD)”; and the flash memory includes “a semiconductor memory such as a USB memory and an SD card”. See Applicant’s specification as filed on October 29, 2025, paragraph [0040]. Next, Applicant discloses that the processor 10a may be any one of integrated circuits (ICs) such as CPUs (Central Processing Units), MPUs (Micro Processing Units), APUs (Accelerated Processing Units), DSPs (Digital Signal Processors), ASICs (Application Specific Integrated Circuits), and FPGAs (Field Programmable Gate Arrays), or combinations of two or more of these ICs. See Applicant’s specification as filed on October 29, 2025, paragraph [0030]. Lastly, Applicant discloses that the memory may be a non-volatile memory, for example, “a
flash memory, a Storage Class Memory (SCM), a Read Only Memory (ROM), and the like”. See Applicant’s specification as filed on October 29, 2025, paragraph [0033]. These descriptions in the specification describe these additional elements as basic computer components and functions, such as well-understood, routine, and conventional computer components. Therefore, the Examiner submits that these computer components and functions represent well-understood, routine, and conventional computer components and functions which are known in the medical industry.
- The Examiner submits that these limitations amount to merely using a computer or other machinery as tools for performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f) and analysis of these limitations under Step 2A, Prong Two above).
- The Examiner submits that these limitations generally link the use of the judicial exception to a particular technological environment or field of use – for example, the limitations directed to: a first machine learning model; a second machine learning model; and the steps directed to: “the first machine learning model is trained, using feature vectors of each of the two data combinations as an explanatory variable, and using a difference in measured value of drug efficacy between the two data combinations as a response variable”; and “the second machine learning model is trained, using the difference in the measured value of the drug efficacy and the feature vectors of each of the two or more data combinations as explanatory variables and using the measured value of the drug efficacy as a response variable”, amount to limiting the abstract idea to the field of machine learning technologies (see MPEP § 2106.05(h) and analysis of these limitations under Step 2A, Prong Two above).
Therefore, the additional elements described in claims 1-9 are deemed to be additional elements which do not amount to significantly more than the abstract idea identified above. Thus, taken alone, the additional elements of claims 1-9 do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functionality of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, claims 1-9 are nonetheless rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over:
- Watanabe et al. (Pub. No. US 2026/0165668), in view of:
- Griffin et al. (Pub. No. US 2023/0178253).
Regarding claims 1, 4, and 7,
- Watanabe et al. (Pub. No. US 2026/0165668) discloses:
- a non-transitory computer-readable recording medium having stored therein a drug efficacy predicting program for causing a computer to execute a process comprising (as described in claim 1) (Watanabe, paragraph [0179]; Paragraph [0179] discloses a non-transitory tangible medium as the recording medium for storing and loading a program supplied to a computer.):
- a computer-implemented method for predicting drug efficacy comprising (as described in claim 4) (Watanabe, paragraph [0006]; Paragraph [0006] discloses a control method for generating drug efficacy prediction information.):
- a drug efficacy predicting device (Watanabe, paragraph [0024]; Paragraph [0024] discloses a prediction device that outputs drug efficacy prediction information regarding an effect of a drug when the drug is administered to a human subject.) comprising: a memory (Watanabe, paragraph [0179]; Paragraph [0179] discloses that the prediction device includes a semiconductor memory treatment evaluator includes memory.); and a processor being coupled to the memory and configured to (as described in claim 7) (Watanabe, paragraph [0179]; Paragraph [0179] discloses that the prediction device includes a computer, that includes, one or more processors (i.e., processor).):
- predicting, by using a first machine learning model, … drug efficacy between one or more of two data combinations under a same experimental system, … (as described in claims 1, 4, and 7) (Watanabe, paragraph [0082]; Paragraph [0082] discloses that the predictor 23 inputs the subject information into the trained drug efficacy prediction model 34 (i.e., a first machine learning model) that predicts an effect of a drug based on the drug information regarding the drug having an action on bones (i.e., predicting, using the first machine learning model, a difference in drug efficacy between two data combinations under a same experimental system, where the two data combinations are (1) the subject information and (2) the drug information), thereby generating and outputting the drug efficacy prediction information regarding the effect of the drug when the drug is administered to the human subject, where the predictor outputs a prediction result indicating a difference between a future bone density and a current bone density of the human subject's bone.); and
- predicting, by using a second machine learning model, one or more drug efficacies of two or more data … (as described in claims 1, 4, and 7) (Watanabe, paragraphs [0089] and [0103], and FIG. 4; Paragraph [0089] discloses that the predictor 23, in response to the subject information (e.g., medical image) and the drug efficacy information being input to an input layer 231 (see FIG. 4) (i.e., the input layer is interpreted as a second machine learning model, where the input layer is described as being embodied by a neural network – see paragraph [0103]), executes operations based on the trained drug efficacy prediction model and outputs the drug efficacy prediction information from an output layer 232 (see FIG. 4) (i.e., predicting, using the second machine learning model, one or more drug efficacies of the two or more data).).
- While Watanabe discloses a non-transitory computer readable medium, method, and device for drug efficacy prediction configured to predict an effect of a drug on a human subject’s bones when the drug is administered to the human subject, Watanabe does not explicitly teach, however, in analogous art of machine learning methods and software systems for identifying drug efficacies, Griffin et al. (Pub. No. US 2023/0178253) teaches a method and system, comprising:
- predicting, by using a first machine learning model, a difference in drug efficacy between one or more of two data combinations under a same experimental system, each of the two data combinations including at least one common item among items of a drug and a cell line (as described in claims 1, 4, and 7) (Griffin, paragraphs [0035] and [0113]; Paragraph [0035] teaches that the machine learning models are trained to predict the efficacy of drug combinations at treating subject morbidities of a patient (i.e., predicting, by using a first machine learning model, drug efficacies for one or more of two data combinations under a same experimental system), where paragraph [0113] teaches that overlapping drug combinations are compared based on a difference in the relative efficiencies between the overlapping drug combinations (i.e., predicting a difference between one or more of two data combinations under a same experimental system). Further, paragraph [0113] teaches that drug combinations can be compared in any desired manner to identify overlapping drug combinations (i.e., each of the two data combinations includes at least one common item among items of a drug and a cell line, where the common item is the overlapping drug).); and
- predicting, by using a second machine learning model, one or more drug efficacies of two or more data using the difference (as described in claims 1, 4, and 7) (Griffin, paragraph [0080]; Paragraph [0080] teaches that the treatment machine learning model (i.e., a second machine learning model) can generate an overall prediction (e.g., the morbidity will or will not be effectively treated) based on the outputs of each of the classification models forming the treatment machine learning model, such as based on the weighted predictions from the multiple logic decision trees (i.e., predicting one or more drug efficacies of two or more data using the difference). Paragraphs [0080] and [0081] teaches that this feature is beneficial for determining whether the drug combination will or will not be effective at treating the subject morbidity for the patient by simulating treatment of the patient by each drug combination from the baseline health data.).
Therefore, it would have been obvious to one of ordinary skill in the art of machine learning methods for identifying drug efficacies at the time of the effective filing date of the claimed invention to modify the non-transitory tangible medium, control method, and taught by Watanabe, to incorporate steps and features directed to: (i) predicting a difference in drug efficacy between one or more of two data combinations using a first machine learning model; and (ii) predicting one or more drug efficacies of two or more data using the difference, as taught by Griffin, in order to determine whether the drug combination will or will not be effective at treating the subject morbidity for the patient by simulating treatment of the patient by each drug combination from the baseline health data. See Griffin, paragraphs [0081] and [0082]; see also MPEP § 2143 G.
Regarding claims 2, 5, and 8,
- The combination of: Watanabe, as modified in view of Griffin, teaches the limitations of: claim 1 (which claim 2 depends on); claim 4 (which claim 5 depends on); and claim 7 (which claim 8 depends on), as described above.
- Watanabe further teaches a non-transitory computer-readable recording medium, computer-implemented method, and drug efficacy prediction device, wherein:
- the first machine learning model is trained, using feature vectors of each of the two data combinations as an explanatory variable, and using a difference in measured value of drug efficacy between the two data combinations as a response variable (as described in claims 2, 5, and 8) (Watanabe, paragraph [0087]; Paragraph [0087] teaches that the trained drug efficacy prediction model 34 (i.e., which includes the first machine learning model) may be trained using the training data 32 (i.e., the first machine learning model is trained) in which multiple pieces of examinee information regarding multiple examinees to whom drug A is administered and multiple pieces of examinee information regarding multiple examinees to whom drug B, which is a different type from drug A, is administered are used as explanatory variables (i.e., using feature vectors of each of the two data combinations as an explanatory variable), and effects of the drugs on the multiple pieces of examinees are used as response variables (i.e., using a difference in measured value of drug efficacy between the two data combinations as a response variable).).
Regarding claims 3, 6, and 9,
- The combination of: Watanabe, as modified in view of Griffin, teaches the limitations of: claim 2 (which claim 3 depends on); claim 5 (which claim 6 depends on); and claim 8 (which claim 9 depends on), as described above.
- Watanabe further teaches a non-transitory computer-readable recording medium, computer-implemented method, and drug efficacy prediction device, wherein:
- the second machine learning model is trained, using the difference in the measured value of the drug efficacy and the feature vectors of each of the two or more data combinations as explanatory variables and using the measured value of the drug efficacy as a response variable (as described in claims 3, 6, and 9) (Watanabe, paragraph [0088]; Paragraph [0088] teaches that the trained drug efficacy prediction model 34 (i.e., which includes the input layer that is interpreted as the second machine learning model) may be trained using the training data 32 (i.e., the second machine learning model is trained) in which multiple pieces of examinee information regarding multiple examinees administered drugs in different combinations are used as explanatory variables (i.e., using the difference in measure value of the drug efficacy and the feature vectors of each of the two data combinations as an explanatory variable), and effects of the drugs on the multiple pieces of examinees are used as response variables (i.e., using a difference in measured value of drug efficacy between the two data combinations as a response variable).).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/N.A.A./Examiner, Art Unit 3686
/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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