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 .
Remarks
In response to communications sent April 15, 2022 claim(s) 16-25 are pending in this application; of these claims 16, 19, 20, and 21 are in independent form. Claims 1-15 are cancelled.
Response to Amendment
The preliminary amendments to the claims and abstract, filed April 15, 2022 are acknowledged and have been entered into the record. For clarity about which version of the abstract was entered, note that the amended abstract can be visually identified and distinguished from the original abstract because it begins on page “9” rather than “47”.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawing(s) filed on April 15, 2022 are accepted by the Examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The browser executable code is located in the Applicant’s Specification at Para [0032] in two instances; Para [0033] in four instances; Para [0039] in two instances; Para [0041]; Para [0146]; and Para [0147].
The use of the terms Wako, Otsuka, Sigma-Aldrich, Bristol Myers Squibb, Medchemexpress, Astellas, Cayman Chemical, Illumina, and Lexogen, each of which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore, the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The trademarks are located in Example 1 starting on at Para [0123].
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: April 15, 2022; July 13, 2022; September 30, 2022; January 3, 2023; and March 11, 2024.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“processing part” in claim 19. (See paragraphs [0063]-[0074] for structure and paragraphs [0076]-[0079] for algorithmic constraints.)
“server device” in claim 21 (See paragraphs [0096]-[0097] for structure and paragraphs [0113] for algorithmic constraints).
“prediction device” in claim 21 (See paragraphs [0113]).
“processing part” in claim 21 (See paragraphs [0096]-[0097] for structure and paragraphs [0113]-[0114] for algorithmic constraints).
“communication part” in claim 21 (See paragraphs [0096]-[0097] for structure and paragraphs [0113]-[0114] for algorithmic constraints).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Specifically, no the following portions of the Applicant’s Specification:
“processing part” in claim 19. (See paragraphs [0063]-[0074] for structure and paragraphs [0076]-[0079] for algorithmic constraints.)
“server device” in claim 21 (See paragraphs [0096]-[0097] for structure and paragraphs [0113] for algorithmic constraints).
“prediction device” in claim 21 (See paragraphs [0113]).
“processing part” in claim 21 (See paragraphs [0096]-[0097] for structure and paragraphs [0113]-[0114] for algorithmic constraints).
“communication part” in claim 21 (See paragraphs [0096]-[0097] for structure and paragraphs [0113]-[0114] for algorithmic constraints).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 17 and 18 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.
- The phrase “existing substance or equivalent substance” is unclear, because the properties that are equivalent are not specified.
- Furthermore, the claim recites that the “test substance does not include an existing substance or an equivalent substance of an existing substance.” However, because the substance does not exist, it is unclear what the metes and bounds of the substance is.
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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to software per se (MPEP § 2106.03(I)), which is not one of the four statutory categories. See Applicant’s Specification at Para [0119]-[0120] for a description of the software per se, which is recited in the claims as the statutory class (“A computer program for predicting…”).
Claims 16-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematical concepts. This judicial exception is not integrated into a practical application because the additional limitations are extra-solution activity. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because precedential courts have determined that receiving data on a general purpose computer is well-understood, routine, and conventional.
(Note claim 20 would be rejected as judicial exceptions but for the issue of statutory class.)
Regarding step 2A prong 1, the limitation of “inputting the first test data set and a second test data set into an artificial intelligence model to predict an indication for the test substance in humans” in claims 16 and 19-21 equates to a mathematical concept because the BRI of "artificial intelligence model" includes models that are purely mathematical equations, for example linear regression, support vector machines, etc. Claim 25 further limits the model to a one-class support vector machine. The limitations regarding the training method in claims 16 and 19-22 are recited in the past tense and merely describe the process by which the model was previously trained but do not require the training to occur in the claimed invention. Therefore, the instant claims recite an abstract idea.
Regarding Step 2A, Prong 2: The additional elements are acquiring data and generic computer components (server device, prediction device with a processing part and communication part). These equate to insignificant extra-solution activity and instructions to apply in a generic computer environment, so there is not a practical application.
Regarding Step 2B: Receiving is a conventional computer function according to the courts therefore the combination of additional elements is also conventional. Thus, there is no inventive concept recited.
Dependent claims 23 and 24 limit the extra-solution activity of the independent claims by limiting the data involved.
Claim Rejections - 35 USC § 103
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.
Claim(s) 16-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20170083670 A1 (“Kosaka”) in view of Betge, Johannes, Niklas Rindtorff, Jan Sauer, Benedikt Rauscher, Clara Dingert, Haristi Gaitantzi, Frank Herweck, Thilo Miersch, Erica Valentini, and Veronika Hauber. "Multiparametric phenotyping of compound effects on patient derived organoids." Biorxiv (2019): 660993.
The latter reference shall hereinafter be referred to as “Betge”.
As to claim 16, Kosaka teaches a method for predicting an indication for a test substance in humans (Kosaka Para [0117]: predicting an indication of medical event in humans; Para [0034] establishes that a medical event may be a medical act), comprising the steps of:
acquiring a first test data set (Kosaka Para [0113]: inputting attribute data from patient diagnostic determinations), the first test data set being a set of data indicating the dynamics (Kosaka Para [0114]: indicating time series of medical events) of a biomarker (Kosaka Para [0114]: a disease; see definition at Para [0083]: “Diagnosed Disease Name” which emphasizes the element of making a diagnostic determination) in one or multiple organs (Kosaka Para [0114]: in a patient) collected from [[ (Kosaka Para [0114]: drugs) has been administered (Kosaka Para [0114]: a disease-drug combination after administration), and
inputting the first test data set (Kosaka Para [0014]: inputting the attribute data including the diagnostic data) and a second test data (Kosaka Para [0014]: inputting attribute data include “other medical events”) set into an artificial intelligence model (Kosaka Para [0223]: inputting into a one-class support vector machine) to predict an indication for the test substance in humans (Kosaka Para [0223]: predict an outlier) based on the first test data set and the second test data set input thereinto (Kosaka Para [0223]: based on data from the examples of the inputted data), the second test data set being a set of data in which labels of multiple known indications are linked with information about adverse events reported correspondingly to each of the multiple known indications (Kosaka Para [0119]: the inputted data using positive examples and negative examples involving diseases and other medical events),
wherein the artificial intelligence model trained by a method (Kosaka Para [0223]: inputting into a one-class support vector machine), comprising:
inputting a first training data set (Kosaka Para [0014]: inputting the attribute data including the diagnostic data), a second training data set (Kosaka Para [0014]: inputting attribute data include “other medical events”) and a third training data set in association with one another into an artificial intelligence model (Kosaka Para [0223]: inputting into a one-class support vector machine) to train the artificial intelligence model (Kosaka Para [0223]: predict an outlier),
the first training data set being a set of data in which a set of data indicating the dynamics (Kosaka Para [0114]: indicating time series of medical events) of a biomarker (Kosaka Para [0114]: a disease; see definition at Para [0083]: “Diagnosed Disease Name” which emphasizes the element of making a diagnostic determination) in one organ or each of multiple different organs collected from respective [[ (Kosaka Para [0114]: in a patient) to which multiple predetermined existing substances with a known indication in humans (Kosaka Para [0114]: drugs) have been individually administered (Kosaka Para [0114]: a disease-drug combination after administration) is linked with labels indicating respective names of the administered predetermined existing substances (Kosaka Para [0183]: linking with Anatomical Therapeutic Chemical Classification System ATC codes),
the second training data set being a set of data in which labels indicating respective names of the multiple predetermined existing substances (Kosaka Para [0183]: ATC codes) are linked (Kosaka Para [0183]: linking ATC codes with ICD codes in event data) with labels indicating the indications reported for each of the multiple predetermined existing substances (Kosaka Para [0183]: labels indicating International Statistical Classification of Diseases and Related Health Problems, or ICD codes),
the third training data set being a set of data in which labels indicating the indications reported for each of the multiple predetermined existing substances are linked with information about adverse events reported correspondingly to each of these indications (Kosaka Para [0183]: data regarding adverse events are converted to encoded data involving ICD and ATC codes).
However, Kosaka does not teach that the animals are non-human animals. Nevertheless, Betge teaches to use non-human animals (Betge, title: “Multiparametric phenotyping of compound effects on patient derived organoids”).
Kosaka and Betge are in the same field of drug discovery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kosaka to include the teachings of Betge because organoids prevent the need for putting patients at risk; instead, there is a reasonable expectation of success because patient-derived organoids are designed to resemble human organs (See the abstract of Betge).
As to claim 17, Kosaka in view of Betge teaches the method according to claim 16, wherein the test substance does not include an existing substance or an equivalent substance of an existing substance (Para [0183]: the 10th Edition of the ICD codes is used, which does not include later substances).
As to claim 18, Kosaka in view of Betge teaches the method according to claim 16, wherein the test substance is one selected from existing substances or equivalent substances of existing substances (Para [0183]: the 10th Edition of the ICD codes is used, which includes existing substances).
As to claim 19, Kosaka teaches a prediction device for predicting an indication for a test substance in humans (Kosaka Para [0117]: predicting an indication of medical event in humans; Para [0034] establishes that a medical event may be a medical act), comprising a processing part,
wherein the processing part inputs a first test data set (Kosaka Para [0113]: inputting attribute data from patient diagnostic determinations) and a second test data set (Kosaka Para [0014]: inputting attribute data include “other medical events”) into an artificial intelligence model (Kosaka Para [0223]: inputting into a one-class support vector machine) to predict an indication for the test substance in humans (Kosaka Para [0223]: predict an outlier) based on the first test data set and the second test data set input thereinto (Kosaka Para [0223]: based on data from the examples of the inputted data),
the first test data set (Kosaka Para [0113]: inputting attribute data from patient diagnostic determinations) being a set of data indicating the dynamics (Kosaka Para [0114]: indicating time series of medical events) of a biomarker (Kosaka Para [0114]: a disease; see definition at Para [0083]: “Diagnosed Disease Name” which emphasizes the element of making a diagnostic determination) in one or multiple organs (Kosaka Para [0114]: in a patient) corresponding to one or multiple organs collected from [[ (Kosaka Para [0114]: drugs) has been administered (Kosaka Para [0114]: a disease-drug combination after administration) to generate the first training data set,
the second test data set being a set of data in which labels of multiple known indications are linked with information, acquired to generate a third training data set, about adverse events reported correspondingly to each of the multiple known indications (Kosaka Para [0119]: the inputted data using positive examples and negative examples involving diseases and other medical events),
wherein the artificial intelligence model trained by a method (Kosaka Para [0223]: inputting into a one-class support vector machine), comprising:
inputting a first training data set (Kosaka Para [0014]: inputting the attribute data including the diagnostic data), a second training data set (Kosaka Para [0014]: inputting attribute data include “other medical events”) and a third training data set in association with one another into an artificial intelligence model (Kosaka Para [0223]: inputting into a one-class support vector machine) to train the artificial intelligence model (Kosaka Para [0223]: predict an outlier),
the first training data set being a set of data in which a set of data indicating the dynamics (Kosaka Para [0114]: indicating time series of medical events) of a biomarker (Kosaka Para [0114]: a disease; see definition at Para [0083]: “Diagnosed Disease Name” which emphasizes the element of making a diagnostic determination) in one organ or each of multiple different organs collected from respective [[ (Kosaka Para [0114]: in a patient) to which multiple predetermined existing substances with a known indication in humans (Kosaka Para [0114]: drugs) have been individually administered (Kosaka Para [0114]: a disease-drug combination after administration) is linked with labels indicating respective names of the administered predetermined existing substances (Kosaka Para [0183]: linking with Anatomical Therapeutic Chemical Classification System ATC codes),
the second training data set being a set of data in which labels indicating respective names of the multiple predetermined existing substances (Kosaka Para [0183]: ATC codes) are linked (Kosaka Para [0183]: linking ATC codes with ICD codes in event data) with labels indicating the indications reported for each of the multiple predetermined existing substances (Kosaka Para [0183]: labels indicating International Statistical Classification of Diseases and Related Health Problems, or ICD codes),
the third training data set being a set of data in which labels indicating the indications reported for each of the multiple predetermined existing substances are linked with information about adverse events reported correspondingly to each of these indications (Kosaka Para [0183]: data regarding adverse events are converted to encoded data involving ICD and ATC codes).
However, Kosaka does not teach that the animals are non-human animals. Nevertheless, Betge teaches to use non-human animals (Betge, title: “Multiparametric phenotyping of compound effects on patient derived organoids”).
Kosaka and Betge are in the same field of drug discovery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kosaka to include the teachings of Betge because organoids prevent the need for putting patients at risk; instead, there is a reasonable expectation of success because patient-derived organoids are designed to resemble human organs (See the abstract of Betge).
As to claim 20, Kosaka teaches a computer program for predicting an indication for a test substance in humans (Kosaka Para [0117]: predicting an indication of medical event in humans; Para [0034] establishes that a medical event may be a medical act) that, when executed by a computer, causes the computer to execute the step of:
inputting a first test data set (Kosaka Para [0113]: inputting attribute data from patient diagnostic determinations) and a second test data set (Kosaka Para [0014]: inputting attribute data include “other medical events”) into an artificial intelligence model (Kosaka Para [0223]: inputting into a one-class support vector machine) to predict an indication for the test substance in humans (Kosaka Para [0223]: predict an outlier) based on the first test data set and the second test data set input thereinto (Kosaka Para [0223]: based on data from the examples of the inputted data),
the first test data set being a set of data indicating the dynamics (Kosaka Para [0114]: indicating time series of medical events) of a biomarker (Kosaka Para [0114]: a disease; see definition at Para [0083]: “Diagnosed Disease Name” which emphasizes the element of making a diagnostic determination) in one or multiple organs (Kosaka Para [0114]: in a patient) corresponding to one or multiple organs collected from [[ (Kosaka Para [0114]: drugs) has been administered (Kosaka Para [0114]: a disease-drug combination after administration) to generate the first training data set,
the second test data set being a set of data in which labels of multiple known indications are linked with information, acquired to generate a third training data set, about adverse events reported correspondingly to each of the multiple known indications (Kosaka Para [0119]: the inputted data using positive examples and negative examples involving diseases and other medical events),
wherein the artificial intelligence model trained by a method (Kosaka Para [0223]: inputting into a one-class support vector machine), comprising:
inputting a first training data set (Kosaka Para [0014]: inputting the attribute data including the diagnostic data), a second training data set (Kosaka Para [0014]: inputting attribute data include “other medical events”) and a third training data set in association with one another into an artificial intelligence model to train the artificial intelligence model (Kosaka Para [0223]: inputting into a one-class support vector machine) ,
the first training data set being a set of data in which a set of data indicating the dynamics (Kosaka Para [0114]: indicating time series of medical events) of a biomarker (Kosaka Para [0114]: a disease; see definition at Para [0083]: “Diagnosed Disease Name” which emphasizes the element of making a diagnostic determination) in one organ or each of multiple different organs collected from respective [[ (Kosaka Para [0114]: in a patient) to which multiple predetermined existing substances with a known indication in humans (Kosaka Para [0114]: drugs) have been individually administered (Kosaka Para [0114]: a disease-drug combination after administration) is linked with labels indicating respective names of the administered predetermined existing substances (Kosaka Para [0183]: linking with Anatomical Therapeutic Chemical Classification System ATC codes),
the second training data set being a set of data in which labels indicating respective names of the multiple predetermined existing substances (Kosaka Para [0183]: ATC codes) are linked (Kosaka Para [0183]: linking ATC codes with ICD codes in event data) with labels indicating the indications reported for each of the multiple predetermined existing substances (Kosaka Para [0183]: labels indicating International Statistical Classification of Diseases and Related Health Problems, or ICD codes),
the third training data set being a set of data in which labels indicating the indications reported for each of the multiple predetermined existing substances are linked with information about adverse events reported correspondingly to each of these indications (Kosaka Para [0183]: data regarding adverse events are converted to encoded data involving ICD and ATC codes).
However, Kosaka does not teach that the animals are non-human animals. Nevertheless, Betge teaches to use non-human animals (Betge, title: “Multiparametric phenotyping of compound effects on patient derived organoids”).
Kosaka and Betge are in the same field of drug discovery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kosaka to include the teachings of Betge because organoids prevent the need for putting patients at risk; instead, there is a reasonable expectation of success because patient-derived organoids are designed to resemble human organs (See the abstract of Betge).
As to claim 21. A prediction system for predicting an indication for a test substance in humans (Kosaka Para [0117]: predicting an indication of medical event in humans; Para [0034] establishes that a medical event may be a medical act), comprising:
a server device (invokes 35 U.S.C. § 112(f)) for transmitting a first test data set (Kosaka Para [0113]: inputting attribute data from patient diagnostic determinations), the first test data set being a set of data indicating the dynamics (Kosaka Para [0114]: indicating time series of medical events) of a biomarker (Kosaka Para [0114]: a disease; see definition at Para [0083]: “Diagnosed Disease Name” which emphasizes the element of making a diagnostic determination) in one or multiple organs (Kosaka Para [0114]: in a patient) collected from [[ (Kosaka Para [0114]: drugs) has been administered (Kosaka Para [0114]: a disease-drug combination after administration), and
a prediction device (invokes 35 U.S.C. § 112(f)) for predicting an action of the test substance on humans (Kosaka Para [0117]: predicting an indication of medical event in humans; Para [0034] establishes that a medical event may be a medical act) connected to the server device via a network (Kosaka computer systems in Figure 1),
the server device comprising a communication part (invokes 35 U.S.C. § 112(f)) for transmitting the first test data set (Kosaka Para [0014]: inputting the attribute data including the diagnostic data) ,
the prediction device comprising a processing part and a communication part (invokes 35 U.S.C. § 112(f)),
wherein the processing part acquires the first test data set transmitted via the communication part of the server device via the communication part of the prediction device (Kosaka computer systems in Figure 1), and
inputs the acquired first test data set (Kosaka Para [0014]: inputting the attribute data including the diagnostic data) and a second test data set (Kosaka Para [0014]: inputting attribute data include “other medical events”) into an artificial intelligence model (Kosaka Para [0223]: inputting into a one-class support vector machine) trained to predict an indication for the test substance in humans (Kosaka Para [0223]: predict an outlier) based on the first test data set and the second test data set input thereinto (Kosaka Para [0223]: based on data from the examples of the inputted data),
the first test data set being a set of data indicating the dynamics (Kosaka Para [0114]: indicating time series of medical events) of a biomarker (Kosaka Para [0114]: a disease; see definition at Para [0083]: “Diagnosed Disease Name” which emphasizes the element of making a diagnostic determination) in one or multiple organs collected from [[ (Kosaka Para [0114]: in a patient) to which the test substance (Kosaka Para [0114]: drugs) has been administered (Kosaka Para [0114]: a disease-drug combination after administration) to generate the first training data set,
the second test data set being a set of data in which labels of multiple known indications are linked with information, acquired to generate a third training data set, about adverse events reported correspondingly to each of the multiple known indications (Kosaka Para [0119]: the inputted data using positive examples and negative examples involving diseases and other medical events),
wherein the artificial intelligence model trained by a method (Kosaka Para [0223]: inputting into a one-class support vector machine), comprising:
inputting a first training data set (Kosaka Para [0014]: inputting the attribute data including the diagnostic data), a second training data set (Kosaka Para [0014]: inputting attribute data include “other medical events”) and a third training data set in association with one another into an artificial intelligence model (Kosaka Para [0223]: inputting into a one-class support vector machine) to train the artificial intelligence model (Kosaka Para [0223]: predict an outlier),
the first training data set being a set of data in which a set of data indicating the dynamics (Kosaka Para [0114]: indicating time series of medical events) of a biomarker (Kosaka Para [0114]: a disease; see definition at Para [0083]: “Diagnosed Disease Name” which emphasizes the element of making a diagnostic determination) in one organ or each of multiple different organs collected from respective [[ (Kosaka Para [0114]: in a patient) to which multiple predetermined existing substances with a known indication in humans (Kosaka Para [0114]: drugs) have been individually administered (Kosaka Para [0114]: a disease-drug combination after administration) is linked with labels indicating respective names of the administered predetermined existing substances (Kosaka Para [0183]: linking with Anatomical Therapeutic Chemical Classification System ATC codes),
the second training data set being a set of data in which labels indicating respective names of the multiple predetermined existing substances (Kosaka Para [0183]: ATC codes) are linked (Kosaka Para [0183]: linking ATC codes with ICD codes in event data) with labels indicating the indications reported for each of the multiple predetermined existing substances (Kosaka Para [0183]: labels indicating International Statistical Classification of Diseases and Related Health Problems, or ICD codes),
the third training data set being a set of data in which labels indicating the indications reported for each of the multiple predetermined existing substances are linked with information about adverse events reported correspondingly to each of these indications (Kosaka Para [0183]: data regarding adverse events are converted to encoded data involving ICD and ATC codes).
However, Kosaka does not teach that the animals are non-human animals. Nevertheless, Betge teaches to use non-human animals (Betge, title: “Multiparametric phenotyping of compound effects on patient derived organoids”).
Kosaka and Betge are in the same field of drug discovery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kosaka to include the teachings of Betge because organoids prevent the need for putting patients at risk; instead, there is a reasonable expectation of success because patient-derived organoids are designed to resemble human organs (See the abstract of Betge).
As to claim 22, Kosaka in view of Betge teaches the prediction method according to claim 16, wherein, in the training, the first training data set and the third training data set are linked by means of the second training data set to generate a fourth training data set, and the fourth training data set is input into the artificial intelligence model (Kosaka Para [0183]: data regarding adverse events are converted to encoded data involving ICD and ATC codes as the fourth data set).
As to claim 23, Kosaka in view of Betge teaches the prediction method according to claim 16, wherein the information about adverse events includes labels indicating the adverse events, and the presence or absence or frequencies of occurrence of the adverse events in the indications (Kosaka Para [0119]: the inputted data using positive examples and negative examples involving diseases and other medical events).
As to claim 24, Kosaka in view of Betge teaches the prediction method according to claim 16, wherein the biomarker is transcriptome (Betge, bottom of page 28 to page 29: “Expression Profiling” as a basis for characterization of organoids used for diagnostic and analytical purposes).
As to claim 25, Kosaka in view of Betge teaches the prediction method according to claim 16, wherein the artificial intelligence model is a One-Class SVM (Kosaka Para [0223]: inputting into a one-class support vector machine) .
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
He, Yuye, Samuel Wen Yan Lim, and Chun Wei Yap. "Determination of torsade-causing potential of drug candidates using one-class classification and ensemble modelling approaches." Current drug safety 7.4 (2012): 298-308.
From the Information Disclosure Statement: WO 2016208776 - PREDICTION DEVICE BASED ON MULTIPLE ORGAN-RELATED SYSTEM AND PREDICTION PROGRAM
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 January 29, 2026