DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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, 3-4, 8, and 10-11 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to abstract idea without significantly more.
Step 1
Claims 1, 3-4, 8, and 10-11 are within the four statutory categories. However, as will be shown below, claims 1, 3-4, 8, and 10-11 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1 and 8 are representative of the inventive concept and recite:
Claim 1
A device for predicting a clinical trial success rate of a new drug by using machine learning, comprising: at least one processor;
and at least one memory for storing a computer program code, wherein the at least one processor, when the computer program code is executed, is:
configured to acquire information about a target gene and chemical information for a new drug candidate;
and configured to predict a clinical trial success rate of the new drug candidate by inputting the information about the target gene and the chemical information into a pre-trained clinical trial success rate prediction model, wherein the information about the target gene comprises a cellular gene essentiality (CGE) indicating degrees of a perturbation effect on the target gene in a cell population, and an organismal gene essentiality (OGE) indicating the degrees of the perturbation effect on the target gene in a population ,
wherein the at least one processor is configured to predict the clinical trial success rate based on the information about the target gene including a discrepancy between the CGE and the OGE and the chemical information,
wherein the pre-trained clinical trial success rate prediction model is trained to improve the clinical trial success rate when the new drug candidate induces tolerate perturbation effects on the cell population and the population, and wherein the chemical information includes at least one of a molecular weight, the number of hydrogen bond donors or acceptors and a polar surface area of the new drug candidate or any combination thereof, wherein the CGE is acquired based on CRISPR-Cas system,
wherein the at least one processor is further configured to train the pre-trained clinical trial success rate prediction model by using learning data including information about a target gene for each of a plurality of drugs and labeled with a clinical trial success rate for each of the plurality of drugs, the at least one processor is configured to train the pre-trained clinical trial success rate prediction model through Monte Carlo cross-validation using the learning data, and wherein the Monte Carlo cross-validation is performed at least 1,000 times to split the learning data into training sets and test sets.
*Claim 8 recites similar limitations as claim 1, but for a method.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
acquiring, inputting, training, and predicting) or using pen and paper. Other than reciting generic computer components/functions such as, “device”, “machine learning”, “memory for storing computer code, “executed computer code”, “model”, “processor”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the system language, the claim encompasses the user collecting information, and processing it to make a prediction about the drug performance. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. Additionally, the recitation of generic computer components and functions such as acquiring information about a target gene for a new drug candidate also covers behavioral or interactions between people (i.e. the computer), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case a person is able to physically follow the steps to gather and analyze data ), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 3-4 and 10-11 recite additional subject matter which further narrows or defines the
abstract idea embodied in the claims (such as claim 3, reciting specific types of information that can be acquired, but for recitation of generic computer components/functions).
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claims
recite the following additional limitations:
Claim 1 recites: device, machine learning, processor, memory for storing computer program code, executed computer program code, model(machine learning and prediction), “inputting the information about the target gene and the chemical information into a pre-trained clinical trial success rate prediction model”, and “and wherein the Monte Carlo cross-validation is performed at least 1,000 times”
In particular, the additional elements do no integrate the abstract idea into a practical
application, other than the abstract idea per se, because the additional elements amount to no more
limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations are recited as being performed by a device, machine learning, processor, memory for storing computer program code, executed computer program code, and model(machine learning and prediction). These limitations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The model and machine learning are used to generally apply the abstract idea without limiting how the model functions. The model is described at a high level such that it amounts to using a computer with a generic model to apply the abstract idea.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “inputting the information about the target gene and the chemical information into a pre-trained clinical trial success rate prediction model”, and “and wherein the Monte Carlo cross-validation is performed at least 1,000 times”.
Dependent claims 3-4 and 10-11 do not include any additional elements beyond those already recited in claims 1 and 8. Hence do not integrate the aforementioned abstract idea into a particular application. 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 functioning of a computer or any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1 and 8 do not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to discussion of integration of the
abstract idea into a practical application, the additional elements: A system in claim 1; amount to no
more than mere instructions to apply an exception to the abstract idea. Additionally, the additional
limitations, other than the abstract idea per se amount to no more than limitations which amount to
elements that have been recognized as well-understood, routine, and conventional activity in particular fields as demonstrated by the recitation of an additional element such as:
Monte Carlo, which is a simulation that predicts possible outcomes of an uncertain event (Section 2.3.6., Eriksen(Eriksen et al. Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study. EBioMedicine. 2020 Aug;58:102932.) discloses Monte Carlo cross-validation (MCCV) consisting of 1000 iterations.) in a manner that is well-understood, routine, and conventional.
Inputting data into a general computer (Para 117, Obeyesekere(US 11386622 B1) discloses: “This conventional input can include, for example, a push button, touch pad, touch screen, wheel, joystick, keyboard, mouse, keypad, or any other such device or element whereby a user can input a command to the device.”) in a manner that is well-understood, routine, and conventional.
CRISPR-Cas refers to a gene-editing technology allowing scientists to modify specific DNA sequences of living organisms (Para 0175, Ferreira(US 20220243170 A1) discloses: “The present invention may be implemented using a conventional CRISPR/Cas genetic tool using a single plasmid comprising a nuclease, a gRNA and a repair matrix such as described by Wang et al. (2015).”) in a manner that is well-understood, routine, and conventional.
Dependent claims 3-4 and 10-11 do not include any additional elements beyond those already
addressed above for claims 1 and 8. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1 and 8 hence do not amount to “significantly more” than the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation.
Subject Matter Free of Prior Art
The following is a statement of reasons for the subject matter free of prior art:
Claims 1 and 8 distinguishes over the prior art for the following reasons.
Claim 1 (in part):
“… wherein the at least one processor is configured to predict the clinical trial success rate based on the information about the target gene including a discrepancy between the CGE and the OGE and the chemical information, wherein the pre-trained clinical trial success rate prediction model is trained to improve the clinical trial success rate when the new drug candidate induces tolerant perturbation effects on the cell population and the population…”
The underlined/italicized limitations indicate the reason for subject matter free of prior art.
The closest available prior art of record as follows:
Rancati (Emerging and evolving concepts in gene essentiality. Nat Rev Genet 19, 34–49 (2018)) discloses CGE and OGE concepts in gene essentiality but does not fairly disclose or suggest the aforementioned configuration for the claimed invention
You (You, Y. et al. A CRISPR-based method for testing the essentiality of a gene. Sci Rep 10, 14779 (2020).) discloses gene essentiality but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Based on the evidence presented above, none of the closest available prior art of record fairly
discloses or suggests the claimed invention. For this reason, claims 1 and 8 would be found to be subject matter free of prior art as would claims 3-4 and 10-11 via dependency.
Response to Arguments
Rejection under 35 U.S.C. 101
(Pages 8-9) Regarding the assertion that claim 1 is directed to patent eligible subject matter per the reasonings provided on page 8-9.
Applicant's arguments filed have been fully considered but they are not persuasive. The claims are directed to an abstract idea without significantly more. Specifically the claim falls within the “mental process” and “certain methods of organizing human activity” grouping of abstract ideas. The specifications cannot be read into the claims and the claim is evaluated as recited as interpreted under BRI for 101 purposes.
(Pages 9-10) Regarding the assertion that the claim presents an improvement to a technology or technical field.
Applicant's arguments filed have been fully considered but they are not persuasive. Claims 1 and 8 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: A system in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation.
Rejection under 35 U.S.C. 103
The 103 has been amended to reflect subject matter free of prior art to reflect advisory action decision on 6/26/2025.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen(US20130144584A1): Chen discloses a system consisting of statistical modeling and machine learning based on integration of systems biology data, drug target data, protein interaction networks, gene ontology, and drug side effects, which are used to predict drug adverse reactions. Some disclosures of this invention are similar to this pending instant application. (Specifications, pages 12-15)
Athey(US20190172584A1): Athey discloses a machine learning engine that can generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Some disclosures of this invention are similar to this pending instant application. (Specifications, pages 12-15)
THIS ACTION IS MADE FINAL. 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.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685