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
Acknowledgment is made of Applicant's claim for priority to the following application(s):
* 63606864 filed 06 December 2023
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on the following date(s) is/are entered and considered by Examiner:
* 09 September 2025
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(s) 19 is/are 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 claim recites a system comprising a plurality of machine learning models.
When read in light of the Specification as originally filed, this claim is found to be directed towards mere information in the form of data and/or software per se (i.e. a mathematical model devoid of any computer structure).
Additional clarification is requested.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 recites:
An apparatus, comprising:
at least one memory; and
at least one processor coupled with the at least one memory and configured to cause the apparatus to:
determine molecular structure information for one or more molecules;
predict one or more potential side effects based on the molecular structure information using a machine learning model; and
provide the predicted one or more potential side effects to a user.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the steps of determining and predicting molecular compound of a drug to predict side-effects are traditionally performed by a person when treating a patient, i.e. managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a generic computer recited with a high level of generality to implement the abstract idea, the determining/predicting steps may be performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 2-18 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
at least one memory; and
at least one processor coupled with the at least one memory and configured to cause the apparatus to:
using a machine learning model; and
provide the predicted one or more potential side effects to a user.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
Regarding the memory and processor, when read in light of the Specification as originally filed, the broadest reasonable interpretation of these limitations would include a general purpose computer invoked with a high level of generality to implement the abstract idea.
Regarding the machine learning model, the Specification as originally filed on 06 December 2024 discloses a variety of AI and ML tools capable of being used with the disclosed invention, and amounts to mere generic computer to apply the judicial exception, i.e. “apply it”.
Accordingly, these limitations amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
The step of providing data to a user merely add(s) insignificant extra-solution activity to the abstract idea (mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application). MPEP 2106.05(g))
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) in the independent claims (such as claim(s) 2 reciting a generic machine learning model, claims 3-6, 16-18 further reciting the generic processor, claim 11-13 reciting training a generic machine learning model, additional limitation(s) which amount(s) to invoking computers as a tool to perform the abstract idea; claim(s) 2 reciting receiving data, claims 7-10 reciting various types of machine learning models, claim 18 reciting an LLM, additional limitation(s) which add(s) insignificant extra-solution activity to the abstract idea which amounts to mere data gathering).
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 improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
Regarding the step of sending data to a user, this limitation amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claim(s) 2 reciting receiving data; e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 7-10 reciting various types of machine learning models, Vaughan (20190019581) discloses 100 trees of a random forest in a manner that would be WURC in the pertinent fields (page 18 paragraph 0232), Colby (20200176087) discloses a plurality of layers with at least 128 dimensions in a manner that would be WURC in the pertinent fields (page 5 paragraph 0031); claim 18 reciting an LLM, Official Notice is taken that using an LLM would be WURC in the pertinent art). MPEP 2106.05(d)(II)(ii))
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 improves any other technology. Their collective functions merely provide conventional computer implementation.
The claim is not patent eligible.
Claim 19 recites:
A system, comprising:
a first machine learning model configured to analyze molecular structure information for one or more molecules to create a molecular embedding vector;
a second machine learning model configured to analyze electronic health information for one or more patients to create a patient embedding vector; and
a third machine learning model configured to analyze a combination of the molecular embedding vector and the patient embedding vector to predict one or more potential side effects.
Step 1:
The claim as a whole does not fall within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter, for the reason stated in the section above and incorporated herein.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mathematical concepts” because calculating embedding vectors is a form of math, i.e. mathematical relationships, mathematical formulas or equations, mathematical calculations. MPEP § 2106.04(a)(2)(I)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
The steps of calculating embedding vectors could be performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
a first machine learning model;
a second machine learning model; and
a third machine learning model
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
As discussed above, the machine learning model has been interpreted to be software and/or data per se.
In the interest of compact prosecution for Applicant, Examiner considers these limitations as being deployed on a generic computer for Alice/Mayo two-part framework analysis.
Accordingly, these models amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
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 improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements of, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
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 improves any other technology. Their collective functions merely provide conventional computer implementation.
The claim is not patent eligible.
Claim 20 recites:
A method, comprising:
determining molecular structure information for one or more molecules;
predicting one or more potential side effects based on the molecular structure information using a machine learning model; and
providing the predicted one or more potential side effects to a user.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the steps of determining and predicting molecular compound of a drug to predict side-effects are traditionally performed by a person when treating a patient, i.e. managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a generic computer recited with a high level of generality to implement the abstract idea, the determining/predicting steps may be performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
using a machine learning model; and
providing the predicted one or more potential side effects to a user.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
Regarding the machine learning model, the Specification as originally filed on 06 December 2024 discloses a variety of AI and ML tools capable of being used with the disclosed invention, and amounts to mere generic computer to apply the judicial exception, i.e. “apply it”.
Accordingly, these limitations amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
The step of providing data to a user merely add(s) insignificant extra-solution activity to the abstract idea (mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application). MPEP 2106.05(g))
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 improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
Regarding the step of sending data to a user, this limitation amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
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 improves any other technology. Their collective functions merely provide conventional computer implementation.
The claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 11-17, 19 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Shtar (WO2023148733).
Claim 1: Shtar discloses:
An apparatus (Figure 2 illustrating a computer), comprising:
at least one memory (page 15 paragraph 0086 illustrating memory); and
at least one processor (page 15 paragraph 0086 illustrating a processor) coupled with the at least one memory and configured to cause the apparatus to:
determine molecular structure information for one or more molecules (page 5 paragraph 0027-0028 illustrating determining a molecule of a drug);
predict one or more potential side effects based on the molecular structure information using a machine learning model (page 6 paragraph 0033 illustrating determining side effects of the drug based on the molecule thereof, page 3-4 paragraph 0015 illustrating using a machine-learning model); and
provide the predicted one or more potential side effects to a user (page 4-5 paragraph 0018-0019 illustrating providing the determined side effects).
Claim 11: Shtar discloses:
The apparatus of claim 1, as discussed above and incorporated herein.
Shtar further discloses:
wherein the at least one processor is configured to cause the apparatus to train the machine learning model using information describing molecules and one or more side effects associated with the molecules (paragraph 0027-0036 illustrating training the machine learning model using molecules and side effects data).
Claim 12: Shtar discloses:
The apparatus of claim 11, as discussed above and incorporated herein.
Shtar further discloses:
wherein the at least one processor is configured to cause the apparatus to group the information by molecules that have a same core structure (calculate one or more first similarity metric values, representing a structural similarity between the first substance of interest and a specific baseline drug, based on the first substance data element and the relevant baseline drug data element; select a first subset of the plurality of baseline drugs, based on the one or more first similarity metric values; predict a DDI between the first substance of interest and a target baseline drug of the plurality of baseline drugs, based on (a) the first selected subset of baseline drugs and (b) the DDI data structure, para [0007]-[0017], para [0018]-[0019]).
Claim 13: Shtar discloses:
The apparatus of claim 1, as discussed above and incorporated herein.
Shtar further discloses:
wherein the at least one processor is configured to cause the apparatus to train the machine learning model using side effect information for one or more drugs (para [007]-[0017], para [0018]-[0019], use the chemical structure of a drug as input; and (2) modality-intensive DDI prediction: methods that use a single domain-expert-engineered drug feature (or fuse several of these features), such as the known drug-drug interactions, drug-target interactions, and side effects of a given drug, to predict its DDis, para [0027]-[0036], leverage the molecular structure, which is available at any stage of the drug development process, to predict occurrence of DDI, para [0043][0050], para [0091]-[0099], para [00108]-[00118], NN 137 may be configured to receive as input the DDI embedding vector 134A and DDI embedding vector 134B from embedding module 133. NN 137 may be trained to calculate, based on this input, a DDI score 137 A, representing expectancy or probability of DDI occurrence between the relevant baseline drugs 30, para [0119]-[0124]).
Claim 14: Shtar discloses:
The apparatus of claim 13, as discussed above and incorporated herein.
Shtar further discloses:
wherein the one or more drugs comprise drugs that are approved by a regulatory organization (the predictions of models based on these features are only available after the drug has been clinically tested or even approved, para [0032]-[0035]).
Claim 15: Shtar discloses:
The apparatus of claim 1, as discussed above and incorporated herein.
Shtar further discloses:
wherein the molecular structure information comprises strings of formatted textual representations of the one or more molecules (page 10-11 paragraph 0068 illustrating strings).
Claim 16: Shtar discloses:
The apparatus of claim 15, as discussed above and incorporated herein.
Shtar further discloses:
wherein the at least one processor is configured to cause the apparatus to convert the strings into vectorial representations of the one or more molecules using circular fingerprints (page 10 paragraph 0065 illustrating converting the strings into fingerprints).
Claim 17: Shtar discloses:
The apparatus of claim 16, as discussed above and incorporated herein.
Shtar further discloses:
wherein each of the one or more molecules is represented as a sparse bit vector (page 21 paragraph 00112 illustrating a sparse input vector) or a molecular graph (page 7 paragraph 0044 illustrating a molecular graph).
Claim 19: Shtar discloses:
A system (Figure 2 illustrating a computer), comprising:
a first machine learning model configured to analyze molecular structure information for one or more molecules to create a molecular embedding vector (Abstract, machine-learning (ML) based prediction of chemical or biological interactions, para [002], applying a vector operation on the first DDI embedding vector and the second DDI embedding vector, to produce a DDI embedding vector; and applying a machine-learning model on the product DDI embedding vector, to obtain the DDI score, para [007][0017], para [0018]-[0019]);
a second machine learning model configured to analyze electronic health information for one or more patients to create a patient embedding vector (Abstract, machine-learning (ML) based prediction of chemical or biological interactions, para [002], collaborative filtering in recommender systems, where calculating a recommendation for a user is done by collecting information about users, para [0027]-[0037], matrix factorization techniques are widely used in recommender systems, where each user and item are represented by a compressed latent vector (embedding), para [0043]-[0050]); and
a third machine learning model configured to analyze a combination of the molecular embedding vector and the patient embedding vector to predict one or more potential side effects (para [0018]-[0019], machine learning approaches for DDI prediction - modality-intensive DDI prediction: methods that use a single domain-expert-engineered drug feature ( or fuse several of these features), such as the known drug-drug interactions, drug-target interactions, and side effects of a given drug, to predict its DDis -collaborative filtering in recommender systems, where calculating a recommendation for a user is done by collecting information about users, para [0027]-[0037], Output devices 8 may include one or more (possibly detachable) displays or monitors, speakers and/or any other suitable output devices, para [0083][0090], (page 6 paragraph 0033 illustrating determining side effects of the drug based on the molecule thereof).
Claim(s) 20 recite(s) substantially similar limitations as those of claim(s) 1 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
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.
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.
Claim(s) 2-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shtar in view of Elemento (20220392580).
Claim 2: Shtar discloses:
The apparatus of claim 1, as discussed above and incorporated herein.
Shtar further discloses:
wherein the at least one processor is configured to cause the apparatus to:
receive electronic health information (Figure 2 illustrating data)
provide the electronic health information to the machine learning model (as discussed above and incorporated herein); and
predict the one or more potential side effects based at least in part on the electronic health information (as discussed above and incorporated herein)
Shtar does not disclose
for one or more patients.
Elemento discloses:
for one or more patients (paragraph 0094-0096 illustrating using EHR data to determine side effects).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the patient data of Elemento within the system of Shtar with the motivation of improving patient care by leveraging known patient outcomes data (Elemento; page 1 paragraph 0003).
Claim 3: Shtar in view of Elemento disclose:
The apparatus of claim 2, as discussed above and incorporated herein.
Shtar further discloses:
wherein the at least one processor is configured to cause the apparatus to generate one or more personalized drug recommendations for the one or more patients (as discussed in view of the combined disclosure of Shtar in view of Elemento above, and incorporated herein).
Claim 4: Shtar in view of Elemento disclose:
The apparatus of claim 2, as discussed above and incorporated herein.
Shtar further discloses:
wherein the at least one processor is configured to cause the apparatus to create a joint embedding comprising the electronic health information for a patient and the molecular structure information (paragraph 0027-0037 illustrating creating a matrix factorization [considered to be a form of “joint” embedding], paragraph 0125-0134 illustrating providing drug side effects based on base drugs and combination of DDI information).
Claim 5: Shtar in view of Elemento disclose:
The apparatus of claim 4, as discussed above and incorporated herein.
Shtar further discloses:
wherein the at least one processor is configured to cause the apparatus to create the joint embedding by concatenating the electronic health information for a patient with the molecular structure information (para [0027]-[0037], matrix factorization techniques are widely used in recommender systems, where each user and item are represented by a compressed latent vector (embedding), para [0043]-[0050], Vector manipulation module 135 may subsequently apply a vector operation on DDI embedding vector 134A and DDI embedding vector 134C, to obtain a product DDI embedding vector 135B. Product DDI embedding vector 135B may represent a combination of DDI information that pertains to both the baseline drug 30 of subset 120A and the baseline drug 30 of subset 120B, para [0125]-[0134]).
Claim 6: Shtar in view of Elemento disclose:
The apparatus of claim 2, as discussed above and incorporated herein.
Shtar does not disclose:
wherein the at least one processor is configured to cause the apparatus to receive the electronic health information for the one or more patients without persistently storing the electronic health information.
Elemento discloses:
wherein the at least one processor is configured to cause the apparatus to receive the electronic health information for the one or more patients without persistently storing the electronic health information (Abstract, Using a variety of machine learning techniques using chemical structure similarity can be used to predict pharmacological/adverse effects and to compute pharmacological similarities and predict new targets, para [0004]-[0014], para [0015]-[0025], para[0073]-[0081], the computational chemical analysis system 210 receives input chemical parameters 205 - chemical structure, reported adverse effects - electronic health record (EHR) data - a datatype can be any characteristic of a chemical (e.g, its structure, etc.) or the effects of the chemical (e.g., side effects, known targets to which it binds, known interactions with other chemicals, etc.), para [0094]-[0096], para [0113][0121]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the patient data of Elemento within the system of Shtar with the motivation of improving patient care by leveraging known patient outcomes data (Elemento; page 1 paragraph 0003).
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shtar in view of Vaughan.
Claim 7: Shtar discloses:
The apparatus of claim 1, as discussed above and incorporated herein.
Shtar does not disclose:
wherein the machine learning model comprises a random forest model.
Vaughan discloses:
wherein the machine learning model comprises a random forest model (page 18 paragraph 0232).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the machine learning techniques of Vaughan within the system of Shtar with the motivation of improving patient care by leveraging known machine learning techniques (Vaughan; page 1 paragraph 0007).
Claim 8: Shtar in view of Vaughan disclose:
The apparatus of claim 7, as discussed above and incorporated herein.
Shtar does not disclose:
wherein the random forest model comprises at least 100 trees.
Vaughan discloses:
wherein the random forest model comprises at least 100 trees (page 18 paragraph 0232).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the machine learning techniques of Vaughan within the system of Shtar in view of Vaughan with the motivation of improving patient care by leveraging known machine learning techniques (Vaughan; page 1 paragraph 0007).
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shtar in view of Colby.
Claim 9: Shtar discloses:
The apparatus of claim 1, as discussed above and incorporated herein.
Shtar does not disclose:
wherein the machine learning model comprises a graph-convolution model.
Colby discloses:
wherein the machine learning model comprises a graph-convolution model (page 5 paragraph 0031).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the machine learning techniques of Colby within the system of Shtar with the motivation of improving patient care by leveraging known machine learning techniques (Colby; page 1 paragraph 0003).
Claim 10: Shtar discloses:
The apparatus of claim 9, as discussed above and incorporated herein.
Shtar does not disclose:
wherein the graph-convolution model comprises at least two convolution layers and at least a 128 dimension output for each of the one or more molecules (page 5 paragraph 0031).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the machine learning techniques of Colby within the system of Shtar in view of Colby with the motivation of improving patient care by leveraging known machine learning techniques (Colby; page 1 paragraph 0003).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shtar in view of Official Notice.
Claim 18: Shtar discloses:
The apparatus of claim 1, as discussed above and incorporated herein.
Shtar does not disclose:
wherein the at least one processor is configured to cause the apparatus to generate an explanation of the one or more potential side effects using a large language model.
Official Notice is taken that using an LLM is old and well known in the pertinent art
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the machine learning techniques of the Official Notice within the system of Shtar with the motivation of improving patient care by leveraging known machine learning techniques.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Apte (20160232312) discloses using microbiome data to diagnose a patient’s disease (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Tran (20180001184) discloses using physiological data to diagnose a patient’s disease (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRAN N NGUYEN whose telephone number is (571)272-0259. The examiner can normally be reached Monday-Friday 9AM-5PM Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KAMBIZ ABDI can be reached on (571)272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.N.N./ Examiner, Art Unit 3685 /KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685