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 .
Status of the Claims
Claims 1-20 are currently pending and have been considered below. Claims 1-7, 9-10, 12-17 and 20 have been amended.
Response to Arguments
Applicant's arguments filed September 9, 2025 have been fully considered but they are not persuasive. Applicant argues on pages 10-14 (A. Analysis at Step 2A, Prong One), that independent claims 1, 5, and 16 cannot be covered under Mental Processes e.g. concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed at using large amounts of real-world data from hospitals that needs to be processed in real time that requires very large processing resources and memory to create a synthetic control group of patients. Examiner disagrees. First, the claims do not recite processing anything in real time. At best, real-world data is being processed but not in real-time as argued. Regarding Mental Processes, amended claim 1 recites creating a (virtual) control group of patients from data (data gathering), applying a weight (filtering or analyzing or processing the data), comparing the data and coming up with a conclusion (efficacy of drug). But for the recitation of generic computer components like a system with a processor executing instructions stored in a memory or on a computer readable medium, the claim when considered as a whole, describe data gathering, observations or evaluations and comparisons of data that a person could make when viewing two sets of data to determine the efficacy or outcomes of certain drugs. Therefore, the series of steps recited in the independent claims describe concepts that can be performed in human mind (including an observation, evaluation, judgement or opinion) and is thus grouped as Mental Processes which is an abstract idea.
Next, Applicant argues on pages 10-14 (B. Analysis at Step 2A, Prong Two) that the instant claims when considered as a whole, integrate the abstract idea into a practical application and that it’s a significant improvement to the technology of testing new drugs. Examiner disagrees. The “significant improvement to the technology of testing new drugs” is at best an improvement to the abstract idea of determining an efficacy of a new drug with the use of generic computer components. The claims do not include additional elements that integrate the judicial exception into a practical application of the exception because the claims do not provide improvements to another technology or technical field or improvements to the functioning of the computer itself. In particular, the claims only recite generic computer components like a system with a processor executing instructions stored in a memory or on a computer readable medium which are recited at a high level of generality (i.e., as a generic processor performing generic computer functions). See Figure 5 and specification paragraphs 45 and 51. Examiner notes that merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) does not integrate a judicial exception into a practical application.
Applicant goes on to cite Technical Problem 1 and Technical Problem 2 on pages 16-18 along with Technical Solutions to Problems 1 and 2. Applicant argues that these technical problems and solution integrate the abstract idea into a practical application. Examiner disagrees. The technical problems and solutions that Applicant is arguing are not technical in nature but rather abstract. In other words, Applicant may be improving the abstract idea at best but nothing technical in nature. Gathering data and evaluating (Technical Problem and Solution 1) and displaying data (Technical Problem and Solution 2) are not improvements to another technology or technical field or improvements to the functioning of the computer itself.
Next, Applicant argues on pages 19-20 (C. Analysis under Step 2B) that the instant claims are similar to Amdocs and result in unconventional ordered combination. Applicant argues that the Office look at the independent claims as a whole to see that the unconventional arrangement of the elements recited in the claims leads to an “inventive concept” or “significantly more” than the individual elements recited in the claims. Examiner disagrees. As seen in Claims 1, 5 and 16, there is no unconventional arrangement of elements as argued by Applicant. For example, claim 1 has a generic processor, memory and computer program executing the steps of gathering data, analyzing and evaluating the data, comparing the data and determining an efficacy. Same can be seen in claims 5 and 16. There is no unconventional arrangement of elements as argued by Applicant. Also, Figure 5 and paragraphs 45 and 51 of specification further reinforces the generic nature and arrangement of the additional elements.
Next, Applicant argues on pages 21-25 the prior art rejection under 35 USC 103. Specifically, Applicant argues on page 21 that the Colley reference does not use “real-world data”. Examiner disagrees. The Colley reference uses real-world data as seen in paragraphs 955 and 1325-1327 (as seen below) and Figures 106-108.
[0955] At 17022 a patient's recurring cancer state is assessed. At 17028 a tumor and healthy tissue are re-biopsied and resequenced. At 17030, real world data is collected about the patient's cancer state. At 17032 organoids (e.g., grown tumor sections from the biopsy) are grown and tested and at 17034 pathway analysis is performed. At 17036, for a specific patient, an N of 1 trial is performed and resulting data is stored for use in helping dial future treatments in for future patients with similar cancer states. At 17040 a patient specific data driven cancer treatment is prescribed for a specific patient.
[1325] Referring to FIG. 106, GUI 3570 shows details corresponding to the “Patients” module. The template (e.g., template 3562) can be used to set one or more data fields that are enabled for use by the abstractors in the “Patient” module for each patient case in the project. In some embodiments, a data field 3572 may have one or more nested elements 3574, such as in the case of the data field “Date of birth,” which can have nested elements “Month,” “Date,” and/or “Year.” A client such as a hospital can provide raw data in the form of clinical documents, pathology reports, progress notes, testing data, electronic medical records (EMRs), or other relevant medical history data for each patient. A client can have any number of projects, and some projects may have the same template. In some embodiments, the same template may be used across multiple projects and/or multiple clients. For instance, if Hospital A and Hospital B each require services to structure their clinical data for stage III ovarian cancer, one template may be prepared with the data fields most relevant to stage III ovarian cancer and then utilized for Hospital A's project and for Hospital B's project. In some embodiments, each data field or nested element can have a data type such as repeatable, select, text, row, text, dropdown, date, Boolean, or combinations thereof such as dropdown and date. Some data fields in each patient case may be pre-populated depending on the type of raw data containing the relevant information for the data field. For example, optical character recognition (OCR) may be run on certain file types, and the data extracted using OCR can be used to pre-populate certain data fields 3572 and/or nested elements 3574.
[1326] Referring broadly to FIGS. 107-109, GUIs 3580-3594 show details corresponding to the “Patients” module. For each patient case in a project, abstractors can view the data fields 3582 of the template corresponding to the project. The abstractors can then populate any unpopulated data fields 3582 in the patient case when extracting information from the raw data (e.g. progress notes, lab reports, genetic testing results, and so forth) of the patient case. Some data fields 3582 can be populated using a dropdown menu containing one or more field values 3596. The field values 3596 can be selected or set in the “Templates” module and/or the “Valuesets” module, as will be explained below.
[1327] One or more patient documents 3586 from the data provided by the client can also be viewed simultaneously, in tabbed fashion as shown in the figures, along with the data fields 3592 to allow the abstractor to efficiently populate the data fields 3592 and any required nested elements (e.g., nested elements 3574) of the data fields, according to some embodiments. The system 3200 can load the patient documents 3586 using a patient identification code of the patient case. The data fields may be categorized by a root category 3582 such as demographics, diagnosis, treatment and outcomes, genetic testing and labs, or any other category that may help better organize the data fields.
Next, Applicant argues that Colley uses the actual data of real patients who do not qualify for the clinical trials. Applicant’s instant claims do not exclude these patients and also, the data used in the Colley reference “omits certain data elements in order to ensure that the structured data is de-identified or that protected health information is securely maintained, encoded, or removed” as seen in paragraph 1690.
Examiner notes that Applicant’s invention also derives data from real patients (see paragraph 18 below).
[0018] In some examples, aspects of the disclosure provide a computerized method for creating synthetic controls in survival analysis by: creating a target group of patients and a control group of patients from data (e.g., real data obtained from a hospital) associated with a plurality of patients, the target group of patients comprising patients to whom a drug is to be administered, the control group of patients comprising patients to whom the drug is not administered, each patient in the target group of patients and the control group of patients having a common feature; applying a weight to the common feature of each patient in the control group of patients so that a linear combination of the common feature of the patients in the control group of patients becomes similar to a particular patient in the target group of patients, applying the weight comprising: minimizing a distance between the common feature of the particular patient in the target group of patients and the linear combination of the common feature of the patients in the control group of patients, the distance being minimized by penalizing the distance using a variance penalty; and creating a synthetic patient for each patient in the control group of patients, the synthetic patient having the common feature similar to the particular patient in the target group of patients.
Applicant's arguments on pages 24-25 have been fully considered but they are not persuasive. See arguments above regarding independent claims 1, 5 and 16.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 1-4 are directed to a system (i.e. a machine), claims 5-15 are directed to a method (i.e. a process), and claims 16-20 is directed to a computer storage medium (i.e. a manufacture). Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea. (Step 1- Yes)
Step 2A- Prong 1
Independent claims 1, 5 and 16 recite steps that, under their broadest reasonable interpretations, cover Mental Processes, e.g. concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Specifically, the claims (claim 1 is shown below) recite:
a processor; and
a memory comprising computer program code, the memory and the computer program code configured to cause the processor to:
create a target group of patients and a synthetic control group of patients from data associated with a plurality of patients, the target group of patients comprising patients to whom anew drug is to be administered, the synthetic control group of patients comprising synthetic patients to whom the new drug is not administered, each patient in the target group of patients and the synthetic control group of patients having a common feature;
apply a weight to the common feature of each patient in the synthetic control group of patients causing so that a linear combination of the common feature of the patients in the synthetic control group of patients becomesto be similar to a particular patient in the target group of patients, wherein applying the weight comprises:
minimizing a distance between the common feature of the particular patient in the target group of patients and the linear combination of the common feature of the patients in the synthetic control group of patients, the distance being minimized by penalizing the distance using a variance penalty; and
using real-world data, create a synthetic patient for each patient in the synthetic control group of patients, the synthetic patient having the common feature similar to the particular patient in the target group of patients;
cause the new drug to be administered to the target group of patients, the target group including all terminally ill patients;
compare the target group of patients who are administered the new drug with the synthetic control group of patients; and
based on the comparison, determine an efficacy of the new drug without using a placebo patient.
But for the recitation of generic computer components like a system with a processor executing instructions stored in a memory or on a computer readable medium, the italicized functions, when considered as a whole, describe observations or evaluations that a person in the pharmaceutical industry would follow to conduct medical trials on patients to determine the efficacy or outcomes of certain drugs. For example, a pharmaceutical researcher could gather data on various groups of patients and set up a target group and a control group. Filtering, analyzing and comparing the data to determine an efficacy can be performed in the human mind or at best with pencil and paper. See also MPEP 2106.04(a)(2) III C where using a generic computer for a judicial exception has been found to be abstract. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a mental process, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 5 and 16 are also abstract for similar reasons.
Dependent claims 2-4, 6-15, and 17-20 inherit the limitations that recite an abstract idea from their dependence on claims 1, 5, or 16, respectively, and thus these claims also recite an abstract idea under the Step 2A - Prong 1 analysis. In addition, claims 2-4, 6-15, and 17-20 recite additional limitations that further describe the abstract idea identified in the independent claims.
Claims 2, 6 and 8 recite “dynamically display the efficacy of the new drug on the target group of patients on a user interface (UI) using an icon, the icon being displayed in a first portion of the UI upon the efficacy being above a threshold, and the icon being displayed in a second portion of the UI upon the efficacy being below a threshold, a location of the displayed icon being automatically altered based on further collection of the real-world data.” (additional element (UI)- insignificant extra-solution activity, namely, data output)
Claims 3, 13 and 20 recite “analyze data associated with a trial participant and real-world control patients to generate the synthetic patient that is closest to the trial participant.” (abstract idea- mental process and mathematical concept)
Claims 4 and 14 recite “automatically generate a report describing an effect of the new drug on the target group of patients and displaying the report on a user interface (UI), dynamically updating the report and moving the report on the UI based on dynamic updates.” (additional element (UI)- insignificant extra-solution activity, namely, data output)
Claims 7 and 17 recites “creating another synthetic patient for each patient in the target group of patients, the other synthetic patient having the common feature similar to a particular patient in the synthetic control group of patients.” (abstract idea- mental process)
Claim 9 recites “determining a time to event outcome of the synthetic patient for each patient in the synthetic control group of patients.” (abstract idea- mental process and mathematical concept)
Claim 10 recites “wherein the synthetic patient for each patient in the synthetic control group of patients is created on an outcome scale or on log scale.” (abstract idea- mathematical concept)
Claim 11 recites “wherein the penalizing the distance comprises using the variance penalty and a covariance penalty.” (abstract idea- mathematical concept)
Claim 12 recites “wherein the target group of patients and the synthetic control group of patients are created from data associated with the plurality of patients by following a biased sampling scheme, the biased sampling scheme comprising: fitting a cox proportional hazards model using all covariates on the plurality of patients; predicting, using the cox proportional hazards model, an expected median survival time for each patient; and based on the expected median survival time, splitting the plurality of patients into the target group of patients and the control group of patients, wherein the target group of patients have the expected median survival time above a threshold.” (abstract idea- mathematical concept)
Claim 18 recites “cause the medical procedure to be performed on a subset of the target group of patients; compare the subset of the target group of patients on whom the medical procedure is performed with the other synthetic patient for each patient in the subset of the target group of patients; and based on the comparison, determine an efficacy of the medical procedure.” (abstract idea- managing personal relationships and mental processes)
Claim 19 recites “generate a report associated with the created synthetic patients and the determined efficacy of the medical procedure; display the generated report on a user interface (UI), including displaying information associated with the determined efficacy in a first location of the UI based on the determined efficacy exceeding a threshold; update the generated report dynamically based on determining additional efficacy information, wherein a value of the determined efficacy is changed based on the update; and move the information associated with the determined efficacy to a second location of the UI based on the changed value of the determined efficacy being less than the threshold.” (additional element (UI)- insignificant extra-solution activity, namely, data output)
The dependent claims above are abstract or they further limit the abstract concepts. Therefore, dependent claims 2-4, 6-15, and 17-20 are not patent eligible. (Step 2A-Prong 1: YES. The claims are abstract).
Step 2A-Prong 2
This judicial exception is not integrated into a practical application. In particular, the claims only recite: “system with a processor”, “memory”, “computer readable medium” and “user interface” as seen in claims 1, 5, 16 and 19. The computer hardware are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. See Applicant's specification paragraphs 45-52 about implementation using various computing devices and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Also, the additional elements add insignificant extra-solution activity to the abstract idea (collecting data, manipulating data, comparing data and outputting data), see MPEP 2106.05(g). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1-20 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept’) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system with a processor”, “memory”, “computer readable medium” and “user interface” as seen in claims 1, 5, 16 and 19 amounts to no more than mere instructions to apply the exception using a generic computer components. Mere instructions to apply an exception using a generic computer components cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1, 5, and 16 are not patent eligible. Dependent claims 2-4, 6-15, and 17-20 further define the abstract idea that is present in their respective independent claims 1, 5, and 16 and thus correspond to Mental Processes and hence are abstract for the reasons presented above. The dependent claims include a user interface (see claim 19) as an additional element; however, it has been determined to be generic, and therefore considered to be insignificant extra-solution activity (see MPEP 2106.05(g)). The dependent claims themselves are abstract or further limit abstract concepts. Therefore, the claims 2-4, 6-15, and 17-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible. (Step 2B: NO. The claims do not provide significantly more).
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.
Claim(s) 1-11 and 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Colley et al., US Patent Application Publication 2021/0090694 (see PTO-892, Ref. A) in view of Optimal Transport Weights for Casual Inference by Eric Dunipace (see Applicant’s IDS filed on 2/21/2025), [Hereinafter Dunipace].
As per claim 1, Colley teaches a system comprising:
a processor; and
a memory comprising computer program code, the memory and the computer program code configured to cause the processor to (see paragraph 266):
create a target group of patients and a synthetic control group of patients from data associated with a plurality of patients, the target group of patients comprising patients to whom anew drug is to be administered, the synthetic control group of patients comprising synthetic patients to whom the new drug is not administered, each patient in the target group of patients and the synthetic control group of patients having a common feature (see paragraph 1689);
apply a weight to the common feature of each patient in the synthetic control group of patients causing so that a linear combination of the common feature of the patients in the synthetic control group of patients becomes to be similar to a particular patient in the target group of patients, wherein applying the weight (see paragraph 1981):
using real-world data, create a synthetic patient for each patient in the synthetic control group of patients, the synthetic patient having the common feature similar to the particular patient in the target group of patients (see paragraph 349),
cause the new drug to be administered to the target group of patients, the target group including all terminally ill patients (see paragraph 349);
compare the target group of patients who are administered the new drug with the synthetic control group of patients (see paragraph 349); and
based on the comparison, determine an efficacy of the new drug without using a placebo patient (see paragraphs 1688 and 1705).
Colley does not explicitly teach minimizing a distance between the common feature of the particular patient in the target group of patients and the linear combination of the common feature of the patients in the synthetic control group of patients, the distance being minimized by penalizing the distance using a variance penalty.
Dunipace teaches minimizing a distance between the common feature of the particular patient in the target group of patients and the linear combination of the common feature of the patients in the synthetic control group of patients, the distance being minimized by penalizing the distance using a variance penalty (see abstract, page 7, optimal transport distance and page 9, L2 penalty).
Therefore, it would be prima facie obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Colley and Dunipace to minimize the distance between the common feature of the particular patient in the target group of patients and the linear combination of the common feature of the patients in the control group of patients because securing distributional balance by reducing variance will make outcome models more robust as taught by Dunipace (see page 3).
As per claim 2, Colley and Dunipace teach the system of claim 1 as seen above. Colley further teaches wherein the memory and the computer program code are configured to further cause the processor to:
dynamically display the efficacy of the new drug on the target group of patients on a user interface (UI) using an icon, the icon being displayed in a first portion of the UI upon the efficacy being above a threshold, and the icon being displayed in a second portion of the UI upon the efficacy being below a threshold, a location of the displayed icon being automatically altered based on further collection of the real-world data (see paragraphs 1094-1099 and Figure 21).
As per claim 3, Colley and Dunipace teach the system of claim 1 as seen above. Colley further teaches analyze data associated with a trial participant and real-world control patients to generate the synthetic patient that is closest to the trial participant (see paragraphs 347-354).
As per claim 4, Colley and Dunipace teach the system of claim 1 as seen above. Colley further teaches wherein the memory and the computer program code are configured to further cause the processor to:
automatically generate a report describing an effect of the new drug on the target group of patients and displaying the report on a user interface (UI), dynamically updating the report and moving the report on the UI based on dynamic updates (see paragraphs 1094-1099 and Figure 21).
Claim 5 recites similar limitations to claim 1 and thus rejected using the same art and rationale in the rejection of claim 1 as set forth above.
Claim 6 recites similar limitations to claim 2 and thus rejected using the same art and rationale in the rejection of claim 2 as set forth above.
As per claim 7, Colley and Dunipace teach the method of claim 5 as seen above. Colley further teaches creating another synthetic patient for each patient in the target group of patients, the other synthetic patient having the common feature similar to a particular patient in the synthetic control group of patients (see paragraph 349).
Claim 8 recites similar limitations to claim 2 and thus rejected using the same art and rationale in the rejection of claim 2 as set forth above.
As per claim 9, Colley and Dunipace teach the method of claim 5 as seen above. Colley further teaches determining a time to event outcome of the synthetic patient for each patient in the synthetic control group of patients (see paragraphs 347-348 and 353).
As per claim 10, Colley and Dunipace teach the method of claim 5 as seen above. Colley further teaches wherein the synthetic patient for each patient in the synthetic control group of patients is created on an outcome scale or on log scale (see paragraph 1962).
As per claim 11, Colley and Dunipace teach the method of claim 5 as seen above. Colley teaches filtering result reduce variance (see paragraph 1654). Dunipace teaches wherein the penalizing the distance comprises using the variance penalty and a covariance penalty (see abstract, page 7, optimal transport distance and page 9, L2 penalty).
Therefore, it would be prima facie obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Colley and Dunipace to utilize a variance penalty and a covariance penalty because securing distributional balance by reducing variance will make outcome models more robust as taught by Dunipace (see page 3).
Claim 13 recites similar limitations to claim 3 and thus rejected using the same art and rationale in the rejection of claim 3 as set forth above.
Claim 14 recites similar limitations to claim 4 and thus rejected using the same art and rationale in the rejection of claim 4 as set forth above.
As per claim 15, Colley and Dunipace teach the method of claim 5 as seen above. Colley further teaches creating the synthetic patient for each patient in the synthetic control group of patients in standard time (see paragraph 349).
Claim 16 recites similar limitations to claims 1 and 5 and thus rejected using the same art and rationale in the rejection of claims 1 and 5 as set forth above.
Claim 17 recites similar limitations to claim 7 and thus rejected using the same art and rationale in the rejection of claim 7 as set forth above.
Claim 18 recites similar limitations to claims 2, 6 and 8 and thus rejected using the same art and rationale in the rejection of claims 2, 6 and 8 as set forth above.
As per claim 19, Colley and Dunipace teach the computer storage medium of claim 18 as seen above. Colley further teaches
generate a report associated with the created synthetic patients and the determined efficacy of the medical procedure (see paragraphs 1094-1099 and Figure 21);
display the generated report on a user interface (UI), including displaying information associated with the determined efficacy in a first location of the UI based on the determined efficacy exceeding a threshold (see paragraphs 1094-1099 and Figure 21);
update the generated report dynamically based on determining additional efficacy information, wherein a value of the determined efficacy is changed based on the update; and
move the information associated with the determined efficacy to a second location of the UI based on the changed value of the determined efficacy being less than the threshold (see paragraphs 1094-1099 and Figure 21).
Claim 20 recites similar limitations to claim 3 and thus rejected using the same art and rationale in the rejection of claim 3 as set forth above.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Colley et al., US Patent Application Publication 2021/0090694 (see PTO-892, Ref. A) in view of Optimal Transport Weights for Casual Inference by Eric Dunipace (see Applicant’s IDS filed on 2/21/2025), [Hereinafter Dunipace] and further in view of Jaber et al., US Patent Application Publication 2023/0030506 (see PTO-892, Ref. B).
As per claim 12, Colley and Dunipace teach the method of claim 5 as seen above. Jaber further teaches wherein the target group of patients and the synthetic control group of patients are created from data associated with the plurality of patients by following a biased sampling scheme, the biased sampling scheme comprising:
fitting a cox proportional hazards model using all covariates on the plurality of patients (see paragraphs 79-82 and 87-91);
predicting, using the cox proportional hazards model, an expected median survival time for each patient (see paragraphs 79-82 and 87-91); and
based on the expected median survival time, splitting the plurality of patients into the target group of patients and the synthetic control group of patients, wherein the target group of patients have the expected median survival time above a threshold (see paragraphs 79-82 and 87-91).
Therefore, it would be prima facie obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Colley, Dunipace and Jaber to predict an expected median survival time for each patient using cox proportional hazards model because a cox proportional hazards model may be applied to determine the correlation of features of a clinical feature which may support better prognosis for an individual as taught by Jaber (see paragraph 5).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wilson et al, (US 20230112187), Shrager et al, (US 20200411199) and Anderson et al, (US 202020210174970) describe various systems of conducting virtual clinical trials (see attached PTO-892, Refs D, E and F).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID R MERCHANT whose telephone number is (571)270-1360. The examiner can normally be reached M-F 7:30-5.
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/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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