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 1-10, 12, 14-19 are currently pending and have been considered below.
Claims 1-6, 14, and 16-19 have been amended.
Claims 11, 13 and 20 have been cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 24, 2026 has been entered.
Response to Arguments
Applicant's arguments filed February 24, 2026 have been fully considered but they are not persuasive. Applicant argues on page 10 (A. Analysis at Step 2A, Prong One), that the claimed features of "minimizing a distance ... wherein minimizing the distance includes ..." as recited in the claims are not "observations or evaluations" nor can they be performed in the human mind (nor with the assistance of pen and paper). Examiner disagrees. These limitations are directed to mathematical calculations, optimization, and data analysis, which are abstract ideas. The fact that the calculations may be complex does not remove them from the abstract idea grouping.
Further, the claimed operations are the type of mental processes and reasoning steps that, at least at a conceptual level, involve evaluation, comparison, and selection among values. The claims do not recite a specific technological mechanism for improving a machine; instead, they recite the result of applying a mathematical model to patient data.
Next, Applicant argues on pages 10-14 (B. Analysis at Step 2A, Prong Two) that the claims integrate any alleged abstract idea into a practical application because they purportedly improve the functioning of computer systems and solve technical problems such as bias, reduced computational time, and improved clinical trial design. Applicant’s alleged improvements relate to:
• reducing bias in synthetic control generation,
• improving statistical fit,
• improving selection of trial patients,
• and reducing the number of actual control patients.
Examiner notes that the above improvements are to the analytic technique or statistical method, not to the computer itself. A claim does not integrate an abstract idea into a practical application merely because it is applied in a useful or real-world setting.
The use of generic computer components to implement the claimed calculations does not supply a practical application. The claims do not recite a particular machine architecture, improved processor operation, improved memory structure, improved network function, or any other computer-specific technical improvement.
Applicant relies on the notion that the claims improve a technological field by enabling clinical trial systems to process fewer patients and reducing computational time.
However, the alleged reduction in computational time is an incidental consequence of using fewer data points or a different modeling approach. That type of benefit does not, by itself, establish an improvement to computer technology. The claims remain focused on using mathematical relationships to generate synthetic controls, not on changing the operation of a computer.
Applicant argues that the variance penalty, nearest-neighbor initialization, and interpolation features amount to a practical application. The Office does not find these recitations sufficient to transform the claim into a practical application. They are still mathematical and statistical steps used to produce a better analytical result. Adding more detail to the mathematical model does not convert the model into a technological improvement.
Next, Applicant argues on pages 14-16 (C. Analysis under Step 2B) that the instant claims are similar to Amdocs and result in unconventional ordered combination. Examiner disagrees. Applicant recites a system, a processor, memory, and a synthetic control arm generator. These additional elements are generic and conventional. These components are recited at a high level of generality and are used as tools to perform the abstract statistical process. They do not impose meaningful limits on the abstract idea.
A generic processor or memory performing generic data processing does not provide an inventive concept.
Applicant relies on Amdocs, arguing that the combination of claim limitations is unconventional. This argument is not persuasive. In Amdocs, the claims were upheld because the ordered combination was tied to a specific technical architecture that improved the functioning of the network itself. Here, the ordered combination of steps merely applies statistical modeling to patient data to generate synthetic controls. The claim still recites the abstract idea itself, implemented on generic computing components. Further, the claims do not recite a specific improvement in computer technology akin to an unconventional network architecture, storage configuration, or processing pipeline. Instead, they recite a particular analytical workflow.
Applicant states that the claimed features are not “well-understood, routine, and conventional.” However, merely asserting unconventionality is insufficient. The claims must recite more than a desired result or a mathematical method applied to conventional computing tools. The cited limitations—weighting, nearest-neighbor initialization, variance penalty, and iterative minimization—remain part of the abstract analysis. They do not themselves add a technological inventive concept.
Next, Applicant argues on pages 16-17 the prior art rejection under 35 USC 103. Specifically, Applicant argues on pages 16-17 that the Colley reference does not teach “apply the weight to a censored time indicator and censored event indicator of the censored data.”
Examiner agrees and will provide new prior art rejection as seen below.
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 1-10, 12, 14-19 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 5-10, 12, 14-15 are directed to a method (i.e. a process), and claims 16-19 is directed to a non-transitory computer readable 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 synthetic control arm (SCA) generator executable by the processor and configured to:
receive real-world data associated with a plurality of patients, the real-world data including a feature of an eligible patient, a patient covariate, a time to event survival outcome, and censored data;
create a target group of patients and a synthetic control group of patients from the real- world data associated with the plurality of patients, the target group of patients comprising patients to whom a new 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;
determine a weight to apply to each patient in the synthetic control group, including:
initialize the weight to correspond to a nearest neighbor patient in the synthetic control group having the common feature most similar to a particular patient in the target group;
apply the weight to a censored time indicator and a censored event indicator of the censored data; and
iteratively minimize a distance function between the common feature of the particular patient in the target group and a weighted linear combination of the common feature of patients in the synthetic control group, wherein minimizing the distance function includes penalizing the distance function using a variance penalty term that reduces interpolation bias in anon-linear survival curve; and
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.
But for the recitation of generic computer components like a processor, synthetic control arm (SCA) generator executable by a processor, and a non-transitory computer readable medium that is executable by a processor, 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. Independent claims 1, 5 and 16 also recite steps that, under their broadest reasonable interpretations, cover Mathematical Concepts, e.g. mathematical relationships, mathematical formulas or equations and mathematical calculations. Steps like initialize the weight to correspond to a nearest neighbor patient in the synthetic control group having the common feature most similar to a particular patient in the target group; apply the weight to a censored time indicator and a censored event indicator of the censored data; and iteratively minimize a distance function between the common feature of the particular patient in the target group and a weighted linear combination of the common feature of patients in the synthetic control group, wherein minimizing the distance function includes penalizing the distance function using a variance penalty term that reduces interpolation bias in anon-linear survival curve may fall under Mathematical Concepts.
Dependent claims 2-4, 6-10, 12, 14-15, and 17-19 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, 6-10, 12, 14-15, and 17-19 recite additional limitations that further describe the abstract idea identified in the independent claims.
Claims 2, 6 and 8 recite “dynamically display an 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; 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 the efficacy of the new drug without using a placebo patient.” (additional element (UI)- insignificant extra-solution activity, namely, data output)
Claim 3 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)
Claim 4 recites “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 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 14 recites “wherein the created synthetic patients are heuristically censored based on the censored data.” (abstract idea- mental process and mathematical concept)
Claim 15 recites “wherein creating the synthetic patient for each patient in the synthetic control group of patients includes at least one of the following: creating the synthetic patient for each patient in the synthetic control group of patients in standard time; and creating a synthetic patient for each patient in the synthetic control group of patients in log-time.” (abstract idea- mental process and 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, 6-10, 12, 14-15, and 17-19 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: processor, synthetic control arm (SCA) generator executable by a processor, non-transitory computer readable medium that is executable by a processor and user interface as seen in claims 1, 2, 4, 5, 6, 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 1-10, 12, 14-19 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 processor, synthetic control arm (SCA) generator executable by a processor, non-transitory computer readable medium that is executable by a processor and user interface as seen in claims 1, 2, 4, 5, 6, 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, 6-10, 12, 14-15, and 17-19 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 claims 2, 4, 6 and 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, 6-10, 12, 14-15, and 17-19 are directed to an abstract idea. Thus, the claims 1-10, 12, 14-19 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) 1-10 and 14-19 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] in further view of Review Article: Handling Censoring and Censored Data in Survival Analysis: A Standalone Systematic Literature Review by Anothony Joe Turkson, Francis Ayiah-Mensah and Vivian Nimoh (See PTO-892, Ref. U) [Hereinafter Turkson].
As per claim 1, Colley teaches a system comprising:
a processor; and
a synthetic control arm (SCA) generator executable by the processor and configured to (see paragraph 266):
receive real-world data associated with a plurality of patients, the real-world data including a feature of an eligible patient, a patient covariate, a time to event survival outcome, and
create a target group of patients and a synthetic control group of patients from the real- world data associated with the plurality of patients, the target group of patients comprising patients to whom a new 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);
determine a weight to apply to each patient in the synthetic control group, including (see paragraph 1981):
initialize the weight to correspond to a nearest neighbor patient in the synthetic control group having the common feature most similar to a particular patient in the target group (see paragraph 1981);
apply the weight to a
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).
Colley does not explicitly teach iteratively minimize a distance function between the common feature of the particular patient in the target group and a weighted linear combination of the common feature of patients in the synthetic control group, wherein minimizing the distance function includes penalizing the distance function using a variance penalty term that reduces interpolation bias in a non-linear survival curve.
Dunipace teaches iteratively minimize a distance function between the common feature of the particular patient in the target group and a weighted linear combination of the common feature of patients in the synthetic control group, wherein minimizing the distance function includes penalizing the distance function using a variance penalty term that reduces interpolation bias in a non-linear survival curve (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 a distance function between the common feature of the particular patient in the target group and a weighted linear combination of the common feature of patients in the synthetic control group because securing distributional balance by reducing variance will make outcome models more robust as taught by Dunipace (see page 3).
Colley does not explicitly teach the data being used in the clinical trials as being censored data. Turkson teaches the use of censored data in clinical trials or survival analysis (see page 1 of article).
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 Turkson to use of censored data in clinical trials or survival analysis because it allows for the use of incomplete information which otherwise may have been discarded and thus affect the understanding of the research and the results as taught by Turkson (see page 14, last paragraph, column 1).
As per claim 2, Colley, Dunipace and Turkson teach the system of claim 1 as seen above. Colley further teaches wherein the SCA generator is 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);
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 the efficacy of the new drug without using a placebo patient (see paragraphs 1688 and 1705).
As per claim 3, Colley, Dunipace and Turkson 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, Dunipace and Turkson 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, Dunipace and Turkson 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, Dunipace and Turkson 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, Dunipace and Turkson 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 14, Colley, Dunipace and Turkson teach the method of claim 5 as seen above. Colley further teaches wherein the created synthetic patients are heuristically
Colley does not explicitly teach the data being censored. Turkson teaches the use of censored data in clinical trials or survival analysis (see page 1 of article).
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 Turkson to use of censored data in clinical trials or survival analysis because it allows for the use of incomplete information which otherwise may have been discarded and thus affect the understanding of the research and the results as taught by Turkson (see page 14, last paragraph, column 1).
As per claim 15, Colley, Dunipace and Turkson 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, Dunipace and Turkson 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(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], in view of Review Article: Handling Censoring and Censored Data in Survival Analysis: A Standalone Systematic Literature Review by Anothony Joe Turkson, Francis Ayiah-Mensah and Vivian Nimoh (See PTO-892, Ref. U) [Hereinafter Turkson] and further in view of Jaber et al., US Patent Application Publication 2023/0030506 (see PTO-892, Ref. B).
As per claim 12, Colley, Dunipace and Turkson 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, Turkson 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
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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