DETAILED ACTION
Status of Claims
This is the first office action on the merits in response to the application filed on 20 March 2025.
Claim(s) 1-19 are currently pending and have been examined.
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
This application claims priority of KR Application No. 10-2024-0038837 filed on 21 March 2024 and 10-2024-0145339 filed on 22 October 2024. Applicant’s claim for the benefit of this prior filed application is acknowledged.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1-19 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims not listed below are rejected for dependency.
Claim 1 recites the original claim limitation of “classifying each of the virtual patients as a responder or a non-responder according to a certain criterion.”
MPEP 2163(I)(A) notes that “issues of adequate written description may arise even for original claims, for example, when an aspect of the claimed invention has not been described with sufficient particularity such that one skilled in the art would recognize that the inventor had possession of the claimed invention at the time of filing.” MPEP 2163.03(V) further notes “An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved.” Here, the identified limitation defines the invention in extremely vague functional language that specifies a high level analytical result. Ex
What appears to be the most relevant disclosure at [00112] states “The processor 110 may classify the virtual patients as responders or non- responders, based on a certain criterion. For example, the processor 110 may classify, among the virtual patients, virtual patients with IIP to be 57 % and virtual patients with non-IIP to be 43 %.” This example does not appear to describe classifying responders and non-responders, but rather classifying users as having an inflamed immune phenotype. This disclosure does not reasonably indicate how the result is achieved. As such, this disclosure does not reasonably support the identified limitation. The remainder of the originally filed disclosure similarly fails to articulate how the function is performed.
The claims encompass a broad functional result without providing a single example of how to achieve that result. One of ordinary skill in the art would regard the claim scope as extremely broad and including complex embodiments which are not supported merely through the articulation of the function. Because of the breadth of the claim, the complexity of the claim scope, and the total lack of supporting disclosure, this situation overcomes the presumption that adequate written description of the claimed invention is present. One of ordinary skill in the art would not recognize applicant as possessing the claimed invention at the time of filing. Therefore the claim is rejected based on the written description requirement. Claim 10 is similarly rejected.
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 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The identified claims recite a “computer-readable recording medium”. The broadest reasonable interpretation of a claim drawn to a “computer-readable recording medium” typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007). The specification does not indicate that a ““computer-readable recording medium” must be interpreted to exclude transitory propagating signals per se. For example, the disclosure at [0054] notes that “The memory may include any non-transitory computer-readable recording medium”, which does not exclude signals per se from the scope of the “computer-readable recording medium.” As such, the claims are interpreted as being directed to non-statutory subject matter.
Applicant advised to amend the claim to recite “non-transitory computer-readable recording medium” to overcome rejection under 35 U.S.C. 101.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 10, which is representative of claim 1, recites “a method of analyzing a biomarker, the method comprising: generating virtual data including information about survival rates of virtual patients included in a first group, based on pre-generated survival data; generating control group data by classifying each of the virtual patients as a responder or a non-responder, according to a certain criterion; generating experimental group data based on at least one of medical images and survival data of actual patients included in a second group to which a specific regime has been applied; and outputting a result of comparison between the control group data and the experimental group data.”
These limitations describe a concept of creating a synthetic control arm and analyzing experimental group data. This concept describes a mental process that a clinical investigator should follow to evaluate treatment efficacy similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping. As such, these limitation set forth a method of organizing human activity. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 10 recites no additional elements. As there are no additional elements, the claim is determined to be directed to an abstract idea.
Claim 1 recites the additional element of a computing device comprising: a memory and a processor. This additional element is recited at an extremely high level of generality, and is interpreted as a generic computing device used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, this additional element does not integrate the abstract idea into a practical application. There are no further additional elements. Therefore the additional element does not integrate the abstract idea into a practical application. As such, the claim is determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, claim 10 recites no additional elements. Thus there are no additional elements to amount to significantly more than the abstract idea.
As previously noted, claim 1 recites an additional element which may be interpreted as a generic computing device used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, this additional element does not amount to significantly more. There are no further additional elements. Therefore the additional elements of the independent claims do not amount to significantly more than the abstract idea. Thus the independent claims are not patent eligible.
Dependent claims 2-9 and 11-18 further describe the abstract idea, but the claims continue to recite an abstract idea, albeit narrowed.
Dependent claims 2-9 recite no further additional elements. The previously identified additional element does not integrate the narrowed abstract idea into a practical application or amount to significantly more than the narrowed abstract idea for the same reasons as indicated above. Dependent claims 11-18 do not recite any additional elements, and thus remain directed to an abstract idea without additional elements that amount to significantly more than the abstract idea. Dependent claim 19 recites the additional element of a computer-readable recording medium. This additional element may be interpreted as a generic computing device used to implement the abstract idea. As previously noted, such additional elements do not either integrate an abstract idea into a practical application or amount to significantly more. Because the dependent claims remain directed to an abstract idea without reciting significantly more, the dependent claims are not patent eligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 2, 5, 7-11, 14, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over González Hernández et al. (US 2025/0174327 A1) [hereafter referenced as Gonzales] in view of Kishore Kumar et al. (US 2023/0343117 A1) [hereafter referenced as Kishore].
Regarding Claim 1, 10, and 19 : Gonzales discloses a computing device comprising: a memory storing at least one program (computer-readable media. See at least [0046]); and a processor (computing apparatus 518. See at least [0048]) configured to perform at least one operation by executing the at least one program, wherein the processor is configured to
generate virtual data including information about survival rates of virtual patients included in a first group, based on pre-generated survival data (The real-world data 110 (e.g., from hospitals) is routinely collected and a machine learning (ML) model (such as a large learning model (LLM)) is applied by the synthetic control arms (SCA) generator 112 to create synthetic (e.g., virtual) patients in the control arm 108 that historically received standard of care (or a placebo). See at least [0024]. Also: The characteristics of the patient may include, but are not limited to, age, menopausal status, tumor size, tumor differentiation grades, number of positive lymph nodes, progesterone receptors and estrogen receptors, information on treatment (e.g., whether hormone therapy and/or chemotherapy was received), and information on a survival outcome (e.g., the time from primary surgery to death or censoring). See at least [0019])
generate control group data (in an example, the SCA generator 112 identifies other patients in the real-world data 110 that are similar to the eligible patient 102 at least with respect to the one or more obtained features. The results over time associated with the identified other patients are weighed and averaged as described herein to generate a synthetic patient that is similar to the eligible patient 102. See at least [0025]. Also: The characteristics of the patient may include, but are not limited to, age, menopausal status, tumor size, tumor differentiation grades, number of positive lymph nodes, progesterone receptors and estrogen receptors, information on treatment (e.g., whether hormone therapy and/or chemotherapy was received), and information on a survival outcome (e.g., the time from primary surgery to death or censoring). See at least [0019])
generate experimental group data based on at least one of medical images and survival data of actual patients included in a second group to which a specific regime has been applied (Patient-level treatment arm data 216 is input and trial summary statistics 218 are extracted from this input data. See at least [0028]), and
output a result of comparison between the control group data and the experimental group data (These synthetic patients are compared with the patients who will receive the new treatment to determine the effect 114 of the new treatment. See at least [0024]. Also: a user interface (UI) shows the efficacy or the determined effect of the new treatment. If the effect or efficacy of the new treatment is above a threshold, the efficacy may be notified in a first portion of the UI. See at least [0026]).
Gonzales does not expressly disclose classifying each of the virtual patients as a responder or a non-responder according to a certain criterion.
However, Kishore teaches classifying each of the patients as a responder or a non-responder according to a certain criterion (Referring back to FIG. 2, the response classification module 240 receives each of the images that were labeled as “Tumor,” and classifies each of those images as “Responder” or “Non-Responder.” In some implementations, a “Responder” classification reflects that the image includes at least one tile that includes imagery indicating that the patient will respond to a chemical substance (e.g., the chemical substance will cause a reduction in the size of a tumor). In some implementations, a “Non-Responder” classification reflects that the image does not include at least one tile including imagery indicating that the patient will not respond to the chemical substance (e.g., the chemical substance will have little to no effect on the size of a tumor). In some implementations, the tiles can be weighted in accordance with their predictive power associated with the Responder/Non-Responder classification. For example, if an image tile of an image includes stronger indications that the patient will respond to a chemical substance, that image tile can be assigned a higher weight than the other image tiles of the same image that have weaker indications that the patient will respond to a chemical substance (or that fail to show indications that the patient will respond to a chemical substance). See at least [0052]).
Gonzales provides a system which generates synthetic control arm data, upon which the claimed invention’s classification of patient data as responding or non-responding can be seen as an improvement. However, Kishore demonstrates that the prior art already knew of predicting whether patients will be responding or non-responding. One of ordinary skill in the art could have easily applied the techniques of Kishore to the system of Gonzales. Further, one of ordinary skill in the art would have recognized that such an application of Kishore would have resulted in a system which better evaluates the efficacy of a treatment by comparing responding treatment patients to responding control patients. As such, the application of Kishore and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Gonzales and the teachings of Kishore.
Regarding Claim 2 and 11: Gonzales in view of Kishore makes obvious the above limitations. Gonzales further discloses wherein the processor is further configured to generate data on at least one of progression-free survival and overall survival of each of the virtual patients by using the pre-generated survival data (A computerized method for creating synthetic controls in survival analysis is described. See at least [0004]. Also: The efficacy may be determined by determining a time to event outcome of the synthetic patient for each patient in the control group of patients. See at least [0021]).
Regarding Claim 5 and 14: Gonzales in view of Kishore makes obvious the above limitations. Kishore further teaches determine a proportion of responders according to at least one parameter value set based on a hypothesis, and generate at least one set in which the virtual patients are classified as responders or non-responders, based on the proportion (Referring back to FIG. 2, the response classification module 240 receives each of the images that were labeled as “Tumor,” and classifies each of those images as “Responder” or “Non-Responder.” In some implementations, a “Responder” classification reflects that the image includes at least one tile that includes imagery indicating that the patient will respond to a chemical substance (e.g., the chemical substance will cause a reduction in the size of a tumor). In some implementations, a “Non-Responder” classification reflects that the image does not include at least one tile including imagery indicating that the patient will not respond to the chemical substance (e.g., the chemical substance will have little to no effect on the size of a tumor). In some implementations, the tiles can be weighted in accordance with their predictive power associated with the Responder/Non-Responder classification. For example, if an image tile of an image includes stronger indications that the patient will respond to a chemical substance, that image tile can be assigned a higher weight than the other image tiles of the same image that have weaker indications that the patient will respond to a chemical substance (or that fail to show indications that the patient will respond to a chemical substance). See at least [0052]). The motivation to combine Gonzales and Kishore is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 7 and 16: Gonzales in view of Kishore makes obvious the above limitations. Kishore further teaches wherein the processor is further configured to determine the proportion of responders based on information about a regime corresponding a drug that is basis of the pre-generated survival data, and generate the at least one set such that at least one parameter value is satisfied (Referring back to FIG. 2, the response classification module 240 receives each of the images that were labeled as “Tumor,” and classifies each of those images as “Responder” or “Non-Responder.” In some implementations, a “Responder” classification reflects that the image includes at least one tile that includes imagery indicating that the patient will respond to a chemical substance (e.g., the chemical substance will cause a reduction in the size of a tumor). In some implementations, a “Non-Responder” classification reflects that the image does not include at least one tile including imagery indicating that the patient will not respond to the chemical substance (e.g., the chemical substance will have little to no effect on the size of a tumor). In some implementations, the tiles can be weighted in accordance with their predictive power associated with the Responder/Non-Responder classification. For example, if an image tile of an image includes stronger indications that the patient will respond to a chemical substance, that image tile can be assigned a higher weight than the other image tiles of the same image that have weaker indications that the patient will respond to a chemical substance (or that fail to show indications that the patient will respond to a chemical substance). See at least [0052]). The motivation to combine Gonzales and Kishore is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 8 and 17: Gonzales in view of Kishore makes obvious the above limitations. Gonzales teaches wherein the processor is further configured to generates at least one set in which the actual patients included in the second group are classified as responders or non-responders (Patient-level treatment arm data 216 is input and trial summary statistics 218 are extracted from this input data. See at least [0028]). Additionally, Kishore teaches group are classified as responders or non-responders, based on biomarkers identified from the medical images (Referring back to FIG. 2, the response classification module 240 receives each of the images that were labeled as “Tumor,” and classifies each of those images as “Responder” or “Non-Responder.” In some implementations, a “Responder” classification reflects that the image includes at least one tile that includes imagery indicating that the patient will respond to a chemical substance (e.g., the chemical substance will cause a reduction in the size of a tumor). In some implementations, a “Non-Responder” classification reflects that the image does not include at least one tile including imagery indicating that the patient will not respond to the chemical substance (e.g., the chemical substance will have little to no effect on the size of a tumor). In some implementations, the tiles can be weighted in accordance with their predictive power associated with the Responder/Non-Responder classification. For example, if an image tile of an image includes stronger indications that the patient will respond to a chemical substance, that image tile can be assigned a higher weight than the other image tiles of the same image that have weaker indications that the patient will respond to a chemical substance (or that fail to show indications that the patient will respond to a chemical substance). See at least [0052]).
Gonzales and Kishore suggest a system which extracts experimental group patient data, upon which the claimed invention’s classification of patients based on image analysis can be seen as an improvement. However, Kishore demonstrates that the prior art already knew of using images to classify patients as responders or non-responders. One of ordinary skill in the art could have trivially applied the techniques of Kishore to the experimental group data of Gonzales to classify experimental group patients. Further, one of ordinary skill in the art would have recognized that such an application of Kishore would have resulted in an improved system which could automate experimental group data analysis. As such, the application of Kishore and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Gonzales and the teachings of Kishore.
Regarding Claim 9 and 18: Gonzales in view of Kishore makes obvious the above limitations. Gonzales further discloses wherein the processor is further configured to generates the result of comparison by comparing at least one set included in the control group data with at least one set included in the experimental group data (These synthetic patients are compared with the patients who will receive the new treatment to determine the effect 114 of the new treatment. See at least [0024]. Also: a user interface (UI) shows the efficacy or the determined effect of the new treatment. If the effect or efficacy of the new treatment is above a threshold, the efficacy may be notified in a first portion of the UI. See at least [0026]).
Claim(s) 3, 4, 12, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over González Hernández et al. (US 2025/0174327 A1) [hereafter referenced as Gonzales] in view of Kishore Kumar et al. (US 2023/0343117 A1) [hereafter referenced as Kishore], and further in view of Tan et al. (Emulating Control Arms for Cancer Clinical Trials Using External Cohorts Created From Electronic Health Record-Derived Real-World Data).
Regarding Claim 3 and 12: Gonzales in view of Kishore makes obvious the above limitations. Gonzales does not appear to disclose wherein the pre-generated survival data comprises a Kaplan-Meier curve. However, Tan teaches generating virtual data including information about survival rates of virtual patients included in a first group based on pre-generated survival data, wherein the pre-generated survival data comprises a Kaplan-Meier curve (Trial data were reconstructed based on data presented in primary trial manuscripts by digitizing published survival curves to obtain estimated patient-level endpoints and by using reported baseline characteristics as cohort-level patient covariates (henceforth named as “reconstructed trial data”). Real-world comparator cohorts were constructed from patient-level EHR-derived real-world databases and based on publicly available information or information on file in trial protocols and publications (henceforth named as “RWD cohort”). See at least Page 2. Also: An example of Kaplan–Meier curves for the reconstructed trial data and RWD cohort is shown for the OAK trial. See at least Page 5).
Gonzales and Kishore suggest a system which generates survival data about virtual patients from real world data, which differs from the claimed invention by the substitution of Gonzales’ unstated techniques for techniques based on Kaplan-Meier curves. However, Tan demonstrates that the prior art already knew of how to generate survival data from Kaplan-Meier curves. One of ordinary skill in the art could have easily substituted in techniques of Tan into the system of Gonzales and Kishore. Further, one of ordinary skill in the art would have recognized that such a substitution would have predictably resulted in a system which would generate treatment effectivity data based on Kaplan-Meier curves. As such the identified substitution and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Gonzales and the teachings of Kishore and Tan.
Regarding Claim 4 and 13: Gonzales, Kishore, and Tan make obvious the above limitations. Tan further teaches obtain the Kaplan-Meier curve for at least one of progression-free survival and overall survival, select a certain number of points on the Kaplan-Meier curve, and generate data for at least one of the progression-free survival and the overall survival of each of the virtual patients by using coordinate values corresponding to the points (Trial data were reconstructed based on data presented in primary trial manuscripts by digitizing published survival curves to obtain estimated patient-level endpoints and by using reported baseline characteristics as cohort-level patient covariates (henceforth named as “reconstructed trial data”). Real-world comparator cohorts were constructed from patient-level EHR-derived real-world databases and based on publicly available information or information on file in trial protocols and publications (henceforth named as “RWD cohort”). See at least Page 2. Also: An example of Kaplan–Meier curves for the reconstructed trial data and RWD cohort is shown for the OAK trial. See at least Page 5). The motivation to combine Gonzales, Kishore, and Tan is the same as explained under claim 3 above, and is incorporated herein.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over González Hernández et al. (US 2025/0174327 A1) [hereafter referenced as Gonzales] in view of Kishore Kumar et al. (US 2023/0343117 A1) [hereafter referenced as Kishore], and further in view of Beck et al. (US 10650929 B1).
Regarding Claim 6 and 15: Gonzales in view of Kishore makes obvious the above limitations. Gonzales does not appear to disclose wherein the at least one parameter value comprises at least one of a hazard ratio of progression-free survival and a hazard ratio of overall survival. However, Beck teaches wherein the at least one parameter value comprises at least one of a hazard ratio of progression-free survival and a hazard ratio of overall survival (In some embodiments, the predicted survival data is sorted into a “Predicted Responder” group of patients expected to respond to the treatment (experimental or control) and a “Predicted Non-Responder” group of patients expected to not respond to the treatment (experimental or control). In order to determine the specificity of the model's prognostic predictions, a hazard ratio and a 95% confidence interval may be computed for the “Predicted Responder” and “Predicted Non-Responder” groups among patients receiving the experimental treatment. It may then be determined whether the confidence interval includes the hazard ratio of the “Predicted Responder” and “Predicted Non-Responder” groups among the patients that received the control treatment. If the confidence interval does not include the hazard ratio among the patients that received the control treatment, it may be inferred that the prognostic power of the predictive model is specific for the experimental treatment. The hazard ratio is defined as the ratio of the hazard rates corresponding to the experimental treatment and the control treatment. For example, patients receiving the control treatment may not survive at twice the rate per unit time as the patients receiving the experimental treatment. See at least Column 21, Lines 19-40).
Gonzales and Kishore suggest a system which generates virtual patients and classifies them as responders and non-responders, upon which the claimed invention’s use of a hazard ratio in the generation can be seen as an improvement. However, Beck demonstrates that the prior art already knew of the use of a hazard ratio to evaluate the classification of responders and non-responders. One of ordinary skill in the art could have applied the techniques of Beck to the system of Gonzales and Kishore to evaluate the classification of patients. One of ordinary skill in the art would have recognized that such an application of Beck would have resulted in a system which would not use poor models to classify patients. As such, the application of Beck and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Gonzales and the teachings of Kishore and Beck.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 Notice of References Cited.
Colley et al. (US 2021/0090694 A1), Brockstedt et al. (US 2023/0395227 A1), Fisher et al. (US 2021/0057108 A1), and Zaharchuk (US 2020/0381096 A1) provide additional details regarding determining treatment efficacy based on comparisons to data generated with real world data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at (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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/Bion A Shelden/Primary Examiner, Art Unit 3685 2026-02-20