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 .
Remarks
In response to communications sent July 17, 2026, claim(s) 1-20 is/are pending in this application; of these claim(s) 1, 8, and 15 is/are in independent form. Claim(s) 1-3, 7-10, and 14-17 are currently amended. Claims 4-6, 11-13, 19, and 20 are original.
Response to Amendment
Applicant sent two sets of claims on July 17, 2026. However, as per the Examiner interview summary sent August 13, 2026, only the set indicating amendments with “Currently Amended” and “Original” markings are entered. Note that the Examiner has stamped the claims with “OK to Enter” for the truly amended claims and “Do not enter” for the claims that the Applicant inadvertently submitted.
The Auxiliary PDF of the application is marked “Do not enter” because it includes a set of claims that is not current and do not reflect the amendments that have been entered.
The drawings are marked “OK to enter.”
Drawings
The drawings were received on July 17, 2026. These drawings are accepted.
Response to Arguments
Applicant’s arguments, see page 12 lines 14-25, filed July 17, 2026, with respect to the drawings have been fully considered and are persuasive. The objection to the drawings mailed April 24, 2026 has been withdrawn.
Applicant’s arguments, see page 13 lines 1-9, filed July 17, 2026, with respect to claims 2, 7, 9, 14, and 16 have been fully considered and are persuasive. The rejection of claims 2, 7, 9, 14, and 16 mailed April 24, 2026 has been withdrawn.
Regarding judicial exceptions to subject matter eligibility: Applicant's arguments filed July 17, 2026 have been fully considered but they are not persuasive. The improvements that the Applicant argues are technological improvements are in fact improvements to the judicial exception itself. The improvements are to the mental process of “determinations” of a type of diagnostic, which is a judgement. In addition, the steps involve a “prediction,” which is also a mental process. The basis of judgements on a computational process is simply performing a mental process with the aid of a general-purpose computer, which amounts to applying the judicial exception on a general-purpose computer. The courts have held that merely applying a judicial exception on a general-purpose computer is not significantly more than an abstract idea. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, 110 USPQ2d 1976, 1981 (2014). The diagnostic approaches recited in claim 3 are also judgements regarding those diagnostic approaches, which is a mental process.
Applicant’s arguments, see page 15 line 25 to 16 line 9, filed July 17, 2026, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 USC 102 and US 20230223145 A1 (“Cohen”).
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a combination of mathematics and mental processes. This judicial exception is not integrated into a practical application because the only additional element beyond the abstract ideas are in the product claims and recite a general-purpose computer that is merely the application of the abstract ideas on a general-purpose computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the application of the abstract ideas on a general-purpose computer is well-understood, routine, and conventional (Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, 110 USPQ2d 1976, 1981 (2014)). The process claims do not recite an additional elements beyond the abstract ideas.
For further explanation of the rejection, see below:
1. A method for predicting cell free DNA (cfDNA) shedding (the entire claim is a combination of abstract ideas with no additional elements and no integration of the abstract ideas with non-abstract ideas),, the method comprising:
training a predicting cfDNA shedding model using a plurality of lesion and cfDNA datasets (the scope of this element encompasses a Lesion Shedding Model, according the Specification at Para [0030] and dependent claim 2; a Lesion Shedding Model is a term of art defined in the inventors’ prior non-patent literature; the prior literature explains that a Lesion Shedding Model involves math; hence, the claim “recites” math because it “describes” math without “setting forth” math, which is a category of abstract ideas; see the prior non-patent literature: Rhrissorrakrai et al., "Lesion Shedding Model: unraveling site-specific contributions to ctDNA." Posted January 29, 2021, https://www.biorxiv.org/content/10.1101/2021.01.28.428297V2.., pages 1-13);
clustering a new cfDNA shedding sample with the plurality of lesion and cfDNA datasets (the Specification does not define “clustering” and does not mention any mathematics for this element; therefore, the Examiner assumes that the claim does not describe nor set forth mathematics as part of the clustering; however, clustering is an evaluation, which is a mental process);
predicting, using the predicting cfDNA shedding model and based on the clustering, a shedding pattern for the new cfDNA shedding sample (mental process of a judgement, which is an abstract idea);
determining, based on the predicted shedding pattern and at least one shedding threshold, a diagnostic type for a subsequent cfDNA shedding sample from a plurality of diagnostic types (mental process of a judgement, which is an abstract idea).
2. The method of claim 1, wherein the training of the predicting cfDNA shedding model using the plurality of lesion and cfDNA datasets includes performing lesion shedding analysis (a Lesion Shedding Model is a term of art defined in the inventor’s prior non-patent literature; the prior literature explains that a Lesion Shedding Model involves math; hence, the claim “recites” math because it “describes” math without “setting forth” math, which is a category of abstract ideas; see the prior non-patent literature: Rhrissorrakrai et al., "Lesion Shedding Model: unraveling site-specific contributions to ctDNA." Posted January 29, 2021, https://www.biorxiv.org/content/10.1101/2021.01.28.428297V2.., pages 1-13).
3. The method of claim 1, wherein the determined diagnostic type is selected from the group consisting of: a urine sample, a stool sample, a blood sample, radiographic imaging, and a tissue biopsy (a limitation of a mental process is still a mental process).
4. The method of claim 1, wherein the determined diagnostic type is selected according to a predetermined threshold of predicted shedding for the subsequent cfDNA shedding sample (a limitation of a mental process is still a mental process).
5. The method of claim 1,
wherein the new cfDNA shedding sample is associated with a plurality of lesions (a limitation of a mental process is still a mental process), and
wherein each lesion of the plurality of lesions has a different determined diagnostic type for the subsequent cfDNA shedding sample based on a corresponding shedding pattern (a limitation of a mental process is still a mental process).
6. The method of claim 5, further comprising:
determining relative cfDNA contributions for each lesion of the plurality of lesions into the new cfDNA shedding sample (a judgement, which is a mental process, which is a type of abstract idea),
wherein the relative cfDNA contributions depend on at least one of time, location, lesion type, and molecular profile (a limitation of a mental process is still a mental process).
7. The method of claim 2, further comprising:
generating a hypothesis blood that matches each of the lesion and cfDNA datasets (“hypothesis blood” in the context of a “Lesion Shedding Model” is described in the inventors’ prior non-patent literature; the prior literature explains that a “hypothesis blood” involves math; hence, the claim “recites” math because it “describes” math without “setting forth” math, which is a category of abstract ideas; see the prior non-patent literature: Rhrissorrakrai et al., "Lesion Shedding Model: unraveling site-specific contributions to ctDNA." Posted January 29, 2021, https://www.biorxiv.org/content/10.1101/2021.01.28.428297V2.., pages 1-13);
generating a consensus shedding network and a shedding level over time from the hypothesis blood (a “consensus shedding network” in the context of a “Lesion Shedding Model” is described in the inventors’ prior non-patent literature; the prior literature explains that generating a “consensus shedding model” involves math; hence, the claim “recites” math because it “describes” math without “setting forth” math, which is a category of abstract ideas; see the prior non-patent literature: Rhrissorrakrai et al., "Lesion Shedding Model: unraveling site-specific contributions to ctDNA." Posted January 29, 2021, https://www.biorxiv.org/content/10.1101/2021.01.28.428297V2.., pages 1-13);
determining a cohort of shedders from the shedding level over time (a judgement, which is a type of mental process, which is an abstract idea); and
clustering the new cfDNA shedding sample with a cohort of substantially similar shedders over time to predict the shedding pattern (the Specification does not define “clustering” and does not mention any mathematics for this element; therefore, the Examiner assumes that the claim does not describe nor set forth mathematics as part of the clustering; however, clustering is an evaluation, which is a mental process).
8. A computer program product for predicting cfDNA shedding, the computer program product comprising:
one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media capable of performing a method (an additional element, beyond the abstract idea, that is not integrated with the abstract idea because it is merely application of the abstract ideas on a general purpose computer that is well-understood, routine, and conventional; see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, 110 USPQ2d 1976, 1981 (2014)), the method comprising:
training a predicting cfDNA shedding model using a plurality of lesion and cfDNA datasets (see the corresponding rationale for claim 1);
clustering a new cfDNA shedding sample with the plurality of lesion and cfDNA datasets to predict a shedding pattern (see the corresponding rationale for claim 1);
predicting, using the predicting cfDNA shedding model and based on the clustering, a shedding pattern for the new cfDNA shedding sample (see the corresponding rationale for claim 1); and
determining, based on the predicted shedding pattern and at least one shedding threshold, a diagnostic type for a subsequent cfDNA shedding sample from a plurality of diagnostic types (see the corresponding rationale for claim 1).
9. The computer program product of claim 8, wherein the training of the predicting cfDNA shedding model using the plurality of lesion and cfDNA datasets includes performing lesion shedding analysis (see the corresponding rationale for claim 2).
10. The computer program product of claim 8, wherein the determined diagnostic type is selected from the group consisting of: a urine sample, a stool sample, a blood sample, radiographic imaging, and a tissue biopsy (see the corresponding rationale for claim 3).
11. The computer program product of claim 8, wherein the determined diagnostic type is selected according to a predetermined threshold of predicted shedding for the subsequent cfDNA shedding sample (see the corresponding rationale for claim 4).
12. The computer program product of claim 8,
wherein the new cfDNA shedding sample is associated with a plurality of lesions (see the corresponding rationale for claim 5), and
wherein each lesion of the plurality of lesions has a different determined diagnostic type for the subsequent cfDNA shedding sample based on a corresponding shedding pattern (see the corresponding rationale for claim 5).
13. The computer program product of claim 12, further comprising:
determining relative cfDNA contributions for each lesion of the plurality of lesions into the new cfDNA shedding sample (see the corresponding rationale for claim 6),
wherein the relative cfDNA contributions depend on at least one of time, location, lesion type, and molecular profile (see the corresponding rationale for claim 6).
14. The computer program product of claim 9, further comprising:
generating a hypothesis blood that matches each of the lesion and cfDNA datasets (see the corresponding rationale for claim 7);
generating a consensus shedding network and a shedding level over time from the hypothesis blood (see the corresponding rationale for claim 7);
determining a cohort of shedders from the shedding level overtime (see the corresponding rationale for claim 7); and
clustering the new cfDNA shedding sample with a cohort of substantially similar shedders over time to predict the shedding pattern (see the corresponding rationale for claim 7).
15. A computer system for predicting cfDNA shedding, the system comprising:
one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method (an additional element, beyond the abstract idea, that is not integrated with the abstract idea because it is merely application of the abstract ideas on a general purpose computer that is well-understood, routine, and conventional; see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, 110 USPQ2d 1976, 1981 (2014)), the method comprising:
training a predicting cfDNA shedding model using a plurality of lesion and cfDNA datasets (see the corresponding rationale for claim 1);
clustering a new cfDNA shedding sample with the plurality of lesion and cfDNA datasets to predict a shedding pattern (see the corresponding rationale for claim 1);
predicting, using the predicting cfDNA shedding model and based on the clustering, a shedding pattern for the new cfDNA shedding sample (see the corresponding rationale for claim 1); and
determining, based on the predicted shedding pattern and at least one shedding threshold, a diagnostic type for a subsequent cfDNA shedding sample from a plurality of diagnostic types (see the corresponding rationale for claim 1).
16. The computer system of claim 15, wherein the training of the predicting cfDNA shedding model using the plurality of lesion and cfDNA datasets includes performing lesion shedding analysis (see the corresponding rationale for claim 2).
17. The computer system of claim 15, wherein the determined diagnostic type is selected from the group consisting of: of a urine sample, a stool sample, a blood sample, radiographic imaging, and a tissue biopsy (see the corresponding rationale for claim 3).
18. The computer system of claim 15, wherein the determined diagnostic type is selected according to a predetermined threshold of predicted shedding for the subsequent cfDNA shedding sample (see the corresponding rationale for claim 4).
19. The computer system of claim 15,
wherein the new cfDNA shedding sample is associated with a plurality of lesions (see the corresponding rationale for claim 5), and
wherein each lesion of the plurality of lesions has a different determined diagnostic type for the subsequent cfDNA shedding sample based on a corresponding shedding pattern (see the corresponding rationale for claim 5).
20. The computer system of claim 19, further comprising:
determining relative cfDNA contributions for each lesion of the plurality of lesions into the new cfDNA shedding sample (see the corresponding rationale for claim 6),
wherein the relative cfDNA contributions depend on at least one of time, location, lesion type, and molecular profile (see the corresponding rationale for claim 6).
Priority
The priority date is July 26, 2022.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-13, and 15-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20230223145 A1 (“Cohen”).
As to claim 1, Cohen teaches a method for predicting cell free DNA (cfDNA) shedding, the method comprising:
training a predicting cfDNA shedding model (Cohen Para [0053]: training a classifier model using biomarker measurements) using a plurality of lesion and cfDNA datasets (Cohen: Para [0078]: using genes in a body fluid as biomarkers);
clustering a new cfDNA shedding sample with the plurality of lesion and cfDNA datasets (Cohen Para [0053]: inputting new biomarker measurements and applying the new input to the classifier model to provide an output to classify patients; Cohen Para [0078]: establishes that the biomarkers may be genes in a body fluid; the term “clustering” is interpreted as “bringing separate data together”);
predicting, using the predicting cfDNA shedding model and based on the clustering, a shedding pattern for the new cfDNA shedding sample (Cohen Para [0034]: a layer the machine learning system determines an organ-system pattern derived from inputted biomarkers; Para [0078]: establishes that the biomarkers may be genes in a body fluid);
determining, based on the predicted shedding pattern and at least one shedding threshold, a diagnostic type for a subsequent cfDNA shedding sample from a plurality of diagnostic types (Coehn Para [0034]: determining, based on the organ-system, a subsequent diagnostic modality, such as a CT scan when the organ-system is chest; Cohen at Para [0017] teaches the use of thresholds with the classifier).
As to claim 2, Cohen teaches the method of claim 1, wherein the training of the predicting cfDNA shedding model using the plurality of lesion and cfDNA datasets includes performing lesion shedding analysis (Cohen Para [0034]: a layer the machine learning system determines an organ-system pattern derived from inputted biomarkers; Para [0078]: establishes that the biomarkers may be genes in a body fluid).
As to claim 3, Cohen teaches the method of claim 1, wherein the determined diagnostic type is selected from the group consisting of: a urine sample, a stool sample, a blood sample, radiographic imaging, and a tissue biopsy (Coehn Para [0034]: determining, based on the organ-system, a subsequent diagnostic modality, such as a CT scan when the organ-system is chest; a CT scan is a type of radiograph imaging that is selected from the group).
As to claim 4, Cohen teaches the method of claim 1, wherein the determined diagnostic type is selected according to a predetermined threshold of predicted shedding for the subsequent cfDNA shedding sample (Cohen at Para [0017] teaches the use of thresholds with the classifier).
As to claim 5, Cohen teaches the method of claim 1,
wherein the new cfDNA shedding sample is associated with a plurality of lesions (Cohen Para [0033]: the biomarker is associated with many possible origins of cancer for a patient), and
wherein each lesion of the plurality of lesions has a different determined diagnostic type for the subsequent cfDNA shedding sample based on a corresponding shedding pattern (Cohen Para [0033]: different diagnostics, such as a CT scan, may be chosen based on organ systems such as lung vs. ear, etc.).
As to claim 6, Cohen teaches the method of claim 5, further comprising:
determining relative cfDNA contributions for each lesion of the plurality of lesions into the new cfDNA shedding sample (Cohen Para [0033]: determining biomarker signatures for organ systems based on the “best balance between sensitivity and specificity”),
wherein the relative cfDNA contributions depend on at least one of time, location, lesion type, and molecular profile (Cohen Para [0033]: the relative biomarker contributes of organ systems depend on exposure history, which includes aspects of time, location, and target lesion type).
As to claim 8, Cohen teaches a computer program product for predicting cfDNA shedding, the computer program product comprising:
one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media capable of performing a method, the method comprising:
training a predicting cfDNA shedding model (Cohen Para [0053]: training a classifier model using biomarker measurements) using a plurality of lesion and cfDNA datasets (Cohen: Para [0078]: using genes in a body fluid as biomarkers);
clustering a new cfDNA shedding sample with the plurality of lesion and cfDNA datasets to predict a shedding pattern (Cohen Para [0053]: inputting new biomarker measurements and applying the new input to the classifier model to provide an output to classify patients; Cohen Para [0078]: establishes that the biomarkers may be genes in a body fluid; the term “clustering” is interpreted as “bringing separate data together”);
predicting, using the predicting cfDNA shedding model and based on the clustering, a shedding pattern for the new cfDNA shedding sample (Cohen Para [0034]: a layer the machine learning system determines an organ-system pattern derived from inputted biomarkers; Para [0078]: establishes that the biomarkers may be genes in a body fluid); and
determining, based on the predicted shedding pattern and at least one shedding threshold, a diagnostic type for a subsequent cfDNA shedding sample from a plurality of diagnostic types (Coehn Para [0034]: determining, based on the organ-system, a subsequent diagnostic modality, such as a CT scan when the organ-system is chest; Cohen at Para [0017] teaches the use of thresholds with the classifier).
As to claim 9, Cohen teaches the computer program product of claim 8, wherein the training of the predicting cfDNA shedding model using the plurality of lesion and cfDNA datasets includes performing lesion shedding analysis (Cohen Para [0034]: a layer the machine learning system determines an organ-system pattern derived from inputted biomarkers; Para [0078]: establishes that the biomarkers may be genes in a body fluid).
As to claim 10, Cohen teaches the computer program product of claim 8, wherein the determined diagnostic type is selected from the group consisting of: a urine sample, a stool sample, a blood sample, radiographic imaging, and a tissue biopsy (Coehn Para [0034]: determining, based on the organ-system, a subsequent diagnostic modality, such as a CT scan when the organ-system is chest; a CT scan is a type of radiograph imaging that is selected from the group).
As to claim 11, Cohen teaches the computer program product of claim 8, wherein the determined diagnostic type is selected according to a predetermined threshold of predicted shedding for the subsequent cfDNA shedding sample (Cohen at Para [0017] teaches the use of thresholds with the classifier).
As to claim 12, Cohen teaches the computer program product of claim 8,
wherein the new cfDNA shedding sample is associated with a plurality of lesions (Cohen Para [0033]: the biomarker is associated with many possible origins of cancer for a patient), and
wherein each lesion of the plurality of lesions has a different determined diagnostic type for the subsequent cfDNA shedding sample based on a corresponding shedding pattern (Cohen Para [0033]: different diagnostics, such as a CT scan, may be chosen based on organ systems such as lung vs. ear, etc.).
As to claim 13, Cohen teaches the computer program product of claim 12, further comprising:
determining relative cfDNA contributions for each lesion of the plurality of lesions into the new cfDNA shedding sample (Cohen Para [0033]: determining biomarker signatures for organ systems based on the “best balance between sensitivity and specificity”),
wherein the relative cfDNA contributions depend on at least one of time, location, lesion type, and molecular profile (Cohen Para [0033]: the relative biomarker contributes of organ systems depend on exposure history, which includes aspects of time, location, and target lesion type).
As to claim 15, Cohen teaches a computer system for predicting cfDNA shedding, the system comprising:
one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method, the method comprising:
training a predicting cfDNA shedding model (Cohen Para [0053]: training a classifier model using biomarker measurements) using a plurality of lesion and cfDNA datasets (Cohen: Para [0078]: using genes in a body fluid as biomarkers);
clustering a new cfDNA shedding sample with the plurality of lesion and cfDNA datasets to predict a shedding pattern (Cohen Para [0053]: inputting new biomarker measurements and applying the new input to the classifier model to provide an output to classify patients; Cohen Para [0078]: establishes that the biomarkers may be genes in a body fluid; the term “clustering” is interpreted as “bringing separate data together”);
predicting, using the predicting cfDNA shedding model and based on the clustering, a shedding pattern for the new cfDNA shedding sample (Cohen Para [0034]: a layer the machine learning system determines an organ-system pattern derived from inputted biomarkers; Para [0078]: establishes that the biomarkers may be genes in a body fluid); and
determining, based on the predicted shedding pattern and at least one shedding threshold, a diagnostic type for a subsequent cfDNA shedding sample from a plurality of diagnostic types (Coehn Para [0034]: determining, based on the organ-system, a subsequent diagnostic modality, such as a CT scan when the organ-system is chest; Cohen at Para [0017] teaches the use of thresholds with the classifier).
As to claim 16, Cohen teaches the computer system of claim 15, wherein the training of the predicting cfDNA shedding model using the plurality of lesion and cfDNA datasets includes performing lesion shedding analysis (Cohen Para [0034]: a layer the machine learning system determines an organ-system pattern derived from inputted biomarkers; Para [0078]: establishes that the biomarkers may be genes in a body fluid).
As to claim 17, Cohen teaches the computer system of claim 15, wherein the determined diagnostic type is selected from the group consisting of: of a urine sample, a stool sample, a blood sample, radiographic imaging, and a tissue biopsy (Coehn Para [0034]: determining, based on the organ-system, a subsequent diagnostic modality, such as a CT scan when the organ-system is chest; a CT scan is a type of radiograph imaging that is selected from the group).
As to claim 18, Cohen teaches the computer system of claim 15, wherein the determined diagnostic type is selected according to a predetermined threshold of predicted shedding for the subsequent cfDNA shedding sample (Cohen at Para [0017] teaches the use of thresholds with the classifier).
As to claim 19, Cohen teaches the computer system of claim 15,
wherein the new cfDNA shedding sample is associated with a plurality of lesions (Cohen Para [0033]: the biomarker is associated with many possible origins of cancer for a patient), and
wherein each lesion of the plurality of lesions has a different determined diagnostic type for the subsequent cfDNA shedding sample based on a corresponding shedding pattern (Cohen Para [0033]: different diagnostics, such as a CT scan, may be chosen based on organ systems such as lung vs. ear, etc.).
As to claim 20, Cohen teaches the computer system of claim 19, further comprising:
determining relative cfDNA contributions for each lesion of the plurality of lesions into the new cfDNA shedding sample (Cohen Para [0033]: determining biomarker signatures for organ systems based on the “best balance between sensitivity and specificity”),
wherein the relative cfDNA contributions depend on at least one of time, location, lesion type, and molecular profile (Cohen Para [0033]: the relative biomarker contributes of organ systems depend on exposure history, which includes aspects of time, location, and target lesion type).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180363063 A1: Pertinent because of the triage of diagnostics based on liquid biopsies from cell-free DNA analysis.
DE 102020122552 A1: Pertinent because of biopsy modalities
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 Jesse P Frumkin whose telephone number is (571)270-1849, Cohen teaches the examiner can normally be reached Monday - Friday, 10-5 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249, Cohen teaches the fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 October 3, 2026
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