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 26, 2022 claim(s) 1-20 are pending in this application; of these claims 1, 8, and 15 are in independent form.
Priority
The priority date is July 26, 2022.
Drawings
The drawings are objected to because Figures 2 and Figures 2A are not properly numbered or do not properly use letters for partial views of the figures. Only Figure 2A uses lettering, whereas Figure 2 does not. Lettering is usually used to distinguish partial views, so it is unclear whether Figure 2A is a partial view of Figure 2, because Figure 2 does not have any letter. See 37 C.F.R. 1.84 (u).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: July 26, 2022.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 7, 9, 14, and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claims 2, 9, and 16 recite the broad recitation “lesion shedding analysis”, and the claim also recites “LSM” (which stands for Lesion Shedding Model, according to the Applicant’s Specification) which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
The Examiner suggests deleting the acronym LSM or adjusting either the phrase or the acronym to specify either Lesion Shedding Analysis or Lesion Shedding Model.
Claims 7 and 14 are rejected because they depend from claims 2 and 9, respectively.
Claims 7 and 14 are also rejected because they recite “shedding level of time”; this appears to be a typographical error, because the specification recites “shedding level over time”. As currently recited, the phrase “shedding level of time” is unclear because “shedding levels” are not durations or instances of “time”.
The Examiner suggests reciting “shedding level over time” if this phrase was intended.
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.
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 and the plurality of lesion and cfDNA datasets to predict a 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); and
determining a diagnostic type for a subsequent cfDNA shedding sample based on the predicted shedding pattern (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 (LSM) (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 at least one of a urine sample, 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 of 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 and the plurality of lesion and cfDNA datasets to predict a shedding pattern (see the corresponding rationale for claim 1); and
determining a diagnostic type for a subsequent cfDNA shedding sample based on the predicted shedding pattern (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 (LSM) (see the corresponding rationale for claim 2).
10. The computer program product of claim 8, wherein the determined diagnostic type is at least one of a urine sample, 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 of time (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 and the plurality of lesion and cfDNA datasets to predict a shedding pattern (see the corresponding rationale for claim 1); and
determining a diagnostic type for a subsequent cfDNA shedding sample based on the predicted shedding pattern (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 (LSM) (see the corresponding rationale for claim 2).
17. The computer system of claim 15, wherein the determined diagnostic type is at least one of a urine sample, 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).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rhrissorrakrai.
Rhrissorrakrai is: 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 (VERSION 2).
Note that VERSION 2 is relied upon as the basis of rejection. Another version, version 3, was filed after the priority date of the instant application and the latter version 3 is NOT relied upon for this rejection. VERSION 2 of this reference was cited on Applicant’s Information Disclosure Statement sent July 26, 2022.
As to claim 1, Rhrissorrakrai teaches a method for predicting cell free DNA (cfDNA) shedding, the method comprising:
training a predicting cfDNA shedding model using a plurality of lesion and cfDNA datasets (Rhrissorrakrai page 3 lines 5-22: computing a lesion shedding model; see also Figure 1 on page 4);
clustering (Rhrissorrakrai page 12 lines 34-45: “combining”) a new cfDNA shedding sample (Rhrissorrakrai page 12 lines 34-45: “assign new alterations found in the cfDNA) and the plurality of lesion and cfDNA datasets to predict a shedding pattern (Rhrissorrakrai page 12 lines 34-45: “combining the LSM with models of tumor clonal evolution, we could develop an inference model of the evolutionary trajectory of lesions based on blood cfDNA”); and
determining a diagnostic type for a subsequent cfDNA shedding sample based on the predicted shedding pattern (Rhrissorrakrai page 12 lines 34-45: “assign new alterations found in the cfDNA as either a likely child of an existing lesion thus showing some kind of evolution or to a new lesion”).
As to claim 2, Rhrissorrakrai 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 (LSM) (Rhrissorrakrai page 3 lines 5-22: a lesion shedding model).
As to claim 3, Rhrissorrakrai teaches the method of claim 1, wherein the determined diagnostic type is at least one of a urine sample, stool sample, a blood sample (Rhrissorrakrai page 12 lines 34-45: “bood cfDNA” sample), radiographic imaging, and a tissue biopsy (these elements are claimed in the alternative and do not all need to be mapped).
As to claim 4, Rhrissorrakrai 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 (Rhrissorrakrai page 10 lines 30-41: determining a diagnostic approach depends on which lesion is a “low shedder” vs. a “high shedder”; the distinction between low and high shedder suggests a predetermined threshold of the shedding sample).
As to claim 5, Rhrissorrakrai teaches the method of claim 1,
wherein the new cfDNA shedding sample is associated with a plurality of lesions (Rhrissorrakrai page 10 lines 30-41: association with a plurality of lesions, including pancreatic and stomach lesions), 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 (Rhrissorrakrai page 10 lines 30-41: the diagnostic type, i.e. diagnostic location, depends on whether the shedding pattern is that of a “low shedder” or a “high shedder”).
As to claim 6, Rhrissorrakrai 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 (Rhrissorrakrai page 10 lines 30-41: determining whether the cfDNA contributions are relatively low or high),
wherein the relative cfDNA contributions depend on at least one of time, location, lesion type, and molecular profile (Rhrissorrakrai page 10 lines 30-41: the lesions that are low vs. high depend on the location of the lesion, such as a stomach vs. a pancreatic lesion).
As to claim 7, Rhrissorrakrai teaches the method of claim 2, further comprising:
generating a hypothesis blood that matches each of the lesion and cfDNA datasets (Rhrissorrakrai page 5 lines 4-24 : generating the hypothesis blood at step 1);
generating a consensus shedding network (Rhrissorrakrai page 5 lines 4-24 : generating the consensus shedding network at step 4) and a shedding level over time from the hypothesis blood (Rhrissorrakrai page 3 lines 5-22: “longitudinal mode” for “dynamics of lesion shedding”);
determining a cohort of shedders from the shedding level of time (Rhrissorrakrai page 12 lines 34-45: determining “the evolutionary trajectory of lesions based on blood cfDNA”; the Examiner interprets each evolutionary trajectory as a cohort of shedders); and
clustering (Rhrissorrakrai page 12 lines 34-45: “combining”) the new cfDNA shedding sample (Rhrissorrakrai page 12 lines 34-45: “assign new alterations found in the cfDNA) with a cohort of substantially similar shedders over time to predict the shedding pattern (Rhrissorrakrai page 12 lines 34-45: “combining the LSM with models of tumor clonal evolution, we could develop an inference model of the evolutionary trajectory of lesions based on blood cfDNA”).
As to claim 8, Rhrissorrakrai 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 (Rhrissorrakrai page 9 line 27: “simulation”), the method comprising:
training a predicting cfDNA shedding model using a plurality of lesion and cfDNA datasets (Rhrissorrakrai page 3 lines 5-22: computing a lesion shedding model; see also Figure 1 on page 4);
clustering (Rhrissorrakrai page 12 lines 34-45: “combining”) a new cfDNA shedding sample (Rhrissorrakrai page 12 lines 34-45: “assign new alterations found in the cfDNA) and the plurality of lesion and cfDNA datasets to predict a shedding pattern (Rhrissorrakrai page 12 lines 34-45: “combining the LSM with models of tumor clonal evolution, we could develop an inference model of the evolutionary trajectory of lesions based on blood cfDNA”); and
determining a diagnostic type for a subsequent cfDNA shedding sample based on the predicted shedding pattern (Rhrissorrakrai page 12 lines 34-45: “assign new alterations found in the cfDNA as either a likely child of an existing lesion thus showing some kind of evolution or to a new lesion”).
As to claim 9, Rhrissorrakrai 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 (LSM) (Rhrissorrakrai page 3 lines 5-22: a lesion shedding model).
As to claim 10, Rhrissorrakrai teaches the computer program product of claim 8, wherein the determined diagnostic type is at least one of a urine sample, stool sample, a blood sample (Rhrissorrakrai page 12 lines 34-45: “bood cfDNA” sample), radiographic imaging, and a tissue biopsy (these elements are claimed in the alternative and do not all need to be mapped).
As to claim 11, Rhrissorrakrai 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 (Rhrissorrakrai page 10 lines 30-41: determining a diagnostic approach depends on which lesion is a “low shedder” vs. a “high shedder”; the distinction between low and high shedder suggests a predetermined threshold of the shedding sample).
As to claim 12, Rhrissorrakrai teaches the computer program product of claim 8,
wherein the new cfDNA shedding sample is associated with a plurality of lesions (Rhrissorrakrai page 10 lines 30-41: association with a plurality of lesions, including pancreatic and stomach lesions), 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 (Rhrissorrakrai page 10 lines 30-41: the diagnostic type, i.e. diagnostic location, depends on whether the shedding pattern is that of a “low shedder” or a “high shedder”).
As to claim 13, Rhrissorrakrai 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 (Rhrissorrakrai page 10 lines 30-41: determining whether the cfDNA contributions are relatively low or high),
wherein the relative cfDNA contributions depend on at least one of time, location, lesion type, and molecular profile (Rhrissorrakrai page 10 lines 30-41: the lesions that are low vs. high depend on the location of the lesion, such as a stomach vs. a pancreatic lesion).
As to claim 14, Rhrissorrakrai teaches the computer program product of claim 9, further comprising:
generating a hypothesis blood that matches each of the lesion and cfDNA datasets (Rhrissorrakrai page 5 lines 4-24 : generating the hypothesis blood at step 1);
generating a consensus shedding network (Rhrissorrakrai page 5 lines 4-24 : generating the consensus shedding network at step 4) and a shedding level over time from the hypothesis blood (Rhrissorrakrai page 3 lines 5-22: “longitudinal mode” for “dynamics of lesion shedding”);
determining a cohort of shedders from the shedding level of time (Rhrissorrakrai page 12 lines 34-45: determining “the evolutionary trajectory of lesions based on blood cfDNA”; the Examiner interprets each evolutionary trajectory as a cohort of shedders); and
clustering (Rhrissorrakrai page 12 lines 34-45: “combining”) the new cfDNA shedding sample (Rhrissorrakrai page 12 lines 34-45: “assign new alterations found in the cfDNA) with a cohort of substantially similar shedders over time to predict the shedding pattern (Rhrissorrakrai page 12 lines 34-45: “combining the LSM with models of tumor clonal evolution, we could develop an inference model of the evolutionary trajectory of lesions based on blood cfDNA”).
As to claim 15, Rhrissorrakrai 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 (Rhrissorrakrai page 9 line 27: “simulation”), the method comprising:
training a predicting cfDNA shedding model using a plurality of lesion and cfDNA datasets (Rhrissorrakrai page 3 lines 5-22: computing a lesion shedding model; see also Figure 1 on page 4);
clustering (Rhrissorrakrai page 12 lines 34-45: “combining”) a new cfDNA shedding sample (Rhrissorrakrai page 12 lines 34-45: “assign new alterations found in the cfDNA) and the plurality of lesion and cfDNA datasets to predict a shedding pattern (Rhrissorrakrai page 12 lines 34-45: “combining the LSM with models of tumor clonal evolution, we could develop an inference model of the evolutionary trajectory of lesions based on blood cfDNA”); and
determining a diagnostic type for a subsequent cfDNA shedding sample based on the predicted shedding pattern (Rhrissorrakrai page 12 lines 34-45: “assign new alterations found in the cfDNA as either a likely child of an existing lesion thus showing some kind of evolution or to a new lesion”).
As to claim 16, Rhrissorrakrai 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 (LSM) (Rhrissorrakrai page 3 lines 5-22: a lesion shedding model).
As to claim 17, Rhrissorrakrai teaches the computer system of claim 15, wherein the determined diagnostic type is at least one of a urine sample, stool sample, a blood sample (Rhrissorrakrai page 12 lines 34-45: “bood cfDNA” sample), radiographic imaging, and a tissue biopsy (these elements are claimed in the alternative and do not all need to be mapped).
As to claim 18, Rhrissorrakrai 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 (Rhrissorrakrai page 10 lines 30-41: determining a diagnostic approach depends on which lesion is a “low shedder” vs. a “high shedder”; the distinction between low and high shedder suggests a predetermined threshold of the shedding sample).
As to claim 19, Rhrissorrakrai teaches the computer system of claim 15,
wherein the new cfDNA shedding sample is associated with a plurality of lesions (Rhrissorrakrai page 10 lines 30-41: association with a plurality of lesions, including pancreatic and stomach lesions), 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 (Rhrissorrakrai page 10 lines 30-41: the diagnostic type, i.e. diagnostic location, depends on whether the shedding pattern is that of a “low shedder” or a “high shedder”).
As to claim 20, Rhrissorrakrai 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 (Rhrissorrakrai page 10 lines 30-41: determining whether the cfDNA contributions are relatively low or high),
wherein the relative cfDNA contributions depend on at least one of time, location, lesion type, and molecular profile (Rhrissorrakrai page 10 lines 30-41: the lesions that are low vs. high depend on the location of the lesion, such as a stomach vs. a pancreatic lesion).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20200004925-A1: pertinent due to Applicant’s own work; see Figure 4 involving cancer trajectory and treatments at different points in an evolutionary tree
US-20240412813-A1: pertinent due to monitoring clonal dynamics of a tumor at different time points using cfDNA
US-12071661-B2: pertinent because it addresses tumor heterogeneity using cfDNA and multifocal samples
Murtaza, Muhammed, et al. "Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer." Nature communications 6.1 (2015): 8760. (Year: 2015)
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. The examiner can normally be reached Monday - Saturday, 10-5 ET.
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 April 17, 2026w
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