DETAILED ACTION
Claim Status
Claims 1-29 are rejected.
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 is a 371 of PCT/US2021/037969. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
This application claims domestic benefit to application #63041103, filed 06/18/2020. Domestic benefit is acknowledged. Therefore, the effective filing date of claim(s) is 06/18/2020.
Information Disclosure Statement
The Information Disclosure Statement filed on 12/16/2022 is in compliance with the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of list of references cited from each IDS is included with this Office Action.
Claim Objections
Claims 3 and 5 objected to because of the following informalities: the abbreviations SSR and IHC need to be defined before they are used. Appropriate correction is required.
Drawings
The drawings filed on 12/16/2022 are accepted.
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 21 and 23 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.
Claim 21 states “a peak width greater than 2 in 5 replicate runs, 3 in 6 replicate runs,” etc. It is unclear whether this number is meant to refer to the peak width or the fractions of the run that need to be greater than some omitted value.
Claim 23 states that the SSR type of an MSI feature must be all of 41 different sequences at once. It is impossible for a nucleotide sequence to be in a superposition of multiple sequences at one time, making it indefinite as to which it is supposed to be.
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.
Claims 14, 27-29 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 claims contain 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.
The preamble of claim 11 (and thus claim 14) recites that the method is computer implemented, and claim 29 is a computer system with a processor that performs a method. However, applicant did not disclose in the specification that that a computer or processor itself is able to administer a treatment to a human. Therefore, these limitations lack written description.
Claim 27 lacks written description because it recites collecting a clinical sample and sequencing the sample through NGS as steps performed by a processor and a processor alone cannot carry out these steps. Applicant has not described a computer processor that is capable of collecting a sample and performing NGS. Claim 28 is additionally rejected because it inherits the issue of claim 27 without resolving it.
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 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
1. (c) extracting a MSI feature from the sequencing data
1. (d) training a machine learning model by mapping a MSI feature data with the estimated MSI status data
2. The computer-implemented method of claim 1, wherein the MSI feature data is calculated by a baseline.
3. The computer-implemented method of claim 2, wherein the baseline is established from a mean of each the MSI feature of each SSR region across normal samples.
4. The computer-implemented method of claim 2, wherein the baseline is established from a mean peak width of each SSR region across normal samples.
6. The computer-implemented method of claim 1, wherein the machine learning model comprises a logistic regression model, a random forest model, an extremely randomized trees model, a polynomial regression model, a linear regression model, a gradient descent model, or an extreme gradient boost model.
7. The computer-implemented method of claim 1, wherein the trained machine learning model comprises a defined weight of each microsatellite locus, and is predictive of the MSI status.
8. (The computer-implemented method of claim 1, wherein the trained machine learning model comprises a defined weight of the MSI feature in each microsatellite locus and is predictive of the MSI status.
9. The computer-implemented method of claim 1, wherein the trained machine learning model has a cutoff value of 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, or 0.5.
11. (c) extracting a MSI feature from the sequencing data;
11. (d) inputting a MSI feature data into the trained machine learning model of claim 1;
11. (e) generating a computed MSI status
13. The computer-implemented method of claim 11, further comprising a step of identifying a treatment based on the computed MSI status data of the subject.
15. The computer-implemented method of claim 13, wherein the treatment comprises surgery, individual therapy, chemotherapy, radiation therapy, or immunotherapy.
16. The computer-implemented method of claim 15, wherein the immunotherapy comprises a step of administering a drug selected from the group consisting of pembrolizumab, nivolumab, MEDIO680, durvalumab and ipilimumab.
17. The computer-implemented method of claim 11, wherein the computed MSI status data indicates MSS or MSI-H.
19. wherein the microsatellite loci with low coverage, unstable peak call, high variability in peak width or low weight are excluded.
20. The computer-implemented method of claim 19, wherein the microsatellite loci with low coverage has a read depth lower than 5x, 10x, 15x, 20x,25x, 30x, 35x, 40x, 45x or 50x from a sample on a locus.
21. The computer-implemented method of claim 19, wherein the microsatellite loci with high variability in peak width has a peak width greater than 2 in 5 replicate runs, 3 in 6 replicate runs, 3 in 7 replicate runs, 3 in 8 replicate runs, 3 in 9 replicate runs, or 4 in 10 replicate runs.
22. wherein the MSI feature comprises peak width, peak height, peak location, simple sequence repeat (SSR) type or any combination thereof.
23. The computer-implemented method of claim 22, wherein the SSR type comprises mononucleotide with at least 10 repeats, dinucleotide with at least 6 repeats, trinucleotide with at least 5 repeats, tetranucleotide with at least 5 repeats,pentanucleotide with at least 5 repeats, and a complex nucleotide type of SEQ ID NOs: 1-37.
27. (a) training a machine learning model by mapping a training MSI feature data with a training estimated MSI status data;
27. (d) computing, by using a trained machine learning model having a MSI feature data extracting from the sequencing data, an estimated MSI status data;
27. (e) generating a computed MSI status data;
28. The system of claim 27, wherein the method further comprises step (g):identifying a treatment for the human subject based on the computed MSI status.
The limitations for “training,” “computing,” and “generating” are all related to the creation and use of a machine learning model, stipulated in claim 1 to be trained by mapping MSI feature data with the estimated MSI status data. This is similar to organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). MPEP 2106.04(a)(2).I.A states: “The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula.” The feature and status data of the instant application are analogous to the first and second data in Digitech. The other limitations simply modify the particulars of the machine learning model. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 1-29 recite performing some aspects of the analysis with a “computer” or “system”, there are no additional limitations that indicate that the computer or system requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-29 recite an abstract idea ( Step 2A, Prong 1 : YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to effect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
1. A computer-implemented method of generating a model for predicting a microsatellite instability (MSI) status, comprising:(a) collecting a clinical sample and an estimated MSI status data thereof; (b) sequencing, through next-generation sequencing (NGS), at least six microsatellite loci of the clinical sample so as to generate a sequencing data;
5. The computer-implemented method of claim 1, wherein the estimated MSI status data is retrieved from a cancer patient through an assay, comprising MSI- PCR assay, IHC or NGS-based MSI testing.
10. The computer-implemented method of claim 1, wherein the estimated MSI status data indicates microsatellite stability (MSS) or microsatellite instability- high (MSI-H).
11. A computer-implemented method for determining a MSI status, comprising: (a) collecting a clinical sample from a subject; (b) sequencing, through NGS, at least six microsatellite loci of the clinical sample so as to generate a sequencing data;
12. The computer-implemented method of claim 11, further comprising step (f): outputting the computed MSI status data to an electronic storage medium or a display.
14. The computer-implemented method of claim 13, further comprising a step of administering a therapeutically effective amount of the treatment to the subject.
18. wherein the microsatellite loci is at least 7, 10, 15, 20, 30, 40, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550 or 600 loci.
24. the clinical sample originates from cell line, biopsy, primary tissue, frozen tissue, formalin-fixed paraffin-embedded (FFPE), liquid biopsy, blood, serum, plasma, buffy coat, body fluid, visceral fluid, ascites, paracentesis, cerebrospinal fluid, saliva, urine, tears, seminal fluid, vaginal fluid, aspirate, lavage, buccal swab, circulating tumor cell (CTC), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), DNA, RNA, nucleic acid, purified nucleic acid, purified DNA, or purified RNA.
25. the clinical sample originates from a patient having cancer, solid tumor, hematologic malignancy, rare genetic disease, complex disease, diabetes, cardiovascular disease, liver disease, or neurological disease.
26. wherein a tumor purity of the clinical sample is at least 5,10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100%.
27. A system for determining a MSI status, comprising:a data storage device storing instructions for determining characteristics of MSI status; and a processor configured to execute instructions to perform a method including:
(b) collecting a clinical sample from a subject;
(c) sequencing, through NGS, at least six microsatellite loci of the clinical sample so as to generate a sequencing data;
(f) outputting the computed MSI status data.
29. The system of claim 28, wherein the method further comprises step (h):administering a therapeutically effective amount of a treatment to the human subject.
There are no limitations that indicate that the claimed “computer” or “system” or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The steps for administering a treatment do not recite a “particular” treatment as there is no indication of the type of drug or treatment that is applied that would have more than a nominal or insignificant relationship to the exception. Rather, these limitations equate to a step of “administering a suitable medication” that merely apply the exception in a generic way and do no integrate the recited exception into a practical application (see MPEP 2106.04(d)(2)).
The steps for “collecting,” “sequencing,” “extracting” and “outputting” are mere data gathering and output activities that are insignificant extra-solution activity. They are similar to Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
As such, claims 1-29 are directed to an abstract idea ( Step 2A, Prong 2 : NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite additional elements enumerated above, in the section on step 2A.
Applicant discloses the methods they use to receive the MSI data in their specification (¶ 10). Li et al. (Cancer Cell Int, (2020) 20:16) details these methods in their review article (pg 2 left col ¶ 2). This shows that the MSI data collection is well understood, routine and conventional.
Detecting DNA or enzymes in a sample, Sequenom, 788 F.3d at 1377-78, 115 USPQ2d at 1157); Cleveland Clinic Foundation 859 F.3d at 1362, 123 USPQ2d at 1088 (Fed. Cir. 2017), amplifying and sequencing nucleic acid sequences, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 764, 113 USPQ2d 1241, 1247 (Fed. Cir. 2014), and analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546, and Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 have all been found to be well-understood, routine and conventional activity.
As discussed above, there are no additional limitations to indicate that the claimed analysis engine requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The limitation for treating the tumor cells equate to mere instructions to apply the judicial exception in a generic way because the treating step is so generically recited. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No). As such, claims 1-29 are not patent eligible.
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.
Claims 1, 2, 5-8, 10-18, and 24-29 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Berger et al (WO2019204208A1, publication date 2019-10-24, IDS reference), as evidenced by DataScienceToday (https://www.datasciencetoday.net/index.php/en-us/machine-learning/167-linear-classifier, 2018).
Claim 1 claims a computer implemented method of generating a model for predicting microsatellite instability profiles. Berger discloses a computer implemented method for generating a model for predicting microsatellite instability (MSI) status, comprising:
(a) collecting clinical samples (pg 27 last paragraph) and their estimated MSI status data; (pg 44 ¶ 1)
(b) by next generation sequencing (NGS), sequencing at least six microsatellite sites of the clinical sample, to generate sequencing data ("receive from a next generation sequencing device (i) a plurality of nucleic acids from a subject (e.g., circulating cell-free DNA (cfDNA) sequence read pairs, each nucleic acid (e.g., cfDNA) sequence read being from the plurality of nucleic acid (e.g., cfDNA) sequence reads, "identifying, by the one or more processors, a first subset of the plurality of nucleic acid (e.g., cfDNA) sequence reads and a second subset of the plurality of leukocyte-derived sequence reads to microsatellite loci.", pg 2, ¶ 1; "about 40 microsatellite loci, pg 43 ¶ 1");
(c) extracting MSI features from the sequencing data ("Claim 11: a machine learning or statistical classifier that generates a decision boundary on coordinate space separating a first set of data points representing the presence of microsatellite instability in the sequence reads from a second set of data points representing the absence of microsatellite instability in the sequence reads");
(d) training a machine learning model by mapping MSI feature data with estimated MSI state data ("one machine learning or statistical classifier, generating a decision boundary on the coordinate space separating a first set of data points representing a presence of microsatellite instability in the sequence reads from a second set of data points representing an absence of microsatellite instability in the sequence reads ", pg 6, ¶ 3);
(e) outputting the trained machine learning model ("the classifier can build a model based on this data. From this model, the classifier can determine whether the first distribution can be classified as having the presence of microsatellite instability" pg 44 ¶ 1)
Berger describes a computer-implemented method (Claim 1). Regarding claim 2, claim 1 of Berger states that a second distribution from a reference sample is used to compare to a first sample in order to detect microsatellite instability. Therefore, the instability is calculated with a baseline.
Regarding claim 5, the description of fig. 10 of Berger states the MSK-IMPACT assay was used to obtain MSI status data from cancer patients.
Regarding claim 6, Berger states: “The SVM can construct a hyperplane in a multi-dimensional space, which can be used for classification or regression. In some examples, the one or more processors can utilize other types of classifiers such as, for example, linear classifiers, quadratic classifiers, kernel estimators, neural networks, learning vector quantization, etc., to classify the first distribution as having microsatellite instability or not having microsatellite instability.” (pg 44 ¶ 1) Therefore, Berger teaches using a linear regression in order to detect MSI status.
Regarding claims 7 and 8, Evidentiary reference DataScienceToday (https://www.datasciencetoday.net/index.php/en-us/machine-learning/167-linear-classifier, 2018) states that a linear classifier has weights for each feature (¶ 1).
Regarding claim 10, the description of figs. 10 and 11 (pg 11 ¶ 4-5) state that MSS and MSI-high can be detected by the method.
Claim 11 claims a computer implemented method of generating a model for predicting microsatellite instability profiles. Berger discloses a computer implemented method for generating a model for predicting microsatellite instability (MSI) status, comprising:
(a) collecting clinical samples (pg 27 last paragraph) and their estimated MSI status data; (pg 44 ¶ 1)
(b) by next generation sequencing (NGS), sequencing at least six microsatellite sites of the clinical sample, to generate sequencing data ("receive from a next generation sequencing device (i) a plurality of nucleic acids from a subject (e.g., circulating cell-free DNA (cfDNA) sequence read pairs, each nucleic acid (e.g., cfDNA) sequence read being from the plurality of nucleic acid (e.g., cfDNA) sequence reads, "identifying, by the one or more processors, a first subset of the plurality of nucleic acid (e.g., cfDNA) sequence reads and a second subset of the plurality of leukocyte-derived sequence reads to microsatellite loci.", pg 2, ¶ 1; "about 40 microsatellite loci, pg 43 ¶ 1");
(c) extracting MSI features from the sequencing data ("Claim 11: a machine learning or statistical classifier that generates a decision boundary on coordinate space separating a first set of data points representing the presence of microsatellite instability in the sequence reads from a second set of data points representing the absence of microsatellite instability in the sequence reads");
(d) inputting a MSI feature data into the trained machine learning model of claim 1 (“In some examples, machine learning tools can be utilized to detect MSI in a sample. As an example, trained classifiers can be used to determine whether the first distribution indicates the presence of MSI. The classifiers may determine the presence of MSI in the first distribution independently of the second distribution.” - Berger pg 40 ¶ 3)
(e) generating a computed MSI status (Berger claim 1 – “generating, by the one or more processors, a second distribution indicating a number of microsatellite loci having distances within the group of distinct distance intervals, the second distribution derived from distances associated with each microsatellite locus of the plurality of microsatellite loci observed in a reference sample; determining, by the one or more processors, that a number of microsatellite loci in the first distribution above a threshold distance metric is greater than a number of microsatellite loci in the second distribution above the threshold distance metric to detect a presence of microsatellite instability in the subject; and
storing, by the one or more processors, responsive to the determination, in one or more data structures, an association between the subject and the presence of microsatellite instability.”)
Regarding claim 12, Berger claim 1 teaches “storing, by the one or more processors, responsive to the determination, in one or more data structures, an association between the subject and the presence of microsatellite instability.” Regarding claims 13-15, Berger used their method to guide and monitor the immunotherapy treatment of two test subjects (pg 45 ¶2-3).
Regarding claim 16, Pembrolizumab was administered in Berger (pg 12 ¶ 1).
Concerning claim 17, the description of figs. 10 and 11 (pg 11 ¶ 4-5) state that MSS and MSI-high can be detected by the method.
Moving on to claim 18, Berger analyzed "about 40 microsatellite loci” (pg 43 ¶ 1).
Regarding claim 24, the samples come from cfDNA in Berger (abstract).
Regarding claim 25, cancer patients had samples taken in Berger (pg 11 ¶ 3).
With respect to claim 26 Berger states: “However, while tumor sequencing is increasingly performed for MSI detection, the current methods typically fail when the tumor purity falls below ~25%.” (pg 37 ¶ 1). Therefore, Berger teaches a tumor purity of at least 25%.
Regarding claim 27, the claim sets forth a system for determining a MSI status, comprising: a data storage device storing instructions for determining characteristics of MSI status; and a processor configured to execute instructions to perform a method including:
(a) training a machine learning model by mapping a training MSI feature data with a training estimated MSI status data; (Berger pg 44 ¶ 3)
(b) collecting a clinical sample from a subject; (pg 27 last paragraph)
(c) sequencing, through NGS, at least six microsatellite loci of the clinical sample so as to generate a sequencing data; (Berger pg 44 ¶ 3 – 311 tumor samples were sequenced for microsatellite loci)
(d) computing, by using a trained machine learning model having a MSI feature data extracting from the sequencing data, an estimated MSI status data; (e) generating a computed MSI status data (Berger claim 1 – “generating, by the one or more processors, a second distribution indicating a number of microsatellite loci having distances within the group of distinct distance intervals, the second distribution derived from distances associated with each microsatellite locus of the plurality of microsatellite loci observed in a reference sample; determining, by the one or more processors, that a number of microsatellite loci in the first distribution above a threshold distance metric is greater than a number of microsatellite loci in the second distribution above the threshold distance metric to detect a presence of microsatellite instability in the subject”)
and (f) outputting the computed MSI status data. (Berger pg 13 ¶ 1 discusses and figs. 16-18 show an output of computed MSI status data.)
Regarding claims 28-29, Pembrolizumab was administered in Berger based on the MSI status results (pg 12 ¶ 1).
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.
Claims 4, 9, 19, 20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Berger as applied to claims 1, 2, 5-8, 10-18, 20, and 24-29 above, and further in view of Thermo Fisher (Microsatellite Analysis Software USER GUIDE, 2018).
Regarding claim 4, Thermo fisher teaches how baselines can be established from peak width in microsatellite analysis (pg 120 ¶ 1).
Claim 9 sets forth half the range of a value that can go from 0 to 1, which would be within the range of routine optimization of someone trying to improve the model through brute force, see In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955).
For claim 19, filtering out unstable peaks is taught by thermo fisher (pg 66 ¶ 1).
For claim 20, Fig. 7B of Berger shows the low coverage of the MSI reads is within the claimed range (pg 10 ¶ 7).
Concerning claim 22, peak width and height as MSI features are taught by Thermo fisher (pg 66 ¶ 1).
Regarding claims 4, 19-20 and 22, An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use peak widths and baselines in the text of Thermo Fisher (pg 66 ¶ 1). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as both methods are adapted to MSI detection. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Berger by using the peak width analysis methods and baseline analysis from Thermo Fisher, in order to boost the predictive power of the MSI analysis.
Claims 3 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Berger and Thermo Fisher as applied to claims 1, 2, 4-20, 22 and 24-29 above, and further in view of Fan et al (Genomics Proteomics Bioinformatics, 2007 Jun 15;5(1):7–14).
Regarding claim 3, Fan teaches that different types of microsatellites have different mutation rates, and states its implications for understanding population structure (abstract, introduction ¶ 1-3).
Concerning claim 23, Fan teaches the length and types of the nucleotide repeats (introduction ¶ 1-2).
Regarding claims 3 and 23, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use consideration of the different SSR types in the text of Fan, in order to better understand microsatellite instability (abstract, introduction ¶ 1-3). There would be a
reasonable expectation of success in making this combination to a person of ordinary skill in the art, as there is nothing blocking the consideration of the types from being added to the method. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Berger by considering the SSR type, in order to boost the power of the MSI detection.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Berger and Thermo Fisher as applied to claims 1, 2, 4-20, 22, and 24-29 above, and further in view of Bramston-Cook (Lotus Consulting, 2009).
Regarding claim 21, Bramston-Cook states that peak responses can “vary dramatically with replicate runs of the same sample” and that “Proper assignment of a peak response involves some judgments to distinguish a real peak from noise.” (pg 2 ¶ 1) Taking at least two replicate runs of the same sample to verify its value is suggested by this disclosure. The disclosed ranges could have been found by routine optimization by the ordinary artisan, see In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955).
Regarding claim 21, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a suggestion to use replicate runs in the text of Bramston-Cook in order to distinguish the peak from noise (pg 2 ¶ 1). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as there is nothing blocking the method from being used more than once. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Berger by using the replicate runs suggested by Bramston-Cook, in order to distinguish the peak from noise.
Conclusion
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/G.M.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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