DETAILED ACTION
Notice of 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 .
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 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.
Election/Restrictions
Applicant’s election without traverse of Group 1 in the reply filed on 04/15/2026 is acknowledged. In this instant application, claims 14-19, 56-60, 62-68 and 72-76 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention there being no allowable generic or linking claim. Claims 1-13, 20-55, 61 and 69-71 are being examined on the merits.
Status of the Claims
Claims 1-78 are pending.
Claims 14-19, 56-60, 62-68 and 72-76 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention, as set forth in the Restriction Response dated 04/15/2026.
Claims 1-13, 20-55, 61 and 69-71 are examined.
Claims 10 and 21 are objected to.
Claims 1-13, 20-55, 61 and 69-71 are rejected.
Priority
This US Application 17/747,851 (05/18/2022) claims priority from US Application 63/190,141 (05/18/2021) and US Application 63/307,009 (02/04/2022) as reflected in the filing receipt mailed on 10/03/2022. The claims to the benefit of priority are acknowledged; and the effective filing date of claims 1-13, 20-55, 61 and 69-71 is 05/18/2021.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/17/2025 was considered.
Specification Objections
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
The disclosure is objected to because of the following informality: Table 6A pg. 102 line 10 reads "Immunoglobulin heavy constant gamma 3" and should read "Immunoglobulin heavy constant gamma 4."
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code in [0168]. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Appropriate correction is required.
Claim objections
Claim 10 is objected to because of the following informality: the recited "one the group" should read "one of the groups" for proper writing.
Claim 21 is objected to because of the following informalities related to grammar. The term "wherein the" is duplicated. Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1-13, 20-55, 61 and 69-71 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
Claims 1, 7, 12, 20-21, 28, 30-31, 36, 40 and 42-43 refer to information listed in a "Table." This table is disclosed on pages 86-88, 91-92 and 100-102 of the instant specification. Independent claims 1, 20 and 42 do not recite the relevant content within the tables in its entirety, thus it is unclear which genes the claims are intended to include. MPEP § 2173.05(s) explains that, where possible, claims are to be complete in themselves. Incorporation by reference to a specific table is "permitted only in exceptional circumstances where there is no practical way to define the invention in words and where it is more concise to incorporate by reference than duplicating a drawing or table into the claim. There is no indication that the invention cannot be defined in words nor that it is more concise to incorporate by reference rather than duplicating the table into the claim. Therefore, the incorporation by reference of "Table" in the claim is improper. Incorporation by reference is a necessity doctrine, not for applicant’s convenience. Ex parte Fressola... (citations omitted)" (MPEP § 2173.05(s)). It is not clear that the instant claim recitation(s) constitute "exceptional circumstances." As one option to overcome this rejection, the referenced genes may be individually recited in the claims, for example as a text list. Alternatively, an entire table may be copied into the claim. Any effective citation to the specification, e.g. recitation of "Table" must be deleted. Regarding the recitation "Table", dependent claims are consequently rejected as they depend from a rejected base claim and do not clarify the issues
Independent claims 1, 20 and 42 recite "peptide structure" identified in tables which disclose multiple categories: PS ID No, Peptide Structure Name, Protein SEQ ID No, Peptide SEQ ID No, Monoisotopic mass, Linking site position in protein sequence, Linking site position in peptide sequence and glycan structure GL No. It is not clear which of these categories the claim intends to refer to when reciting “peptide structure… identified in each recited table in the claims. It is unclear if only one, multiple, or all of the categories is intended to describe the peptide structure. For compact examination, it is assumed that the peptide structure recited in the claims corresponds to Peptide SEQ ID or its corresponding Protein SEQ ID. The rejection may be overcome by amending the claim to clarify the metes and bounds of the limitation. Claims 2-13, 21-41, 43-55, 61 and 69-71 are rejected based on their dependency.
Claim 40 recites "the first group" which lacks antecedent basis because there is no previous recitation of "a first group."
Claim 50 recites “the treatment output” which is indefinite because it lacks antecedent basis. A treatment output has not been previously recited. The examiner suggests an amendment to overcome the rejection.
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 1-13, 20-55, 61 and 69-71 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 1-13, 20-55, 61 and 69-71) which falls within one of the categories of statutory subject matter.
[Step 1: claims 1-13, 20-55, 61 and 69-71: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) as well as a law of nature or a natural phenomenon are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "analyzing the peptide structure data using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences an ovarian cancer disease state based on at least three peptide structures selected from one of a first group of peptide structures identified in Table 1A and a second group of peptide structures identified in Table 2A, wherein the first group of peptide structures and the second group of peptide structures are associated with the ovarian cancer disease state; wherein each of the first group of peptide structures in Table 1A and the second group of peptide structures in Table 2A is listed in order of relative significance to the disease indicator" (independent claim 1);
• "generating the diagnosis output based on the score falling above the selected threshold, wherein the diagnosis output includes a positive or negative diagnosis for the ovarian cancer disease state" (claim 3);
• "analyzing the peptide structure data using a binary classification model" (claims 6 and 27);
• "training the supervised machine learning model using training data, wherein the training data comprises a plurality of peptide structure profiles for a plurality of subjects and a plurality of subject diagnoses for the plurality of subjects" (claims 8 and 32);
• "analyzing the peptide structure data using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor based on at least three peptide structures selected from one of a group of peptide structures identified in Table 3A" (independent claim 20);
• "generating the diagnosis output based on the score falling above the selected threshold, wherein the diagnosis output includes a positive diagnosis for the ovarian cancer disease state" (claim 23);
• "generating the diagnosis output based on the score falling below the selected threshold, wherein the diagnosis output includes a negative diagnosis for the ovarian cancer disease state" (claim 24);
• "analyzing the peptide structure using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor based on at least five, at least 10 at least 15, at least 20, at least 25, at least 30, or at least 35 peptide structures selected from one of a group of peptide structures identified in Table 3A" (claim 30);
• "analyzing the peptide structure using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor based on each of the peptide structures selected from one of a group of peptide structures identified in Table 3A, comprising an amino acid sequence set forth in SEQ ID NOS: 111, 114, 115, 131, 132, 133, 134, 137, 138, 140, 142, 144, 145, 146, or 153-165" (claim 31);
• "performing a differential expression analysis using initial training data to compare a first portion of the plurality of subjects diagnosed with the positive diagnosis for the ovarian cancer disease state versus a second portion of the plurality of subjects diagnosed with the negative diagnosis for the ovarian cancer disease state" (claim 35);
• "identifying a training group of peptide structures based on the differential expression analysis for use as prognostic markers for the ovarian cancer disease state" (claim 35);
• " forming the training data based on the training group of peptide structures identified" (claim 35);
• "reducing the training group of peptide structures to a final group of peptide structures identified in Table 3A" (claim 36);
• "analyzing the peptide structure data using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor based on at least three peptide structures selected from one of a group of peptide structures identified in Table 1A, Table 2A, and/or Table 3A" (independent claim 42);
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed either in the mind and/or by mathematical operation. Without further detail as to the methodology involved in "analyze peptide structure data to generate a disease indicator score based on a trained algorithm", under the BRI, one may simply, for example, use pen and paper to perform mathematical steps to arrive at the described steps. Further support for the mathematical techniques used in the claims is provided in the specification at [0346-0348], which discloses mathematical techniques and parametric models that may be used to arrive at such calculations. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "generating a diagnosis output based on the disease indicator" (independent claims 1, 20 and 42);
• "determining that the score falls above a selected threshold" (claims 3 and 23-24);
• "providing a treatment recommendation based upon the diagnosis" (claim 44);
• "generating a report identifying that the biological sample evidences the ovarian cancer disease state" (claim 49); and
• "generating a report recommending that a biopsy be performed for the subject in response to the diagnosis output indicating a positive diagnosis for the ovarian cancer disease state" (claim 54).
Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of generating a diagnosis and recommendation based on data evaluation, comparing data and determining where a score falls within a threshold and generating a report noting that a biopsy be performed.
Dependent claims 2, 4,-5, 9, 22, 25-26, 33-34, 43, 50, 52, 61, 69-71 recite further steps that limit the judicial exceptions in independent claims 1, 20 and 42 and, as such, also are directed to those abstract ideas. For example, claims 2, 4, 22, 25 and 43 recite further details about the disease indicator; claims 5 and 26 recite further details about the selected threshold; claims 9, 33-34, 61 and 71 recite further details about the subject diagnosis based on the generated disease indicator; claims 69-70 recite further details about the ovarian cancer disease state; and claims 50 and 52 recite further details about the treatment output generated.
Furthermore, the instant claims recite a natural correlation by correlating the presence of peptide structures naturally found in the body with an ovarian cancer disease indicator (see MPEP 2106.04(b).I).
[Step 2A Prong One: claims 1-13, 20-55, 61 and 69-71: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1, 11-14, 16, and 23-26 recite additional elements that are not abstract ideas:
• "receiving peptide structure data corresponding to a biological sample obtained from the subject" (independent claims 1, 20 and 42);
• "administering a treatment for ovarian cancer" (claim 45);
• "preparing a sample of the biological sample using reduction, alkylation, and enzymatic digestion to form a prepared sample that includes a set of peptide structures" (claim 47);
• "generating the peptide structure data from the prepared sample using multiple reaction monitoring mass spectrometry (MRM-MS)" (claim 48)
• "administering the identified treatment or treatment plan to the subject" (claim 51);
• "performing a biopsy of the subject in response to the diagnosis output indicating a positive diagnosis for the ovarian cancer disease state" (claim 53);
• "performing a biopsy of the subject in response to the diagnosis output indicating a positive diagnosis for the ovarian cancer disease state" (claim 55).
Dependent claims 7, 10, 12-13, 21, 28-29, 37-38, 40-41 and 46 recite further details about the peptide structure data received and dependent claims 11, 39 recite further details about the machine learning model.
Considerations under Step 2A, Prong Two
The recited judicial exceptions in claims 1-13, 20-55, 61 and 69-71 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer. Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. The instant claims state nothing more than that a generic computer performs the functions that constitute the abstract idea (MPEP 2106.05(f)).
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Claims directed to "receiving peptide structure data" (claims 1, 20 and 42) read on receiving or transmitting data over a network -Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and therefore correspond to insignificant extra-solution activity.
The recited "preparing a sample of the biological sample using reduction, alkylation, and enzymatic digestion to form a prepared sample that includes a set of peptide structures" (claim 47) and "generating the peptide structure data from the prepared sample using multiple reaction monitoring mass spectrometry (MRM-MS)" (claim 48) read on data gathering activities; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means.
The recited "administering a treatment" (claims 45 and 51) and "performing a biopsy .. in response to the diagnosis output" (claims 53 and 55) read on a generic "apply it" step and do not recite a particular treatment. because the claim recites an idea of a solution or outcome without any indication of how the judicial exception impacts or influences this step. As it appears in the claims, the limitations fail to determine whether or how the judicial element employed is integrated into a particular treatment (see MPEP 2106.04(d)(2)). Therefore, all embodiments of the invention do not include a practical application. Furthermore, providing a recommendation for a treatment cannot be a practical application because the limitation does not provide an actual treatment to the patient. Here, there are no additional limitations to indicate details of exactly how the judicial exception is being integrated into the recited treatment administration.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-13, 20-55, 61 and 69-71: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1-13, 20-55, 61 and 69-71 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The computer-related elements or the general purpose computer and the machine learning model do not rise to the level of significantly more than the judicial exception. The claims state a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)).
Further, the courts have found that receiving data is a well-understood, routine, and conventional function of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
With respect to the instant claims, the prior art review to Leung ("Integrating High-Throughput Technologies for the Identification and Validation of Novel Ovarian Cancer Biomarkers." University of Toronto (Canada) (2016); newly cited) discloses that the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (i.e. treatment administered) (pg. 5 para. 1) wherein definitive diagnosis by biopsy and histopathology is needed for acceptable sensitivity and specificity for ovarian cancer (pg. 17 para. 2) is routine, well-understood and conventional in the art.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-13, 20-55, 61 and 69-71: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
A. Claims 1-5, 7-10, 12-13, 20-26, 28-38, 40-55, 61 and 69-71 are rejected under 35 U.S.C. 103(a) as being unpatentable over Swiatly "MALDI-TOF-MS analysis in discovery and identification of serum proteomic patterns of ovarian cancer." Bmc Cancer 17(1):472 (2017) in view of Lowenthal ("Analysis of albumin-associated peptides and proteins from ovarian cancer patients." Clinical chemistry 51(10):1933-1945 (2005)) in view of Leung ("Integrating High-Throughput Technologies for the Identification and Validation of Novel Ovarian Cancer Biomarkers." University of Toronto (Canada) (2016)), as cited on the attached Form PTO-892. Claims 4-5 and 25-26 are additionally evidenced by Nolen ("Biomarker testing for ovarian cancer: clinical utility of multiplex assays." Molecular diagnosis & therapy 17(3):139-146 (2013)).
Independent claim 1 recites:
receiving peptide structure data corresponding to a biological sample obtained from the subject;
analyzing the peptide structure data using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences an ovarian cancer disease state
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein serum proteomic patterns in samples from OC patients were obtained using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (i.e. receiving peptide structure data corresponding to a biological sample obtained from the subject) (pg. 1 Abstract); wherein data analysis comprised chemometric algorithms: supervised neural network (i.e. supervised machine learning), genetic algorithm, and quick classifier used for model analysis and selection of peptide/protein peak clusters (i.e. analyzing the peptide structure data using a supervised machine learning model) (pg. 4 col. 2 para. 1); wherein a proprietary algorithm – OVA1 -combines serum concentrations of five markers (CA125, apolipoprotein A-1, β2-microglobulin, transthyretin and transferrin) and calculates a malignancy risk index score (i.e. generate a disease indicator that indicates whether the biological sample evidences an ovarian cancer disease state) (pg. 2 col. 1 para. 1).
based on at least three peptide structures selected from one of a first group of peptide structures identified in Table 1A and a second group of peptide structures identified in Table 2A, wherein the first group of peptide structures and the second group of peptide structures are associated with the ovarian cancer disease state; wherein each of the first group of peptide structures in Table 1A and the second group of peptide structures in Table 2A is listed in order of relative significance to the disease indicator
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P05155, P05156, P00450, P01857, P02790, P04114, P00738, P01860 and P01861 (corresponding to protein SEQ IDs 101-110 from Table 1A respectively) and proteins identified by Uniprot IDs P00450, P01857, P04114, P06681, P02763, P01011, P01023, P01009, P04004, P01859, P08603, P02749, P05090, P02787 (corresponding to SEQ IDSs 104-105, 107, 120-130 from Table 2A, respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A); wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2).
generating a diagnosis output based on the disease indicator
• Swiatly does not teach the recitation above. However Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating a diagnosis output based on the disease indicator) (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients (i.e. generating a diagnosis output based on the disease indicator) (pg. 32 para. 1).
Independent claim 20 recites:
receiving peptide structure data corresponding to a biological sample obtained from the subject; analyzing the peptide structure data using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein serum proteomic patterns in samples from OC patients were obtained using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (i.e. receiving peptide structure data corresponding to a biological sample obtained from the subject) (pg. 1 Abstract); wherein data analysis comprised chemometric algorithms: supervised neural network (i.e. supervised machine learning), genetic algorithm, and quick classifier used for model analysis and selection of peptide/protein peak clusters (i.e. analyzing the peptide structure data using a supervised machine learning model) (pg. 4 col. 2 para. 1); wherein a proprietary algorithm – OVA1 -combines serum concentrations of five markers (CA125, apolipoprotein A-1, β2-microglobulin, transthyretin and transferrin) and calculates a malignancy risk index score (i.e. generate a disease indicator that indicates whether the biological sample evidences an ovarian cancer disease state) (pg. 2 col. 1 para. 1).
based on at least three peptide structures selected from one of a group of peptide structures identified in Table 3A
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P00450, P01857, P02790, P00738, P04114, P06681, P02763, P01009, P04004, P05090, P02787 (corresponding to protein SEQ IDs 101, 104-106, 108, 120-122, 125-126 and 129-130 from Table 3A respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A)
generating a diagnosis output based on the disease indicator
• Swiatly does not teach the recitation above. However Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients (pg. 32 para. 1).
Independent claim 42 recites:
receiving peptide structure data corresponding to a biological sample obtained from the subject; analyzing the peptide structure data using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein serum proteomic patterns in samples from OC patients were obtained using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (i.e. receiving peptide structure data corresponding to a biological sample obtained from the subject) (pg. 1 Abstract); wherein data analysis comprised chemometric algorithms: supervised neural network (i.e. supervised machine learning), genetic algorithm, and quick classifier used for model analysis and selection of peptide/protein peak clusters (i.e. analyzing the peptide structure data using a supervised machine learning model) (pg. 4 col. 2 para. 1); wherein a proprietary algorithm – OVA1 -combines serum concentrations of five markers (CA125, apolipoprotein A-1, β2-microglobulin, transthyretin and transferrin) and calculates a malignancy risk index score (i.e. generate a disease indicator that indicates whether the biological sample evidences an ovarian cancer disease state) (pg. 2 col. 1 para. 1)
based on at least three peptide structures selected from one of a group of peptide structures identified in Table 1A, Table 2A, and/or Table 3A
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P05155, P05156, P00450, P01857, P02790, P04114, P00738, P01860 and P01861 (corresponding to protein SEQ IDs 101-110 from Table 1A respectively) and proteins identified by Uniprot IDs P00450, P01857, P04114, P06681, P02763, P01011, P01023, P01009, P04004, P01859, P08603, P02749, P05090, P02787 (corresponding to SEQ IDSs 104-105, 107, 120-130 from Table 2A, respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A); wherein listed proteins identified by Uniprot IDs P25311 (i.e. parent protein of peptide SEQ ID 111), P00450 (i.e. parent protein of peptide SEQ ID 114), P01857 (i.e. parent protein of peptide SEQ ID 115), P06681 (i.e. parent protein of peptide SEQ ID 131), P02763 (i.e. parent protein of peptide SEQs ID 132 and 142), P01011 (i.e. parent protein of peptide SEQ IDs 133 and 137), P01023 (i.e. parent protein of peptide SEQ IDs 134 and 146), P01009 (i.e. parent protein of peptide SEQ ID 138), P01859 (i.e. parent protein of peptide SEQ ID 140), P05090, (i.e. parent protein of peptide SEQ ID 144), P02787 (i.e. parent protein of peptide SEQ IDs 145 and 157), P04004 (i.e. parent protein of peptide SEQ ID 153), P02765 (i.e. parent protein of peptide SEQ IDs 154 and 163), P02750 (i.e. parent protein of peptide SEQ ID 155), P00738 (i.e. parent protein of peptide SEQ IDs 156 and 160), P02790 (i.e. parent protein of peptide SEQ ID 158), P02766 (i.e. parent protein of peptide SEQ ID 159), P43652 (i.e. parent protein of peptide SEQ ID 161), P02647 (i.e. parent protein of peptide SEQ IDs 162 and 164), and P04003 (i.e. parent protein of peptide SEQ ID 165) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A) - which comprises all protein parents from all peptide sequences in Table 3A; wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2).
generating a diagnosis output based on the disease indicator
• Swiatly does not teach the recitation above. However Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients (pg. 32 para. 1).
Claims 2 and 22 recite:
wherein the disease indicator comprises a score
• Swiatly teaches a proprietary algorithm – OVA1 – that combines serum concentrations of five markers (CA125, apolipoprotein A-1, β2-microglobulin, transthyretin and transferrin) and calculates a malignancy risk index score (pg. 2 col. 1 para. 1).
Claim 3 recites:
wherein generating the diagnosis output comprises determining that the score falls above a selected threshold; and generating the diagnosis output based on the score falling above the selected threshold, wherein the diagnosis output includes a positive or negative diagnosis for the ovarian cancer disease state
Claim 23 recites:
wherein generating the diagnosis output comprises: determining that the score falls above a selected threshold; and generating the diagnosis output based on the score falling above the selected threshold, wherein the diagnosis output includes a positive diagnosis for the ovarian cancer disease state
Claim 24 recites:
wherein generating the diagnosis output comprises: determining that the score falls below a selected threshold; and generating the diagnosis output based on the score falling below the selected threshold, wherein the diagnosis output includes a negative diagnosis for the ovarian cancer disease state
• Swiatly does not teach the recitation above. However, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (pg. 5 para. 1); wherein said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating the diagnosis output based on the score) (pg. 22 para. 2); wherein scores >= 5.0 represent high likelihood of finding malignancy (i.e. positive diagnosis as in claims 3 and 23) and scores < 5.0 represent low likelihood of finding malignancy (i.e. negative diagnosis as in claims 3 and 24) (pg. 31 Fig. 1.5); wherein said scoring corresponded to positive and negative predicted values (pg. 32 para. 1); wherein said OVA1 test was used to profile (153 invasive EOCs, 42 other ovarian cancers, 166 benign pelvic masses, and 142 healthy controls) (pg. 29 para. 2).
Claims 4 and 25 recite:
wherein the score comprises a probability score and the selected threshold is 0.5.
• Swiatly does not teach the recitation above. However, Leung teaches that, for premenopausal patients, an OVA1 score of less than 5.0 indicates a low probability of malignancy while 5.0 or above indicates a high probability of malignancy (i.e. reading of a threshold of 50% or 0.05), as the OVA1 test produces a risk assessment score within the range of 0–10. (as evidenced by Nolen pg. 143 col. 1 para. 2).
Claims 5 and 26 recites:
wherein the selected threshold falls within a range between 0.30 and 0.65
• Swiatly does not teach the recitation above. However, Leung teaches that, for postmenopausal patients, an OVA1 score less than 4.4 indicates a low probability of malignancy while 4.4 or above indicates a high probability of malignancy (i.e. reading of a threshold within 30% - 65% or 0.30 – 0.65), as the OVA1 test produces a risk assessment score within the range of 0–10. (as evidenced by Nolen pg. 143 col. 1 para. 2).
Claim 7 recites:
wherein a peptide structure of the at least three peptide structures comprises a glycopeptide structure defined by a peptide sequence and a glycan structure linked to the peptide sequence at a linking site of the peptide sequence, as identified in Table 1A or Table 2A, with the peptide sequence being one of SEQ ID NOS: 111-119 in Table 1A as defined in Table 5A or one of SEQ ID NOS: 114, 115, and 131-146 in Table 2A as defined in Table 5A
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311 (i.e. protein SEQ ID 101 in Table 1A), P02763 (i.e. protein SEQ ID 121 in Table 2A), P02749 (i.e. protein SEQ ID 128 in Table 2A) present a glycopeptide structure defined by a peptide sequence and a glycan structure linked to the peptide sequence at a linking site of the peptide sequence.
Claims 8 and 32 recite:
further comprising: training the supervised machine learning model using training data, wherein the training data comprises a plurality of peptide structure profiles for a plurality of subjects and a plurality of subject diagnoses for the plurality of subjects
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein serum proteomic patterns in samples from OC patients were obtained using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (pg. 1 Abstract); wherein data analysis comprised chemometric algorithms: supervised neural network (i.e. supervised machine learning) (pg. 4 col. 2 para. 1) and training data comprised a plurality of subjects diagnoses (pg. 3 Table 1) and peptide related peak clusters (i.e. plurality of peptide structure profiles) (pg. 5 col. 1 para. 2).
Claim 9 recites:
wherein the plurality of subject diagnoses includes a positive diagnosis for any subject of the plurality of subjects determined to have the ovarian cancer disease state and a negative diagnosis for any subject of the plurality of subjects determined to have a healthy state or a benign tumor state
• Swiatly does not teach the recitation above. However, Leung teaches the OVA1 algorithm used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating the diagnosis output based on the score) (pg. 22 para. 2); wherein scores >= 5.0 represent high likelihood of finding malignancy (i.e. reading on a positive ovarian cancer disease state) and scores < 5.0 represent low likelihood of finding malignancy (i.e. reading on healthy state or a benign tumor state) (pg. 31 Fig. 1.5); wherein said scoring corresponded to positive and negative predicted values (pg. 32 para. 1).
Claims 10 and 37 recites:
wherein each peptide structure profile of the plurality of peptide structure profiles comprises a feature selected from one the group consisting of a relative abundance and a concentration for a corresponding peptide structure
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); reporting the relative abundances of the different functional classes of proteins found in ovarian cancer samples (pg. 1941 col. 1 para. 1 and Fig. 3).
Claim 12 recites:
wherein the first group of peptide structures in Table 1A is used to distinguish between the ovarian cancer disease state and a healthy state and wherein the second group of peptide structures in Table 2A is used to distinguish between the ovarian cancer disease state and a benign tumor state.
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2).
Claim 13 recites:
wherein the peptide structure data comprises at least one of an abundance, a relative abundance, a normalized abundance, a relative quantity, an adjusted quantity, a normalized quantity, a relative concentration, an adjusted concentration, or a normalized concentration.
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); reporting the relative abundances of the different functional classes of proteins found in ovarian cancer samples (pg. 1941 col. 1 para. 1 and Fig. 3).
Claim 21 recites:
wherein the wherein the group of peptide structures in Table 3A is listed in order of relative significance to the disease indicator
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P00450, P01857, P02790, P00738, P04114, P06681, P02763, P01009, P04004, P05090, P02787 (corresponding to protein SEQ IDs 101, 104-106, 108, 120-122, 125-126 and 129-130 from Table 3A respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences classified as high risk, stage I or stage III-IV (Supplemental Table 1A); wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2).
Claim 28 recites:
wherein a peptide structure of the at least three peptide structures comprises a glycopeptide structure defined by a peptide sequence and a glycan structure linked to the peptide sequence at a linking site of the peptide sequence, as identified in Table 3A, with the peptide sequence being one of SEQ ID NOS: 111, 114, 115, 131, 132, 133, 134, 137, 138, 140, 142, 144, 145, 146, 153-165
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311 (i.e. protein SEQ ID 101 which represents the parent protein for the peptides SEQ ID 111 in Table 3A) and P02763 (i.e. protein SEQ ID 121 which represents the parent protein for the peptides SEQ IDs 132 and 142 in Table 3A) present a glycopeptide structure defined by a peptide sequence and a glycan structure linked to the peptide sequence at a linking site of the peptide sequence.
Claim 29 recites:
wherein the peptide structure comprises an amino acid sequence set forth in SEQ ID NOS: 111, 114, 115, 131, 132, 133, 134, 137, 138, 140, 142, 144, 145, 146, or 153-165
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311 (i.e. parent protein of peptide SEQ ID 111), P00450 (i.e. parent protein of peptide SEQ ID 114), P01857 (i.e. parent protein of peptide SEQ ID 115), P06681 (i.e. parent protein of peptide SEQ ID 131), P02763 (i.e. parent protein of peptide SEQs ID 132 and 142), P01011 (i.e. parent protein of peptide SEQ IDs 133 and 137), P01023 (i.e. parent protein of peptide SEQ IDs 134 and 146), P01009 (i.e. parent protein of peptide SEQ ID 138), P01859 (i.e. parent protein of peptide SEQ ID 140), P05090, (i.e. parent protein of peptide SEQ ID 144), P02787 (i.e. parent protein of peptide SEQ IDs 145 and 157), P04004 (i.e. parent protein of peptide SEQ ID 153), P02765 (i.e. parent protein of peptide SEQ IDs 154 and 163), P02750 (i.e. parent protein of peptide SEQ ID 155), P00738 (i.e. parent protein of peptide SEQ IDs 156 and 160), P02790 (i.e. parent protein of peptide SEQ ID 158), P02766 (i.e. parent protein of peptide SEQ ID 159), P43652 (i.e. parent protein of peptide SEQ ID 161), P02647 (i.e. parent protein of peptide SEQ IDs 162 and 164), and P04003 (i.e. parent protein of peptide SEQ ID 165) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A).
Claim 30 recites:
wherein the method comprises analyzing the peptide structure using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein data analysis comprised chemometric algorithms: supervised neural network (i.e. supervised machine learning), genetic algorithm, and quick classifier used for model analysis and selection of peptide/protein peak clusters (pg. 4 col. 2 para. 1). Furthermore, Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor)(pg. 22 para. 2).
based on at least five, at least 10 at least 15, at least 20, at least 25, at least 30, or at least 35 peptide structures selected from one of a group of peptide structures identified in Table 3A
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P00450, P01857, P02790, P00738, P04114, P06681, P02763, P01009, P04004, P05090, P02787 (corresponding to protein SEQ IDs 101, 104-106, 108, 120-122, 125-126 and 129-130 from Table 3A respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A).
Claim 31 recites:
wherein the method comprises analyzing the peptide structure using a supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein data analysis comprised chemometric algorithms: supervised neural network (i.e. supervised machine learning), genetic algorithm, and quick classifier used for model analysis and selection of peptide/protein peak clusters (pg. 4 col. 2 para. 1). Furthermore, Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor)(pg. 22 para. 2).
based on each of the peptide structures selected from one of a group of peptide structures identified in Table 3A, comprising an amino acid sequence set forth in SEQ ID NOS: 111, 114, 115, 131, 132, 133, 134, 137, 138, 140, 142, 144, 145, 146, or 153-165
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311 (i.e. protein SEQ ID 101 which represents the parent protein for the peptides SEQ ID 111 in Table 3A) and P02763 (i.e. protein SEQ ID 121 which represents the parent protein for the peptides SEQ IDs 132 and 142 in Table 3A) present a glycopeptide structure defined by a peptide sequence and a glycan structure linked to the peptide sequence at a linking site of the peptide sequence.
Claim 33 recites:
wherein the plurality of subject diagnoses includes a positive diagnosis for any subject of the plurality of subjects determined to have the malignant pelvic tumor and a negative diagnosis for any subject of the plurality of subjects determined to have a healthy state
Claim 34 recites:
wherein the plurality of subject diagnoses includes a positive diagnosis for any subject of the plurality of subjects determined to have the ovarian cancer disease state and a negative diagnosis for any subject of the plurality of subjects determined to have a benign pelvic
• Swiatly does not teach the recitation above. However, Leung teaches the OVA1 algorithm used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. subjects with pelvic tumor) (pg. 22 para. 2); wherein scores >= 5.0 represent high likelihood of finding malignancy (i.e. reading on a positive ovarian cancer disease state) and scores < 5.0 represent low likelihood of finding malignancy (i.e. reading on healthy state or a benign tumor state as in claims 33-34) (pg. 31 Fig. 1.5); wherein said scoring corresponded to positive and negative predicted values (pg. 32 para. 1).
Claim 35 recites:
further comprising: performing a differential expression analysis using initial training data to compare a first portion of the plurality of subjects diagnosed with the positive diagnosis for the ovarian cancer disease state versus a second portion of the plurality of subjects diagnosed with the negative diagnosis for the ovarian cancer disease state; and
identifying a training group of peptide structures based on the differential expression analysis for use as prognostic markers for the ovarian cancer disease state; and
forming the training data based on the training group of peptide structures identified
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein serum proteomic patterns in samples from OC patients were obtained using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (pg. 1 Abstract); wherein data analysis comprised chemometric algorithms: supervised neural network (i.e. supervised machine learning) (pg. 4 col. 2 para. 1) and training data comprised a plurality of subjects diagnoses (pg. 3 Table 1) and peptide related peak clusters (i.e. forming the training data based on the training group of peptide structures identified) (pg. 5 col. 1 para. 2). Furthermore, Leung teaches presence/absence and differential expression analyses to identify proteins that were strongly upregulated in ovarian cancer patients (i.e. identifying a training group of peptide structures based on the differential expression analysis for use as prognostic markers for the ovarian cancer disease state) (pg. 171 para. 1) comparing levels between mucinous ovarian carcinoma (i.e. positive diagnosis for the ovarian cancer disease state), mucinous cystdenoma, clear cell ovarian carcinoma and endometriosis patients (i.e. negative diagnosis for the ovarian cancer disease state).
Claim 36 recites:
wherein training the supervised machine learning model comprises reducing the training group of peptide structures to a final group of peptide structures identified in Table 3A
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein serum proteomic patterns in samples from OC patients were obtained using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (pg. 1 Abstract); wherein data analysis comprised chemometric algorithms: supervised neural network (i.e. supervised machine learning) (pg. 4 col. 2 para. 1) and training data comprised a plurality of subjects diagnoses (pg. 3 Table 1) and peptide related peak clusters (i.e. forming the training data based on the training group of peptide structures identified) (pg. 5 col. 1 para. 2). Furthermore,, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311 (i.e. parent protein of peptide SEQ ID 111), P00450 (i.e. parent protein of peptide SEQ ID 114), P01857 (i.e. parent protein of peptide SEQ ID 115), P06681 (i.e. parent protein of peptide SEQ ID 131), P02763 (i.e. parent protein of peptide SEQs ID 132 and 142), P01011 (i.e. parent protein of peptide SEQ IDs 133 and 137), P01023 (i.e. parent protein of peptide SEQ IDs 134 and 146), P01009 (i.e. parent protein of peptide SEQ ID 138), P01859 (i.e. parent protein of peptide SEQ ID 140), P05090, (i.e. parent protein of peptide SEQ ID 144), P02787 (i.e. parent protein of peptide SEQ IDs 145 and 157), P04004 (i.e. parent protein of peptide SEQ ID 153), P02765 (i.e. parent protein of peptide SEQ IDs 154 and 163), P02750 (i.e. parent protein of peptide SEQ ID 155), P00738 (i.e. parent protein of peptide SEQ IDs 156 and 160), P02790 (i.e. parent protein of peptide SEQ ID 158), P02766 (i.e. parent protein of peptide SEQ ID 159), P43652 (i.e. parent protein of peptide SEQ ID 161), P02647 (i.e. parent protein of peptide SEQ IDs 162 and 164), and P04003 (i.e. parent protein of peptide SEQ ID 165) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A) - which comprises all protein parents from all peptide sequences in Table 3A.
Claim 38 recites:
wherein the plurality of peptide structure profiles includes a first peptide structure profile with a relative abundance for a corresponding peptide structure and a second peptide structure profile with a concentration for the corresponding peptide structure
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); reporting the relative abundances of the different functional classes of proteins found in ovarian cancer samples (pg. 1941 col. 1 para. 1 and Fig. 3); wherein the concentration for the corresponding biomarker analyzed can be calculated (pg. 1934 col. 2 para. 2); wherein the amounts for said fragments of larger proteins necessitate validation by anti-peptide antibodies (1942 col. 1para. 3).
Claim 40 recites:
wherein the first group of peptide structures in Table 3A is used to distinguish between the ovarian cancer disease state having the malignant pelvic tumor and a non-ovarian cancer state having a benign pelvic tumor
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2).
Claim 41 recites:
wherein the peptide structure data comprises quantification data selected from the group consisting of an abundance, a relative abundance, a normalized abundance, a relative quantity, an adjusted quantity, a normalized quantity, a relative concentration, an adjusted concentration, or a normalized concentration
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); reporting the relative abundances of the different functional classes of proteins found in ovarian cancer samples (pg. 1941 col. 1 para. 1 and Fig. 3).
Claim 43 recites:
wherein the disease indicator is based on at least three peptide structures from one of a group of peptide structures identified in Table 3A
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311 (i.e. parent protein of peptide SEQ ID 111), P00450 (i.e. parent protein of peptide SEQ ID 114), P01857 (i.e. parent protein of peptide SEQ ID 115), P06681 (i.e. parent protein of peptide SEQ ID 131), P02763 (i.e. parent protein of peptide SEQs ID 132 and 142), P01011 (i.e. parent protein of peptide SEQ IDs 133 and 137), P01023 (i.e. parent protein of peptide SEQ IDs 134 and 146), P01009 (i.e. parent protein of peptide SEQ ID 138), P01859 (i.e. parent protein of peptide SEQ ID 140), P05090, (i.e. parent protein of peptide SEQ ID 144), P02787 (i.e. parent protein of peptide SEQ IDs 145 and 157), P04004 (i.e. parent protein of peptide SEQ ID 153), P02765 (i.e. parent protein of peptide SEQ IDs 154 and 163), P02750 (i.e. parent protein of peptide SEQ ID 155), P00738 (i.e. parent protein of peptide SEQ IDs 156 and 160), P02790 (i.e. parent protein of peptide SEQ ID 158), P02766 (i.e. parent protein of peptide SEQ ID 159), P43652 (i.e. parent protein of peptide SEQ ID 161), P02647 (i.e. parent protein of peptide SEQ IDs 162 and 164), and P04003 (i.e. parent protein of peptide SEQ ID 165) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A) - which comprises all protein parents from all peptide sequences in Table 3A.
Claim 44 recites:
further providing a treatment recommendation based upon the diagnosis
• Swiatly does not teach the recitation above. However Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients of whether a pelvic mass patient should continue with surgery (i.e. treatment recommendation based upon the diagnosis) (pg. 32 para. 1).
Claim 45 recites:
further comprising administering a treatment for ovarian cancer
• Swiatly does not teach the recitation above. However, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (i.e. reading on response after treatment administration which comprises the step of administering said treatment) (pg. 5 para. 1).
Claim 46 recites:
wherein the peptide structure data is generated using multiple reaction monitoring mass spectrometry (MRM-MS)
Claim 48 recites:
further comprising generating the peptide structure data from the prepared sample using multiple reaction monitoring mass spectrometry (MRM-MS).
• Swiatly does not teach the recitation above. However, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management (pg. 5 para. 1); wherein multiple reaction monitoring can performed in a high-throughput, multiplexed manner via MS-based quantification (pg. 192 para. 1).
Claim 47 recites:
further comprising preparing a sample of the biological sample using reduction, alkylation, and enzymatic digestion to form a prepared sample that includes a set of peptide structures
• Swiatly does not teach the recitation above. However, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management (pg. 5 para. 1); wherein the majority of glycomic-based biomarker studies have employed the use of MALDI MS coupled with extensive pre-analytical enrichment methods for glycans (such as peptide-N-glycosidase digestion (i.e. enzymatic digestion), chromatographic separation (i.e. reduction), and solid phase permethylation (i.e. type of alkylation) (pg. 46 para. 1).
Claim 49 recites:
wherein generating the diagnosis output comprises: generating a report identifying that the biological sample evidences the ovarian cancer disease state
• Swiatly does not teach the recitation above. However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein a report identifies peptides sequences and its correlation with ovarian cancer stage specific and normal serum (pg. 1938 Table 1).
Claim 50 recites:
wherein the treatment output comprises at least one of an identification of a treatment to treat the subject or a treatment plan
• Swiatly does not teach the recitation above. However Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients of whether a pelvic mass patient should continue with surgery (i.e. identification of a treatment to treat the subject or a treatment plan) (pg. 32 para. 1).
Claim 51 recites:
further comprising administering the identified treatment or treatment plan to the subject
• Swiatly does not teach the recitation above. However, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (i.e. reading on response after treatment administration which comprises the step of administering said treatment) (pg. 5 para. 1).
Claim 52 recites:
wherein the treatment comprises at least one of surgery, radiation therapy, a targeted drug therapy, chemotherapy, immunotherapy, hormone therapy, or neoadjuvant therapy
• Swiatly does not teach the recitation above. However, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (i.e. reading on response after treatment administration which comprises the step of administering said chemotherapy treatment) (pg. 5 para. 1).
Claim 53 recites:
further comprising performing a biopsy of the subject in response to the diagnosis output indicating a positive diagnosis for the ovarian cancer disease state
• Swiatly does not teach the recitation above. However, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (i.e. reading on response after treatment administration which comprises the step of administering said treatment) (pg. 5 para. 1); wherein definitive diagnosis by biopsy and histopathology is needed for acceptable sensitivity and specificity for ovarian cancer (pg. 17 para. 2).
Claim 54 recites:
further comprising generating a report recommending that a biopsy be performed for the subject in response to the diagnosis output indicating a positive diagnosis for the ovarian cancer disease state
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein external validation seems an important step in defining accuracy and robustness of the prediction models (pg. 7 col. 1 para. 1). Furthermore, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (i.e. reading on response after treatment administration which comprises the step of administering said treatment) (pg. 5 para. 1); wherein definitive diagnosis by biopsy and histopathology is needed for acceptable sensitivity and specificity for ovarian cancer (i.e. biopsy performed for the subject in response to the diagnosis output indicating a positive diagnosis for the ovarian cancer disease state) (pg. 17 para. 2).
Claim 55 recites:
further comprising performing a biopsy of the subject in response to the diagnosis output indicating a positive diagnosis for the ovarian cancer disease state
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein external validation seems an important step in defining accuracy and robustness of the prediction models (pg. 7 col. 1 para. 1). Furthermore, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (i.e. reading on response after treatment administration which comprises the step of administering said treatment) (pg. 5 para. 1); wherein definitive diagnosis by biopsy and histopathology is needed for acceptable sensitivity and specificity for ovarian cancer (i.e. biopsy performed for the subject in response to the diagnosis output indicating a positive diagnosis for the ovarian cancer disease state) (pg. 17 para. 2).
Claim 61 recites:
wherein a negative diagnosis for the ovarian cancer disease state indicates a non-ovarian cancer state comprising a benign tumor state
• Swiatly does not teach the recitation above. However, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (pg. 5 para. 1); wherein said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating the diagnosis output based on the score) (pg. 22 para. 2); wherein scores >= 5.0 represent high likelihood of finding malignancy (i.e. positive diagnosis) and scores < 5.0 represent low likelihood of finding malignancy (i.e. negative diagnosis) (pg. 31 Fig. 1.5); wherein said scoring corresponded to positive and negative predicted values (pg. 32 para. 1); wherein said OVA1 test was used to profile (153 invasive EOCs, 42 other ovarian cancers, 166 benign pelvic masses, and 142 healthy controls) (pg. 29 para. 2)
Claim 69 recites:
wherein the ovarian cancer disease state comprises a malignant pelvic tumor
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein data analysis comprised chemometric algorithms: supervised neural network (i.e. supervised machine learning), genetic algorithm, and quick classifier used for model analysis and selection of peptide/protein peak clusters (pg. 4 col. 2 para. 1). Furthermore, Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. disease indicator that indicates whether the biological sample evidences the ovarian cancer disease state of having a malignant pelvic tumor)(pg. 22 para. 2).
Claim 70 recites:
wherein the ovarian cancer disease state is epithelial ovarian cancer, or optionally malignant epithelial ovarian cancer
• Swiatly does not teach the recitation above. However, Leung teaches the use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy (pg. 5 para. 1); wherein said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (pg. 22 para. 2); wherein serum from 10 confirmed late stage (III-IV) epithelial ovarian cancer cases were used for data samples (pg. 62 para. 2).
Claim 71 recites:
wherein the subject is a human
• Swiatly teaches serum analysis in discovery and identification of proteomic patterns (i.e. comprising protein and peptides) of ovarian cancer (pg. 1 Title); wherein serum proteomic patterns in human samples from OC patients were obtained using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (pg. 1 Abstract).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-5, 7-10, 12-13, 20-26, 28-38, 40-55, 61 and 69-71, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Swiatly in view of Lowenthal and Leung because all references disclose methods for investigating peptide structure data as indicator for ovarian cancer diagnosing. The motivation would have been to:
• identify proteins and peptides associated with serum carrier proteins and predicted sequences that represent fragments of larger molecules that may eventually be found suitable for full clinical diagnostic testing (pg. 1944 col. 1 para. 1 Lowenthal); and
• use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy Leung).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the investigation of peptide structure data as indicator for ovarian cancer diagnosing of Swiatly to the methods by Lowenthal and Leung because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigating peptide structure data as indicator for ovarian cancer diagnosing.
Regarding claims 1, 7, 20-21, 28-31, 36 and 42-43, one of ordinary skill in the art would be motivated to identify the peptide sequence of each taught protein listed in the recited Tables in its peptide sequence entirety (i.e. comprising all possible fragments of such protein) to optimize the ovarian cancer diagnosing method for routine optimization reasons and determination of the optimum or workable ranges to identify ovarian cancer with higher accuracy – as indicated by the prior art to Lowenthal disclosing the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1). MPEP 2144.05 II states - The Supreme Court has clarified that an "obvious to try" line of reasoning may properly support an obviousness rejection. In In re Antonie, 559 F.2d 618, 195 USPQ 6 (CCPA 1977), the CCPA held that a particular parameter must first be recognized as a result-effective variable, i.e., a variable which achieves a recognized result, before the determination of the optimum or workable ranges of said variable might be characterized as routine experimentation, because "obvious to try" is not a valid rationale for an obviousness finding. Another motivation to combine the teachings originates from the "obvious to optimize" 2144.05 rationale, which states that choosing from a finite number of identified, predictable solutions (i.e., in this case a finite number of loci in the art that are related to the disease/condition), with a reasonable expectation of success would motivate one of ordinary skill in the art. See MPEP 2143 (I). Also see MPEP 2144.05 (a change in form, proportions, or degree "will not sustain a patent"); In re Williams, 36 F.2d 436, 438, 4 USPQ 237 (CCPA 1929) ("It is a settled principle of law that a mere carrying forward of an original patented conception involving only change of form, proportions, or degree, or the substitution of equivalents doing the same thing as the original invention, by substantially the same means, is not such an invention as will sustain a patent, even though the changes of the kind may produce better results than prior inventions."). See also KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416, 82 USPQ2d 1385, 1395 (2007) (identifying "the need for caution in granting a patent based on the combination of elements found in the prior art").
B. Claims 6, 11, 27 and 39 are rejected under 35 U.S.C. 103(a) as being unpatentable over Swiatly, Lowenthal and Leung as applied to claims 1, 20 and 42 above further in view of Lu ("Using machine learning to predict ovarian cancer." International journal of medical informatics 141:104195 (2020)), as cited on the attached Form PTO-892.
Claims 6 and 27 recites:
wherein analyzing the peptide structure data comprises analyzing the peptide structure data using a binary classification model
• Neither Swiatly or Lowenthal or Leung does not teach the recitation above. However Lu teaches using machine learning to predict ovarian cancer (pg. 1 Title); wherein serum samples were analyzed for tumor markers by a modular analyzed (pg. 2 col. 2 para. 3); wherein a constructed decision tree was used as a binary classification model (pg. 4 col. 2 para. 3).
Claims 11 and 39 recites:
wherein the supervised machine learning model comprises a logistic regression model
• Neither Swiatly or Lowenthal or Leung does not teach the recitation above. However Lu teaches using machine learning to predict ovarian cancer (pg. 1 Title); wherein serum samples were analyzed for tumor markers by a modular analyzed (pg. 2 col. 2 para. 3); wherein biomarkers are identified via multiparametric modeling approaches that includes logistic regression analysis (pg. 56 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claims 6, 11, 27 and 39, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Swiatly, Lowenthal and Leung in view of Lu because all references disclose methods for investigating peptide structure data as indicator for ovarian cancer diagnosing. The motivation would have been to accurately classify ovarian cancer (pg. 1 Abstract Lu).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the investigation of peptide structure data as indicator for ovarian cancer diagnosing of Swiatly, Lowenthal and Leung to the methods by Lu because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigating peptide structure data as indicator for ovarian cancer diagnosing.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
A. Claims 1, 20 and 42 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 13, 20, 26, 31, 33, 38, 53 and 64 of U.S. Application No. 17/433,971 in view of Lowenthal and Leung, as cited in the attached PTO-892 Form.
U.S. Application No. 17/433,971 teaches claim 1 except "based on at least three peptide structures selected from one of a first group of peptide structures identified in Table 1A and a second group of peptide structures identified in Table 2A; wherein the first group of peptide structures and the second group of peptide structures are associated with the ovarian cancer disease state; wherein each of the first group of peptide structures in Table 1A and the second group of peptide structures in Table 2A is listed in order of relative significance to the disease indicator; and generating a diagnosis output based on the disease indicator." However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P05155, P05156, P00450, P01857, P02790, P04114, P00738, P01860 and P01861 (corresponding to protein SEQ IDs 101-110 from Table 1A respectively) and proteins identified by Uniprot IDs P00450, P01857, P04114, P06681, P02763, P01011, P01023, P01009, P04004, P01859, P08603, P02749, P05090, P02787 (corresponding to SEQ IDSs 104-105, 107, 120-130 from Table 2A, respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A); wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2). Further, Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating a diagnosis output based on the disease indicator) (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients (i.e. generating a diagnosis output based on the disease indicator) (pg. 32 para. 1).
U.S. Application No. 17/433,971 teaches claim 20 except "based on at least three peptide structures selected from one of a group of peptide structures identified in Table 3A; and generating a diagnosis output based on the disease indicator." However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P00450, P01857, P02790, P00738, P04114, P06681, P02763, P01009, P04004, P05090, P02787 (corresponding to protein SEQ IDs 101, 104-106, 108, 120-122, 125-126 and 129-130 from Table 3A respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A). Further, Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating a diagnosis output based on the disease indicator) (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients (i.e. generating a diagnosis output based on the disease indicator) (pg. 32 para. 1).
U.S. Application No. 17/433,971 teaches claim 42 except "based on at least three peptide structures selected from one of a group of peptide structures identified in Table 1A, Table 2A, and/or Table 3A; and generating a diagnosis output based on the disease indicator." However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P05155, P05156, P00450, P01857, P02790, P04114, P00738, P01860 and P01861 (corresponding to protein SEQ IDs 101-110 from Table 1A respectively) and proteins identified by Uniprot IDs P00450, P01857, P04114, P06681, P02763, P01011, P01023, P01009, P04004, P01859, P08603, P02749, P05090, P02787 (corresponding to SEQ IDSs 104-105, 107, 120-130 from Table 2A, respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A); wherein listed proteins identified by Uniprot IDs P25311 (i.e. parent protein of peptide SEQ ID 111), P00450 (i.e. parent protein of peptide SEQ ID 114), P01857 (i.e. parent protein of peptide SEQ ID 115), P06681 (i.e. parent protein of peptide SEQ ID 131), P02763 (i.e. parent protein of peptide SEQs ID 132 and 142), P01011 (i.e. parent protein of peptide SEQ IDs 133 and 137), P01023 (i.e. parent protein of peptide SEQ IDs 134 and 146), P01009 (i.e. parent protein of peptide SEQ ID 138), P01859 (i.e. parent protein of peptide SEQ ID 140), P05090, (i.e. parent protein of peptide SEQ ID 144), P02787 (i.e. parent protein of peptide SEQ IDs 145 and 157), P04004 (i.e. parent protein of peptide SEQ ID 153), P02765 (i.e. parent protein of peptide SEQ IDs 154 and 163), P02750 (i.e. parent protein of peptide SEQ ID 155), P00738 (i.e. parent protein of peptide SEQ IDs 156 and 160), P02790 (i.e. parent protein of peptide SEQ ID 158), P02766 (i.e. parent protein of peptide SEQ ID 159), P43652 (i.e. parent protein of peptide SEQ ID 161), P02647 (i.e. parent protein of peptide SEQ IDs 162 and 164), and P04003 (i.e. parent protein of peptide SEQ ID 165) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A) - which comprises all protein parents from all peptide sequences in Table 3A; wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2). Further, Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating a diagnosis output based on the disease indicator) (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients (i.e. generating a diagnosis output based on the disease indicator) (pg. 32 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1, 20 and 42, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of U.S. Application No. 17/433,971 in view of Lowenthal and Leung because all references disclose methods for investigating peptide structure data as indicator for ovarian cancer diagnosing. The motivation would have been to:
• identify proteins and peptides associated with serum carrier proteins and predicted sequences that represent fragments of larger molecules that may eventually be found suitable for full clinical diagnostic testing (pg. 1944 col. 1 para. 1 Lowenthal); and
• use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy Leung).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the investigation of peptide structure data as indicator for ovarian cancer diagnosing of U.S. Application No. 17/433,971 to the methods by Lowenthal and Leung because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigating peptide structure data as indicator for ovarian cancer diagnosing.
Regarding claims 1, 20 and 42, one of ordinary skill in the art would be motivated to identify the peptide sequence of each taught protein listed in the recited Tables in its peptide sequence entirety (i.e. comprising all possible fragments of such protein) to optimize the ovarian cancer diagnosing method for routine optimization reasons and determination of the optimum or workable ranges to identify ovarian cancer with higher accuracy – as indicated by the prior art to Lowenthal disclosing the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1). MPEP 2144.05 II states - The Supreme Court has clarified that an "obvious to try" line of reasoning may properly support an obviousness rejection. In In re Antonie, 559 F.2d 618, 195 USPQ 6 (CCPA 1977), the CCPA held that a particular parameter must first be recognized as a result-effective variable, i.e., a variable which achieves a recognized result, before the determination of the optimum or workable ranges of said variable might be characterized as routine experimentation, because "obvious to try" is not a valid rationale for an obviousness finding. Another motivation to combine the teachings originates from the "obvious to optimize" 2144.05 rationale, which states that choosing from a finite number of identified, predictable solutions (i.e., in this case a finite number of loci in the art that are related to the disease/condition), with a reasonable expectation of success would motivate one of ordinary skill in the art. See MPEP 2143 (I). Also see MPEP 2144.05 (a change in form, proportions, or degree "will not sustain a patent"); In re Williams, 36 F.2d 436, 438, 4 USPQ 237 (CCPA 1929) ("It is a settled principle of law that a mere carrying forward of an original patented conception involving only change of form, proportions, or degree, or the substitution of equivalents doing the same thing as the original invention, by substantially the same means, is not such an invention as will sustain a patent, even though the changes of the kind may produce better results than prior inventions."). See also KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416, 82 USPQ2d 1385, 1395 (2007) (identifying "the need for caution in granting a patent based on the combination of elements found in the prior art").
B. Claims 1, 20 and 42 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 16, 20, 22-24 and 33 of U.S. Application No. 17759714 in view of Lowenthal and Leung, as cited in the attached PTO-892 Form.
U.S. Application No. 17759714 teaches claim 1 except "based on at least three peptide structures selected from one of a first group of peptide structures identified in Table 1A and a second group of peptide structures identified in Table 2A; wherein the first group of peptide structures and the second group of peptide structures are associated with the ovarian cancer disease state; wherein each of the first group of peptide structures in Table 1A and the second group of peptide structures in Table 2A is listed in order of relative significance to the disease indicator; and generating a diagnosis output based on the disease indicator." However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P05155, P05156, P00450, P01857, P02790, P04114, P00738, P01860 and P01861 (corresponding to protein SEQ IDs 101-110 from Table 1A respectively) and proteins identified by Uniprot IDs P00450, P01857, P04114, P06681, P02763, P01011, P01023, P01009, P04004, P01859, P08603, P02749, P05090, P02787 (corresponding to SEQ IDSs 104-105, 107, 120-130 from Table 2A, respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A); wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2). Further, Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating a diagnosis output based on the disease indicator) (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients (i.e. generating a diagnosis output based on the disease indicator) (pg. 32 para. 1).
U.S. Application No. 17759714 teaches claim 20 except "based on at least three peptide structures selected from one of a group of peptide structures identified in Table 3A; and generating a diagnosis output based on the disease indicator." However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P00450, P01857, P02790, P00738, P04114, P06681, P02763, P01009, P04004, P05090, P02787 (corresponding to protein SEQ IDs 101, 104-106, 108, 120-122, 125-126 and 129-130 from Table 3A respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A). Further, Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating a diagnosis output based on the disease indicator) (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients (i.e. generating a diagnosis output based on the disease indicator) (pg. 32 para. 1).
U.S. Application No. 17759714 teaches claim 42 except "based on at least three peptide structures selected from one of a group of peptide structures identified in Table 1A, Table 2A, and/or Table 3A; and generating a diagnosis output based on the disease indicator." However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P05155, P05156, P00450, P01857, P02790, P04114, P00738, P01860 and P01861 (corresponding to protein SEQ IDs 101-110 from Table 1A respectively) and proteins identified by Uniprot IDs P00450, P01857, P04114, P06681, P02763, P01011, P01023, P01009, P04004, P01859, P08603, P02749, P05090, P02787 (corresponding to SEQ IDSs 104-105, 107, 120-130 from Table 2A, respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A); wherein listed proteins identified by Uniprot IDs P25311 (i.e. parent protein of peptide SEQ ID 111), P00450 (i.e. parent protein of peptide SEQ ID 114), P01857 (i.e. parent protein of peptide SEQ ID 115), P06681 (i.e. parent protein of peptide SEQ ID 131), P02763 (i.e. parent protein of peptide SEQs ID 132 and 142), P01011 (i.e. parent protein of peptide SEQ IDs 133 and 137), P01023 (i.e. parent protein of peptide SEQ IDs 134 and 146), P01009 (i.e. parent protein of peptide SEQ ID 138), P01859 (i.e. parent protein of peptide SEQ ID 140), P05090, (i.e. parent protein of peptide SEQ ID 144), P02787 (i.e. parent protein of peptide SEQ IDs 145 and 157), P04004 (i.e. parent protein of peptide SEQ ID 153), P02765 (i.e. parent protein of peptide SEQ IDs 154 and 163), P02750 (i.e. parent protein of peptide SEQ ID 155), P00738 (i.e. parent protein of peptide SEQ IDs 156 and 160), P02790 (i.e. parent protein of peptide SEQ ID 158), P02766 (i.e. parent protein of peptide SEQ ID 159), P43652 (i.e. parent protein of peptide SEQ ID 161), P02647 (i.e. parent protein of peptide SEQ IDs 162 and 164), and P04003 (i.e. parent protein of peptide SEQ ID 165) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A) - which comprises all protein parents from all peptide sequences in Table 3A; wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2). Further, Leung teaches that said OVA1 algorithm is used for the determination of the likelihood of ovarian malignancy in women presenting with an adnexal mass (i.e. generating a diagnosis output based on the disease indicator) (pg. 22 para. 2); wherein the OVA1 test is used as a supplementary tool for clinical decision making for preoperative adnexal mass patients (i.e. generating a diagnosis output based on the disease indicator) (pg. 32 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1, 20 and 42, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of U.S. Application No. 17759714 in view of Lowenthal and Leung because all references disclose methods for investigating peptide structure data as indicator for ovarian cancer diagnosing. The motivation would have been to:
• identify proteins and peptides associated with serum carrier proteins and predicted sequences that represent fragments of larger molecules that may eventually be found suitable for full clinical diagnostic testing (pg. 1944 col. 1 para. 1 Lowenthal); and
• use of high-throughput technologies for the identification of biomarkers for numerous aspects of ovarian cancer management including early diagnosis, prognosis, prediction, and monitoring disease progression and response to chemotherapy Leung).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the investigation of peptide structure data as indicator for ovarian cancer diagnosing of U.S. Application No. 17759714 to the methods by Lowenthal and Leung because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigating peptide structure data as indicator for ovarian cancer diagnosing.
Regarding claims 1, 20 and 42, one of ordinary skill in the art would be motivated to identify the peptide sequence of each taught protein listed in the recited Tables in its peptide sequence entirety (i.e. comprising all possible fragments of such protein) to optimize the ovarian cancer diagnosing method for routine optimization reasons and determination of the optimum or workable ranges to identify ovarian cancer with higher accuracy – as indicated by the prior art to Lowenthal disclosing the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1). MPEP 2144.05 II states - The Supreme Court has clarified that an "obvious to try" line of reasoning may properly support an obviousness rejection. In In re Antonie, 559 F.2d 618, 195 USPQ 6 (CCPA 1977), the CCPA held that a particular parameter must first be recognized as a result-effective variable, i.e., a variable which achieves a recognized result, before the determination of the optimum or workable ranges of said variable might be characterized as routine experimentation, because "obvious to try" is not a valid rationale for an obviousness finding. Another motivation to combine the teachings originates from the "obvious to optimize" 2144.05 rationale, which states that choosing from a finite number of identified, predictable solutions (i.e., in this case a finite number of loci in the art that are related to the disease/condition), with a reasonable expectation of success would motivate one of ordinary skill in the art. See MPEP 2143 (I). Also see MPEP 2144.05 (a change in form, proportions, or degree "will not sustain a patent"); In re Williams, 36 F.2d 436, 438, 4 USPQ 237 (CCPA 1929) ("It is a settled principle of law that a mere carrying forward of an original patented conception involving only change of form, proportions, or degree, or the substitution of equivalents doing the same thing as the original invention, by substantially the same means, is not such an invention as will sustain a patent, even though the changes of the kind may produce better results than prior inventions."). See also KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416, 82 USPQ2d 1385, 1395 (2007) (identifying "the need for caution in granting a patent based on the combination of elements found in the prior art").
C. Claims 1-2, 4-6, 8, 10-11, 13, 20, 22, 25-27, 35, 37, 39, 42, 46 and 48 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-2, 5-7, 9, 12, 14, 16, 19 and 26 of U.S. Application No. 19110885 in view of Lowenthal and Leung, as cited in the attached PTO-892 Form.
U.S. Application No. 17759714 teaches claim 1 except "based on at least three peptide structures selected from one of a first group of peptide structures identified in Table 1A and a second group of peptide structures identified in Table 2A; wherein the first group of peptide structures and the second group of peptide structures are associated with the ovarian cancer disease state; wherein each of the first group of peptide structures in Table 1A and the second group of peptide structures in Table 2A is listed in order of relative significance to the disease indicator." However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P05155, P05156, P00450, P01857, P02790, P04114, P00738, P01860 and P01861 (corresponding to protein SEQ IDs 101-110 from Table 1A respectively) and proteins identified by Uniprot IDs P00450, P01857, P04114, P06681, P02763, P01011, P01023, P01009, P04004, P01859, P08603, P02749, P05090, P02787 (corresponding to SEQ IDSs 104-105, 107, 120-130 from Table 2A, respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A); wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2).
U.S. Application No. 17759714 teaches claim 20 except "based on at least three peptide structures selected from one of a group of peptide structures identified in Table 3A." However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P00450, P01857, P02790, P00738, P04114, P06681, P02763, P01009, P04004, P05090, P02787 (corresponding to protein SEQ IDs 101, 104-106, 108, 120-122, 125-126 and 129-130 from Table 3A respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A).
U.S. Application No. 17759714 teaches claim 42 except "based on at least three peptide structures selected from one of a group of peptide structures identified in Table 1A, Table 2A, and/or Table 3A." However, Lowenthal teaches the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1); wherein listed proteins identified by Uniprot IDs P25311, P05155, P05156, P00450, P01857, P02790, P04114, P00738, P01860 and P01861 (corresponding to protein SEQ IDs 101-110 from Table 1A respectively) and proteins identified by Uniprot IDs P00450, P01857, P04114, P06681, P02763, P01011, P01023, P01009, P04004, P01859, P08603, P02749, P05090, P02787 (corresponding to SEQ IDSs 104-105, 107, 120-130 from Table 2A, respectively) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A); wherein listed proteins identified by Uniprot IDs P25311 (i.e. parent protein of peptide SEQ ID 111), P00450 (i.e. parent protein of peptide SEQ ID 114), P01857 (i.e. parent protein of peptide SEQ ID 115), P06681 (i.e. parent protein of peptide SEQ ID 131), P02763 (i.e. parent protein of peptide SEQs ID 132 and 142), P01011 (i.e. parent protein of peptide SEQ IDs 133 and 137), P01023 (i.e. parent protein of peptide SEQ IDs 134 and 146), P01009 (i.e. parent protein of peptide SEQ ID 138), P01859 (i.e. parent protein of peptide SEQ ID 140), P05090, (i.e. parent protein of peptide SEQ ID 144), P02787 (i.e. parent protein of peptide SEQ IDs 145 and 157), P04004 (i.e. parent protein of peptide SEQ ID 153), P02765 (i.e. parent protein of peptide SEQ IDs 154 and 163), P02750 (i.e. parent protein of peptide SEQ ID 155), P00738 (i.e. parent protein of peptide SEQ IDs 156 and 160), P02790 (i.e. parent protein of peptide SEQ ID 158), P02766 (i.e. parent protein of peptide SEQ ID 159), P43652 (i.e. parent protein of peptide SEQ ID 161), P02647 (i.e. parent protein of peptide SEQ IDs 162 and 164), and P04003 (i.e. parent protein of peptide SEQ ID 165) have been identified in ovarian cancer stage specific and normal serum by multiple peptide sequences (Supplemental Table 1A) - which comprises all protein parents from all peptide sequences in Table 3A; wherein a candidate protein can be identified in one disease pool - e.g., high risk - but not found in a second disease category - e.g., stage I cancer (i.e. reading on a group for peptide structures associated with the ovarian cancer disease state and a group for the order of relative significance to the disease indicator) (pg. 143 col. 2 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding 1-2, 4-6, 8, 10-11, 13, 20, 22, 25-27, 35, 37, 39, 42, 46 and 48, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of U.S. Application No. 17759714 in view of Lowenthal because all references disclose methods for investigating peptide structure data as indicator for ovarian cancer diagnosing. The motivation would have been to:
• identify proteins and peptides associated with serum carrier proteins and predicted sequences that represent fragments of larger molecules that may eventually be found suitable for full clinical diagnostic testing (pg. 1944 col. 1 para. 1 Lowenthal).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the investigation of peptide structure data as indicator for ovarian cancer diagnosing of U.S. Application No. 17759714 to the methods by Lowenthal because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigating peptide structure data as indicator for ovarian cancer diagnosing.
Regarding claims 1-2, 4-6, 8, 10-11, 13, 20, 22, 25-27, 35, 37, 39, 42, 46 and 48, one of ordinary skill in the art would be motivated to identify the peptide sequence of each taught protein listed in the recited Tables in its peptide sequence entirety (i.e. comprising all possible fragments of such protein) to optimize the ovarian cancer diagnosing method for routine optimization reasons and determination of the optimum or workable ranges to identify ovarian cancer with higher accuracy – as indicated by the prior art to Lowenthal disclosing the analysis of albumin associated peptides and proteins from ovarian cancer patients (pg. 1933 Title); wherein a large proportion of the predicted peptides sequences represent fragments of larger molecules (i.e. peptides inherently fragmented from proteins) (pg. 1944 col. 1 para. 1). MPEP 2144.05 II states - The Supreme Court has clarified that an "obvious to try" line of reasoning may properly support an obviousness rejection. In In re Antonie, 559 F.2d 618, 195 USPQ 6 (CCPA 1977), the CCPA held that a particular parameter must first be recognized as a result-effective variable, i.e., a variable which achieves a recognized result, before the determination of the optimum or workable ranges of said variable might be characterized as routine experimentation, because "obvious to try" is not a valid rationale for an obviousness finding. Another motivation to combine the teachings originates from the "obvious to optimize" 2144.05 rationale, which states that choosing from a finite number of identified, predictable solutions (i.e., in this case a finite number of loci in the art that are related to the disease/condition), with a reasonable expectation of success would motivate one of ordinary skill in the art. See MPEP 2143 (I). Also see MPEP 2144.05 (a change in form, proportions, or degree "will not sustain a patent"); In re Williams, 36 F.2d 436, 438, 4 USPQ 237 (CCPA 1929) ("It is a settled principle of law that a mere carrying forward of an original patented conception involving only change of form, proportions, or degree, or the substitution of equivalents doing the same thing as the original invention, by substantially the same means, is not such an invention as will sustain a patent, even though the changes of the kind may produce better results than prior inventions."). See also KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416, 82 USPQ2d 1385, 1395 (2007) (identifying "the need for caution in granting a patent based on the combination of elements found in the prior art").
Conclusion
No claims are allowed.
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/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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