DETAILED ACTION
Applicant’s response, filed Feb 25 2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim Status
Claims 1-9 are pending.
Claims 8-9 are newly added.
Claims 1, 4, and 8 are objected to.
Claims 1-9 are rejected.
Priority
The instant Application claims domestic benefit to US provisional application 63/160,486, filed Mar 12 2021.
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the prior application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, US provisional application 63/160,486, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The provisional application does not provide support for selecting a portion of the candidate peaks with the lowest second derivative as recited in instant claim 4 or for assigning one or two additional candidate peaks to secondary peaks comprising secondary beta-2 or secondary gamma as recited in instant claim 6.
Accordingly, each of claims 1-3 and 7-9 are afforded the effective filing date of the Mar 12 2021 and claims 4-6 are afforded the effective filing date of Mar 15 2022.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: User 332 in FIG. 1.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Response to Applicant Arguments
At p. 1, Applicant submits that a replacement drawing sheet has been submitted. However, no replacement drawing sheet is present in the Feb 25 2026 submission. Therefore the objection is maintained.
Specification
The disclosure is objected to for the following informalities. It is noted that for purposes of the instant Office Action, any reference to the specification pertains to the specification as originally filed on Mar 15 2022.
Disclosure
The specification is objected to because the paragraph numbering starting on p. 9 is incorrect. The paragraph on p. 9 which should be labeled as [0051] is not labelled, as are the next 3 paragraph on p. 9-10. Other instances of paragraphs which are not numbered are noted, such as what should be [0053] at p. 11. The specification should be resubmitted with correct paragraph numbering.
Appropriate correction for all objections to the specification is required.
Response to Applicant Arguments
At p. 1, Applicant submits that a substitute specification has been submitted. However, no substitute specification is present in the Feb 25 2026 submission. Therefore the objection is maintained.
Claim Objections
Unless otherwise noted, the outstanding objections to the claims are withdrawn in view of the amendments submitted herein.
The claims are objected to because of the following informalities.
Claims 1 and 8 are objected to for reciting “a.”, “b.”, “c.”, and “d.” to delineate steps of the method. According to MPEP 608.01(m), “Each claim begins with a capital letter and ends with a period. Periods may not be used elsewhere in the claims except for abbreviations. See Fressola v. Manbeck, 36 USPQ2d 1211 (D.D.C. 1995)”. The applicant may consider an amendment to replace “a.”, “b.” and “c.” with “(a)”, “(b)” and “(c)”, respectively. The objection is newly stated for claim 1 based upon further consideration of the claims, and is newly stated for claim 8 based on amendment.
Claim 4 should be amended to recite “wherein extracting the feature set further comprises:
determining…; and
selecting…”. As set forth in 37 CFR 1.75, where a claim sets forth a plurality of steps, each step of the claim should be separated by a line indentation (see MPEP 608.01(i)). The objection is maintained from the previous Office Action.
Claim Rejections- 35 USC § 112
Unless otherwise noted, the outstanding rejections to the claims are withdrawn in view of the amendments submitted herein.
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 1-9 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. The instant rejection is either newly stated and is necessitated by claim amendment or is maintained from the previous Office Action.
Claim 1, limitation b., recites “the feature vector comprising at least one feature…, wherein the at least one feature comprises: at least one identified peak, at least one region corresponding to each identified peak, at least one peak feature associated with each identified peak, and at least one region feature associated with each region”. It is not clear if the feature vector may comprise only one of the recited features or if it must comprise at least one of each, because the limitation first recites that that the feature vector comprises at least one feature, indicating that only one feature at minimum is required, but then describes that the feature comprises four separate features. Each of the “identified peak”, “region”, “peak feature”, and “region feature” are considered to recite a feature, as is supported by the specification as published at least at [0055], which describes a “feature” as comprising data describing one aspect of the SPEP profile. Therefore, the metes and bounds of the claim are not clear. For compact examination, it is assumed that the claim requires a feature vector comprising at least one feature, where the feature may be selected from the recited features. The rejection may be overcome by clarifying the relationship between the terms by, for example, amending the lists to be separated by an “or” rather than an “and”. Claims 2-9 are rejected based on their dependency from claim 1. The rejection is maintained from the previous Office Action.
Claim 8 recites “a. the local curvature is extracted over a 3-unit window; b. the local curvature is extracted over a 3-unit window”. It is not clear why the same limitation is recited twice. For compact examination, it is assumed that b. should be amended to recite “the local angle”, which was previously recited in claim 2 to have a 3 unit window.
Response to Applicant Arguments
Applicant has provided no remarks regarding the 35 USC 112(b) rejections. However, Applicant’s response is considered a bona fide response because the claims were amended to attempt to address the rejections.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portions are necessitated by claim amendment.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows 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.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
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;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to a method, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
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 mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claim 1: b. extracting… a feature vector from the two-dimensional SPEP profile, the feature vector comprising at least one feature of the SPEP profile, wherein the at least one feature comprises: at least one identified peak, at least one region corresponding to each identified peak, at least one peak feature associated with each identified peak, and at least one region feature associated with each region; and
c. transforming…the feature vector into the diagnostic comment and a corresponding confidences of the diagnostic comment.
Dependent claims 2-6 and 8-9 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claims 2 and 8 further limit the peak features to comprising various coordinates and mathematical descriptions of the peak; claims 3 and 9 further limit the region features to comprising various mathematical descriptions of the peak in the region; claim 4 further limits the extracting to comprising determining a plurality of candidate peaks, selecting a portion of the candidate peaks with lowest second derivatives; claim 5 further limits the extracting to comprising assigning each candidate peak of the portion to a corresponding reference peak; and claim 6 further limits the assigning to comprising assigning one or two additional candidate peaks to secondary peaks comprising secondary beta-2 or secondary gamma.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually generate diagnostic comments and confidences based on two-dimensional serum protein electrophoresis. Without further detail as to the methodology involved in “extracting”, “transforming”, “determining”, and “assigning”, under the BRI, one may simply, for example, use pen and paper to extract various features from the peaks in electrophoresis data and transform those features into diagnostic comments. Some of these steps and those recited in the dependent claims, such as “extracting” feature vectors from the SPEP profile which include features such as the curvature, angle, derivatives, and areas under the curve of the peaks and slopes of lines in the regions, require mathematical techniques as the only supported embodiments, as is disclosed in the specification at least at “the features correspond to mathematical expressions capturing one or more characteristics of each peak” [0048].
Therefore, claim 1 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claim 1: a. providing a two-dimensional serum protein electrophoresis (SPEP) profile comprising a plurality of measured abundances and corresponding times; and
c. … using a machine-learning model; and
d. displaying, using the computing device, the diagnostic comment and the corresponding confidence to a practitioner for use in diagnosing a hemoglobinopathy in the subject.
Dependent claim 7 further limit the recited additional elements of the machine-learning model to comprising one of KNN, elastic net regression, random forests, and gradient boosting machine.
The claims also include non-abstract computing elements. For example, independent claim 1 includes that the method is computer-implemented on a computing device.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “providing” data, and to data outputting, such as “displaying” data, perform functions of collecting and outputting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of a computing device 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, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than 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, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Further, the limitation reciting “using a machine-learning model” provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the limitations merely serve to link the judicial exception of “transforming” the feature vector to the technological environment of machine learning.
The specification discloses that automatically-generated interpretative comments would save several hours of hands-on time per week, decrease turnaround time, reduce the training needed by technologists, standardize the results reported to clinicians, and decrease transcriptional errors, at [0007] (see also [0054]), but does not provide a clear explanation for how the additional elements provide these improvements. Therefore, the additional elements do not clearly improve the functioning of a computer, or comprise an improvement to any other technical field. Further, the additional elements do not clearly affect a particular treatment; they do not clearly require or set forth a particular machine; they do not clearly effect a transformation of matter; nor do they clearly provide a nonconventional or unconventional step (MPEP2106.04(d)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
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 prosecution 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).
With respect to the instant claims, the prior art review to Zampieri et al. (PLOS Computational Biology, 2019, 15(7):1-24; previously cited) discloses that using machine learning models comprising KNN, elastic net regression, random forests, and gradient boosting machines for analyzing biological data is an additional element which “applies” the judicial exception to a generic computer that is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, Table 1. Further, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely 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), Versata 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)(II)(i)). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 1 and those claims dependent therefrom, 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 nothing more than 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. at 225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)). The specification as published also notes that computer processors and systems, as example, are commercially available or widely used at [0063], and that any suitable machine learning model may be used without limitation at p. 10, par. 2. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Response to Applicant Arguments
At p. 3-4, Applicant submits that amended claim 1 includes the additional element of “displaying” the diagnostic comment and confidence indicator, which integrates the judicial exceptions into a practical application of diagnosing a hemoglobinopathy, thereby informing treatment selection. Applicant further submits that the claimed method is an improvement over existing automated methods of analyzing two-dimensional SPEP profiles because the method includes transforming the feature vector into a diagnostic comment and a corresponding confidence, which provides the practitioner an indication of the certainty of the diagnostic comment.
It is respectfully submitted that this is not persuasive. The newly added additional element of “displaying, using the computing device, the diagnostic comment and the corresponding confidence to a practitioner for use in diagnosing a hemoglobinopathy in the subject” does not provide a practical application of the judicial exceptions at Step 2A, Prong 2, because it merely recites the outputting of the result of the judicial exception for display to a practitioner. As set forth in the above rejection, data outputting is insignificant extra-solution activity to the judicial exception because the limitation merely amounts to data outputting required by the judicial exception (see MPEP 2106.05(g)). The claim does not require a diagnosis of hemoglobinopathy or a selection of treatment. Therefore, Applicant’s arguments are not commensurate with the scope of the claim because those elements which Applicant has argued are improved are not present in the claim. Even if those elements were actively required, they would be considered to recite a judicial exception and would therefore not represent a technical field which could be improved to provide a practical application.
Applicant further submits that the claimed method is an improvement over existing automated methods of analyzing two-dimensional SPEP profiles. However, steps directed to analyzing the two-dimensional SPEP profile or that provide the supposed improvement (i.e., transforming the vector into a diagnostic comment and a corresponding confidence) in the instant claims are steps that are, themselves, the judicial exceptions and cannot therefore be a practical application of the judicial exception. The courts have made clear that a judicial exception is not eligible subject matter (Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)) if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, it is the additional elements (if any) in the claim that must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. It is submitted here that the instant claims do not include any additional elements that provide for a practical application. Rather, the “additional element” in the instant claims (see exemplary claim 1) includes only the step of “providing” and “displaying” data, as well as the use of a computing device and a single machine-learning model. As set forth above, said steps operate in the claim as data gathering and “apply it” steps and do not integrate any of the recited judicial exceptions into a practical application, nor do the claims as a whole include any inventive concept beyond well-understood, routine and conventional steps.
Claim Rejections - 35 USC § 102
The outstanding rejections from the previous Office Action are withdrawn in view of the amendments submitted herein. Specifically, Kratzer et al. (Journal of Clinical Pathology, 1992, 45(7):612-615) does not teach transforming, using a machine-learning model, the feature vector into a confidence that corresponds to the diagnostic comment, as submitted by Applicant at p. 5, par. 5.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
A. Claims 1-3 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Kratzer et al. (Journal of Clinical Pathology, 1992, 45(7):612-615; previously cited) in view of Wu et al. (CN-110443789-A; newly cited). The instant rejection is newly stated and is necessitated by claim amendment.
The prior art to Kratzer discloses a system of neuronal networks which can classify the densitometric patterns of serum protein electrophoresis (abstract). Kratzer, indicated by the closed circles, teaches the instant features as follows. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
Claim 1 discloses a computer-implemented method for automatically generating a diagnostic comment for protein capillary electrophoresis data obtained for a subject, the method comprising:
Kratzer teaches analyzing digitized data on a computer (i.e., a computer-implemented method) (abstract; Figure 2; p. 613, col. 2; p. 614, col. 1, par. 1).
a. providing a two-dimensional serum protein electrophoresis (SPEP) profile comprising a plurality of measured abundances and corresponding times;
Kratzer teaches that protein electrophoresis was run and the digitized data were transferred to a computer (p. 614, col. 1, par. 1). Kratzer teaches that the serum electrophoresis densitograms comprise peaks of separated fractions along the x axis (i.e., time) with a height along the y axis (i.e., abundances) (Figures 2 and 4):
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. The serum electrophoresis densitograms of Kratzer are considered to read on a two-dimensional SPEP profile as instantly claimed because the instant specification discloses that FIG. 5 illustrates exemplary two-dimensional serum protein electrophoresis (SPEP) profiles:
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b. extracting, using a computing device, a feature vector from the two-dimensional SPEP profile, the feature vector comprising at least one feature of the two-dimensional SPEP profile, wherein the at least one feature comprises: at least one identified peak, at least one region corresponding to each identified peak, at least one peak feature associated with each identified peak, and at least one region feature associated with each region;
Kratzer teaches preprocessing (i.e., extracting) the electrophoresis data in three different ways to present them to different networks, by evaluating the integral (i.e., region feature) of the curves of the albumin, alpha 1, alpha 2, beta, and gamma fractions (i.e., region) (network 1) or the shape (i.e., peak feature) of the albumin, beta, and gamma fractions (i.e., peak) (networks 2, 3, 4) (p. 614, col. 1 through col. 2, par. 3). Kratzer teaches subdividing each peak into different regions with bars (i.e., regions) and performing a Fourier transformation on the gamma region (i.e., region feature) (Fig. 4):
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.
Kratzer teaches that the data are input into the neural networks as input vectors (p. 612, col. 1, par. 8; p. 613, col. 1, par. 2; Figure 1).
c. transforming, using a single machine-learning model implemented on the computing device, the feature vector into the diagnostic comment and a corresponding confidence of the diagnostic comment; and
Kratzer teaches training and using neuronal networks (i.e., machine-learning model) to output diagnoses of normal or pathological based on the data from the preprocessed serum electrophoresis densitograms (p. 614, col. 2, par. 5-6; Tables 1-3). Although Kratzer teaches four different networks (p. 614, col. 1, par. 2 through col. 2, par. 2), each network operates on a specific type of input data: network 1, integrated fractions of the proteins (i.e., region feature); network 2, shape of the albumin fraction; network 3, shape of the β fraction; and network 4, shape of the γ fraction (i.e., peak feature) (p. 614, col. 2, par. 2). Kratzer teaches that each network outputs a diagnosis (Table 1). Therefore, as the instant claims require in b. that the feature vector comprises only one feature, it is considered that Kratzer fairly teaches a single machine-learning model that transforms a single feature in a feature vector to a diagnostic comment.
See below for teachings by Wu regarding a corresponding confidence of the diagnostic comment.
d. displaying, using the computing device, the diagnostic comment and the corresponding confidence to a practitioner for use in diagnosing a hemoglobinopathy in the subject.
Kratzer teaches displaying the results of the analysis as either pathological or normal (Tables 2-3). As it is considered that Kratzer teaches a method using a computing device as described above (p. 614, col. 1, par. 1), it is considered that Kratzer fairly teaches displaying the diagnostic comment using a computing device. The limitation “to a practitioner for use in diagnosing a hemoglobinopathy in the subject” is considered to recite an intended use of displaying the diagnostic comment. As Kratzer is considered to fairly teach displaying the diagnostic comment, it is considered that Kratzer also fairly teaches the intended use recited in the claim.
See below for teachings by Wu regarding displaying the corresponding confidence.
Kratzer does not teach generating or displaying a corresponding confidence of the diagnostic comment.
However, the prior art to Wu discloses a method for automatically identifying immune electrophoresis images (abstract). Wu teaches that the method comprises data preparation, cleaning, pre-processing, and division, followed by a convolutional neural network for extracting protein electrophoresis area with image features, training a LSTM model, and using the trained LSTM model to predict immunofixation electrophoresis (IFE) identification (abstract). Wu teaches that the model performs multiple classification that includes a prediction probability output (p. 4, par. 4-6; p. 7, par. 3, 6, and 8). Therefore, it is considered that Wu fairly teaches transforming, using a single machine-learning model, the feature vector into the diagnostic comment and a corresponding confidence of the diagnostic comment, as instantly claimed.
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, Kratzer and Wu because each reference discloses methods for automatic analysis of protein electrophoresis data with neural networks. The motivation to combine the methods would have been to provide an automatic method for judgment classification rather than a manual one, as taught by Wu (p. 2, par. 3). Further, although Wu teaches applying their method to IFE rather than SPEP data, it is considered that one of ordinary skill in the art would have understood how to modify the method of Wu to be applied to SPEP data instead, as IFE similarly analyzes two-dimensional data of similar immune proteins (p. 2, par. 2 of Wu) as those analyzed in SPEP protocols. Therefore, it is considered that SPEP and IFE two-dimensional images could be interchanged in the methods.
Regarding claim 2, Kratzer in view of Wu teaches the method of claim 1 as described above. Claim 2 further adds that the at least one peak feature is selected from: an x- coordinate, a y-coordinate, a local curvature, a local angle, a leading first derivative, a lagging first derivative, a leading second derivative, a lagging second derivative (, and any combination thereof.
Kratzer teaches that the serum electrophoresis densitograms have values associated with the x and y coordinates (Figures 2-3), as well as the shape of curves (i.e., curvature) (p. 614, col. 2, par. 2), which are input into separate single neuronal networks.
Regarding claim 3, Kratzer in view of Wu teaches the method of claims 1-2 as described above. Claim 3 further adds that the at least one region feature is selected from: at least one of an area under the curve, a skew, a number of inflection points, a mean curvature, a minimum of the leading second derivative, a minimum of the lagging second derivative, a mean sum of squares of the leading second derivative, a mean sum of squares of the lagging second derivative, at least one slope of a segment connecting each region boundary to its associated peak, an angle formed by adjacent peaks through a joining boundary, at least one root mean squared error of polynomial fit and any combination thereof.
Kratzer teaches evaluating the integrals (i.e., area under the curve) of each of the fractions (i.e., regions) using the neuronal networks (p. 614, col. 1, par. 2).
Regarding claim 8, Kratzer in view of Wu teaches the method of claims 1-2 as described above. Claim 8 further adds that a. the local curvature is extracted over a 3-unit window; b. the local curvature is extracted over a 3-unit window; c. the leading first derivative is extracted as a mean over a 5-unit window; and d. the lagging first derivative is extracted as a mean over a 5-unit window.
Claim 8 does not limit the at least one peak feature to being a local curvature, a local angle, a leading first derivative, or a lagging first derivative. As Kratzer teaches a peak feature of an x and y coordinates (Figures 2-3), it is considered that Kratzer fairly teaches the limitations of claim 8 as required.
It is noted that Kratzer teaches the shape of curves (i.e., local curvature) normalized to a length of 8 units (p. 614, col. 2, par. 2). Kratzer therefore teaches an example of an 8-unit window which makes obvious the instantly claimed example of a 3-unit window. It would have been prima facie obvious to one of ordinary skill in the art to select any portions of the disclosed ranges including the instantly claimed ranges from the ranges disclosed in the prior art references, particularly in view of the fact that: "The normal desire of scientists or artisans to improve upon what is already generally known provides the motivation to determine where in a disclosed set percentage ranges is the optimum combination of percentages" In re Peterson 65 USPQ2d 1379 (CAFC 2003). See also In re Malagari, 182 USPQ 549,533 (CCPA 1974) and MPEP 2144.05.
Regarding claim 9, Kratzer in view of Wu teaches the method of claims 1-3 as described above. Claim 9 further adds that the polynomial fit further comprises a degree of fit selected from 2, 4, 6, 8, or 10.
Claim 9 does not limit the at least one region feature to being a polynomial fit. As Kratzer teaches a region feature of an area under the curve as described above (p. 614, col. 1, par. 2), it is considered that Kratzer fairly teaches the limitations of claim 9 as required.
B. Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Kratzer in view of Wu, as applied to claims 1-3 above, and further in view of Matos et al. (Journal of Chromatography B, 2012, 910:31-45; previously cited). The instant rejection is newly stated and is necessitated by claim amendment.
Regarding claim 4, Kratzer in view of Wu teaches the method of claims 1-3 as described above. Claim 4 further adds determining, using the computing device, a plurality of candidate peaks, and selecting a portion of the plurality of candidate peaks with lowest second derivatives, which Kratzer does not teach.
However, the prior art to Matos reviews data processing methods of two-dimensional chromatography (abstract). Matos teaches that peak detection of chromatographic data can be performed using the second-order derivatives, where the peak coincides with the minimum of the second-order derivative (p. 37, col. 2, par. 2; Fig. 4).
Regarding claim 5, Kratzer in view of Wu teaches the method of claims 1-3 and, in further view of Matos, the method of claim 4 as described above. Claim 5 further adds assigning, using the computing device, each candidate peak of the portion to a corresponding reference peak, wherein each reference peak is a known serum protein selected from albumin, alpha-1, alpha-2, beta-1, beta-2, and gamma.
Kratzer teaches assigning the serum electrophoresis densitograms to either albumin, alpha 1, alpha 2, beta, or gamma fractions (abstract; p. 614, col. 1, par. 2 through col. 2, par. 2; Figure 2). It is considered that Kratzer fairly teaches the limitations of claim 5 because Kratzer assigns each of the identified candidate peaks to reference peaks as instantly claimed. The claim is not considered to require identifying each of the recited reference peaks.
Regarding claim 6, Kratzer in view of Wu teaches the method of claims 1-3 and, in further view of Matos, the method of claim 4-5 as described above. Claim 6 further adds that assigning each candidate peak further comprises assigning one or two additional candidate peaks to secondary peaks comprising secondary beta-2 or secondary gamma.
Kratzer teaches that the gamma region has different monoclonal fractions, normal, MG 1, and MG 2 (i.e., secondary gamma) (p. 614, col. 2, par. 3; Figure 4).
Regarding claims 4-6, 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 Kratzer in view of Wu with Matos because both references disclose methods for processing two-dimensional data. The motivation would have been to perform peak detection using a known method as taught by Matos (p. 37, col. 2, par. 2). Therefore it would have been obvious to one of ordinary skill in the art to substitute the peak finding method of Kratzer with the peak finding method of Matos because such a substitution is no more than the simple substitution of one known element for another.
C. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kratzer in view of Wu, as applied to claim 1 above, and further in view of Chen et al. (Am. J. Clin Pathol, 2020, 154:S7-S8; previously cited). The instant rejection is newly stated and is necessitated by claim amendment.
Regarding claim 7, Kratzer in view of Wu teaches the method of claim 1 as described above. Claim 7 further adds that the machine learning model comprises one of KNN, elastic net regression, random forests, and gradient boosting machine, which Kratzer does not teach.
However, the prior art to Chen discloses an automated detected method of serum protein electrophoresis data using machine learning (Introduction, p. S7). Chen teaches examining three different models, including random forest (Method, p. S7).
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 method of Kratzer in view of Wu and Chen because both references disclose methods for analyzing serum protein electrophoresis data using machine learning. The motivation would have been to perform machine learning using known methods as taught by Chen (Method, p. S7). Therefore it would have been obvious to one of ordinary skill in the art to substitute the machine learning method of Kratzer with the machine learning method of Chen because such a substitution is no more than the simple substitution of one known element for another.
Response to Applicant Arguments
With respect to Applicant’s arguments under 35 USC 103, the arguments have been fully considered but are moot in view of the new grounds of rejection set forth above as necessitated by claim amendment herein.
It is noted that Applicant considers that Kratzer does not teach a single machine-learning model (p. 5). As explained in the above rejection, although Kratzer teaches four different networks (p. 614, col. 1, par. 2 through col. 2, par. 2), each network operates on a specific type of input data: network 1, integrated fractions of the proteins (i.e., region feature); network 2, shape of the albumin fraction; network 3, shape of the β fraction; and network 4, shape of the γ fraction (i.e., peak feature) (p. 614, col. 2, par. 2). Kratzer teaches that each network outputs a diagnosis (Table 1). Therefore, as the instant claims require in b. that the feature vector comprises only one feature, it is considered that Kratzer fairly teaches a single machine-learning model that transforms a single feature in a feature vector to a diagnostic comment. It is therefore considered that Kratzer teaches a single-machine learning model to generate a diagnostic comment.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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