DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-17 are pending and examined herein. No claims are canceled.
Priority
As detailed on the 24 May 2022 filing receipt, the application claims priority as early as 12 July 2019. At this point in examination, all claims have been interpreted as being accorded this priority date as the effective filing date.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 12 January 2022. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the references are being considered by the examiner.
Specification
The disclosure is objected to because of the following informality: the “c” in “corresponding” is separated onto the previous line (pg. 13, lines 14-15).
Appropriate correction is required.
Claim Interpretation under 35 USC § 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“an input module” in claim 11,
“a threshold calculation module” in claim 11,
“a processing module” in claim 11, and
“a baseline value calculation module” in claim 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The corresponding structure in the instant specification and independent claims is a "processor" (pg. 13, paragraphs [57-59) and thus the steps are interpreted as computer-implemented. The specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b) (MPEP 2181 (II)(B)).
Regarding the input module, the specification discloses the computer can receive a data set (pg. 14, paragraph [63]). The steps or algorithm performed by the threshold calculation module and processing module to perform the classifying is disclosed in the specification (pg. 4, paragraph [15]). The baseline value calculation module is described within claim 12 which instantiates it.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-18 are rejected under 35 USC § 101 because the claimed inventions are directed to an abstract idea without significantly more. "Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), 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)). The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea of determining thresholds between clusters of data.
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below.
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)?
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The claims are directed to a method (claims 1-8), a computer system (claim 11), and a non-transitory computer-readable medium (claims 9-10). The computer readable medium is neither recited nor disclosed to be required to be stored on a physical, non-transitory medium. (pg. 36-37, paragraph [183]). [Step 1: Claims 1-8 and 11: Yes; Claims 9-10: No]
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))?
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)).
Mathematical concepts recited in claims 1 and 9-11 include calculating a threshold, where a calculation is interpreted as a mathematical process. A mathematical relationship may be expressed in words and there is no particular word or set of words that indicates a claim recites a mathematical calculation (MPEP 2106.04(a)(2)). Mental processes, defined as concepts practically performed in the human mind such as steps of observing, evaluating, or judging information, recited in claims 1 and 9-11 include classifying values into clusters using the threshold, where classification is interpreted as performable by the human mind as a data evaluation or judgment.
Claims 2 and 12 recite further calculating steps and a classification step, interpreted as mathematical concepts and a mental process for the reasons above, respectively.
Claims 4 and 13 recite producing a histogram, which is interpreted as data evaluation and organization which can be done by hand with pen and paper, and thus a mental process; performing noise removing based on comparison to a reference noise value, and thus is interpreted as performable by the human mind or as a mathematical calculation on the data to produce different data; searching for data, where the human mind is practically equipped to search for data; and performing a calculation, which is interpreted as a mathematical calculation.
Claims 5 and 14 recite updating the data set by modifying the dataset and producing a new histogram, where filtering the data to remove high and low values and producing a graph are performable by the human mind and with pen and paper.
Claims 6 and 15 recite producing a histogram by sorting data in bins, which is a mental process; equalizing and differencing the data, which are mathematical concepts; searching for data, where the human mind is practically equipped to search for data; and performing a calculation, which is interpreted as a mathematical calculation.
Claims 7 and 16 recite reducing bin width, which is interpreted as mental process, and performing the steps of claim 6, which are treated above.
Claims 8 and 17 recite producing histogram data by classifying data, where data classification is interpreted as a mental process; equalizing the data, which is interpreted as a mathematical concept; and searching the data, which is interpreted as a mental process.
Hence, the claims explicitly recite numerous elements that, individually and in combination,
constitute abstract ideas. The claims must therefore be examined further to determine whether they
integrate that abstract idea into a practical application (MPEP 2106.04(d)). [Step 2A: 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))?
Additional elements recited in the claims include receiving input numerical values from a reference (claim 1), adding fluorescent dye to detect a mutation and performing PCR (claim 3), a computer program (claim 9), a computer readable recording medium (claim 10), and a data processing system comprising modules (claim 11).
Receiving data to perform the mathematical and mental steps is interpreted as a data gathering step. This data gather is insignificant extra-solution activity and as such does not integrate the abstract idea into a practical application (MPEP 2106.05(g)). The remaining additional elements are interpreted as reciting a computer or computer elements at a high level of generality. Thus, the claims state nothing more than that a generic computer performs the functions that constitute the abstract idea. Instructions to apply the abstract idea using a computer does not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; and MPEP 2106.05(f)). The step of adding dye and performing PCR is interpreted as a data gathering step necessary to generate data for the abstract steps of calculating a threshold and classifying the numerical values. Therefore, this step is interpreted as insignificant extra-solution activity and does not integrate the abstract idea into a practical application. [Step 2A Prong Two: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself. Step 2B of 101 analysis determines whether the claims contain additional elements that amount to an inventive concept, and an inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05). Additional elements recited in the claims include receiving input numerical values from a reference (claim 1), adding fluorescent dye to detect a mutation and performing PCR (claim 3), a computer program (claim 9), a computer readable recording medium (claim 10), and a data processing system comprising modules (claim 11).
Receiving data is interpreted as a conventional computer function (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); MPEP 2106.05(d)). The claims recite a computer, 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)). Storing data on a computer is a conventional computer function (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; MPEP 2106.05(d)).
Determining specific mutations and performing PCR. The specification teaches such a process as “Droplet DigitalTM PCR” (pg. 15, paragraph [68]), which is commercially available from BioRad (BioRad 2013; newly cited). Therefore, this step is interpreted as conventionally available prior to the effective filing date in 2019.
Therefore, the recited additional elements, alone or in combination with the judicial exceptions, do not appear to provide an inventive concept. [Step 2B: No]
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole,
are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not
constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as
being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 9-11
Claims 1 and 9-11 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Watanabe (US 2006/0190195 A1; previously cited on the 12 January 2022 IDS form).
Claim 1 recites receiving, as an input, a plurality of individual numerical values included in a reference data set having two or more clusters through a data processing system.
Claim 1 recites calculating a threshold for classifying the clusters the reference data set has through the data processing system, based on the respective numerical values included in the reference
data set received.
Claim 1 recites classifying at least one or more analysis subject data sets having a plurality of individual numerical values into different clusters using the threshold through the data processing system.
Watanabe teaches using a “reference pattern” (abstract) which has at least two clusters (Fig. 5).Watanabe teaches determining if a distance is greater than the threshold value (paragraph [40]). Watanabe teaches the application of the classification to the present data (paragraph [54]).
Claim 9 recites a computer program installed to perform the steps of claim 1. Watanabe teaches a program for performing the steps of claim 1 (paragraph [1]).
Claim 10 recites a computer-readable storage medium recording the program of claim 1. Watanabe teaches a computer readable medium storing the instructions for claim 1 (claim 34).
Claim 11 recites a computer system performing the steps of claim 1. Watanabe teaches a device including a hardware configuring including a CPU, RAM, network access, and peripherals which is interpreted as a computer system (paragraph [28]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 2-3 and 12
Claims 2-3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe as applied to claims 1 and 9-11 above and further in view of Li (Bioinformatics 33(21): 3423-3430, 2017; newly cited), Lerner (US 2016/0004815 A1; newly cited), and Trypsteen (Analytical and Bioanalytical Chemistry 407: 5827-5834, 2015; newly cited).
Claims 2 and 12 recite the step of calculating a baseline value of the cluster having the smallest average value among the clusters the reference data set has, through the data processing system, based on the individual numerical values included in the reference data set received, the step of classifying at least one or more analysis subject data sets having a plurality of individual numerical values into different clusters using the threshold through the data processing system comprising the steps of: calculating the baseline value of the cluster having the smallest average value among the clusters the reference data set has through the data processing system, based on the individual numerical values included in the reference data set received; calculating a compensation threshold obtained by compensating for the threshold through the data processing system, based on a difference between the baseline value of the
reference data set and the baseline value of the analysis subject data sets; and classifying the respective numerical values included in the analysis subject data sets through the data processing system, based on the compensation threshold.
Li teaches a method of clustering in which the smallest average distance is selected as a reference (pg. 3425, col. 2, first paragraph).
Lerner teaches averaging values as part of cluster determination, and using the values as seeds for parameterization to determine the optimal solution (pg. 8, col. 1, paragraphs [105-107]).
Trypsteen teaches “baseline correction to account for… shifts” (pg. 5829, col. 2, first paragraph) as part of classification of a positive or negative state of ddPCR data, where compensation is interpreted as reading on correction.
Claim 3 recites the respective numerical values included in the reference data set and the at least one or more analysis subject data sets are amplitude values of fluorescent signals measured for droplets obtained by adding a fluorescent dye thereto to detect a specific mutation and then performing a polymerase chain reaction (PCR) to gene sequences corresponding to the specific mutation.
Trypsteen teaches determining automated thresholding to distinguish fluorescence patterns from ddPCR output (pg. 5828, Fig. 1). Lerner also teaches application to overlapping peaks in ddPCR data (pg. 1, paragraph [2]).
Combining Watanabe, Li, Lerner, and Trypsteen
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention.
One would have been motivated to combine the work of Watanabe, which teaches pattern matching against a reference, with the work of Li, which teaches categorization of biomedical data in the form of mass cytometry data, because Li teaches using the smallest average distance within a cluster in a reference for gating, or classifying, samples (pg. 3425, col. 2, first paragraph). Li teaches this is used as a “ground truth… to train the classifier” and such a ground truth to determine classifications would be desirable in the classifications performed by Watanabe. One would be further motivated to combine the work of Lerner because Lerner teaches deconstructing overlapping peaks in ddPCR data, and determining parameters by using average values as seeds and working out from there iteratively to recover peaks (pg. 8, paragraph [105]). Lerner teaches such a method would be useful because it optimizes the parameterization of the peaks, and when combined with the work of Watanabe and Li may result in better peak or cluster prediction for ddPCR data specifically. Finally, further combination with Trypsteen, which teaches threshold determination in ddPCR experiments, including baseline correction. Application of a baseline correction, interpreted as a correction based on discrepancies compared to a negative template control (pg. 5830, col. 2, second paragraph), which could be applied to the reference taught by Watanabe as a basis for correction to teach the required compensation.
The cited pieces of prior art are related to categorization or classification of biomedical data and their combination would be expected to succeed, thus rendering the instant invention prima facie obvious.
Claims 4-5 and 13-14
Claims 4-5 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe as applied to claims 1 and 9-11 above and further in view of Jacobs (Analytical Chemistry 89: 4461-4467, 2017; newly cited).
Claims 4 and 13 recite the step of calculating a threshold for classifying the clusters the reference data set has through the data processing system, based on the respective numerical values included in the reference data set received comprises the steps of: producing histogram data having a plurality of bins with a predetermined bin width using the respective numerical values included in the reference data set through the data processing system; performing a noise removing process for allowing the bins having frequencies less than a predetermined noise reference value to have zero frequencies and thus producing histogram data from which noise is removed through the data processing system; searching a first target bin existing on the left end of a first cluster in the reference data set through the data processing system, based on the histogram data from which the noise is removed; searching a second target bin existing on the right end of a second cluster in the reference data set through the data processing system, based on the histogram data from which the noise is removed; and calculating the threshold as any one of the numerical values between the first target bin and the second target bin.
The described method is interpreted as applying hard thresholding based on the histogram data as a means of distinguishing peaks, where hard thresholding is understood as treating any bins having frequency below a cutoff value as zero.
Jacobs teaches application of model-based classification to digital PCR data. Jacobs teaches transformation of this data to a histogram (pg. 4462, Fig. 2), with the third panel indicating a data set with multiple peaks/clusters. Jacobs further teaches hard thresholding where values are either classified as negative or positive (pg. 4463, col. 2, second paragraph).
Claims 5 and 14 recite the step of producing histogram data having a plurality of bins with a predetermined bin width using the respective numerical values included in the reference data set through the data processing system comprises the steps of: producing an updated data set from which given top-level numerical values and given bottom-level numerical values are removed from the respective numerical values included in the reference data set; and producing the histogram data using the respective numerical values included in the updated data set.
These claims are interpreted as producing the histogram resulting from the hard thresholding taught by Jacobs above. Jacobs teaches the concept of applying a hard threshold when there is lower frequency data between the higher peaks, taught as “rain,” as the possibility of a hard threshold misclassifying intermediate data (Supplement 1, Fig. S-1).
Combining Watanabe and Jacobs
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the work of Watanabe, which teaches pattern matching against a reference, with the work of Jacobs, which teaches methods for thresholding ddPCR data, because Jacobs teaches imaging frequency data by histogram and determining a threshold between frequency peaks (Fig. 1), which may be classified in a binary fashion when hard thresholding is applied (pg. 4463, col. 2, second paragraph), where hard thresholding is understood as omitting data below a certain threshold. Such a method would be desirable in data classification such as that taught by Watanabe because Jacobs teaches a method which removes problematic partitions as a trade off to produce a binary classification without the low frequency occurrences termed “rain” (pg. 4461, col. 2, second paragraph). The cited pieces of prior art are related to categorization or classification of biomedical data and their combination would be expected to succeed, thus rendering the instant invention prima facie obvious.
Claims 6, 8, 15, and 17
Claims 6, 8, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe as applied to claims 1 and 9-11 above and further in view of Jacobs and Smith (Computer-Aided Flow Visualization, pgs. 375-391, in Handbook of Flow Visualization, Routledge: New York, 272 pp., 2001; newly cited).
Claims 6 and 15 recite the step of calculating a threshold for classifying the clusters the reference data set has through the data processing system, based on the respective numerical values included in the reference data set received comprises the steps of: (a) producing histogram data by classifying the range of the numerical values into a plurality of bins having given widths to allow the number of individual data having the respective numerical values of the classified bins to have the frequencies of the respective bins through the data processing system; (b) performing histogram data equalizing through the data processing system; (c) performing differencing for the equalized histogram data through the data processing system; (d) searching a first target bin satisfying a given reference condition and existing on the left end of a first cluster in the reference data set through the data processing system, based on the histogram data with the differencing; (e) searching a second target bin satisfying the given reference condition and existing on the right end of a second cluster in the reference data set through the data processing system, based on the histogram data with the differencing; and (f) calculating the threshold as any one of the numerical values between the first target bin and the second target bin through the data processing system.
Jacobs teaches application of model-based classification to digital PCR data. Jacobs teaches transformation of this data to a histogram (pg. 4462, Fig. 2), with the third panel indicating a data set with multiple peaks/clusters and determining a threshold value between the peaks (pg. 4462, Fig. 1D). Together, these suggest the concept of a hard threshold determined based on gaps between peaks where low frequency bins are not examined.
Jacobs does not teach equalizing or differencing the histogram data.
Smith teaches histogram processing, including equalization (pg. 376, col. 2, last paragraph) and differencing (pg. 381, col. 1, last paragraph).
Claims 8 and 17 recite calculating a threshold for classifying the clusters the reference data set has through the data processing system, based on the respective numerical values included in the reference data set received comprises the steps of: (a) producing histogram data by classifying the range of the numerical values into a plurality of bins having given widths to allow the number of individual data having the respective numerical values of the classified bins to have the frequencies of the respective bins through the data processing system; (b) performing histogram data equalizing through the data processing system; (c) searching a first target bin satisfying a given reference condition and existing on the left end of a first cluster in the reference data set through the data processing system, based on the equalized histogram data; and (d) searching a second target bin satisfying the given reference condition and existing on the right end of a second cluster in the reference data set through the data processing system, based on the equalized histogram data.
Jacobs teaches application of model-based classification to digital PCR data. Jacobs teaches transformation of this data to a histogram (pg. 4462, Fig. 2), with the third panel indicating a data set with multiple peaks/clusters and determining a threshold value between the peaks (pg. 4462, Fig. 1D). Together, these suggest the concept of a hard threshold determined based on gaps between peaks where low frequency bins are not examined.
Jacobs does not teach equalizing or differencing the histogram data.
Smith teaches histogram processing, including equalization (pg. 376, col. 2, last paragraph).
Combining Watanabe, Jacobs, and Smith
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the previously combined work of Watanabe and Jacobs, which together teach pattern matching against a reference applied to ddPCR data for thresholding peaks or clusters based on a histogram, with the work of Smith, which teaches a review of histogram-based data processing including equalizing and differencing, because not only are such steps widely known in the art (specification: pg. 23, paragraph [121]). Furthermore, equalizing allows for improved discrimination in histogram-based analyses (pg. 376, col. 2, last paragraph) and is described as an enhancement producing increased quality and clarity (pg. 386, col. 2, first paragraph. Meanwhile, Smith teaches differencing as another enhancement technique to increase perception of details (pg. 381, col. 1-2, last paragraphs). Given these techniques are admittedly well known in the field of histogram-based analyses and are described as enhancing the result of the histogram by Smith, application of these techniques is understood as desirable. Thus, the instant invention is prima facie obvious.
Claims 7 and 16
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe in view of Jacobs and Smith as applied to claims 1, 6, 8-11, 15, and 17 above and further in view of Gao (Analytical Chemistry 89: 4461-4467, 2017; newly cited).
Claims 7 and 16 recite reducing the bin width by a given value through the data processing system if the first target bin or the second target bin satisfying the given reference condition is not searched; and performing the steps (a) to (e) again using the reduced bin width through the data processing system.
This claim recites iterative bin reduction as part of the noise reduction to determine minima for thresholding. Jacobs teaches application of model-based classification to digital PCR data. Jacobs teaches transformation of this data to a histogram (pg. 4462, Fig. 2), with the third panel indicating a data set with multiple peaks/clusters. Jacobs further teaches hard thresholding where values are either classified as negative or positive (pg. 4463, col. 2, second paragraph). Jacobs does not teach an iterative process to determine the thresholding.
Gao teaches iteratively and automatically searching bins until a terminating threshold is satisfied (pg. 167, col. 2, second paragraph).
Combining Watanabe, Jacobs, Smith, and Gao
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the previously combined work of Watanabe, Jacobs, and Smith, which together teach pattern matching against a reference applied to ddPCR data for thresholding peaks or clusters based on a histogram, with the work of Gao, which teaches clustering histogram data by iteratively detecting dense areas to determine a threshold (abstract). Jacobs teaches hard thresholding but repeating the method to satisfy a given reference condition, but this limitation is taught by Gao was automatically testing different thresholds until a terminating threshold is satisfied (pg. 167, col. 2, second paragraph), where the reference condition is interpreted as reading on the terminating threshold, and where the signatures are then generated, which is interpreted as similar to the clusters taught by Jacobs. The cited pieces of prior art are related to categorization or classification of data, and in cases based on histogram data and/or ddPCR data, and their combination would be expected to succeed, thus rendering the instant invention prima facie obvious.
Conclusion
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/R.J.K./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685