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 .
Claim Status
Claims 1-4, 6-9, and 17-25 are pending and are examined on the merit.
Claims 5, 10-15 were previously canceled.
Claim 16 is canceled.
Priority
The instant application claims the benefit of foreign priority to JP2019-039262, filed 03/05/2019. As such, the effective filing date assigned to each of claims 1-4, 6-9, and 16-19 is 03/05/2019.
Claim rejection - 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 1-4, 6-9, and 17-25 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 9 recite “… generates a prediction model to improve the prediction accuracy by learning from a data set comprising an electrophoresis result of a DNA sample with the standard base length and the environmental information during the electrophoresis”, however, it is unclear whether the model is performing an active step of predicting.
Instant claims 2-4, 6-8, and 17-25 are rendered indefinite by virtue of their dependency to claim 1 and said dependent claims do not clarify the above issue.
Response to Arguments
Applicant's arguments filed 02/13/2026 have been fully considered but they are not persuasive.
Applicant states:
the specification provides the scope of the invention such that one of ordinary skill in the art would know what was meant, and that the subject matter of the invention has been described and is supported in such a way as to reasonably convey to one skilled in the relevant art that the inventors, at the time the application was filed, had possession of the claimed invention.
It is respectfully submitted that this is not persuasive. According to MPEP 2173.05(q) claiming a process by merely reciting a use without specifying active, positive, steps on how that use is practiced are considered indefinite. In instant claims 1 and 9, it is unclear whether the model is performing an active step of predicting by applying the model.
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 1-4, 6-9, and 17-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claims 1-4, 6-8, and 17-25 being representative) is directed to a system and (claim 9 being representative) a method. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1-4, 6-9, and 17-25 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
Claim 1 recites determining a measured based length of DNA based upon the spectrum and analyzing a genotype with respect to a standard base length; the limitations “determining” and “analyzing” given the plain meanings of “determining” and “analyzing” encompass concepts that that can be practically performed in human mind (mental process) including observation, evaluation, judgement, and opinion, since human mind is capable of determine a measured base length and analyze a genotype, for example by inspecting graphical representation of the detected fluorescence peaks (data) and compare measured peaks with a standard and determine the genotype. See MPEP 2106.04 (a)(2).
Claim 1 further recites predicting a correspondence between the standard base length and the measured base length of the DNA based upon the spectrum and the environmental information in the electrophoresis; the limitation predicting a correspondence is considered a mathematical calculation, as disclosed in the present specification (pages 35-36), “The prediction model may be a parametric model in which f can be represented by a form of a specific function when d = f(p, v)”. A such, the recited limitation falls into mathematical concepts groupings of abstract ideas.
claim 1 further recites generating a prediction model to improve the prediction accuracy by learning from a data set comprising an electrophoresis result of a DNA sample with the standard base length and the environmental information during the electrophoresis; the limitations generating a model and learning are considered mathematical calculations, as disclosed in the present specification (pages 35-36), “The prediction model may be a parametric model in which f can be represented by a form of a specific function when d = f(p, v)”. A such, the recited limitation falls into mathematical concepts groupings of abstract ideas. Said limitations are also considered mental process of generating a model and learning from a dataset.
Claim 2 recites storing a plurality of prediction models to be used for the prediction; the limitations storing models given the plain meaning of “storing” can be practically performed in human mind (mental process), since human mind is capable of storing data.
Claim 2 further recites selecting the prediction model according to an environmental condition based upon the environmental information when predicting the correspondence therebetween; the limitations selecting a model given the plain meaning of “selecting” can be practically performed in human mind (mental process), since human mind is capable of selecting data.
Claim 3 recites storing a plurality of prediction models to be used for the prediction; the limitations storing models given the plain meaning of “storing” can be practically performed in human mind (mental process), since human mind is capable of storing data.
Claim 3 further recites applying the prediction model in a predetermined priority order when predicting the correspondence therebetween; the limitation applying the prediction model, as stated above, is considered a mathematical calculation, and as such falls within mathematical concepts grouping of abstract ideas. further, the limitation applying the model in a predetermined priority can be practically performed in human mind (mental process), since human mind is capable of giving priority, for example, giving a predetermined precedence to a model.
Claim 6 recites selecting the data set according to an environmental condition based upon the environmental information; the limitations selecting a model given the plain meaning of “selecting” can be practically performed in human mind (mental process), since human mind is capable of selecting data.
Claim 6 further recites learning from the selected data set to generate the prediction model; the limitation learning from a dataset falls within mathematical concepts groupings of abstract ideas, as disclosed in the present specification (page 37), “model may be modeled by using known machine learning algorithms such as a random forest that combines the decision trees, a related vector machine (RVM), a neural network”. As such, recited limitation falls within mathematical concepts groupings of abstract ideas.
Claim 7 recites evaluating accuracy of the prediction by referring to a base length obtained by electrophoresis of an actual sample that always contains DNA whose standard base length is known, when predicting the correspondence therebetween; the limitation evaluating accuracy of prediction given the plain meaning of “evaluating” can be practically performed in human mind (mental process), since human mind is capable of evaluating the accuracy of predication by referring to the base length of the known marker, see specification page 54-55.
Claim 8 recites changing the prediction model or newly learning a prediction model according to an evaluation result of the accuracy of the prediction; the limitation changing the model given the plain meaning of “changing” can be practically performed in human mind (mental process), since human mind is capable of change a model according to the result of an analysis.
Claim 9 recites determining a measured based length of DNA based upon the spectrum and analyzing a genotype with respect to a standard base length; the limitations “determining” and “analyzing” given the plain meanings of “determining” and “analyzing” encompass concepts that that can be practically performed in human mind (mental process) including observation, evaluation, judgement, and opinion, since human mind is capable of determine a measured base length and analyze a genotype, for example by inspecting graphical representation of the detected fluorescence peaks (data) and compare measured peaks with a standard and determine the genotype. See MPEP 2106.04 (a)(2).
Claim 9 further recites generating a prediction model to be used for improving the prediction accuracy; the limitation generating a model is considered a mathematical calculation, as disclosed in the present specification (pages 35-36), “The prediction model may be a parametric model in which f can be represented by a form of a specific function when d = f(p, v)”. A such, the recited limitation falls into mathematical concepts groupings of abstract ideas.
Claim 22 recites comparing a difference from a correction value measured using a mean square error (mathematical calculation/mathematical concepts).
Claim 23 recites dividing dataset into test and training sets (mental process of dividing a dataset)
Claims 17-21 and 24-25 provide additional information.
Additionally, claims 1-4, 6-9, and 17-25 recite a correlation between length of DNA and a prediction. As such, said claims fall into judicial exception of Laws of nature and natural phenomena. See MPEP 2106(b) I.
The identified claims recite a law of nature, a natural phenomenon (product of nature) and/or fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the reasons set forth above. Therefore, the claims are directed to one or more judicial exception(s) and require further analysis in Prong Two. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claims include the following.
Claims 1 and 9 recite a genotype analysis device that obtains a spectrum by an electrophoresis device, a data analysis device, and a mobility model management unit.
Claim 9 recites at least one processor coupled to a memory and outputting a correspondence prediction.
Claim 4 recites a user interface unit for displaying prediction models.
The additional elements of a genotype analysis device and a data analysis device/ a computer system, at least a processor coupled to a memory, a mobility management unit/a computer program, and a user interface unit/display unit are generic computer component and/or process. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, the additional elements of an electrophoresis device serve to collect the information for use by the abstract idea.
Furthermore, the limitation of outputting a correspondence prediction (data) amount to necessary data outputting and as such, considered insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additionally recited elements amount to generic computer components and/or insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application. See MPEP 2106.05(g). Thus, claims 1-4, 6-9, and 17-25 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. An inventive concept cannot be furnished by an abstract idea itself. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
The additional elements of claims include the following.
Claims 1 and 9 recite a genotype analysis device that obtains a spectrum by an electrophoresis device, a data analysis device, and a mobility model management unit.
Claim 9 recites at lest one processor coupled to a memory and outputting a correspondence prediction.
Claim 4 recites a user interface unit for displaying prediction models.
The additional elements of a genotype analysis device and a data analysis device/ a computer system, at least a processor coupled to a memory, a mobility management unit/a computer program, and a user interface unit/display unit are conventional computer component and/or process. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TU Communications LLC v. AV Auto, LLC, 823 F.3d 607,613,118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Furthermore, the additional elements of an electrophoresis device amount to conventional methods and systems for separating molecules according to their size. This position is supported by Novo et al. (Current advances and challenges in microfluidic free-flow electrophoresis—A critical review, Analytica Chimica Acta, Volume 991, 23 October 2017, Pages 9-29). Novo reviews current advances in electrophoresis and states that electrophoresis is a popular technique in the laboratory for the separation of charged species upon application of a voltage generating an electric field across a separation compartment filled with gel matrix in case of gel electrophoresis (GE), or free liquid solution in case of capillary electrophoresis (CE) (introduction).
Furthermore, the limitation of outputting a correspondence prediction amounts to necessary data outputting and as such, considered insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional element is not sufficient to amount to significantly more than the judicial exception.
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]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
Applicant's arguments filed 02/13/2026 have been fully considered but they are not persuasive.
Applicant states:
Regarding Step 2A, Prong 1, Applicant submits that claim 1 as amended is not directed to an abstract idea. Claim 1 as amended recites a genotype analysis device "wherein the mobility model management unit generates a prediction model to improve the prediction accuracy by learning from a data set comprising an electrophoresis result of a DNA sample with the standard base length and the environmental information during the electrophoresis." The amended claims do not merely recite mathematical calculations in the abstract. Rather, claim 1 as amended recites a specific technical process: generating a prediction model from actual electrophoresis results of a DNA sample with the standard base length, then applying that model to correct measured base lengths in actual samples.
The Applicant remarks are directed to Step 2A Prong One of 101 analysis, specifically that whether the claims recite a judicial exception. It is respectfully submitted that the above statements are not persuasive. As stated above, generating a model (mathematical algorithm) to improve accuracy using known data and learning from the data are abstract ideas. MPEP recognized that a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, as an example of claims that recite mental processes. See MPEP 2106.04(a)(2) III. A. Instant claims do not provide any details about how the model operates or how learning is done. The plain meaning of generating a model and learning from a dataset encompasses mental observations or evaluations, e.g. a person creating a mathematical algorithm using pen and paper and learning from the data. See MPEP 2106.04(a)(2), subsection III.
Applicant further states:
Regarding Step 2A, Prong 2, Applicant submits that the amended claims integrate any alleged abstract idea into a practical application. 5. Claim 1 as amended recites that the electrophoresis device "implements an electrophoresis comprising environmental information and that determines a spectrum by the electrophoresis." This is not merely data gathering but rather the physical process of electrophoresis that generates the spectrum used for analysis.
It is respectfully submitted that this is not persuasive. The Applicant remarks are directed to Step 2A Prong Two of 101 analysis, specifically whether the additional elements integrate the recited judicial exception into a practical application of the exception. As noted by the Applicant, the recites electrophoresis device generates the spectrum used for analysis. In other words, the electrophoresis device provides the data to be used by the recited judicial exceptions; this data gathering is performed in order to gather data for the mental and mathematical analysis step, and is a necessary precursor for all uses of the recited exception. The courts have identified limitations that merely gather data or stores data as insignificant extra-solution activity that does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Applicant further states:
claim 1 recites that the electrophoresis device "implements an electrophoresis comprising environmental information and that determines a spectrum by the electrophoresis," and the mobility model management unit generates a prediction model by learning from a data set comprising electrophoresis results and environmental information. This ordered combination provides a specific technological improvement to the electrophoresis-based genotype analysis process. The claims do not merely recite a correlation between DNA length and a prediction. Rather, claim 1 recites a specific technical process that uses environmental information from electrophoresis to generate and apply prediction models, enabling accurate genotype analysis while reducing the frequency of allelic ladder use.
It is respectfully submitted that this is not persuasive. The Applicant remarks are directed to Step 2A Prong Two of 101 analysis, specifically whether the additional elements integrate the recited judicial exception into a practical application of the exception. With respect to the arguments regarding the alleged improvement, it is unclear that the independent claims recite all the necessary and sufficient steps required to achieve that improvement. MPEP 2106.05(a): “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102- 03; DDR Holdings, 773F.3d at 1259, 113 USPQ2d at 1107.”. Instant claims recite the idea of a solution, a prediction model to improve accuracy. It is unclear whether the model is performing an active step of predicting by applying the model. Therefore, this recitation does not clearly identify or set forth the necessary and sufficient steps required to achieve the improved accuracy.
Applicant further states:
Regarding Step 2B, Applicant submits that the claims recite significantly more than any alleged abstract idea. The combination of elements in claim 1 as amended-an electrophoresis device implementing electrophoresis with environmental information, a data analysis device with a mobility model management unit that generates a prediction model by learning from electrophoresis results-provides significantly more than any alleged abstract idea. The specification explains that "the electrophoresis results when the allelic ladder is measured are stored, and the electrophoresis results are used as training data to update the prediction model." As-Filed Specification, page 45, Line 15. This specific technical process of using stored electrophoresis results as training data is not a conventional or routine activity.
The Applicant’s remarks are directed to Step 2B of 101 analyses, specifically evaluating additional elements to determine whether they amount to an inventive concept by considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. It is respectfully submitted that these are not persuasive.
The additional elements of a genotype analysis device and a data analysis device/ a computer system, at least a processor coupled to a memory, a mobility management unit/a computer program, and a user interface unit/display unit are conventional computer component and/or process. Furthermore, the additional elements of an electrophoresis device amount to conventional methods and systems for separating molecules according to their size. Furthermore, the limitation of outputting a correspondence prediction amounts to necessary data outputting and as such, considered insignificant extra-solution activity. See MPEP 2106.05(g). Therefore, the additional elements alone or in combination are not sufficient to amount to significantly more than the judicial exception.
As stated above, a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," are abstract ideas. It is important to note that any improvement or non-routine step or nonconventional element cannot be found in the judicial exceptions alone. “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016).”.
As such, the rejection of claims under U.S.C. 101 is maintained.
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 1, 9, 18-21, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Ikuta et al. (US 20070044541 A) in view of Yokoyama et al. (US9605300B2), and further in view of Tan et al. (US20190019290A1).
Regarding claims 1 and 9, Ikuta discloses an electrophoretic analysis method comprising a standard substance is added to a sample containing an unknown substance (unknown sample). Ikuta further discloses that this unknown sample to which the standard substance is added is introduced into a capillary, and capillary electrophoresis is executed.
Ikuta further discloses that by executing this capillary electrophoresis, the various parameters of the standard substance and the unknown sample are determined. Ikuta further discloses that on the basis of the various parameters and a theoretical equation, the effective mobility of the unknown sample is calculated (abstract, Figure 1).
Ikuta further discloses determining the mobility of an unknown sample having a system peak (for example, size determinant) and a known sample having a system peak in a standard substance (claim 1).
Ikuta further discloses that the unknown sample having the standard substance added thereto is introduced into the capillary, and the capillary zone electrophoresis is executed [0094].
Ikuta further discloses that the migration velocity of the polymers or colloid particles in the solution that is influenced by not only the kind and concentration of an electrolyte in the solution but also the shape and size of the particles of the polymers or colloid particles themselves and that for this reason, capillary zone electrophoresis (CZE) that is one of the analysis methods utilizing this electrophoresis is being watched as, for example, an important analysis method for analyzing the length of DNA fragments contained in proteins, …[0003].
Ikuta further discloses measuring migration time and migration velocity (claim 1 and [0041]) (inherently disclosing separating DNA fragments by length by using the fact that migration speed is different for different DNA fragment lengths (see instant specification pg. 2, last para. and pg. 3, first para.)).
Further regarding claims 1 and 9, Ikuta does not expressly disclose determining a measured base length of deoxyribonucleic acid (DNA) based upon the spectrum.
Yokoyama discloses a device for genotypic analysis characterized by obtaining reference fluorescence spectra using a size standard and an allelic ladder, which provide information concerning known DNA fragments used for electrophoresis of an actual sample, (abstract) FIG. 6 & 12A. Yokoyama further discloses conducting an electrophoresis process for an actual sample to be analyzed, calculating fluorescence intensities of respective fluorescent dyes from spectral waveform data obtained by electrophoresis, detecting peaks, determining a relationship between time and DNA fragment length by mapping the peak times and information concerning known DNA fragment lengths of a size standard (size calling), and obtaining fluorescence spectra of a reference capillary (col. 7, last para. col. 8, L. 1-23).
Yokoyama further discloses the process of size calling for detecting a DNA fragment by electrophoresis to the DNA fragment length. Yokoyama further discloses labelling a reagent containing DNA fragments which have known lengths/size standard with florescent dyes. Yokoyama further discloses matching peak times obtained in peak detection and finding the relational expression of the electrophoresis time and the known DNA fragment length (col. 13, last para., col. 14, last para.).
Yokoyama further discloses applying a high voltage to generate an electric field and that the sample components are detected in the order of arrival depending on their speed (col. 10, para. 2).
Yokoyama further discloses allele information contained in an allelic ladder (FIG. 15; col. 5, L. 28) (see also, instant specification pg. 6, para. 1; FIG. 10); reading on limitations of predicting a correspondence between the standard base length and the measured base length based upon the spectrum and environmental information.
Yokoyama further discloses using mathematical algorithms, such as Gaussian fitting, the least square error, Gauss-Newton for calculating parameters and values, and to minimize a loss function. Yokoyama further discloses means for improving the accuracy when peaks are asymmetric (col. 13, para. 3). Yokoyama further discloses using offset information for improving the peak matching process (col. 20, para. 1); reading on limitations of a genotype analysis device for improving prediction accuracy of a base correction length, the genotype analysis device comprising: an electrophoresis device that implements an electrophoresis comprising environmental information and that determines a spectrum by electrophoresis; and a data analysis device that determines a measured base length of deoxyribonucleic acid (DNA) based upon the spectrum and analyzes a genotype with reference to a standard base length, wherein the data analysis device includes a mobility model management unit that predicts a correspondence between the standard base length and the measured base length of DNA based upon the spectrum and the environmental information in the electrophoresis.
Further regarding claims 1 and 9 Ikuta and Yokoyama do not expressly disclose generating a prediction model to improve the prediction accuracy by learning from a data set comprising an electrophoresis result of a DNA sample with the standard base length and the environmental information during the electrophoresis. Tan discloses an adaptive expert system that receives sample data comprising at least one of raw data, optical data, and electropherogram data from a DNA analysis device, said data generated from a sample containing DNA and determines at least one characteristic of the sample data and utilize it to classify the sample data and apply a predefined parameter set to the said sample data to generate an output (abstract; claim 1). Tan further discloses that other classes of DNA IDs are possible based on a wide range of factors including sample types, processing approaches, instrumentation, reagents, and consumables [0094]. Tan discloses that the source of DNA can be from capillary electrophoresis instrument [0310] and that system determines fragment size (claim 37).
Tan further provides using a model to dynamically generate one or more synthetic allelic ladders, based on analysis of fragment sizing data sets obtained from previously conducted sample runs to measure fragment sizes; DNA IDs generated by ANDE have been categorized into 6 classification and criteria for each classification has been defined. The definition of the phenotypical DNA ID categories above allows an AES parameter set to be used for each. These parameters have been established based on the dataset generated and tested by measuring alleles called and dropout/dropin appearance. The final parameter set selected generates the greatest number of called alleles while minimizing dropins and dropouts [0424] [0578] [0579]. Tan further discloses that the DNA sample comprises SRT loci [0085] and evaluating prediction by referring to STR markers [0207-0208].
Regarding claim 18, Yokoyama discloses a correction process in FIG. 21 (a step 2104), where a reference fluorescence spectrum 2106 is corrected based on the comparison of the ideal fluorescence spectrum 2107 and the reference fluorescence spectrum 2106 (col. 21, para. 7); reading on limitations of wherein an output of the prediction model comprises a correction length for predicting the measured base length.
Regarding claim 19, Yokoyama discloses fluorescence intensity calculations (col. 11, para. 4; FIGs 4 and 6); reading on limitations of wherein the environmental information includes fluorescence intensity of each fluorescent dye calculated from multiplying an intensity ratio of each fluorescent dye.
Regarding claim 20, Tan discloses that the model is a prametric model [0484-0646]; reading on limitations of wherein the prediction model is a parametric model.
Regarding claim 21, Tan discloses that he separation and detection instrument comprise an excitation and detection subsystems for interrogating the DNA sample. Although DNA samples are described in the examples, the sample can include one or more biological molecules including but not limited to DNA, RNA, and proteins that are labeled with one or more fluorescent dyes [0373]; reading on limitations of wherein the prediction model is generated for each fluorescent dye based on mobility characteristics of DNA that differ depending on the fluorescent dye.
Regarding claim 23, Tan discloses that in building an AES, an initial step is to establish a dataset from which real-world DNA IDs from a wide range of sample types with known DNA ID (truth) can be generated and specific characteristics of the DNA IDs can be quantified. Furthermore, from these datasets, quantitative AES rules can be derived and back tested [0381]. Tan further discloses Allow the software to iterate outside of the initial limits provided. This adaptation allows the AES parameter values to be effectively unlimited in range, and outside of the initial limits that were initially set. This adaptation is particularly effective when additional data sets are generated for test. The limits that were initially set typically represent ranges that have been observed, however, with additional testing, sample data that exceed the limits may be encountered. The ability of the algorithm to iterate outside the initial limits will allow new data sets to be processed without having to readjust the initial input parameters [0574]; reading on limitations of wherein the mobility model management unit divides a training data set and a test data set again and relearns the prediction model when an evaluation index does not satisfy a predetermined pass level.
Regarding claim 24, Tan discloses performing analytics such re-amplifying DNA to confirm interpretation [0007]. Tan further discloses a computing device having a set of decision-making nodes, said nodes capable of self-learning when presented with a set of sample data with known outputs (claim 34) [0116]; reading on limitations of wherein the marker comprising the known base length is a positive control containing DNA having a known base length for confirming that a polymerase chain reaction (PCR) is correctly performed.
Regarding claim 25, Ikuta discloses that the environmental information is temperature (claims 1-3), applied voltage and flow velocity [0042-0046]. Additionally, Tan discloses taking into account factors including sample types, processing approaches, instrumentation, reagents, and consumables [0094]; reading on limitations of wherein the environmental information comprises at least one of temperature, current value, polymer characteristics, or buffer solution characteristics during the electrophoresis.
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Ikuta, Yokoyama and Tan, the examiner concludes that the combination of Ikuta, Yokoyama and Tan represents the use of known techniques to improve similar methods. Ikuta, Yokoyama and Tan are directed to methods and systems of analyzing electrophoresis data. Ikuta only disclosed the electrophoresis device and the data analysis device and identifying an unknown sample based on environmental parameters. In the same field of research, Yokoyama and Tan provided the base length analysis/size calling and the prediction model that trains on electrophoresis result of an allelic ladder to improve the mobility prediction accuracy. Combining the mobility analysis of Ikuta and Yokoyama with artificial allelic ladder of Tan would have allowed for more precise base length identification and subsequent identification of unknown samples. One ordinary skilled in the art before the effective filing data of the claimed invention would have had a reasonable expectation of success at combining these methods, that is combining the prediction model of Ikuta that take into account environmental information with artificial ladder of Tan to improve the accuracy of the model prediction. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claims 2-4 and 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Ikuta et al. (US 20070044541 A) in view of Yokoyama et al. (US9605300B2), in view of Tan et al. (US20190019290A1), as applied to claims 1, 9, 18-21, and 23-25 above, and further of Sugimoto et al. (US 20070256935 A1).
Regarding claim 2, Ikuta does not expressly disclose using plurality of prediction models. Sugimoto discloses a method for predicting the migration time (for example, electrophoresis result and numerically expressed descriptors that represent select features of an ionic compound (for example, environmental parameters) by electrophoresis measurement with respect to a substance having a known electrophoretic migration time (for example, DNA sample with a standard base length) and numerically expressed descriptors ([0010], (claim 1)). Sugimoto further discloses a prediction model that learns about the relationship between the resulting descriptors and the mobility of the substance (for example, electrophoresis result) having an unknown migration time in the electrophoresis by employing a neural network [0010] and improves accuracy [0049].
Sugimoto further discloses that the ANN ensemble method can be used for learning with improved accuracy, in which the same learning data are learnt by multiple neural networks (for example plurality of prediction models) [0049]; reading on limitations of the mobility model management unit storing a plurality of prediction models to be used for the prediction, and selects the prediction model according to an environmental condition based upon the environmental information when predicting the correspondence therebetween.
Regarding claim 3, Sugimoto discloses that of the ANN's found by ANN computation, those outputs of learning data with the highest to the 30th highest correlation coefficients between the measured value and the predicted value were averaged, and the resulting value was employed as the predicted relative migration time of the compound [0072]; reading on limitations of applying the prediction model in a predetermined priority order when predicting the correspondence therebetween.
Regarding claim 4, Sugimoto discloses that the processing of data can be performed using a personal computer, inherently disclosing a user interface for displaying networks ANN1 to ANNN [0049-0050]; reading on limitations of the data analysis device includes a user interface unit, and displays a list of the applicable prediction models on the user interface unit.
Regarding claim 6, Ikuta further discloses that controlling each of a migration voltage (current), a temperature of a separation chamber, and electroosmotic flow are very important factors in their analysis [0004] and determines an effective mobility of the standard substance at the standard temperature using an equation (claim 2) and migration voltage value in an equation [0042-0047]. Sugimoto disclose that with respect to a substance having a known electrophoretic migration time, its characteristic quantities (descriptors) which can be numerically expressed are computed to predict the relation between the characteristic quantities (descriptors) and the migration time. Then, the migration times of some substances are measured by electrophoresis or an electrophoresis mass spectrometer to learn about the relation to predict the mobility of the substance [0010-0013]; reading on limitations of the mobility model management unit selects the data set according to an environmental condition based upon the environmental information, and learns from the selected data set to generate the prediction model.
Regarding claim 7, Ikuta discloses determining an effective mobility of the unknown sample on the basis of the migration times of the standard substance and the unknown sample as measured in the measuring step, and a known effective mobility of the standard substance at a standard temperature (claim 6); reading on limitations of evaluating accuracy of the prediction by referring to a base length obtained by electrophoresis of an actual sample that always contains DNA whose standard base length is known, when predicting the correspondence therebetween.
Regarding claim 8, Sugimoto discloses that the ANN ensemble method can be used for learning with improved accuracy, in which the same learning data are learnt by multiple neural networks (for example plurality of prediction models) [0049]; reading on limitations of mobility model management unit changes the prediction model or newly learns a prediction model according to an evaluation result of the accuracy of the prediction.
Applying the KSR standard to Ikuta, Yokoyama, Tan, and Sugimoto, the examiner concludes that this combination represents the use of known techniques to improve similar methods. Ikuta, Yokoyama, Tan, and Sugimoto are directed to methods and systems of analyzing electrophoresis data. Ikuta Yokoyama, and Tan only disclosed the electrophoresis device and the data analysis device and identifying an unknown sample based on environmental parameters and using an artificial ladder to improve the mobility prediction accuracy. In the same field of research, Sugimoto provided the specifics of the prediction model that uses the result of the electrophoresis data and a set of descriptors to predict the mobility of a compound. Combining the mobility analysis of Ikuta, Yokoyama, and Tan with prediction model of Sugimoto would have allowed for more precise base length identification and subsequent identification of unknown samples. One ordinary skilled in the art before the effective filing data of the claimed invention would have had a reasonable expectation of success at combining these methods, that is combining the prediction model of Ikuta, Yokoyama, and Tan that take into account environmental information with artificial ladder with specifics of the prediction model (choosing from plurality of models based on the environmental information), as disclosed by Sugimoto, to improve the accuracy of the model prediction. This combination would have been expected to have provided a more precise electrophoresis data analysis and base length prediction. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Ikuta et al. (US 20070044541 A) in view of Yokoyama et al. (US9605300B2), in view Tan et al. (US20190019290A1), as applied to claims 1, 9, 18-21, and 23-25 above, and further in view of Holland et al. (GeneMarker® HID: A Reliable Software Tool for the Analysis of Forensic STR Data, 3 January 2011, American Academy of Forensic Sciences, pages 29-35).
Claim 17 depends on claim 1. Limitations of claim 1 have been taught in the above rejections.
Regarding claim 17, Yokoyama discloses that peaks in the fluorescence intensity waveforms correspond to the DNA fragment lengths (col. 3, para. 4) and that in the size calling process, relational expression of the electrophoresis time and the DNA fragment length can be derived from the combinations of the peak time and the known DNA fragment length (col. 14, L. 8-12).
Further regarding claim 17, Ikuta, Yokoyama, and Tan do not disclose that the output of the model is directly related to the base length.
Holland discloses a software tool for the analysis of forensic short tandem repeat (STR) data (abstract). Holland further discloses that GeneMarker HID allows for sizing of data (for determining the size of DNA fragments in bps) using either the Local Southern or Cubic Spline methods (mathematical models). A trace comparison method is used to match the representative size standard peak with peaks in the ILS for each sample. A lane score is generated (a quality metric for reflecting how well the matching process is performed) based on how successful the software was in identifying all expected peaks, and if necessary, manual changes can be made to the position of individual sizing fragments in the ILS (for example, corrections) (pg. 31, para. 1); reading on limitations of wherein an output of the prediction model comprises a direct value of the base correction length.
Applying the KSR standard to Ikuta, Yokoyama, Tan and Holland, the examiner concludes that the combination of Ikuta, Yokoyama and Tan represents the use of known techniques to improve similar methods. Ikuta, Yokoyama, Tan and Holland are directed to methods and systems of analyzing electrophoresis data. Ikuta and Tan only disclosed the electrophoresis device and the data analysis device and identifying an unknown sample based on environmental parameters. In the same field of research, Yokoyama and Tan provided the base length analysis/size calling and the prediction model that trains on electrophoresis result of an allelic ladder to improve the mobility prediction accuracy. Holand discloses the specific output of size calling in the context of a model. Combining the mobility analysis of Ikuta, Yokoyama and Tan with output details of Holland would have allowed for more precise base length identification and subsequent identification of unknown samples. One ordinary skilled in the art before the effective filing data of the claimed invention would have had a reasonable expectation of success at combining these methods. This combination would have been expected to have provided a more precise electrophoresis data analysis and base length prediction. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Ikuta et al. (US 20070044541 A) in view of Yokoyama et al. (US9605300B2), in view Tan et al. (US20190019290A1), as applied to claims 1, 9, 18-21, and 23-25 above, and further in view of Raschka (Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning, University of Wisconsin–Madison Department of Statistics November 2018, pages 1-49).
Claim 22 depends on claim 1. Limitations of claim 1 has bee taught in the above rejections.
Regarding claim 22, Ikuta, Yokoyama, and Tan do not expressly disclose evaluating the performance of the model using a mean square error. Raschka discloses the techniques that can be used for correct model evaluation, model selection, and algorithm selection in machine learning (abstract). Raschka further discloses mean squared error as one of the standard techniques for prediction accuracy and error measurements (pg. 12, subsection 2.2).
It would have been obvious to one ordinary skilled in the art before the effective filing data of invention to use the known standard statistical technique of using mean square error for the purpose of measuring model accuracy to measure how closely a model’s predictions align with actual values. One ordinary skilled in the art could have applied the known technique of using mean squared error and the results would have been predictable to one ordinary skilled in the art.
Response to Arguments
Applicant's arguments filed 02/13/2026 have been fully considered but they are not persuasive. Applicant’s amendments to claims necessitated a new round of art rejections. As such, the combination of Ikuta, Yokoyama, Tan, Holland, and Raschka teach all the recited limitations.
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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/G.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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