DETAILED ACTION
This office action is in response to communication filed on February 4, 2026.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on February 4, 2026 has been entered.
Response to Amendment
Amendments filed on January 24, 2026 have been entered.
Claims 1 and 8 have been amended.
Claim 6 remains canceled.
Claims 1-5 and 7-8 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 5), filed on 01/24/2026, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn. However, upon further consideration, new objections to the claims are presented in order to address additional informalities.
Applicant’s arguments, see Remarks (p. 7), filed on 01/24/2026, with respect to the rejection of claims 1-5 and 7-8 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, have been fully considered. In view of the amendments to the claims addressing the issues raised in the previous office action, the rejection has been withdrawn.
Applicant’s arguments, see Remarks (p. 5-7), filed on 01/24/2026, with respect to the rejections of claims 1-5 and 7-8 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argues (p. 5) that Amended claim 1 integrates any alleged judicial exception into a practical application within analytical instrumentation by reciting a specific, machine-implemented visualization and output workflow that improves how chromatogram/spectrum analysis results are presented and used.
These arguments are not persuasive.
The examiner submits that the claimed invention, when considered as a whole, recites data analysis using mental processes and/or mathematical concepts (see rejection below), while appending extra-solution activities (e.g., source/type of data being manipulated, data outputting), generic computer components (e.g., a processor, a memory, see specification at p. 4, lines 14-16) and computer implementation (e.g., trained model produced by machine learning, see specification at p. 6, lines 28-29; p. 7, lines 2-3) used to facilitate the application of the judicial exception, and a field of use (e.g., waveform analysis, see specification at p. 1, lines 5-6), which as explained in the MPEP:
“A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (see MPEP 2106.04(a)(2), section “I. Mathematical Concepts”; see also specification at p. 10, lines 4-7); and
“In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: 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, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)” (see MPEP 2106.04(a)(2), section “III. Mental Processes”; see also specification at p. 10, lines 15-24).
Moreover, the examiner submits that displaying the results of the data analysis does not integrate the judicial exception into a practical application, particularly when displaying the results of the analysis of chromatograms or spectrum are conventional steps in the related field, as evidenced by applicant’s own disclosure (see p. 1, lines 8-12, 23-24; p. 2, lines 6-8) as described in the MPEP: “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional … When determining whether an additional element is insignificant extra-solution activity, examiners may consider the following: … (3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output)” (see MPEP 2106.05(g)).
Furthermore, regarding the argument about claim 1 reciting a specific, machine-implemented visualization and output workflow that improves how chromatogram/spectrum analysis results are presented and used, the examiner submits that the claimed invention recites a series of mental/mathematical steps used to analyze particular data and obtain a result for displaying purposes, and as explained in the October 2019 Update: Subject Matter Eligibility: “Note, a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two” (p. 11) and “However, it is important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology” (p. 13).
Applicant also argues (p. 5-6) that Claim 1 is directed to an analysis device that processes physical measurement data (a chromatogram or spectrum generated by analytical instruments). The processor executes a defined sequence including: … The actions performed by the claimed invention are not generic data analysis. Rather, these recited actions are technically constrained by the structure of waveform data, time-axis segmentation, trained-model inference, and certainty-factor computation tied to model outputs. This processing occurs within a defined analytical workflow that begins with instrument-generated signals and ends with device-driven visualization.
These arguments are not persuasive.
Regarding the argument about claim 1 being directed to an analysis device that processes physical measurement data, the examiner submits that the analysis device refers to a generic computer (see specification at p. 4, lines 14-16), and as explained in the MPEP: “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 or provide significantly more” (see MPEP 2106.05(f)).
Also, the examiner submits that selecting a particular data source or type of data (e.g., a chromatogram or spectrum) to be manipulated are activities that the courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)) and “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) (“The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point”). See also Genetic Technologies Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1547 (Fed. Cir. 2016) (steps of DNA amplification and analysis are not “sufficient” to render claim 1 patent eligible merely because they are physical steps)”. (see MPEP 2106.04(d)).
Regarding the arguments about the actions performed by the claimed invention not being generic data analysis but rather a defined sequence, the examiner submits that as described in the MPEP: “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions” (see MPEP 2106.04) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence: “Even if the judicial exception is narrow (e.g., a particular mathematical formula or detailed mental process), the Court has held that a claim may not preempt that judicial exception” (see “III. Update on Certain Areas of the USPTO’s Patent Subject Matter Eligibility Guidance Applicable to AI Inventions”, section “A. Evaluation of Whether a Claim Is Directed to a Judicial Exception (Step 2A)”).
Applicant further argues (p. 6) that As recited, the device does not merely “display results.” Rather, the claimed invention generates a display signal that causes the display device to present the partial waveform of the original chromatogram/spectrum and the determination-result waveform derived from certainty factors together. This enables a user to visually correlate the measured signal with the model-derived certainty of peak determination. This is a practical application under MPEP §2106.04(d) because the claimed output and display features meaningfully limit the use of any mathematical or mental concept to a particular technological implementation that improves the operation of the analysis device and the analytical workflow itself. The improvement is not aesthetic or abstract; it directly enhances how peak-picking results are understood and acted upon in chromatographic or spectral analysis.
These arguments are not persuasive.
First, the examiner submits that, according to the original disclosure, a certainty factor and a determination result are presented (see Fig. 8 in which, see original specification at p. 12, lines 10-11) with partial waveforms having widths smaller than a peak width (see specification at p. 5, lines 15-18). Therefore, in light of the specification, the examiner interprets that a certainty factor and a determination result are displayed (not a partial waveform, see claim 8).
Regarding the argument about the claimed output and display features meaningfully limiting the use of any mathematical or mental concept to a particular technological implementation that improves the operation of the analysis device, the examiner submits that there is no improvement of the operation of the analysis device because, as explained above, the analysis device refers to a generic computer which is employed to facilitate the application of the judicial exception, which according to the MPEP: “As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible” (see MPEP 2106.05(f)).
Regarding the argument about the claimed output and display features meaningfully limiting the use of any mathematical or mental concept to a particular technological implementation that improves the analytical workflow itself, the examiner submits that generating a display signal for displaying results refers to mere computer implementation (e.g., generating signals) for extra-solution activities (e.g., mere data outputting).
Furthermore, the examiner submits that as explained in the MPEP: “The courts have also identified limitations that did not integrate a judicial exception into a practical application:
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h)” (see MPEP 2106.04(d)).
Additionally, applicant argues (p. 6-7) that In the Office Action, the Examiner characterizes the output and display elements as merely extra-solution activity (OA at pp. 13-14). However, this characterization overlooks the functional relationship between the calculated certainty factor and the generated determination-result waveform that is displayed alongside the partial waveform. The claim requires the device to transform internal model outputs into a new waveform representation and to drive a display device with a signal that presents that representation in conjunction with the measured waveform.
These arguments are not persuasive.
The examiner submits that, under the broadest reasonable interpretation in light of the specification, calculating a certainty factor and the determination result waveform are part of the judicial exception recited in the claimed invention (see rejection below). Furthermore, the examiner submits that transforming data using a computer device for displaying purposes does not provide a practical application under Step 2A – Prong Two or provide significantly more under Step 2B of the test as explained in the analysis.
Regarding the argument about the claim requiring the device to transform internal model outputs into a new waveform representation, the examiner submits that as indicated in the MPEP: “For data, mere “manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’” has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994))” (see MPEP 2106.05(c)).
Moreover, applicant argues (p. 7) that Under MPEP §2106.04(d), integrating an exception into a practical application includes applying it “in a meaningful way beyond generally linking the use of the exception to a particular technological environment.” Here, the claimed visualization is part of the solution to a technical problem in analytical chemistry instrumentation (i.e., enabling reliable interpretation of automated peak picking by conveying certainty information at each point of the chromatogram/spectrum). The display is thus integral to the claimed technical solution.
This argument is not persuasive.
The examiner submits that displaying the results of waveform analysis amounts to necessary data outputting in the corresponding field (see MPEP 2106.05(g)).
Moreover, the examiner submits that as explained in the October 2019 Update: Subject Matter Eligibility: “Note, a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two” (p. 11) and “However, it is important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology” (p. 13).
Specification
The disclosure is objected to because of the following informalities (see original specification):
Page 1, lines 19-20: Language “In recent years, an attempt to automate the peak picking using deep learning have been made” should read “In recent years, an attempt to automate the peak picking using deep learning has been made” in order to correct for minor informalities.
Page 14, lines 3-4: Language “The weight of the peak start point output from the trained model and the weight of the peak start point output from the trained model were added” should read “The weight of the peak start point output from the trained model and the weight of the peak end point output from the trained model were added” in order to clarify the description details.
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “obtains, as outputs, a determination result for each of the plurality of partial waveforms and a weight value of the determination result, and calculates a certainty factor of the determination result from the weight value , and” should read “obtains, as outputs, a determination result for each of the plurality of partial waveforms and a weight value of the determination result, and calculates a certainty factor of the determination result from the weight value[[ ]], and” in order to correct for minor informalities (i.e., remove extra space).
Claim language “an output port that outputs a display signal for displaying the partial waveform and the determination result waveform” should read “an output port that outputs a display signal for displaying the certainty factor and the determination result waveform” in order to clarify the recited subject matter (e.g., see Fig. 8 in which a certainty factor and a determination result are presented, see original specification at p. 12, lines 10-11).
Claim language “a display device that displays the partial waveform and the determination result waveform” should read “a display device that displays the certainty factor and the determination result waveform” in order to clarify the recited subject matter (e.g., see Fig. 8 in which a certainty factor and a determination result are presented, see original specification at p. 12, lines 10-11).
Appropriate correction is required.
Claim 8 is objected to because of the following informalities:
Claim language “outputting a display signal for displaying partial waveform and the determination result waveform” should read “outputting a display signal for displaying the certainty factor and the determination result waveform” in order to clarify the recited subject matter (e.g., see Fig. 8 in which a certainty factor and a determination result are presented, see original specification at p. 12, lines 10-11).
Claim language “displaying the determination result and the certainty factor based on the display signal” should read “displaying the determination result waveform and the certainty factor based on the display signal” in order to provide appropriate antecedence basis.
Appropriate correction is required.
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 1-5 and 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a machine/manufacture, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test (see italic text):
the limitation “a memory that stores a trained model produced by machine learning using a plurality of sets including a plurality of reference partial waveforms produced by dividing a reference waveform in which a position of a peak portion is known” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts (e.g., divide data; see Fig. 3 and specification at p. 5-7). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated, data storing), the particular technological environment or field of use, and the generic computer elements/steps (e.g., a memory for storing purposes, a trained model produced by machine learning), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to divide data for training a model for storing purposes.
the limitation “wherein the processor: divides the target waveform into a plurality of partial waveforms in a time-axis direction” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts (e.g., segment data; see Fig. 5, item S12 and specification at p. 9). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the particular technological environment or field of use, and the generic computer elements (i.e., processor), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to partition data.
the limitation “wherein the processor: determines a peak waveform that becomes the peak portion among the plurality of partial waveforms using the trained model” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to compare data and obtain a result (e.g., determine whether data portions corresponds to certain data characteristics using the trained model; see Fig. 5, item S14 and specification at p. 9). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the particular technological environment or field of use, and the generic computer elements (i.e., processor), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to compare data and obtain a result.
the limitation “wherein the processor: obtains, as outputs, a determination result for each of the plurality of partial waveforms and a weight value of the determination result, and calculates a certainty factor of the determination result from the weight value” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to obtain results (i.e., a determination result for each of the plurality of partial waveforms and a weight value of the determination result, a certainty factor; see Fig. 5, items S14, S17; Figs. 6-8, 11 and specification at p. 9-12 and 14). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated, inputting data to a model), the particular technological environment or field of use, and the generic computer elements (i.e., processor), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to obtain various results.
the limitation “wherein the processor: obtains a determination result waveform from the certainty factor” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to obtain results (i.e., a determination result waveform based on the certainty factor; see Fig. 5, item S18; Figs. 6-8 and specification at p. 10-12). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated, inputting data to a model), the particular technological environment or field of use, and the generic computer elements (i.e., processor), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to obtain various results.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application. In particular, the additional elements recited in the claim (see non-italic text):
“An analysis device that analyzes a target waveform that is a chromatogram or a spectrum, the analysis device comprising: a processor; and a memory that stores a trained model produced by machine learning using a plurality of sets including a plurality of reference partial waveforms produced by dividing a reference waveform in which a position of a peak portion is known” generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), add extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) using elements recited at a high level of generality (i.e., an analysis device) (see MPEP 2106.05(g)), and/or add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f));
“wherein the processor: inputs the plurality of partial waveforms to the trained model” add extra-solution activities (e.g., inputting data to a model) (see MPEP 2106.05(g)) and/or add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); and
“the analysis device further comprises: an output port that outputs a display signal for displaying the partial waveform and the determination result waveform; and a display device that displays the partial waveform and the determination result waveform” add extra-solution activities (e.g., output data) (see MPEP 2106.05(g)) and/or add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Accordingly, these additional elements, when considered individually and in combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, the claim does not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
generally link the use of the judicial exception to a particular technological environment or field of use (e.g., analysis of chromatogram or spectrum), which as indicated in the MPEP: “As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (see MPEP 2106.05(h));
recite extra-solution activities (i.e., mere data gathering by selecting a particular data source/type to be manipulated, inputting data to a model, outputting data) using elements (i.e., an analysis device) specified at a high level of generality, which as indicated in the MPEP: “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more” (see MPEP 2106.05(b), section III); and
append generic computer components (i.e., a processor, memory) used to facilitate the application of the abstract idea (i.e., mere computer implementation), which as indicated in the MPEP: “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 MPEP 2106.05(f), item 2).
The claim, when considered as a whole, does not provide significantly more under Step 2B of the test.
Based on the analysis, the claim is not patent eligible.
Similarly, independent claim 8 is directed to a judicial exception (abstract idea) without significantly more as explained above with regards to claim 1.
With regards to the dependent claims they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional limitations (Claims 2-5 and 7), that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, refer to extra-solution activities (e.g., mere data outputting) and/or generic computer components (i.e., a processor, memory) used to facilitate the application of the abstract idea (Claim 7), which as indicated in the Office’s guidance does not integrate the judicial exception into a practical application (Step 2A – Prong Two) and/or does not provide significantly more (Step 2B).
Subject Matter Not Rejected Over Prior Art
Claims 1-5 and 7-8 are distinguished over the prior art of record for the following reasons:
Regarding claim 1.
Osoekawa (US 20200292509 A1) discloses/teaches:
An analysis device (Fig. 1, item 11 – “data-analyzing unit”) that analyzes a target waveform that is a chromatogram or a spectrum ([0001], [0049]-[0053]: a waveform analyzer configured to analyze a chromatogram or spectrum waveform is presented), the analysis device comprising:
a processor (Fig. 1, item 111 – “peak detection processor”; [0051]-[0052]: the data-analyzing unit includes a peak detection processor (see also [0054] regarding the data-analyzing unit being a computer, which implies the use of a processor)); and
a memory (Fig. 1, item 114 – “trained model storage”; [0051]: the data-analyzing unit includes a trained model storage (see also [0054] regarding the data-analyzing unit being a computer, which implies the use of memory)) that stores a trained model ([0053]: a trained model is created and stored in a storage unit) produced by machine learning using a plurality of sets including a plurality of reference partial waveforms produced by a reference waveform in which a position of a peak portion is known ([0058]-[0062], [0065]-[0071]: the model-creating unit uses machine learning to train the model using chromatogram waveform data combined with exact peak information, wherein the chromatogram waveform data is segmented as a black and white image (see Figs. 4-5) and used for training the model (see also [0020])),
wherein the processor:
divides the target waveform (Fig. 3, items S11-S14; [0074]-[0075]: chromatogram waveform data of a target sample is acquired and segmented as a black and white image),
determines a peak waveform that becomes the peak portion among the plurality of partial waveforms using the trained model (Fig. 3, item S15; [0076]: trained model is employed to detect five-dimensional information for each segment of the chromatogram waveform acquired for the target sample using the black and white image, the information including starting and ending points of a peak in the image (see Figs. 6-7)), and
obtains, as outputs, a determination result for each of the plurality of partial waveforms, and calculates a certainty factor of the determination result (Fig. 3, item S15; [0076]-[0077]: trained model is employed to detect five-dimensional information including confidence information for each segment of the chromatogram waveform acquired for the target sample using the black and white image (see Figs. 6-7)), and
obtains a determination result waveform from the certainty factor ([0079]: confidence information (highest confidence) is used to determine starting point and ending point of peak);
the analysis device further comprises:
an output port that outputs a display signal for displaying the partial waveform and the determination result waveform (Fig. 3, items S16 or S18; [0079]-[0080], [0090]: peak detection processor components determine peak detection result, which is displayed by display unit, which implies the use of a display signal and an output port from the data-analyzing unit to the display unit), and
a display device (Fig. 1, item 13 – “display unit”) that displays the partial waveform and the determination result waveform (Fig. 3, items S16 or S18; [0079]-[0080], [0090]: peak detection processor components determine peak detection result, which is displayed by display unit (see Figs. 8-9)).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“a trained model produced by machine learning using a plurality of sets including a plurality of reference partial waveforms produced by dividing a reference waveform in which a position of a peak portion is known;
divides the target waveform into a plurality of partial waveforms in a time-axis direction;
inputs the plurality of partial waveforms to the trained model,
obtains, as outputs, a determination result for each of the plurality of partial waveforms and a weight value of the determination result, and calculates a certainty factor of the determination result from the weight value”
in combination with all other limitations within the claim, as claimed and defined by the applicant.
Regarding claim 8.
Osoekawa (US 20200292509 A1) discloses/teaches:
An analysis method for analyzing a target waveform that is a chromatogram or a spectrum ([0001], [0049]-[0053], [0074]: a waveform analyzer configured to analyze a chromatogram or spectrum waveform is presented), the analysis method comprising:
producing a trained model that specifies a peak portion included in an input waveform by machine learning using a plurality of sets of a plurality of reference partial waveforms produced by a reference waveform in which a position of the peak portion is known ([0058]-[0062], [0065]-[0071]: a model-creating unit uses machine learning to train a model using chromatogram waveform data combined with exact peak information, wherein the chromatogram waveform data is segmented as a black and white image (see Figs. 4-5) and used for training the model (see also [0020]));
dividing the target waveform (Fig. 3, items S11-S14; [0074]-[0075]: chromatogram waveform data of a target sample is acquired and segmented as a black and white image);
determining a peak waveform that becomes the peak portion among the plurality of partial waveforms using the trained model (Fig. 3, item S15; [0076]: trained model is employed to detect five-dimensional information for each segment of the chromatogram waveform acquired for the target sample using the black and white image, the information including starting and ending points of a peak in the image (see Figs. 6-7)); and
obtaining, as outputs, a determination result for each of the plurality of partial waveforms, and calculating a certainty factor of the determination result (Fig. 3, item S15; [0076]-[0077]: trained model is employed to detect five-dimensional information including confidence information for each segment of the chromatogram waveform acquired for the target sample using the black and white image (see Figs. 6-7));
obtaining a determination result waveform from the certainty factor ([0079]: confidence information (highest confidence) is used to determine starting point and ending point of peak);
outputting a display signal for displaying partial waveform and the determination result waveform (Fig. 3, items S16 or S18; [0079]-[0080], [0090]: peak detection processor components determine peak detection result, which is displayed by display unit, which implies the use of a display signal and an output port from the data-analyzing unit to the display unit), and
displaying the determination result and the certainty factor based on the display signal (Fig. 3, items S16 or S18; [0079]-[0080], [0090]: peak detection processor components determine peak detection result, which is displayed by display unit (see Figs. 8-9)).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“producing a trained model that specifies a peak portion included in an input waveform by machine learning using a plurality of sets of a plurality of reference partial waveforms produced by dividing a reference waveform in which a position of the peak portion is known;
dividing the target waveform into a plurality of partial waveforms in a time-axis direction;
inputting the plurality of partial waveforms to the trained model,
obtaining, as outputs, a determination result for each of the plurality of partial waveforms and a weight value of the determination result, and calculating a certainty factor of the determination result from the weight value”
in combination with all other limitations within the claim, as claimed and defined by the applicant.
Regarding claim 2-5 and 7.
They are also distinguished over the prior art of record due to their dependency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bernard; Marc et al., US 5274548 A, Method for the automatic analysis of signals by means of segmentation and classification
Reference discloses a method for analysis of signals by segmentation and classification.
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/LINA CORDERO/Primary Examiner, Art Unit 2857