DETAILED ACTION
This office action is in response to communication filed on May 15, 2026.
Response to Amendment
Amendments filed on May 15, 2026 have been entered.
The specification has been amended.
Claims 1-2 and 8 have been amended.
Claim 6 remains canceled.
Claims 9-10 have been added
Claims 1-5 and 7-10 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 6), filed on 05/15/2026, with respect to the objections to the specification have been fully considered. In view of the amendments to the specification addressing the informalities raised in the previous office action, the objections to the specification have been withdrawn.
Applicant’s arguments, see Remarks (p. 6), filed on 05/15/2026, 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to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn.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. 7-8), filed on 05/15/2026, 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. 7) that Claim 1 has been amended to improve clarity regarding the recited “determination result,” “weight value,” and “certainty factor.” In particular, the amendments more clearly identify that the trained model outputs an output label for the peak partial waveform, a label weight value for that output label, and weight values for the plurality of label candidates, and that the certainty factor is calculated from those values. In addition, consistent with the Examiner’s comments during the interview, the last element of claim 1 has been amended to more positively recite outputting the peak partial waveform and the certainty factor to the display device, such that the display device displays the peak partial waveform and the certainty factor. These claim amendments clarify: (1) the manner in which the certainty factor is derived, (2) how the certainty factor relates to the model output, and (3) that the peak partial waveform and certainty factor are presented to the user on the display device.
These arguments are not persuasive.
The examiner submits that the current argued amendments refer to the judicial exception 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).
Additionally, the examiner submits that according to the current Office’s guidance: “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. For example, the mathematical formula in Flook, the laws of nature in Mayo, and the isolated DNA in Myriad were all novel or newly discovered, but nonetheless were considered by the Supreme Court to be judicial exceptions because they were “‘basic tools of scientific and technological work’ that lie beyond the domain of patent protection.” Myriad, 569 U.S. 576, 589, 106 USPQ2d at 1976, 1978 (noting that Myriad discovered the BRCA1 and BRCA1 genes and quoting Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 (“the novelty of the mathematical algorithm is not a determining factor at all”); Mayo, 566 U.S. 73-74, 78, 101 USPQ2d 1966, 1968 (noting that the claims embody the researcher’s discoveries of laws of nature). The Supreme Court’s cited rationale for considering even “just discovered” judicial exceptions as exceptions stems from the concern that “without this exception, there would be considerable danger that the grant of patents would ‘tie up’ the use of such tools and thereby ‘inhibit future innovation premised upon them.’” Myriad, 569 U.S. at 589, 106 USPQ2d at 1978-79 (quoting Mayo, 566 U.S. at 86, 101 USPQ2d at 1971). See also Myriad, 569 U.S. at 591, 106 USPQ2d at 1979 (“Groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry.”). The Federal Circuit has also applied this principle, for example, when holding a concept of using advertising as an exchange or currency to be an abstract idea, despite the patentee’s arguments that the concept was “new”. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) (“a new abstract idea is still an abstract idea”) (emphasis in original)” (see MPEP 2106.04).
Regarding applicant’s arguments with respect to the Examiner’s comments during the interview, the examiner submits that, while no agreement was reached, multiple suggestions were presented (see interview summary 05/11/2026), however, the amended independent claims presented in the current response do not positively reflect the suggestions presented during the interview.
In general, the examiner continues to submit 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 display device, 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 is not patent eligible subject matter under the current Office guidance.
Applicant also argues (p. 8) that Thus, rather than merely presenting a label or determination result, the claimed invention provides the user, via the display device, with the peak partial waveform together with information reflecting how strongly the trained model supports the output label relative to other label candidates. By calculating the certainty factor in this manner and causing the display device to display the peak partial waveform together with the certainty factor, the claimed invention enables the user to understand the degree of reliability or accuracy associated with the output for the peak partial waveform. This is particularly useful in waveform analysis, where the user must decide whether to rely on the machine learning result for subsequent processing, review, or correction … The amended claims now more positively outputting the peak partial waveform and the certainty factor to the display device. As discussed during the interview, this further emphasizes that the claims are directed to a concrete application of the trained model output in the operation of the analysis device. The claimed invention therefore provides a technical improvement in the operation of the analysis device by generating and presenting additional machine-derived information tied to the trained model output, thereby improving the practical usability of the model in analyzing chromatograms or spectra. Accordingly, the claims integrate any alleged judicial exception into a practical application and satisfy Step 2A, Prong 2 of the USPTO eligibility guidance.
These arguments are not persuasive.
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)).
Applicant’s arguments, see Remarks (p. 9), filed on 05/15/2026, with respect to new claims 9-10 have been fully considered. The examiner submits that, after evaluating claims 9-10 for compliance under 35 U.S.C. 101, these claims were found eligible (see Examiner’s Note section below).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/15/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “and obtains from the trained model an output label …” should read “[[and]] obtains from the trained model an output label …” in order to correct for minor informalities.
Claim language “and calculates a certainty factor of the output label from the label weight value and the plurality of weight values,” should read “[[and]] calculates a certainty factor of the output label from the label weight value and [[the]]a plurality of weight values, and” in order to provide appropriate antecedence basis.
Claim language “output the peak partial waveform and certainty factor to the display device such that the display device displays the peak partial waveform and the certainty factor” should read “outputs the peak partial waveform and the certainty factor to the display device such that the display device displays the peak partial waveform and the certainty factor” in order to correct for minor informalities and provide appropriate antecedence basis.
Appropriate correction is required.
Claim 3 is objected to because of the following informalities:
Claim language should read “The analysis device according to claim 1, wherein the processor labels the peak partial waveform to calculate the certainty factor” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 7 is objected to because of the following informalities:
Claim language should read “The analysis device according to claim 1, wherein the processor receives an operation for correcting the peak partial waveform when peak partial waveform and the certainty factor are displayed on the display device” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 8 is objected to because of the following informalities:
Claim language “and calculating a certainty factor of the output label from the label weight value and the plurality of weight values” should read “[[and]] calculating a certainty factor of the output label from the label weight value and [[the]]a plurality of weight values” in order to provide appropriate antecedence basis.
Claim language “outputting the peak partial waveform and certainty factor to the display device” should read “outputting the peak partial waveform and the certainty factor to the display device” in order to correct for minor informalities and provide appropriate antecedence basis.
Claim language “displaying with the display device the outputted peak partial waveform and the certainty factor” should read “displaying with the display device the
Appropriate correction is required.
Claim 9 is objected to because of the following informalities:
Claim language “output the generated target waveform to the analysis device” should read “output the .
Appropriate correction is required.
Claim 10 is objected to because of the following informalities:
Claim language “outputting the generated target waveform from the chromatograph” should read “outputting the .
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, 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, wherein each of the plurality of reference partial waveforms is assigned one label among a plurality of label candidates” 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 and assign labels; 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 and assign labels to the divided data for training a model for storing purposes.
the limitation “wherein the processor: divides the target waveform subject to waveform processing into a plurality of target 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 partial waveform that is a partial waveform including the peak portion among the plurality of target 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/steps (i.e., processor, trained model), 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 from the trained model an output label for the peak partial waveform, a label weight value output by the trained model for the output label, and a weight value for each of the plurality of label candidates” 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., output label, a label weight value and a weight value for each of the plurality of label candidates; see Fig. 5, item S14; 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), the particular technological environment or field of use, and the generic computer elements/steps (i.e., processor, trained model), 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: calculates a certainty factor of the output label from the label weight value and the plurality of weight values” 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 manipulate data and obtain a result (i.e., certainty factor; see Fig. 5, item 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), 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 transform data.
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; a display device; 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, wherein each of the plurality of reference partial waveforms is assigned one label among a plurality of label candidates” 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 specification at p. 4, lines 14-15; see also MPEP 2106.05(f));
“wherein the processor: inputs the plurality of target 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
“wherein the processor: output the peak partial waveform and certainty factor to the display device such that the display device displays the peak partial waveform and the certainty factor” 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, display device) 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).
Examiner’s Note
Claims 9-10 were evaluated for patent eligibility under 35 U.S.C. 101 using the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) to determine patent eligibility under 35 U.S.C. 101.
Regarding claim 9, the examiner submits that under Step 1 of the test for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a machine, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test, the examiner submits that claim 9 recites the judicial exception as indicated above with respect to claim 1.
Furthermore, under Step 2A - Prong Two of the test, the claim recites the additional elements as indicated above with respect to claim 1 and:
“An analysis system comprising:
the analysis device of claim 1; and
a chromatograph configured to:
generate the chromatogram or the spectrum as the target waveform by measuring a sample; and
output the generated target waveform to the analysis device”, which when considering the claim as a whole, integrate the judicial exception into a practical application by applying the judicial exception with, or by use of, a particular machine (i.e., chromatograph; see MPEP 2106.05(b)).
Therefore, these additional elements, when considered individually and in combination, integrate the judicial exception into a practical application. The claim, when considered as a whole, is eligible at Prong Two of the Revised Step 2A (see 2019 Revised Patent Subject Matter Eligibility Guidance – Revised Step 2A, see also MPEP 2106.04(d)).
Similarly, claim 10 is directed to patent eligible subject matter as explained above with regards to claim 9.
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 1 – “liquid chromatograph system”) that analyzes a target waveform that is a chromatogram or a spectrum ([0001], [0049]-[0053]: a liquid chromatograph system is configured to analyze a chromatogram or spectrum waveform), the analysis device comprising:
a processor (Fig. 1, item 111 – “peak detection processor”; [0050]-[0052]: the liquid chromatograph system comprises a data-analyzing unit, which includes a peak detection processor (see also [0054] regarding the data-analyzing unit being a computer, which implies the use of a processor));
a display device (Fig. 1, item 13 – “display unit”; [0050]: the liquid chromatograph system comprises a display unit); 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 subject to waveform processing (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 partial waveform that is a partial waveform including the peak portion among the plurality of target 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 from the trained model an output label for the peak partial waveform (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))
calculates a certainty factor of the output label (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
output the peak partial waveform and certainty factor to the display device such that the display device displays the peak partial waveform and the certainty factor (Fig. 3, items S16 or S18; [0079]-[0080], [0090]: peak detection processor components determine peak detection result, which is displayed by display unit, 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;
wherein each of the plurality of reference partial waveforms is assigned one label among a plurality of label candidates;
divides the target waveform subject to waveform processing into a plurality of target partial waveforms in a time-axis direction;
inputs the plurality of target partial waveforms to the trained model,
obtains a label wight value output by the trained model for the output label and a weight value for each of the plurality of label candidates, and
calculates a certainty factor of the output label from the label weight value and the plurality of weight values”
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 including 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 subject to waveform processing (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 partial waveform that is a partial waveform including the peak portion among the plurality of target 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));
obtaining from the trained model an output label for the peak partial waveform (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
calculating a certainty factor of the output label (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));
outputting the peak partial waveform and certainty factor to the display device (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 with the display device the outputted peak partial waveform and the certainty factor (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 including a plurality of reference partial waveforms produced by dividing a reference waveform in which a position of the peak portion is known;
wherein each of the plurality of reference partial waveforms is assigned one label among a plurality of label candidates;
dividing the target waveform subject to waveform processing into a plurality of target partial waveforms in a time-axis direction;
inputting the plurality of target partial waveforms to the trained model,
obtaining a label weight value output by the trained model for the output label, and a weight value for each of the plurality of label candidates, and
calculating a certainty factor of the output label from the label weight value and the plurality of weight values”
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.
Allowable Subject Matter
Claims 9-10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DING; Dajun et al., US 20240046604 A1, IMAGE PROCESSING METHOD AND APPARATUS, AND ELECTRONIC DEVICE
Reference discloses processing images by obtaining plurality of frames of original images and mapping texture information between small FOV images and large FOV images to improve definition of large FOV images.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINA CORDERO whose telephone number is (571)272-9969. The examiner can normally be reached 9:30 am - 6:00 pm.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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