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 .
DETAILED ACTION
Claim Interpretation - 35 USC § 112
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim limitations “means for” has/have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder “means for” coupled with functional language “storing, acquiring, etc.” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, Claims 1, 5, 6, and 7 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
According to MPEP 2181, II, B, “In cases involving a special purpose computer-implemented means-plus-function limitation, the Federal Circuit has consistently required that the structure be more than simply a general purpose computer or microprocessor and that the specification must disclose an algorithm for performing the claimed function. See, e.g., Noah Systems Inc. v. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239.
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… the specification must sufficiently disclose an algorithm to transform a general purpose microprocessor to a special purpose computer so that a person of ordinary skill in the art can implement the disclosed algorithm to achieve the claimed function. Aristocrat, 521 F.3d at 1338, 86 USPQ2d at 1242.”
A review of the specification shows that the following appears to be the corresponding algorithm for performing the claimed functions (enlarge or reduce a target signal waveform, using a learned model, determine a magnitude of a variation of signal intensity, performing processing of enlarging or reducing a signal waveform) as described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: Figs.1 and 2; algorithms in [0031, 0035, 0071], as published.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 1-3 and 5-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“An analysis device comprising: a waveform deformation unit configured to enlarge or reduce a target signal waveform obtained by analysis and indicating a change in signal intensity depending on a change in a value of a predetermined parameter by a scale factor of N (where N is a positive value other than 0 and 1) in the direction of the signal intensity axis and/or enlarge or reduce by a scale factor of M (where M may be a positive value other than 0 and 1 and may be a same value as N) in the direction of the predetermined parameter axis; a peak detection unit configured to use a learned model generated in advance by machine learning using, as teaching data, a signal waveform and a start point and an end point of a correct solution, and use, as an input, a signal waveform after deformation by the waveform deformation unit, and output, as a detection result, the start point and the end point of the peak; and a waveform inverse deformation unit configured to reduce or enlarge information on a start point and an end point of a peak, the information being output by the peak detection unit, by a scale factor of 1/N in the direction of the signal intensity axis and/or by a scale factor of 1/M in the direction of the predetermined parameter axis inverse to that at the time of deformation by the waveform deformation unit, and obtain a peak detection result for the target signal waveform.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (machine).
Under the Step 2A, Prong One, we consider whether the claim recites train
a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
Similar limitations comprise the abstract ideas of Claim 6.
Next, under the Step 2A, Prong Two, we consider whether the above claims that recites a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: An analysis device comprising: a waveform deformation unit, a peak detection unit, a waveform inverse deformation unit; a learned model generated in advance by machine learning using, as teaching data;
In Claim 6: A non-transitory computer-readable recording media, a computer; using a learned model generated in advance by machine learning using, as teaching data, a signal waveform
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as a non-transitory computer-readable medium and a computer (Claim 6) and functional units (Claim 1) are examples of generic computer equipment (components) that are generally recited and not meaningful and, therefore, are not qualified as particular machines to indicate a practical application. The limitations that generically recite obtaining a signal waveform (Claim 1) and a target signal waveform (Claim 6) represent insignificant extra-solution activity of mere data gathering. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
The limitation that recite using a learned model generated in advance by machine learning using, as teaching data, a signal waveform are not meaningful to indicate a practical application (recited in generality).
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record. For example, Melnikov, Takeshi, and Taya disclose a learned model generated in advance by machine learning using, as teaching data, a signal waveform.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-3, 5, 7-8 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
For example, additional elements in Claims 2 and 3 (chromatogram waveform and deep learning) are all recited in generality and not meaningful to indicate a practical application and/or qualify for significantly more.
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 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.
Claims 1-3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Arsenty D. Melnikov et al., “Deep Learning for the Precise Peak Detection in High-Resolution LC−MS Data”, Anal. Chem. 2020, 92, pp. 588−592, hereinafter “Melnikov’ and supplementary information to this article, “Supplementary information for “Deep learning for the precise peak detection in high-resolution LC-MS data"' (27 pages), hereinafter ‘Supplementary’ (submitted in IDS dated 6/24/2024) in view of Osoekawa Takeshi (US 20200279408), hereinafter ‘Takeshi’.
With regards to Claim 1, Melnikov discloses
An analysis device (CNN for ROI Classification, p.589) comprising: a waveform deformation unit configured to enlarge or reduce a target signal waveform obtained by analysis and indicating a change in signal intensity depending on a change in a value of a predetermined parameter by a scale factor of N (where N is a positive value other than 0 and 1) in the direction of the signal intensity axis and/or enlarge or reduce by a scale factor of M (where M may be a positive value other than 0 and 1 and may be a same value as N) in the direction of the predetermined parameter axis (The signal intensities in ROIs were scaled to unity at the maximum, p.590); a peak detection unit (CNN for Peak Integration, p.590) configured to use a learned model (Authors applied CNN, p.588) generated in advance by machine learning using, as teaching data, a signal waveform and a start point and an end point of a correct solution (Section “Data Mining”, p.589), and output, as a detection result, the start point and the end point of the peak (Peak boundaries, p.590), a waveform inverse deformation unit (CNN for Peak Integration, p.590), and reducing or enlarging (scaling) information (p. 590, left column) and calculating an integrated area for measured peaks (p.590, Left Column: The length of each ROI was linearly interpolated, transforming the ROI size to 256 points. The signal intensities in ROIs were scaled to unity at the maximum; additionally, “Evaluation of the algorithm” section) that implies reducing or enlarging information on a start point and an end point of a peak, the information being output by the peak detection unit, by a scale factor of 1/N in the direction of the signal intensity axis and/or by a scale factor of 1/M in the direction of the predetermined parameter axis inverse to that at the time of deformation by the waveform deformation unit, and obtain a peak detection result for the target signal waveform.
“Supplementary” also discloses generated in advance by machine learning using, as teaching data, a signal waveform and a start point and an end point of a correct solution (Fig. S2, p.5) and further discloses use, as an input, a signal waveform after deformation by the waveform deformation unit (CNN for peak integration, last paragraph on p.6), and output, as a detection result, the start point and the end point of the peak (Table S1, p.10);
However, Melnikov does not specifically disclose reducing or enlarging information on a start point and an end point of a peak, the information being output by the peak detection unit, by a scale factor of 1/N in the direction of the signal intensity axis and/or by a scale factor of 1/M in the direction of the predetermined parameter axis inverse to that at the time of deformation by the waveform deformation unit, and obtain a peak detection result for the target signal waveform.
Takeshi discloses a waveform deformation unit [0070, 0055] that describes waveform scaling in both intensity and tine axis, a peak detection unit [0071-0072] that discloses converting pixel positions of the start/end points of peak back to time and intensity and outputting results (Fig.7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Melnikov/Supplemental in view of Takeshi to obtain a peak detection result for the target signal waveform by reducing or enlarging information on a start point and an end point of a peak, the information being output by the peak detection unit, by a scale factor of 1/N in the direction of the signal intensity axis and/or by a scale factor of 1/M in the direction of the predetermined parameter axis inverse to that at the time of deformation by the waveform deformation unit as known in art techniques of scaling/zooming in/out using specific magnification factors as discussed in Takeshi above.
With regards to Claims 2 and 3, Melnikov/Supplemental in view of Takeshi discloses the claim limitations as discussed in regards to Claim 1.
Melnikov/Supplemental additionally discloses he predetermined parameter is time, and the signal waveform is a chromatogram waveform, wherein the machine learning is deep learning (Melnikov, Title and Abstract) and so does Takeshi [0049].
With regards to Claim 6, Melnikov/Supplemental in view of Takeshi discloses the claim limitations as discussed in regards to Claim 1.
In addition, Melnikov/Supplemental disclose a non-transitory computer-readable recording media recording a waveform processing program for an analysis device, the program configured to process, on a computer, a target signal waveform (p.588).
Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Melnikov/Supplemental in view of Takeshi, in further view of Tanaka Yasufumi (JP 2001165923), hereinafter ‘Yasufumi’.
Melnikov/Supplemental in view of Takeshi discloses the claim limitations as discussed in regards to Claim 1 including performing processing of enlarging or reducing a signal waveform as discussed above.
However, Melnikov/Supplemental does not specifically disclose determining a magnitude of a variation in a signal intensity level in the target signal waveform, wherein the waveform deformation unit is configured to perform processing of enlarging or reducing a signal waveform for a parameter value range for which the determination unit determines that a variation in the signal intensity level is lower or higher than a specified value.
Yasufumi discloses a signal processing system [0002] with a determination unit (Comparator 31, Abstract) that determine whether a signal strength (strength of fluctuations in signal intensity) is high or low compared to specified value such as a reference value [0004, 0011, 0012].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Melnikov/Supplemental in view of Takeshi, and Yasufumi to perform processing of enlarging or reducing a signal waveform for a parameter value range for which the determination unit determines that a variation in the signal intensity level is lower or higher than a specified value to increase dynamic range (ensures a wide dynamic range while preventing a decrease in the S / N ratio, Yasufumi [0008]).
With regards to Claim 7, Melnikov/Supplemental in view of Takeshi, and Yasufumi discloses the claim limitations as discussed in regards to Claims 1 and 5.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Melnikov/Supplemental in view of Takeshi, in further view of Akira Noda (US 20190011408), hereinafter ‘Noda’.
Melnikov/Supplemental in view of Takeshi discloses the claim limitations as discussed in regards to Claim.
However, Melnikov/Supplemental does not specifically disclose wherein the value of N and/or the value of M is a predetermined value or a value selected by a user.
Noda discloses the value of N and/or the value of M is a predetermined value (a scale factor changed within a predetermined range [0018]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Melnikov/Supplemental in view of Takeshi, and Noda to use a value of N and/or the value of M as a predetermined value to ensure true (noise-free) picks (Noda [0040]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Akihiroa Taya et al. (US 12105068) discloses a learned model generated in advance by machine learning using, as teaching data, a signal waveform are not meaningful to indicate a practical application (recited in generality).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2863