DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The preamble of claim 17 recites a “method for Raman spectroscopy, includes:”. The examiner interprets “includes” as open-ended “comprises”.
Claim Objections
Claims 1, 14, 17, 18, and 20 are objected to because of the following informalities: the preamble of the claims recite “comprises” or “includes”. It is suggested to amend the claims to recite “comprising”, for grammatical correctness and standard practices. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5 and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kekkonen, Jere, Tuomo Talala, and Ilkka Nissinen. "Time-and spectrally-resolved mesoscopic raman and fluorescence imaging of carious enamel by a cmos spad-based spectrometer." 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2023, hereinafter “Kekkonen”, and further in view of Luo, Ruihao, Juergen Popp, and Thomas Bocklitz. "Deep learning for Raman spectroscopy: A review." Analytica 3.3 (2022): 287-301, hereinafter “Luo”.
Regarding claim 1, Kekkonen teaches a method for Raman spectroscopy (abstract, Figs. 1-2), comprises:
irradiating a location of a sample with multiple light pulses (as shown in Fig. 1a, pages 2-3, Sec. IIA, paragraph 1) and acquiring photons from the sample, wherein acquiring the photons includes recording an arrival time of each of the photons (as shown in Fig. 1b, page 3, Sec. IIA, paragraph 2);
generating a spectrum series based on the acquired photons, wherein the spectrum series include a plurality of spectra, and each spectrum of the plurality of spectra is constructed from photons with a same arrival time from a corresponding irradiation of the light pulse (as shown in Fig. 1b, page 3, Sec. IIA, paragraph 2);
processing the spectrum series (page 3, Sec. IIC, paragraph 2); and
generating a Raman spectrum (page 3, Sec. IIC, paragraph 2).
Kekkonen is silent regarding using a trained 2D neural network.
However, Luo teaches deep learning algorithm analysis of Raman spectra (abstract) including using a trained 2D neural network (pages 292-94, Secs. 3, 3.1).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Kekkonen with the teaching of Luo by including using a trained 2D neural network in order to efficiently process the data.
Regarding claim 2, Kekkonen teaches wherein a portion of the plurality of spectra acquired at an earlier arrival time include both a Raman signal and a fluorescence signal, and a portion of the plurality of spectra acquired at a later arrival time do not include the Raman signal (Fig. 2, Sec. IIC, pages 3-4).
Regarding claim 3, Kekkonen teaches wherein processing the spectrum series and generating the Raman spectrum includes removing the fluorescence signal from the spectrum series and generating the Raman spectrum (Sec. IIA, paragraph 2, removing later bins and only summing the first 15 bins).
Kekkonen is silent regarding using a trained 2D neural network.
However, Luo teaches deep learning algorithm analysis of Raman spectra (abstract) including using a trained 2D neural network (pages 292-94, Secs. 3, 3.1).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Kekkonen with the teaching of Luo by including using a trained 2D neural network in order to efficiently process the data.
Regarding claim 4, Kekkonen is silent regarding generating the trained 2D neural network by training a 2D neural network with training data including multiple training spectrum series.
However, Luo teaches generating the trained 2D neural network by training a 2D neural network with training data including multiple training spectrum series (pages 296-97, Sec. 3.2).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Kekkonen with the teaching of Luo by including generating the trained 2D neural network by training a 2D neural network with training data including multiple training spectrum series in order to train the neural network.
Regarding claim 5, Kekkonen is silent regarding generating the training spectrum series based on pure Raman spectra and pure fluorescence spectra.
However, the examiner takes Official Notice that training a spectrum series based on a pure spectra is well-known in the art. One would train based on a pure spectra in order to have the most accurate training, as any noise in an un-pure spectra would cause inaccurate analysis.
Regarding claim 9, Kekkonen teaches determining a composition of the sample based on the Raman spectrum (Fig 5, page 5, col. 2).
Regarding claim 10, Kekkonen is silent regarding wherein determining the composition of the sample based on the Raman spectrum includes determine the composition using a second neural network.
However, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include wherein determining the composition of the sample based on the Raman spectrum includes determine the composition using a second neural network as it has been held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960). One would use a second neural network in order to have accurate and rapid processing of the data analysis.
Regarding claim 11, Kekkonen is silent regarding wherein the trained 2D neural network is a convolutional neural network or an autoencoder.
However, Luo teaches wherein the trained 2D neural network is a convolutional neural network or an autoencoder (pages 289-90, Sec. 2.1; and Fig. 2).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Kekkonen with the teaching of Luo by including wherein the trained 2D neural network is a convolutional neural network or an autoencoder in order to accurately process the data.
Regarding claim 12, Kekkonen teaches wherein the spectrum series is a 2D dataset, wherein a length of the Raman spectrum is the same as at least one dimension of the spectrum series (Fig. 2, pages 3-4, Sec. IIC).
Regarding claim 13, Kekkonen teaches wherein acquiring photons from the sample includes acquiring the photons via time-correlated single-photon counting (Fig. 1b, pages 2-3, Sec. IIA).
Regarding claim 14, Kekkonen teaches obtaining a training data, and generating the training data based on a time profile of an intensity of the light pulse (page 3, Sec. IIC, Fig. 2).
Kekkonen is silent regarding using a trained 2D neural network.
However, Luo teaches deep learning algorithm analysis of Raman spectra (abstract) including using a trained 2D neural network (pages 292-94, Secs. 3, 3.1).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Kekkonen with the teaching of Luo by including using a trained 2D neural network in order to efficiently process the data.
Regarding claim 15, Kekkonen teaches generating the training data based further on a pure Raman spectrum from a library and a fluorescence lifetime (page 3, Sec. IIC, Fig. 2).
Regarding claim 16, Kekkonen teaches obtaining a time profile of an intensity of the light pulse (page 3, Sec. IIC, Fig. 2), but is silent regarding re-training the trained 2D neural network based on the time profile.
However, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include re-training the trained 2D neural network based on the time profile as it has been held that mere duplication has no patentable significance unless a new and unexpected result is produced. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960). One would re-train the neural network in order to have accurate and rapid processing of the data analysis.
Regarding claim 17, Kekkonen teaches a method for Raman spectroscopy (abstract, Figs. 1-2), includes:
receiving a spectrum series including multiple spectra acquired at a sample location, wherein each of the spectrum of the multiple spectra corresponds to a different arrival time, and wherein the multiple spectra are acquired by irradiating the sample location with multiple light pulses and acquiring photons from the sample, wherein acquiring the photons includes recording an arrival time of each of the photons (as shown in Figs. 1a, 1b, page 3, Sec. IIA, paragraphs 1-2);
processing the spectrum series (page 3, Sec. IIC, paragraph 2); and
generating a Raman spectrum (page 3, Sec. IIC, paragraph 2).
Kekkonen is silent regarding using a trained 2D neural network.
However, Luo teaches deep learning algorithm analysis of Raman spectra (abstract) including using a trained 2D neural network (pages 292-94, Secs. 3, 3.1).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Kekkonen with the teaching of Luo by including using a trained 2D neural network in order to efficiently process the data.
Regarding claim 18, Kekkonen teaches a microscopy system (abstract, Figs. 1-2), comprises:
a detector (Fig. 1, detector);
a pulsed laser for generating light pulses (Fig. 1a, pulsed laser);
a sample holder for positioning a sample (as shown in Fig. 1a, the tooth cannot be floating in free space); and
a controller includes a processor and non-transitory memory for storing computer readable instructions, by executing the instructions in the processor (Fig. 1a, computer), the microscopy system is configured to:
irradiate, via the pulsed laser, a location of the sample with multiple light pulses (as shown in Fig. 1a, pages 2-3, Sec. IIA, paragraph 1) and acquire, via the detector, photons from the sample, wherein acquiring the photons includes recording an arrival time of each of the photons from a corresponding irradiation of the light pulse (as shown in Fig. 1b, page 3, Sec. IIA, paragraph 2);
generate a spectrum series based on the acquired photons, wherein the spectrum series include a plurality of spectra, wherein each spectrum of the plurality of the spectra is constructed from photons with the same arrival time (as shown in Fig. 1b, page 3, Sec. IIA, paragraph 2);
process the spectrum series (page 3, Sec. IIC, paragraph 2); and
generate a Raman spectrum (page 3, Sec. IIC, paragraph 2).
Kekkonen is silent regarding using a trained 2D neural network.
However, Luo teaches deep learning algorithm analysis of Raman spectra (abstract) including using a trained 2D neural network (pages 292-94, Secs. 3, 3.1).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Kekkonen with the teaching of Luo by including using a trained 2D neural network in order to efficiently process the data.
Regarding claim 19, Kekkonen teaches wherein the detector is a single-photon avalanche diode detector (page 2, paragraph 2).
Regarding claim 20, Kekkonen teaches a scanner for directing the light pulses to different sample locations of the sample (page 3, Sec. IIC).
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Kekkonen and Luo as applied to claims 1, 4, and 5 above, and further in view of Kekkonen, Jere, et al. "Chemical imaging of human teeth by a time-resolved Raman spectrometer based on a CMOS single-photon avalanche diode line sensor." Analyst 144.20 (2019): 6089-6097, hereinafter “Kekkonen II”.
Regarding claim 6, Kekkonen is silent regarding generating the training spectrum series further based on one or more characteristics of the light pulses.
However, Kekkonen II teaches a Raman spectroscopy method including generating the training spectrum series further based on one or more characteristics of the light pulses (Fig. 1, page 6090, col. 1 last paragraph to col. 2).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Kekkonen with the teaching of Kekkonen II by including generating the training spectrum series further based on one or more characteristics of the light pulses as the time profile of the laser intensity is an important parameter for retrieving the Raman signal.
Regarding claim 7, Kekkonen is silent regarding wherein the one or more characteristics of the light pulses include a time profile of an intensity of the light pulses.
However, Kekkonen II teaches a Raman spectroscopy method including wherein the one or more characteristics of the light pulses include a time profile of an intensity of the light pulses (Fig. 1, page 6090, col. 1 last paragraph to col. 2).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Kekkonen with the teaching of Kekkonen II by including wherein the one or more characteristics of the light pulses include a time profile of an intensity of the light pulses as the time profile of the laser intensity is an important parameter for retrieving the Raman signal.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kekkonen and Luo as applied to claims 1 and 4 above, and further in view of Chouaib (US 2025/0251283 A1).
Regarding claim 8, Kekkonen is silent regarding wherein generating the trained 2D neural network further includes, training the 2D neural network first with a simulated training spectrum series, and then training the 2D neural network with an experimentally obtained training spectrum series.
However, Chouaib teaches machine learning (abstract) including wherein generating the trained 2D neural network further includes, training the 2D neural network first with a simulated training spectrum series, and training the 2D neural network with an experimentally obtained training spectrum series (paragraph [0095]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Kekkonen with the teaching of Chouaib by including wherein generating the trained 2D neural network further includes, training the 2D neural network first with a simulated training spectrum series, and training the 2D neural network with an experimentally obtained training spectrum series in order to have more accurate training of the network.
Chouaib does not teach the claimed sequence. However, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include the claimed sequence as it has been held that selection of any order of performing process steps is prima facie obvious in the absence of new or unexpected results. In re Burhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946). One would train in the claimed order as simulated data is more easily available compared to experimental data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lambert (US 2004/0019283) teaches a conventional Raman spectroscopy device that includes measuring fluorescence, and a neural network. It appears Lambert could be combined with prior art of record to render the claims obvious.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOMINIC J BOLOGNA whose telephone number is (571)272-9282. The examiner can normally be reached Monday - Friday 7:30am-3:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kara E Geisel can be reached at (571) 272-2416. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DOMINIC J BOLOGNA/Primary Examiner, Art Unit 2877
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