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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2 September 2025 has been entered.
Response to Amendment
The Amendment filed 27 January 2025 has been entered. Claims 1-7, 11, 13, 14, 16, 18-20, 22, 25, 26, 28 and 29 remain pending in the application. Applicant’s amendments to the Drawings overcome the previous objections. However, the amendments bring forth a new objection. Further, there are no amendments to the claims, and the U.S.C. 103 rejections are not overcome.
Response to Arguments
Applicant’s arguments, see Remarks, filed 27 January 2025, with respect to the U.S.C. 103 rejection of claims 1-7, 11, 13, 14, 16, 18-20, 22, 25, 26 and 29, have been fully considered and are not persuasive.
Applicant’s Remarks
Applicant remarks that the systems of Chekalyuk and Lussier operate differently, and thus the modification would not work because the computer of Chekalyuk would not necessarily characterize the same condition of the photosynthetic object that Lussier discloses. Applicant understands Lussier is referenced by the Examiner to teach the concept of specific assessment of a health condition or a stress condition, which in the Examiner’s view can be easily applied to Chekalyuk. Applicant disagrees.
Applicant remarks that it is not guaranteed that the computer of Chekalyuk would automatically be capable of analyzing the recorded fluorescence information to distinguish a health or stress condition of a photosynthetic object from other physiological conditions of the photosynthetic object simply because health and stress are examples of physiological status. Chekalyuk system is not capable of identifying and assessing health or stress conditions from fluorescence data.
Applicant remarks that a person having ordinary skill in the relevant art(s) would not be able to modify the computer of Chekalyuk to be capable of identifying and characterizing a health/stress condition of a photosynthetic object. Lussier’s system is set up with a slow time resolution (measuring in the time range of ca 15 seconds) and a wavelength resolution limited to two fixed wavelengths in order to measure the Kautsky effect on plants. This time resolution and wavelength resolution are not compatible with the shorter time resolution and wavelength resolution used in Chekalyuk.
Examiner’s Responses
Examiner respectfully disagrees. Examiner respectfully points out that the computer of Chekalyuk is analyzing the recorded fluorescence information to determine a condition of the photosynthetic object (Chekalyuk fig. 4, Abstract, [0054]). The teachings of Chekalyuk do not prevent the computer from analyzing specific conditions of the photosynthetic object. Examiner respectfully points out that the computer of Lussier is also analyzing the recorded fluorescence information to determine a condition of the photosynthetic object (Lussier [0005], [0012], [0013]). As Applicant mentioned, Lussier is referenced to teach the concept of specific assessment of a health condition or a stress condition. Thus, the computer of Chekalyuk in view of Lussier is capable of performing a specific analysis with the same fluorescence information to determine specific conditions, i.e. “a health condition” and “a stress condition”.
Examiner respectfully disagrees. Because the computer of Chekalyuk is analyzing the recorded fluorescence information to determine a condition of the photosynthetic object, it would appear that the computer could perform a different analysis with the same fluorescence information to determine another condition. Further, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. The structure of the spectroscopy apparatus does not include the object being worked upon, i.e. the photosynthetic object.
Examiner respectfully points out that the photosynthetic object is the object being worked upon. The photosynthetic object is not a part of the claimed spectroscopy apparatus. Language in an apparatus or product claim directed to the function, operation, intended use, and materials upon which the components of the structure work that does not structurally limit the components or patentably differentiate the claimed apparatus or product from an otherwise identical prior art structure will not support patentability. See, e.g., In re Rishoi, 197 F.2d 342, 344-45 (CCPA 1952); In re Otto, 312 F.2d 937, 939-40 (CCPA 1963); In re Ludtke, 441 F.2d 660, 663-64 (CCPA 1971); In re Yanush, 477 F.2d 958, 959 (CCPA 1973).
Drawings
The drawings are objected to because the title of the drawing submitted on 2 September 2025 “Figure 1” should be corrected to say –Figure 6--. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 103
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 of this title, 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 1-7, 11, 13, 14, 16, 18-20, 22, 25, 26, 28 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Chekalyuk (US20150000384A1) in view of Lussier (US20100111369A1).
As to Claim 1, Chekalyuk teaches a spectroscopy apparatus for measuring fluorescence signals from a photosynthetic object, the spectroscopy apparatus (fig. 4; [0054]; an instrument configuration for spectral characterization of fluorescence constituents in natural water samples) comprising:
one or more light excitation sources (fig. 4; [0054]; wherein the excitation module 101 includes three lasers: a first laser at 375 nm, a second at 405 nm, and a third at 514 nm) operable to carry out time-varying excitation ([0052]; the control circuit 415 can issue control signals to the one or more excitation sources, such as to selectively initiate, cease, or adjust the one or more excitation sources) of the fluorescence from the photosynthetic object ([0054]; fluorescence constituents);
and one or more fluorescence-sensitive detection channels ([0052]; PMT (photomultiplier) 453, spectrometer 410) configured to simultaneously record the fluorescence as a function of time ([0052]; configured to receive information about temporally resolved emissions) with a microsecond to millisecond time resolution (fig. 17B; the time resolution, i.e. the number of bits per sample, is shown as within 1 microsecond to 1 millisecond with the smoothness of the curves and the time microsecond axis) and as a function of wavelength with a wavelength resolution of 10nm or better (fig. 17A; the wavelength resolution, i.e. the number of bits per sample, is shown as 10nm or better, with the smoothness of the curves and the wavelength nm axis), responsive to the excitation of the fluorescence from the photosynthetic object by the or each light excitation source (fig. 17A; [0092]; In fig. 17A, the wavelength resolution, i.e. the number of bits per sample, is shown as 10nm or better with the smoothness of the curves);
and an electronic circuit ([0067]-[0068]; the computer 727), wherein the electronic circuit includes a processor and memory including computer program code ([0067]-[0068]; an instrument computer 727 for processing and storage), the memory and computer program code configured to, with the processor, enable the electronic circuit at least to analyse the recorded fluorescence information from the photosynthetic object so as to identify or characterise a condition of the photosynthetic object ([0054]; spectral characterization and assessment);
wherein the condition of the photosynthetic object includes at least one of: a physiological condition of the photosynthetic object (Abstract; the systems and methods are used for assessments of physiology).
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Chekalyuk Fig. 4
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Chekalyuk Fig. 17A-17B
However, Chekalyuk does not explicitly disclose wherein the condition of the photosynthetic object includes at least one of: a health condition of the photosynthetic object; and a stress condition of the photosynthetic object.
Lussier, in the same field of endeavor as the claimed invention, teaches wherein the condition of the photosynthetic object includes at least one of: a health condition of the photosynthetic object ([0013]; diagnosing plant health); and a stress condition of the photosynthetic object ([0005]; a plant stress diagnostic).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chekalyuk to incorporate the teachings of Lussier to include wherein the condition of the photosynthetic object includes at least one of: a health condition of the photosynthetic object; and a stress condition of the photosynthetic object; for the advantage of enhancing analysis by diagnosing the plant’s condition using library data ([0015]-[0016]).
As to Claim 2, Chekalyuk teaches wherein the one or more fluorescence-sensitive detection channels ([0052]; PMT (photomultiplier) 453, spectrometer 410) includes one or more fluorescence-sensitive detection units or devices ([0052]; fig. 4; the second detector 153 corresponds to the PMT 453, and the first detector 151 corresponds to the spectrometer 410).
As to Claim 3, Chekalyuk teaches wherein the wavelength resolution of the recorded fluorescence information is achieved continuously across the entire recorded fluorescence spectrum (fig. 17A; [0092]; In fig. 17A, the wavelength resolution is achieved continuously across the entire recorded fluorescence spectrum, as shown with the smoothness of the curves).
As to Claim 4, Chekalyuk does not explicitly disclose wherein the wavelength resolution of the recorded fluorescence information is achieved using three or more distinct narrow wavelength bands.
Lussier, in the same field of endeavor as the claimed invention, teaches wherein the wavelength resolution of the recorded fluorescence information is achieved using three or more distinct narrow wavelength bands ([0015]; the filter wheel 6 carries a plurality of narrow band filters, which includes the range of three or more).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chekalyuk to incorporate the teachings of Lussier to include wherein the wavelength resolution of the recorded fluorescence information is achieved using three or more distinct narrow wavelength bands, for the advantage of increasing the options for narrow-band selection ([0032]).
As to Claim 5, Chekalyuk teaches wherein the recorded fluorescence information includes fluorescence induction information ([0075]; the passed Chl-a fluorescence is used for temporarily-resolved measurements of Chl-a fluorescence induction to retrieve the magnitude of variable fluorescence).
As to Claim 6, Chekalyuk does not explicitly disclose wherein the recorded fluorescence information includes non-photochemical quenching information.
Lussier, in the same field of endeavor as the claimed invention, teaches wherein the recorded fluorescence information includes non-photochemical quenching information ([0031]; The fluorescence signal's decay or quenching time from Fp to Fs (steady state) provides information on how the stress response affects the plant's thylakoid cells).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chekalyuk to incorporate the teachings of Lussier to include wherein the recorded fluorescence information includes non-photochemical quenching information, for the advantage of enhancing analysis by diagnosing stress conditions ([0008]).
As to Claim 7, Chekalyuk teaches wherein the time-varying excitation is in the form of a repeating pulsed excitation that has a microsecond to millisecond pulse duration (fig. 17A-17B; [0092]; the fluorescence induction was observed over about 100 microseconds, which is in the range of a microsecond to millisecond pulse duration).
As to Claim 11, Chekalyuk teaches wherein the time-varying excitation is in the form of a periodically modulated excitation (fig. 17A-17B; [0092]; the laser excitation is shown in fig. 17A-17B in which the excitation is periodically modulated).
As to Claim 13, Chekalyuk teaches wherein the time resolution is in the range of 0.5 microseconds to 10 milliseconds (fig. 17B; [0092]; In fig. 17B, the time resolution, i.e. the number of bits per sample, is shown as within 0.5 microseconds to 10 milliseconds with the smoothness of the curves and the time microsecond axis).
As to Claim 14, Chekalyuk teaches wherein the wavelength resolution is in the range of 1 nm to 10 nm (fig. 17A; [0092]; In fig. 17A, the wavelength resolution, i.e. the number of bits per sample, is shown as within 1-10nm with the smoothness of the curves and the wavelength nm axis).
As to Claim 16, Chekalyuk teaches wherein the memory and computer program code are configured to, with the processor ([0067]-[0068]; an instrument computer 727 for processing and storage), enable the electronic circuit at least to analyse modified derivative functions ([0091]; [0092]; a correction function is used to correct spectral measurements conducted and a full-functioning operating system is used which inherently allows for the analysis of modified derivative functions) of the recorded fluorescence information from the photosynthetic object so as to identify or characterise a condition of the photosynthetic object ([0054]; spectral characterization and assessment).
As to Claim 18, Chekalyuk teaches wherein the memory and computer program code are configured to, with the processor ([0067]-[0068]; an instrument computer 727 for processing and storage), enable the electronic circuit at least to analyse the recorded fluorescence information from the photosynthetic object to identify or characterise the condition of the photosynthetic object ([0054]; spectral characterization and assessment).
However, Chekalyuk does not explicitly disclose providing the recorded fluorescence information as input to a machine learning algorithm or model and identify or characterise the condition of the photosynthetic object based on an output of the machine learning algorithm or model.
Lussier, in the same field of endeavor as the claimed invention, teaches providing the recorded fluorescence information as input to a machine learning algorithm or model and identify or characterise the condition of the photosynthetic object based on an output of the machine learning algorithm or model ([0033]; the expert computer algorithm, an artificial intelligence software which is known in the art to include machine learning algorithms or models, characterizes the fluorescence measurement).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chekalyuk to incorporate the teachings of Lussier to include providing the recorded fluorescence information as input to a machine learning algorithm or model and identify or characterise the condition of the photosynthetic object based on an output of the machine learning algorithm or model, for the advantage of enhancing the analysis via disease diagnostics ([0033]).
As to Claim 19, Chekalyuk does not explicitly disclose wherein the machine learning algorithm or model includes a long short-term memory algorithm or a neural network.
Lussier, in the same field of endeavor as the claimed invention, teaches wherein the machine learning algorithm or model ([0033]; the expert computer algorithm, an artificial intelligence software which is known in the art to include machine learning algorithms or models, characterizes the fluorescence measurement) includes a long short-term memory algorithm or a neural network ([0033]; the artificial intelligence software inherently includes a neural network because machine learning is known in the art to include neural networks (including LSTM, long short-term memory, which is a type of neural network)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chekalyuk to incorporate the teachings of Lussier to include wherein the machine learning algorithm or model includes a long short-term memory algorithm or a neural network, for the advantage of enhancing the analysis via disease diagnostics ([0033]).
As to Claim 20, Chekalyuk teaches wherein the condition of the photosynthetic object includes a physiological condition of the photosynthetic object (Abstract; the systems and methods are used for assessments of physiology).
As to Claim 22, Chekalyuk a method of measuring fluorescence signals from a photosynthetic object using the spectroscopy apparatus according to Claim 1 (Abstract; the methods used to characterize natural aquatic environments), the method comprising the steps of:
by the or each light excitation source, carrying out time-varying excitation ([0052]; the control circuit 415 can issue control signals to the one or more excitation sources, such as to selectively initiate, cease, or adjust the one or more excitation sources) of the fluorescence from the photosynthetic object ([0054]; fluorescence constituents);
and by the or each fluorescence-sensitive detection channel ([0052]; PMT (photomultiplier) 453, spectrometer 410), simultaneously recording the fluorescence as a function of time ([0052]; configured to receive information about temporally resolved emissions) with a microsecond to millisecond time resolution (fig. 17B; the time resolution, i.e. the number of bits per sample, is shown as within 1 microsecond to 1 millisecond with the smoothness of the curves and the time microsecond axis) and as a function of wavelength with a wavelength resolution of 10 nm or better (fig. 17A; the wavelength resolution, i.e. the number of bits per sample, is shown as 10nm or better, with the smoothness of the curves and the wavelength nm axis), responsive to the excitation of the fluorescence from the photosynthetic object by the or each light excitation source (fig. 17A; [0092]; In fig. 17A, the wavelength resolution, i.e. the number of bits per sample, is shown as 10nm or better with the smoothness of the curves).
As to Claim 25, Chekalyuk teaches a computer-implemented method of identifying or characterising a condition of a photosynthetic object ([0054]; [0067]-[0068]; the computer 727 performs the spectral characterization and assessment), the method comprising the steps of:
recording fluorescence information from the photosynthetic object by carrying out the method according to Claim 22 ([0033]; advanced laser fluorescence methods);
and analysing the recorded fluorescence information from the photosynthetic object so as to identify or characterise a condition of the photosynthetic object ([0054]; spectral characterization and assessment).
As to Claim 26, Chekalyuk teaches a computer-implemented method of identifying or characterising a condition of a photosynthetic object ([0054]; [0067]-[0068]; the computer 727 performs the spectral characterization and assessment), the method comprising the steps of:
collecting a set of data by carrying out the method according to Claim 22, wherein the collected set of data includes the recorded fluorescence information from the photosynthetic object ([0034]; the stimulated emissions include fluorescence).
However, Chekalyuk does not explicitly disclose creating a training set including the collected set of data; training a machine learning algorithm or model using the training set; and identifying or characterising the condition of the photosynthetic object based on an output of the machine learning algorithm or model.
Lussier, in the same field of endeavor as the claimed invention, teaches creating a training set including the collected set of data ([0014]; the plant fluorescence-intensity data is stored in a library);
training a machine learning algorithm or model using the training set ([0028]; [0033]; the expert computer algorithm, an artificial intelligence software, is trained by building up the disease library);
and identifying or characterising the condition of the photosynthetic object based on an output of the machine learning algorithm or model ([0034]; the computer compares the search disease diagnostic to the buildable plant disease library).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chekalyuk to incorporate the teachings of Lussier to include creating a training set including the collected set of data; training a machine learning algorithm or model using the training set; and identifying or characterising the condition of the photosynthetic object based on an output of the machine learning algorithm or model; for the advantage of enhancing the analysis via disease diagnostics ([0033]).
As to Claim 28, Chekalyuk does not explicitly disclose wherein the step of identifying or characterising the condition of the photosynthetic object based on an output of the machine learning algorithm or model includes analysis of stress phenomena associated with a stress condition of the photosynthetic object.
Lussier, in the same field of endeavor as the claimed invention, teaches wherein the step of identifying or characterising the condition of the photosynthetic object based on an output of the machine learning algorithm or model ([0033]; the characterization of the fluorescence measurement via the expert computer algorithm, the artificial intelligence software) includes analysis of stress phenomena associated with a stress condition of the photosynthetic object ([0031]; [0036]; [0037]; the stress information is used to diagnose the stress (i.e. water stress, root pathogen stress)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chekalyuk to incorporate the teachings of Lussier to include wherein the step of identifying or characterising the condition of the photosynthetic object based on an output of the machine learning algorithm or model includes analysis of stress phenomena associated with a stress condition of the photosynthetic object; for the advantage of enhancing the analysis via stress diagnostics ([0037]).
As to Claim 29, Chekalyuk does not explicitly disclose wherein the step of identifying or characterising the condition of the photosynthetic object based on an output of the machine learning algorithm or model includes plant phenotyping or genotyping.
Lussier, in the same field of endeavor as the claimed invention, teaches wherein the step of identifying or characterising the condition of the photosynthetic object based on an output of the machine learning algorithm or model includes plant phenotyping or genotyping ([0016]; the buildable library comprises data relating to one or more of plant photosynthetic spectral wavelength signatures, leaf physiology, environmental information and visual plant image data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chekalyuk to incorporate the teachings of Lussier to include wherein the step of identifying or characterising the condition of the photosynthetic object based on an output of the machine learning algorithm or model includes plant phenotyping or genotyping; for the advantage of enhancing analysis with more data, i.e. information on the plant’s phenotype and genotype ([0016]).
Citation of pertinent art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kramer et al. (WO2002016895A1), hereinafter Kramer, cited in the IDS, teaches a spectroscopy apparatus for measuring fluorescence signals from a photosynthetic object (Kramer abstract; kinetic spectrophotometers 510 configured to collect spectral data from the sample 540, i.e. a plant leaf), wherein the condition of the photosynthetic object includes at least one of: a health condition of the photosynthetic object; and a stress condition of the photosynthetic object (Kramer page 6 ln. 8-13; The determined photosynthetic parameters can be used to ascertain whether the subject plant is experiencing one or more of a variety of environmental and/or physiological stresses, such as temperature stress, drought stress and nutrient stress, i.e. a health condition and a stress condition).
Conclusion
THIS ACTION IS MADE FINAL. 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 KEMAYA NGUYEN whose telephone number is (571)272-9078. The examiner can normally be reached Mon - Fri 8:30 am - 5:00pm ET.
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/KEMAYA NGUYEN/Examiner, Art Unit 2877
/TARIFUR R CHOWDHURY/Supervisory Patent Examiner, Art Unit 2877