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 .
Response to Amendment
The Amendment filed 16 June 2026 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 replacement for Figure 6 has overcome the previous objection. However, Applicant has not amended the claims and they do not appear to overcome the U.S.C. 103 rejections.
Response to Arguments
Applicant’s arguments, see Remarks, filed 16 June 2026, with respect to the U.S.C. 103 rejection of claims 1-7, 11, 13, 14, 16, 18-20, 22, 25, 26, 28 and 29, have been fully considered and are not persuasive.
Applicant Remarks
Applicant remarks that there are fundamental differences between the claimed disclosure of the current application, Chekalyuk and Lussier. Although they all teach a fluorescence detection apparatus, the required specific technical features depend heavily on the exact purpose for which fluorescence signals are to be measured. Likewise, the chosen methods of analysis of the fluorescence signals are crucial and determine the specific details of the apparatus and the possible measuring protocols. That is to say, it is not technically correct to say that any fluorescence detection apparatus is capable of obtaining any kind of information from a fluorescence signal; on the contrary, it must be specifically set up to first detect a fluorescence signal within specific detection parameters/limits and then analyze said detected fluorescence signal in certain ways in order to obtain certain information. Otherwise, one would be faced with the illogical conclusion that a conventional fluorescence detection apparatus is capable of obtaining any kind of information from a fluorescence signal, without requiring a specific configuration or further enhancements/improvements.
Applicant remarks: The Application is based on chlorophyll fluorescence detection, a first novel aspect of which is the simultaneous fast time-resolved detection and high-resolution wavelength-resolved detection. The Application further includes a second novel aspect of a processor specifically programmed to analyze the combined detected signals, such as, for example, by using AI-based methods and machine learning methods, in order to achieve the above-mentioned goals of characterizing the health/stress condition of the photosynthetic object. For the purpose of efficient stress characterization, having the ability to determine both the type of stress as well as the degree of a particular stress, the combination of both fast and intermediate time resolution with wavelength resolution of the fluorescence signals on the detection side is a fundamental requirement for the successful application of AI and machine learning techniques in the analysis. Without the availability of the proper measured signal combinations carrying the required information content, application of AI methods for the analysis would not be successful. Importantly, no spatial resolution is required for the Application.
Applicant remarks: The main focus of Chekalyuk is on measuring spectral information from a wide range of liquids, but there is no disclosure in Chekalyuk of simultaneously combining the spectral measurement with time-resolved information to achieve specific aims. Specifically, Chekalyuk does not disclose detection of fluorescence from plants or characterization of a health or stress condition of a photosynthetic object, and thereby does not disclose any automatic analysis/characterization procedures using special advanced analysis methods like machine learning, AI methods, or anything else which would be crucial for achieving specific aims in health/stress characterization.
Applicant remarks: With regard to the disclosure of Lussier, Applicant notes that Lussier discloses an apparatus focused on stress detection on plants using primarily spatially resolved fluorescence detection on leaves (or larger parts of plants, such as several leaves on small plants, or little branches with several leaves). Lussier is furthermore explicitly focused on employing a special effect in plants (namely the Kautsky effect) in order to detect the stress effects. This requires the Lussier apparatus to employ: i) Only slow time resolution (measuring in the time range of ca 15 seconds); and ii) Only very minor wavelength resolution (only detection at two fixed wavelengths). The slow time resolution and the limitation to two fixed wavelengths are intrinsic properties and limitations of the Lussier apparatus, which is thus limited to only detect/resolve fluorescence signals based on that single effect (Kautsky effect).
Applicant remarks: Regarding the combination of Chekalyuk and Lussier, if one were to use the computer of Chekalyuk to perform the analysis method of Lussier using the detected fluorescence data of Chekalyuk to determine a health/stress condition as intended by Lussier, then it is logically necessary for the analysis method of Lussier to be technically compatible with the detected fluorescence data of Chekalyuk. However, the detected fluorescence data of Chekalyuk with the time and wavelength resolutions shown in Figures 17A and 17B is not the same as the slow time resolution (measuring in the time range of ca 15 seconds) and the very minor wavelength resolution (only detection at two fixed wavelengths) in Lussier. In other words, the recorded fluorescence data of Chekalyuk is fundamentally different from the recorded fluorescence data of Lussier. Accordingly, because the analysis method of Lussier is specifically designed to handle a very specific type of detected fluorescence data (particularly spatially-resolved fluorescence data and the Kautsky effect) outlined in Lussier, the analysis method of Lussier would be incapable of extracting any meaningful information from the specific detected fluorescence data of Chekalyuk, let alone health/stress information. The reverse is also true when applying the analysis method of Chekalyuk to the recorded fluorescence data of Lussier; that is, the analysis method of Chekalyuk is specifically designed to handle the specific type of detected fluorescence data (which is not spatially-resolved fluorescence data) outlined in Chekalyuk. That is to say, the technical realizations for fluorescence signal detection and analysis in Chekalyuk and Lussier are so technically inconsistent and incompatible with each other to the point of not being combinable in the same fluorescence detection apparatus. Applicant respectfully submits that it is an unreasonably speculative technical leap to suggest that the analysis method of Lussier would be successful in deriving health/stress information from the detected fluorescence data of Chekalyuk; notably, the Examiner has failed to provide any technical evidence that this is possible.
Examiner Responses
Examiner acknowledges Applicant’s remark that a conventional fluorescence detection apparatus is not capable of obtaining simply any kind of information from a fluorescence signal, without requiring a specific configuration. However, Examiner respectfully asserts that the configuration of independent claim 1 does not seem to limit the apparatus in a specific configuration in a way that would overcome the current U.S.C. 103 rejections. Examiner respectfully suggests further limiting claim 1 to reflect why the present application teaches a specific configuration that is capable of obtaining the specific data. For example, the one or more fluorescence-sensitive detection channels could be further limited, or the health condition and stress condition could be further limited.
Examiner respectfully notes that Chekalyuk para. [0109], [0110], [0120], [0121] state examples wherein there is a determination of the chlorophyll-a fluorescence induction to identify at least one of a photo-physiological status or a photochemical efficiency of phytoplankton. Examiner respectfully notes that Lussier teaches machine learning in para. [0033] for example. Examiner respectfully suggests further limiting claim 1 to demonstrate how spatial resolution is not required.
Examiner respectfully points out that Chekalyuk teaches a physiological condition of the photosynthetic object (abstract). Yes, Chekalyuk does not explicitly mention “health” and “stress”, but by monitoring physiological conditions, a person of ordinary skill in the art can gauge the health and stress status of a photosynthetic object. For instance, the stress and the health directly affect the physiological conditions. From Gjindali (Gjindali A, Johnson GN. Photosynthetic acclimation to changing environments. Biochem Soc Trans. 2023 Apr 26) Introduction, “Plants are equipped with a plethora of mechanisms to cope with transient or sustained changes, adjusting their photosynthesis, primary metabolism, and overall physiology”. Please note Gjindali is simply mentioned to demonstrate an example of why Chekalyuk’s teachings go hand in hand with Lussier’s, and not used as a prior art reference. Thus, Examiner asserts that “health” and “stress” under broadest reasonable interpretation are types of physiological conditions. Stress is a physiological condition because it involves changes in response to stimuli. Health is a physiological state that can be maintained or compromised by physiological processes, including those influenced by stress.
Examiner respectfully points out that Lussier’s teachings in regards to claim 1 of the present application is used to teach “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”, rather than the specific methodologies employed.
Examiner respectfully points out that Lussier’s teachings in regards to claim 1 of the present application is used to teach “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”, rather than the specific methodologies employed. Therefore Examiner asserts that Chekalyuk and Lussier’s technical inconsistencies are not relevant in the way that the two prior art references are being combined. Examiner agrees with Applicant that it is an unreasonably speculative technical leap to suggest that the exact detailed analysis method of Lussier would be successful in deriving health/stress information from the detected fluorescence data of Chekalyuk. However, they are not being combined in this manner. Lussier in claim 1 is simply referenced to teach “health” and “stress” when Chekalyuk does not explicitly say these terms. The motivation to combine is to enhance the analysis by diagnosing the plant’s condition, which can be done when physiological conditions of Chekalyuk are related to health and stress.
Examiner respectfully suggests amending the claims to further limit the one or more fluorescence-sensitive detection channels configured to simultaneously record the fluorescence as a function of time. Potentially limit the number to more than one fluorescence-sensitive detection channel in order to more clearly demonstrate the significance of simultaneous recording.
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
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