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 .
Priority
This application is a 371 of PCT/IB2022/055712, filed 6/20/2022. This application claims benefit to U.S. Provisional Application Serial Number 63/212,672, filed 6/20/2021. Claims 1-20 are pending and have been examined on the merits.
Information Disclosure Statement
No information disclosure statement has been submitted.
Drawings
The drawings are objected to for the following reasons:
37 CFR 1.84(i) states "Words must appear in a horizontal, left-to-right fashion when the page is either upright or turned so that the top becomes the right side, except for graphs utilizing standard scientific convention to denote the axis of abscissas (of X) and the axis of ordinates (of Y)." In the instant application, words in Fig. 7 (“Predicted species”) are not in a left-to-right hand fashion.
Additionally, the text inside the circle in Fig. 6 is blurry (appears to be a low-resolution bit-map image rather than text) wherein the equations are illegible. It would appear that the text size is adequate if text, rather than an image, were used.
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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 13-15 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by He et al., 2021 (cite U, attached PTO-892; herein “He”).
Note claims 13-20 are composition claims drawn to a system. Intended use recitations in the claims are met by systems which COULD be used for the intended use regardless of whether the prior art discloses said use.
He discloses a system, which could be used for monitoring invasive species in a bioreactor, comprising a liquid sample cell with blackout cover and shading box, i.e., a dark chamber wherein a sample comprising a biomass is intended to be placed inside wherein the biomass can comprise an algal species (Spirulina platensis in He; see Fig. 1) which could comprise an invasive species, which is illuminated by a laser source which can be continuous; a fiber-optic probe which collects light scattered by the sample; a dedicated spectrometer which measures a scattered light intensity over a Raman spectral range; and a digital computer configured to implement signal processing algorithms (p. 2, “2.3. Raman Spectrum Acquisition”; Figures 1 and 3; pp. 3-4, “2.4.2. Predictive Model”); wherein the signal processing algorithms comprise one or more Support Vector Machine (SVM) models which incorporate a radial basis function kernel (pp. 3-4, “2.4.2. Predictive Model”; misspelled “redial basis function” in He), which could be used for determining an output vector (Fs) containing one or more probability values corresponding to the presence of one or more invasive species in the sample and output vector (Fe) containing one or more probability values corresponding to concentrations of the one or more invasive species in the sample, anticipating claims 13-15 and 18-20.
Claims 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nadadoor et al., 2012 (cite V, attached PTO-892; herein “Nadadoor”).
Nadadoor discloses a system, which could be used for monitoring invasive species in a bioreactor, comprising a liquid sample cell, i.e., a 2 L bioreactor (pp. 40-41, “4.1. Experiment setup”; Fig. 2), i.e., a chamber wherein a sample comprising a biomass is intended to be placed inside wherein the biomass can comprise an algal species (A. protothecoides in Nadadoor; see pp. 40-41, “4.1. Experiment setup”; Fig. 2) which could comprise an invasive species (NOTE: “dark” is considered a broad, relative term wherein a broadest reasonable interpretation would consider the sample cell of Nadadoor to be ‘dark’ because it is inside a room (Fig. 2) not in full outdoor sunlight), which is illuminated by a laser source which can be continuous; a fiber-optic probe which collects light scattered by the sample; a dedicated spectrometer which measures a scattered light intensity over a Raman spectral range; and a digital computer configured to implement signal processing algorithms (pp. 40-41, “4.1. Experiment setup”; p. 41, “4.2.1. Raman spectra preprocessing”; p. 41, “4.2.2. Preprocessing of the measured concentrations”; pp. 41-42, “4.3. Optimal selection of model parameters”); wherein the signal processing algorithms comprise an autoscale function and/or a logarithmic transformation and a spectrum preprocessing algorithm which differentiates a Raman measurement vector with respect to a Raman frequency shift (p. 41, “4.2.1. Raman spectra preprocessing”) and comprise one or more Support Vector Machine (SVM) models which incorporate a radial basis function kernel (pp. 41-42, “4.3. Optimal selection of model parameters”), which could be used for determining an output vector (Fs) containing one or more probability values corresponding to the presence of one or more invasive species in the sample and output vector (Fe) containing one or more probability values corresponding to concentrations of the one or more invasive species in the sample, anticipating claims 13-20.
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 1-6 and 8-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nadadoor in view of Maruthamuthu et al., 2020 (cite W, attached PTO-892; herein “Maruthamuthu”).
The discussion of Nadadoor regarding claims 13-20 set forth in the rejection above is incorporated herein.
Nadadoor teaches monitoring cultures in a bioreactor (Abst.) wherein the cultures comprise A. protothecoides (pp. 40-41, “4.1. Experiment setup”; Fig. 2), i.e., a biomass comprising an algae species, wherein the monitoring is with a low-resolution Raman spectrometer (LRRS) system and a digital computer configured for implementing signal processing algorithms (pp. 40-41, “4.1. Experiment setup”; Fig. 2), wherein the LRRS system comprises a laser source which emits pulsed or continuous illumination, a spectrometer and a fiber-optic probe (pp. 40-41, “4.1. Experiment setup”; Fig. 2), wherein the bioreactor is a photo-bioreactor, wherein the biomass comprises an algal species.
Nadadoor teaches that the method comprises building a model (p. 41, full ¶2) for classification of unknown culture samples regarding the concentration of biomass, glucose and oil content (p. 41, full ¶2) comprising providing known samples of a suspension comprising biomass as a calibration set of known samples of a suspension, each known sample comprising the biomass in a defined culture condition (p. 41, full ¶6; Table 1) wherein the Raman spectra, i.e., LRRS spectra, of the calibration set are collected, preprocessed with spectrum preprocessing algorithms comprising standard normal variate (SNV) transformation; Savitzky-Golay (SG) filtering comprising a smoothing filter based on polynomial regression with a third order polynomial; and/or linear polynomial baseline correction (Polyfit) wherein a peak selection algorithm is used to identify peaks, then a linear polynomial is fitted to the baseline values for each of the obtained peaks and the resulting polynomial curve is then subtracted from the raw Raman spectra (p. 41, “4.2.1. Raman spectra preprocessing”). Nadadoor then compared the fit (R2 value, correlation coefficient) of the calibrated dataset using the different preprocessing algorithms or combinations of different preprocessing algorithms to determine which preprocessing algorithms were most suitable (pp. 43-44, “5.1. Effect of processing on correlation coefficient”; Table 3).
Nadadoor’s method of building the model further comprises using the preprocessed Raman spectra of the calibration set of known samples comprising the biomass in a defined culture condition as input, i.e., normalized spectral data vectors, for generating a Support Vector Machine (SVM) model for classifying the normalized spectral data vectors, wherein the SVM model incorporates a radial basis kernel function and wherein the SVM model comprises SVM regression (pp. 41-42, “4.3. Optimal selection of model parameters”); hence, a person of ordinary skill in the art at the time of filing would have found it obvious to practice the method made obvious by Nadadoor for determining a concentration of an algae species comprising an ex-situ method further comprising steps (i) providing a known sample of a suspension comprising a biomass, (ii) providing a calibration set of one or more known samples of a suspension, each known sample comprising the biomass in a defined culture condition, (iii) measuring LRRS spectra for the samples in steps (i) and (ii), (iv) using a spectrum preprocessing algorithm to transform the LRRS spectra into normalized spectral data vectors, (v) generating one or more Support Vector Machine (SVM) models for classifying the normalized spectral data vectors, and (vi) determining an SVM support vector associated with each of the one or more SVM models.
Nadadoor’s method makes obvious providing an unknown sample of a suspension comprising a biomass, measuring at least one LRRS spectrum for the unknown sample, using spectrum preprocessing algorithms to transform the LRRS spectra into normalized spectral data vectors, and using the SVM model and SVM support vectors to determine an output vector containing one or more probability values corresponding to the concentrations of the algae in the unknown sample (pp. 45-47, “5.4. On-line estimation of the compositions in the bioreactor”); hence, a person of ordinary skill in the art at the time of filing would have found it obvious to practice the method made obvious by Nadadoor for determining a concentration of an algae species comprising an in-situ method further comprising steps (vii) providing an unknown sample of a suspension comprising a biomass, (viii) measuring at least one LRRS spectrum for the unknown sample, (ix) using the spectrum preprocessing algorithm to transform the at least one LRRS spectrum into at least one normalized spectral data vector, and (x) using the one or more SVM models and the SVM support vectors to determine an output vector (Fs) containing one or more probability values corresponding to the culture being classified as one of the defined culture conditions (p. 2, “2.3. Raman Spectrum Acquisition”; Figures 1 and 3; pp. 3-4, “2.4.2. Predictive Model”).
The method made obvious by Nadadoor differs from the method of claim 1 in that Nadadoor only recites using the system comprising Raman spectroscopy and SVM classification to measure the concentration of a single algal species, i.e., using the one or more SVM models and the SVM support vectors to determine an output vector (Fc) containing one or more probability values corresponding to the concentration of the algal species in the unknown sample, wherein the method of claim 1 is drawn to determining the presence and concentration of one or more invasive species in the bioreactor. However, a person of ordinary skill in the art at the time of filing would have found it obvious for Nadadoor’s method to be used to determine the presence and concentration of one or more invasive species in the bioreactor in view of the disclosures of Maruthamuthu.
Maruthamuthu teaches identifying microbial contamination in cultures using Raman spectroscopy and machine learning models (Abst.) which Maruthamuthu teaches can be applied to monitoring contamination in bioreactors (p. 6, full ¶3) wherein the method comprises generating a classification model comprising collecting Raman spectra for representative contaminating microorganisms and training the machine learning model with the spectra from the representative contaminating microorganisms, collecting the Raman spectrum of an unknown sample, inputting the spectral data vector into the model which generates a vector containing the probability distribution over all the classes of microbes or cells, i.e., using the machine learning model to determine an output vector (Fs) containing one or more probability values corresponding to the presence of one or more invasive species in the unknown sample.
Hence, a person of ordinary skill in the art at the time of filing would have found it obvious to use the system and method made obvious by Nadadoor to monitor invasive species in the bioreactor by modifying the ex-situ method for generating the SVM classification model to include calibration samples of possible contaminating species and using the SVM models and the SVM support vectors to determine an output vector (Fs) containing one or more probability values corresponding to the presence of one or more invasive species in the unknown sample; therefore, claims 1, 3-6, 9-10 and 13-20 are prima facie obvious.
Regarding claim 2, Nadadoor teaches using their system and method for quantifying the concentration of the algae A. protothecoides in the bioreactor (pp. 45-47, “5.4. On-line estimation of the compositions in the bioreactor”); hence, a person of ordinary skill in the art at the time of filing would have found it obvious for the method made obvious by Nadadoor in view of Maruthamuthu to further comprise providing an additional output vector (Fc) containing one of more probability values corresponding to concentrations of the one or more invasive species in the unknown sample; therefore, claim 2 is prima facie obvious.
Regarding claim 8, a person of ordinary skill in the art at the time of filing would have found it obvious to determine contamination, i.e., invasion, of the algae culture with the method made obvious by Nadadoor in view of Maruthamuthu wherein the microorganisms used in the classification set are microorganisms which commonly invade algae cultures, such as invasive algae species and/or cyanobacteria; therefore, claim 8 is prima facie obvious.
Regarding claims 11-12, Nadadoor teaches trying different preprocessing algorithms and comparing the fit (R2 value, correlation coefficient) of the expected value to the actual values of the calibrated dataset to determine which preprocessing algorithms were most suitable; hence, a person of ordinary skill in the art at the time of filing would have found it obvious for the method to comprise spectrum preprocessing algorithms comprises an autoscale function and/or a logarithmic transformation and a spectrum preprocessing algorithm comprising differentiation of a Raman measurement vector with respect to a Raman frequency shift because these common preprocessing algorithms would give the best fit of the expected value to the actual values, which in a classification scheme the values would be the probability of the identity of the invasive microbial species as one of the calibration set species; therefore, claims 11-12 are prima facie obvious.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nadadoor in view of Maruthamuthu and He.
The discussion of Nadadoor and Maruthamuthu regarding claims 1-6 and 8-20 and He regarding claims 13-15 and 18-20 in the rejections set forth above is incorporated herein.
Nadadoor does not teach that the algal biomass in the bioreactor is spirulina; however, He teaches real-time monitoring of Spirulina platensis cultures using a system encompassed by instant claims 13-15 and 18-20; hence, a person of ordinary skill in the art at the time of filing would have found it obvious for the algal species cultivated in the bioreactor in the method made obvious by Nadadoor in view of Maruthamuthu and Gallager to be spirulina; therefore, claim 6 is prima facie obvious.
Additionally, He discloses that the sample Raman spectra acquisition chamber can be a liquid cell comprising the spirulina liquid covered by a blackout cover, i.e., a dark chamber (Fig. 3) which would allow less interference in the Raman spectrum from ambient light than the Raman spectra acquisition chamber of Nadadoor; hence, a person of ordinary skill in the art at the time of filing would have found it obvious to incorporate He’s dark chamber into the system and method made obvious by Nadadoor in view of Maruthamuthu and Gallager; therefore, claims 13-20 are also obvious over Nadadoor in view of Maruthamuthu, Gallager and He.
Conclusion
No claims are allowed.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gallager et al., US 20190293565 (cite A, attached PTO-892) teaches identifying the species of harmful algal blooms using Raman spectroscopy and machine learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Trent R Clarke whose telephone number is (571)272-2904. The examiner can normally be reached M-F 10-7 MST.
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/TRENT R CLARKE/ Examiner, Art Unit 1651
/DAVID W BERKE-SCHLESSEL/ Primary Examiner, Art Unit 1651