Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Status of the Claims
Claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 are currently pending and under exam herein.
Claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 are rejected.
Priority
The instant application claims a continuation of an application 12/780,066 which takes priority from a provisional application 61/178,251 filed on 5/14/2009. Thus, the effective filing date of the instant application is 5/14/2009
Drawings
The Drawings filed on 12/23/2022 were considered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/28/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 are directed to a method for inverse modeling for characteristic prediction from multi-spectral and hyper-spectral remote sensed datasets for the purpose of growing plants.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
constructing a predictive model for the plant characteristic, the predictive model being a multivariate relation constructed from: (a) phenotypic characteristic information extracted from a hyperspectral image of a plant from a first plant population, and (b) a first set of whole-plant spectroscopic absorbance spectra comprising absorbance at a range of wavelengths from the first plant population, and (c) a corresponding set of measured plant characteristic data from the first plant population, the multivariate relation maximizing covariance between the first set of whole-plant spectroscopic absorbance spectra and the measured plant characteristic data, the multivariate relation comprising a loading vector representative of absorbance data of the first set of whole-plant spectroscopic absorbance spectra, and the multivariate relation comprising a plurality of scores relating a weight of the loading vector in the measured plant characteristic data; (mathematical concept, this just describes how the mathematical model is built.)
applying the predictive model to a second set of whole-plant spectroscopic absorbance spectra from the target plant so as to estimate the measured plant characteristic in the target plant (mathematical concept)
selecting or removing the target plant for use in a plant breeding program based on the estimated plant characteristic in the target plant, the measured plant characteristic comprising an agronomic trait, drought tolerance, herbicide resistance, insect resistance, or any combination thereof. (mental process, one just needs to mentally select a plant w
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first set of whole-plant spectroscopic data, the second set of whole-plant spectroscopic data, or both, comprise spectra from one or more wavelengths from the visible light spectrum, from the infrared spectrum, the near- infrared spectrum, the ultraviolet spectrum, or any combination thereof. (mathematical concept, this just limits what the math is done on)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first set of whole-plant spectroscopic data, the second set of whole-plant spectroscopic data, or both, comprise multiple spectra (mathematical concept, this just limits what the math is done on)
Dependent claim 5 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first set, the second set, or both sets of whole-plant spectroscopic data are from a predetermined wavelength range. (mathematical concept, this just limits what the math is done on)
Dependent claim 6 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first set of whole-plant spectroscopic absorbance data, the second set of whole-plant spectroscopic absorbance data, or both, comprise hyperspectral data. (mathematical concept, this just limits what the math is done on)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the predictive model comprises a partial least squares regression analysis, a partial least squares discriminant analysis, a principal component analysis, or any combination thereof. (mathematical concept, this just limits what the math is done on)
Dependent claim 12 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
a. assigning, on the basis of the predictive model, a first relative score to at least one plant in the first population; (mathematical process, this just limits what the math is done on)
b. assigning, on the basis of the predictive model, a second relative score to the target plant; and (mathematical concept, this just limits what the math is done on)
c. calculating a difference between the first relative score and the second relative score. (mathematical concept, this just limits what the math is done on)
Dependent claim 13 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
further comprising adjusting the predictive model to reduce the difference between the estimate of the characteristic in the target plant and a corresponding measurement of the characteristic in the target plant (mathematical concept, this just limits what the math is done on)
Dependent claim 14 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the method estimates the characteristic at a future point in time. (mathematical concept, this just limits what the math is done on)
Independent claim 15 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
constructing a predictive model using whole-plant spectroscopic absorbance data collected from a first population of plants and corresponding measured drought tolerance data from the first population of plants, the predictive model being a multivariate relation constructed from: (a) phenotypic characteristic information extracted from a hyperspectral image of a plant from a first plant population and (b) a first set of whole-plant spectroscopic absorbance spectra comprising absorbance at a range of wavelengths from the first plant population, and (c) a corresponding set of measured drought tolerance data from the first plant population, and, the multivariate relation maximizing covariance between the whole-plant spectroscopic absorbance spectra and the measured drought tolerance data, the multivariate relation comprising a loading vector representative of absorbance data of the first set of whole-plant absorbance spectra, and the multivariate relation comprising a plurality of scores relating a weight of the loading vector in the measured drought tolerance data; (mathematical concept, this just describes how the mathematical model is built.)
applying the predictive model to whole-plant spectroscopic absorbance spectra collected from a target plant to estimate the drought tolerance of the target plant, and (mathematical concept)
selecting a plant or its seed on the estimated drought tolerance of the target plant (mental process)
Independent claim 18 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
a. based on chemometric analysis of spectroscopic data from at least a first plant and corresponding measured level of genome introgression data as input variables, constructing a predictive model being a multivariate relation constructed only from:(a) phenotypic characteristic information extracted from a hyperspectral image of a plant from a first plant population, and (b) a first set of whole-plant, spectroscopic absorbance spectra from the first plant population, the first set of whole-plant spectroscopic absorbance spectra comprising absorbance at a range of wavelengths, and (c) a corresponding set of measured genome introgression data set from the first plant population, and, the multivariate relation maximizing covariance between the whole-plant spectroscopic absorbance spectra and the measured genome introgression data set, the multivariate relation comprising a loading vector representative of absorbance s data at the range of wavelengths of the first set of whole-plant spectroscopic absorbance data, and the multivariate relation comprising a plurality of scores relating a weight of the loading vector in the measured genome introgression data; and (mathematical concept, this just describes how the mathematical model is built.)
applying the predictive model to a whole-plant spectroscopic data set from a target plant to estimate the level of genome introgression in the target plant (mathematical concept)
selecting the target plant or its seed on the estimated level of genome introgression in the target plant. (mental process)
Dependent claim 21 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the measured data and whole- plant spectroscopic data set are based on differing growing conditions, or differing environmental conditions, or both (mathematical concept, this just limits what the math is done on)
Dependent claim 25 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein building the predictive model comprises a. obtaining spectroscopic data from one or more progeny plants of a backcrossing experiment relative to a desired parental line of plants; and b. correlating the spectroscopic data to the one or more progeny plants (mathematical concept, this just describes how the mathematical model is built)
Dependent claim 26 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the phenotypic characteristic information comprises branching, plant height, ear height, or flowering time plants (mathematical concept, this just limits what the prediction is of the mathematical concept)
Dependent claim 27 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
further comprising multiplying the loading vector by the scores and subtracting the result from the first set of whole-plant spectroscopic absorbance spectra so as to produce a new set of spectra (mathematical concept)
Dependent claim 29 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first set of whole-plant spectroscopic absorbance spectra comprises an average of sample spectra (mathematical concept)
Dependent claim 30 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the multivariate relation is further constructed from spectral data of a part of a plant of the first plant population. (mathematical concept)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 1 includes:
A method of estimating a plant characteristic of a target plant, comprising: a. using a computer processor
The additional element in independent claim 15 includes:
A method of predicting drought tolerance of a target plant, comprising: a. using a computer processor
The additional element in independent claim 18 includes:
A method of predicting a level of genome introgression of a single plant for a backcross experiment, comprising
The additional element in dependent claim 25 includes:
wherein the measured plant characteristic data comprising absorbance at the range of wavelengths from the first plant population is collected from plants subjected to different growing conditions.
The additional element in dependent claim 31 includes:
wherein the part of the plant is a leaf
The additional elements of wherein the measured plant characteristic data comprising absorbance at the range of wavelengths from the first plant population is collected from plants subjected to different growing conditions (Claim 25), wherein the part of the plant is a leaf (claim 31) are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)).
The additional elements of a method of estimating a plant characteristic of a target plant, comprising: a. using a computer processor (claim 1), a method of predicting drought tolerance of a target plant, comprising: a. using a computer processor (Claim 15), a method of predicting a level of genome introgression of a single plant for a backcross experiment, comprising (Claim 18) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of wherein the measured plant characteristic data comprising absorbance at the range of wavelengths from the first plant population is collected from plants subjected to different growing conditions (Claim 25), wherein the part of the plant is a leaf (claim 31) are are conventional and part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). Evidence for conventionality is shown by Jacquemoud et al. (Remote Sensing of Environment Vol. 74, pages 471-481 (2000)) which discusses how spectra were subject to different growing conditions and performing spectra analysis on them as well as discussing the prior state and how preforming spectra on leaves was a conventional and routine.
The additional elements of a method of estimating a plant characteristic of a target plant, comprising: a. using a computer processor (claim 1), a method of predicting drought tolerance of a target plant, comprising: a. using a computer processor (Claim 15), a method of predicting a level of genome introgression of a single plant for a backcross experiment, comprising (Claim 18) are conventional fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
When taken alone, all additional elements in claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1, 3, 4, 5, 6, 7, 12-14, 15, 18, 19, 21-22, 25, 27, 28, 29, 30, 31 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Jacquemoud et al. (Remote Sensing of Environment Vol. 74, pages 471-481 (2000)) in view of Orr et al. (United States Patent No. 5,764,819) in view of Hansen et al. (Remote Sensing of Environment Vol. 86, pages 542-553 (2003)) in view of Rocchini et al. (Applied Vegetation Science vol. 10, pages 325-331 (2007)) in view of Rodarmel et al. (Surveying and Land Information Systems vol. 62, pages 115-123 (2002)) in view of Asner et al. (Proceedings of the National Academy of Sciences USA Vol. 101, pages 6039-6044 (2004)) in view of Bro (Analytica Chimica Acta vol. 500, pages 185-194 (2003) in view of Zheng et al. (Sensors vol. 9, pages 2719-2745 (2009))
The subject matter of claim 1 is a computer-mediated process of constructing a predictive model between architectural information extracted from a hyperspectral image from a first plant population corresponding to the whole-plant population spectroscopic absorbance data obtained from a first plant population and a corresponding set of measured plant characteristic data from the first plant population, comprising a multivariate relation comprising a loading vector derived from principal component analysis, and using the predictive model to interpret spectroscopic data of a second plant to assay the characteristic, where the measured characteristic is an agronomic trait, drought tolerance, herbicide resistance or insect resistance, followed by selection of a plant for use in plant breeding. In the embodiment of claim 3 the spectroscopic data is from visible, infrared, near infrared, or ultraviolet wavelengths or a combination thereof. In the embodiment of claim 4 multiple spectra are analyzed. In the embodiment of claim 5 the spectral wavelengths are predetermined. In the embodiment of claim 6 the spectroscopic data is hyperspectral data. In an embodiment of claim 7 the predictive model comprises a partial least squares regression analysis or a principal component analysis.The subject matter of independent claim 15 is the process of claim 1 further limited to a plant characteristic that is drought tolerance and selection of a plant based on an estimated drought tolerance. Claim 27 further recites multiplying a loading vector by the score and subtracting from the first set of spectra. Claim 28 further recites absorbance wavelengths of plants grown in different growing conditions are measured. In the embodiment of claim 29 the method of claim 1 uses an average of sample spectra. In the embodiment of claim 30 the method of claim 1 the multivariate relation is further constructed from spectral data of a part of a plant of the first plant population. In the embodiment of claim 30 the method of claim 1 where the part of the plant is a leaf.
Jacquemoud et al. shows in the abstract and throughout a process of using reflectance spectra measurements of plant populations to develop models for determining chlorophyll content and leaf area index Jacquemoud et al. shows in the first column of page 473 that an inverse PROSPECT model has good agreement with measured data of chlorophyll, water, and dry matter. Figure 1 shows absorption coefficients of plant components from 400nm to 2400nm wavelengths. Figure 2 shows low error at different wavelengths of an inverted PROSPECT model between measured and simulated data. Six wavelengths are shown in Table 1 for the PROSAIL model, and use of ten wavelengths in the visible and near infrared are noted in the abstract. On page 472, column 2, Jacquemoud et al. discusses prior art availability of hyperspectral measurement of vegetation. On pages 475-477 Jacquemoud et al. shows development of four inverse models and validation of the inverse models by use of synthetic data. The models comprise vectors of parameters. Computer processing times for running the four different models and measurements of plant characteristics are shown in Table 2. On pages 477-478, Table 3, and Figures 5 and 6, three validated inverse models were applied to field spectroscopic measurements of soybean and corn (maize) plants compared to measured chlorophyll content. Jacquemoud et al. shows analysis of different crops at different growing parcels in the first column of page 477
Jacquemoud et al. does not show Jacquemoud et al. does not show selecting a plant for breeding, measuring spectroscopic absorbance data and a corresponding measured plant characteristic from the same plant population, an example of using hyperspectral imaging of a plant population, averaging spectroscopic absorbance data of a sample, a multivariate relation comprising a loading vector and scores of the loading vector derived from principal component analysis, multiplying a loading vector by the score and subtracting from the first set of spectra, or selecting a target plant or its seed from the predicted value of the analyte. Jacquemoud et al. does not show determining phenotypic characteristic information of plants from plant images or the multivariate relation is further constructed from spectral data of a part of a plant of the first plant population.
Orr et al. shows in the abstract use of remote sensing to select plants for breeding. Orr et al. shows selection of a plant with desired characteristics at columns 6, 8, 19 (example 1), 26 (example 3), 38 (example 4), 41 (example 5), and claims 1, 2, 4, and 5. Orr et al. shows measurement of crop yield (an agronomic trait) and insect infestation levels in column 2. Orr et al. also shows genotypic differences were detected for canopy temperatures during an 11:00 a.m. to noon scan for 24 hybrids grown under irrigated conditions. The present invention employs remote sensing technology to classify inbred and hybrid plants and segregating populations for commercially important traits such as yield, environmental stress responses, disease resistance, insect and herbicide resistance, and drought resistance (SUMMARY OF THE INVENTION, 1st paragraph). Factor analysis is a method of data-reduction the purpose of which is to uncover an underlying pattern of relationships in data. Reduction of, for example, a large array of correlation coefficients, to a smaller set of factors or components that may be taken as source variables which account for observed interrelationships in the data, facilitate interpretation of complex relationships in large, multivariate data sets. Uses of factor analysis include discovering new concepts, confirming hypotheses about structuring of variables, and constructing indices to be used as new variables in later analysis. (Factor analysis and principal components, paragraph 1)
Hansen et al. shows in the abstract that hyperspectral reflectance data was used to
determine plant characteristics. Hansen et al. shows on page 544 in sections 2.2 and 2.4 a device
for obtaining spectroscopic measurements of plants grown in a plot of land, and storing the spectroscopic scans. Hansen et al. shows on page 544 that canopy reflectance was measured
using a dual spectrometer and that plant sampling of the analyzed plants was performed to assay
plant characteristics including chlorophyll content. Regarding averaging spectra, Hansen et al.
states on page 544 column 1: The same reference spectrum made by the average of 10 repeated scans for each channel was used for all measurements. Hansen also shows use of averaged spectral data on page 544, column 2: One of the replicates showed outlier tendency in 98 cases, corresponding to 6.8% of all scans performed, and was excluded from further calculations. The remaining replicates were averaged and used in the analysis. On page 545 multivariate analysis of the plant spectra is detailed. Hansen et al. shows in Table 2 multiple wavelengths that were selected and used to analyze plant properties.
Rocchini et al. shows in the abstract that multispectral data from satellite image data was
analyzed using principal component analysis and the components from near infrared were useful
to determine plant species richness in the image. Rocchini et al. discusses in the first column of
page 327 the use of eigenvectors and first and second principal components of the multispectral
data. In the first column of page 329 and Table 1 Rocchini et al. shows that near infrared
principal components contained the information of most interest in the images.
Rodarmel et al. shows in the abstract analysis of hyperspectral images using principal
component analysis. Rodarmel et al. discusses the technique of principal component analysis on
pages 116-118 and shows in Figures 3 and 4 that the first few bands of a hyperspectral land
image contains the most information and the remaining band comprise mainly noise. Rodarmel
et al. concludes on page 121 that principal component analysis (PCA) is beneficial when
analyzing hyperspectral images. PCA was found to reduce the amount of data that is handled. PCA also shows
Asner et al. shows in the abstract that hyperspectral metrics of canopy water content and
light-use efficiency are highly sensitive to drought. Asner et al. shows their spectroscopic data
can be used to measure drought stress using the SWAM metric (which measures water content,
see the second column on page 6041) (see figure 4). Asner et al. concludes in the first column of
page 6044 that the SWAM metric is the best indicator of the metrics measured in the paper to
determine drought stress. This shows whole plant measurements as well as hyperspectral imaging.
Bro discusses use of multivariate analysis of analytes. Bro shows advantages of
multivariate analysis of noise reduction, analysis of samples with interferents, and other
advantages on pages 186-193. Bro provides guidance to use principal component analysis that
uses loading vectors and scores of the loading vectors on pages 186-193, particularly at Figures
1-5 and the second column of page 186, the first column of page 187, and the second column of
page 189. The second column of page 189 provides guidance to multiply the loading vector by
the score.
Zheng et al. reviews determining leaf area index from remote sensing plant images.
Zheng et al. states on page 2720 that leaf area index is a useful parameter that is related to gas-
vegetation exchange processes of plants such as photosynthesis, evaporation and transpiration,
rainfall interception, and carbon flux, which provides an understanding of productivity and
climate impacts on forest ecosystems. Zheng et al. reviews on pages 2721-2728 and Table 1
general concepts of determining leaf area index. Zheng et al. shows determining leaf area index
from remote plant images on pages 2730-2733.
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify the process of Jacquemoud et al. by selecting a plant for use in a plant breeding program because Orr et al. provides guidance to select plants on the basis of remote sensing for use in plant breeding. It would have been further obvious to modify the spectral data used by Jacquemoud et al. to perform spectroscopic and plant characteristic analysis on the same population of plants because Hansen et al. shows developing a predictive model by use of those techniques, and because it is obvious to use a known technique to improve a similar method. It would have been further obvious to average spectroscopic absorbance spectra because Hansen et al. provides guidance to average replicate spectroscopic scans without outlier tendencies. Regarding the limitation of using a multivariate relation comprising a vector derived from principal component analysis, it would have been further obvious to use principal component analysis of hyperspectral images of plants because Rocchini et al. provides guidance to use principal component analysis of multispectral plant images to analyze the data with the most information and Rodarmel et al. shows that use of principal component analysis of multispectral images provides advantages in reducing the amount of data that is analyzed. It would have been further obvious to measure drought tolerance because Asner et al. shows that hyperspectral data of plant populations can be used to determine drought stress and the quantification of drought tolerance is an inverse of drought stress. It would have been further obvious to use a multivariate analysis comprising a loading vector and scores of the loading vector derived from principal component analysis and multiplying a loading vector by the score and subtracting from the first set of spectra which is a simple mathematical variation of score analysis shown in Bro because Bro provides guidance to use multivariate analysis comprising principal component analysis using loading vectors and scores which has advantages of multivariate analysis of noise reduction, analysis of samples with interferents, and other advantages on pages 186-193. It would have been further obvious to determine phenotypic characteristic information such as leaf area index from plant remote sensing images because Zheng et al. provides guidance to determine leaf area index from remote sensing images and shows that the leaf area index is of interest to gain insights into plant metabolism by remote sensing.
Claims 18, 19 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Jacquemoud et al. in view of Orr et al. in view of Hansen et al. in view of Rocchini et al. in view of Rodarmel et al. in view of Asner et al. in view of Bro in view of Zheng et al. as applied to claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31 above, and further in view of Stewart (Trends in Plant Science Vol. 10, pages 390-396 (2006))
Independent claim 18 recites the process of claim 1 further limited to a plant characteristic that is a plant analyte concentration and a further limitation of correlating a level of genome introgression using the multivariate relation and a plant or seed is selected but is not limited to be used for plant breeding. Dependent claim 19 recites the further limitation wherein the whole-plant spectroscopic data comprises hyper-spectral imaging of reflectance.
Regarding the limitations of dependent claim 19, Hansen et al. shows in the abstract that hyperspectral reflectance data was used to determine plant characteristics. (abstract, wherein the whole-plant spectroscopic data comprises hyper-spectral imaging of reflectance (Claim 19))
Jacquemoud et al. in view of Orr et al. in view of Hansen et al. in view of Rocchini et al. in view of Rodarmel et al. in view of Asner et al. in view of Bro in view of Zheng et al. as applied to claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31 above does not show analysis of transgenic traits or levels of introgression
Stewart shows in the abstract that transgenes in plants can be tagged with a green fluorescent protein (GFP) encoding gene to monitor transgene escape to wild relatives. Stewart discusses on page 390 that plant transgenes may encode for insect resistance or herbicide resistance, and that introgression of transgenes into wild relative plants is a concern of regulators, consumer groups, and farmers. On page 392 Stewart shows that GFP can be linked to a gene of interest to monitor the transgenic. Stewart shows on page 392 that GFP expression can be monitored by spectrofluorometers or standoff laser induced fluorescence imaging devices. The measurement of spread of the transgene to wild relatives is measurement of gene introgression, as noted on page 390.
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify the plant analysis process of Jacquemoud et al. in view of Orr et al. in view of Hansen et al. in view of Rocchini et al. in view of Rodarmel et al. in view of Asner et al. in view of Bro in view of Zheng et al. as applied to claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31 above by performing analysis on GFP expression because Stewart shows that GFP can be linked to a transgene expressing a trait such as insect or herbicide resistance and that spectroscopic assay of GFP can be used in the field to determine gene introgression into wild relatives. It would have been further obvious to select crop plants for production on the basis of expression of a desired transgene for commercial production, or to select plants for removal if those plants are undesirable wild relatives with transgene introgression, and because Orr et al. provides guidance to select plants for breeding on the basis of remote sensing.
Claims 12-14, 21-22, 25 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Jacquemoud et al. in view of Orr et al. in view of Hansen et al. in view of Rocchini et al. in view of Rodarmel et al. in view of Asner et al. in view of Bro in view of Zheng et al. as applied to claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31 above, and further in view of Stewart as applied to claims 18, 19 above, and further in view of Halfhill et al. (Theoretical and Applied Genetics Vol. 107, pages 1533-1540 (2003))
Jacquemoud et al. shows analysis of different crops at different growing parcels in the
first column of page 477.
Stewart provides guidance on page 390 to use real-time data to monitor escape of plant
transgenes to wild relatives.
Jacquemoud et al. in view of Orr et al. in view of Hansen et al. in view of Rocchini et al.
in view of Rodarmel et al. in view of Asner et al. in view of Bro in view of Zheng et al. as applied to claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31, and further in view of Stewart as applied to claims 18, 19 above does not show data based on diploid or polyploidy organisms, or a model based on plants of a backcrossing experiment.
Halfhill et al. shows a process of measuring gene introgression between transgenic plants and wild relatives by measuring expression of GFP linked to the transgene. On page 1534 Halfhill et al. shows that canola X B. rapa crosses generate triploid F₁ hybrid generations that can be restored to the original B. rapa phenotype and diploid level after several backcross generations, but that backcrossed generations can retain the transgene of the original cross. Details of a quantitative score for GFP expression is shown on page 1535. GFP levels of crosses are shown to be related to the homozygous or hemizygous state of the transgene in transgenic plants at page 1536 and Figures 1 and 2.
It would have been obvious to a person of ordinary skill in the art at the time the
invention was made to modify the process of Jacquemoud et al. in view of Orr et al. in view of
Hansen et al. in view of Rocchini et al. in view of Rodarmel et al. in view of Asner et al. in view
of Bro in view of Zheng et al. as applied to claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31,
above, and further in view of Stewart as applied to claims 18, 19 above by using scores of plant populations in developing a model of plant characteristics because Halfhill et al. shows a process of quantitating GFP expression by optical measurements to determine gene introgression in plants. The GFP measurements are scores that are equivalent to the optical reflectance data used by Jacquemoud et al. It would have been further obvious to determine the difference between the scores of plant populations to determine the difference in a characteristic among the populations and to determine the occurrence of transgene escape. It would have been further obvious to measure transgene escape to wild relatives at nearby locations to fields planted with transgenic plants because Stewart provides guidance to monitor transgene escape and Jacquemoud et al. shows comparisons of different crops at different growing parcels
Claim 26 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Jacquemoud et al. in view of Orr et al. in view of Hansen et al. in view of Rocchini et al. in view of Rodarmel et al. in view of Asner et al. in view of Bro in view of Zheng et al. as applied to claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31 above, further in view of Stewart as applied to claims 18, 19 above, further in view of Halfhill et al. as applied to claims 12-14, 21-22, 25, and in further view of Enquist et al. (Enquist et al. A General Integrative Model for Scaling Plant Growth, Carbon Flux, and Functional Trait Spectra. Nature 2007, 449 (7159), 218–222)
Jacquemoud et al. in view of Orr et al. in view of Hansen et al. in view of Rocchini et al.
in view of Rodarmel et al. in view of Asner et al. in view of Bro in view of Zheng et al. as applied to claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31, and further in view of Stewart as applied to claims 18, 19 above, further in view of Halfhill et al. as applied to claims 12-14, 21-22, 25, does not show the phenotypic characteristic information comprises branching, plant height, ear height, or flowering time.
Regarding the limitations of claim 26, Enquist et al. building a model to predict the phenotypic traits of plants including plant heights (abstract, wherein the phenotypic characteristic information comprises branching, plant height, ear height, or flowering time (Claim 26)
It would have been obvious to a person of ordinary skill in the art at the time the
invention was made to modify the process of Jacquemoud et al. in view of Orr et al. in view of
Hansen et al. in view of Rocchini et al. in view of Rodarmel et al. in view of Asner et al. in view
of Bro in view of Zheng et al. as applied to claims 1, 3, 4, 5, 6, 7, 15, 27, 28, 29, 30, 31,
above, and further in view of Stewart as applied to claims 18, 19, further in view of Halfhill et al. as applied to claims 12-14, 21-22, 25 by using the knowledge of predicting future plant traits including plant heights taught by Enquist et al. There is a reasonable expectation of success because adding the prediction of height is a simple well-known process and does not change the way the underlying model works. Each part works individually so it is expected to work when height is also predicted.
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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