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 Arguments
Applicant's arguments filed 1/22/2026 have been fully considered but they are not persuasive.
Claims 1-20 are pending in this application and have been considered below. Applicant’s Arguments:
The applicant argues Candes is different because it generates a “new set” of knockoff variables rather than operating within the original set.
Examiner’s Response:
Claim 1 recites, “determining substitute variable values for the spectral variable of interest using the variable-to-variable model.” The claims uses the preposition “for,” which indicates purpose (replacement) not source. Arguendo, the claim would have a different meaning if it were to say, “determining substitute variable values from the spectral variable of interest using the variable-to-variable model.” Therefore, the claim broadly covers any value generated to replace the original, including the knockoff values (
X
j
~
) generated by Candes to as a substitute for the original variable (
X
j
). The limitation requiring the substitute values to remain “in the original set” does not exist in the claim. In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Even if the claim language was changed, Candes would likely still disclose these limitations because the “knockoff” (substitute) variable is not random noise created from nothing. It is mathematically calculated from the original dataset. To generate
X
j
~
Candes looks at the other covariates.
Applicant’s Arguments:
The applicant argues that Lightner fails to teach the variable-to-variable model.
Examiner’s Response:
While Lightner discloses predicting traits from spectra (an X-to-Y model), the rejection relies on Candes to supply the missing limitation. Candes explicitly disclose the “Model-X Knockoff” framework, which models the conditional distribution of on variable given other (an X-to-X model) to generate substitute values for variable selection. The combination of Lightner’s spectral data with Candes’ selectin algorithm renders the claimed method obvious. To the extent that the applicant implies Lightner’s use of PCA (which generates orthogonal variables) renders the “variable-to-variable” prediction model impossible, this is contradicted by the applicant’s specification. Paragraph [0073] lists PCA-derived components as a valid embodiment of “spectral variables.” The applicant cannot argue that a combination is technically inoperative when their own specification admits that the invention functions using the same data types found in the prior art.
The applicant’s method uses a variable-to-variable model to sample a new value for a spectral value based on its neighbors. The Candes method uses a conditional distribution model to sample a new value for a covariate based on its neighbors. The math is identical. The applicant is simply applying the mathematics of Candes (typically used for genetics or general statistics) to a specific data type, spectroscopy. The “variable-to-variable” model in the claims (predicting a variable based on other in the set) is mathematically identical to the conditional sampling defined in Candes equation in Table 1 (
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X
j
X
-
j
,
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1
:
j
-
1
. Furthermore, the step of “comparing influence metrics” is identical to equation 3.6’s lasso coefficient difference (LCD) ( Wj = Zj –
Z
~
j) which compares the coefficient of the original variable against the coefficient of the substitute (knockoff) variable. The applicant is simply applying the standard statistical equation disclosed in Candes to the spectral data disclosed in Lightner.
Therefore, the argued limitations were written broad such that they read upon the cited references or are shown explicitly by the references. As a result, the claims stand as follows.
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, 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.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lightner, et al. (US 2023/0367272 A1 – hereinafter “Lightner”) in view of Candes, et al. (Panning for gold: ‘model-X’ knockoffs for high dimensional controlled variable selection – hereinafter “Candes”).
Claims 1, 19, and 20.
Lightner discloses a method for generating organisms having a target trait (Lightner ¶79: “desired trait.” Lightner further teaches: "Progeny are selfed and selected so that the newly developed inbred has many of the attributes of the recurrent parent and yet several of the desired attributes of the non-recurrent parent “ (¶301).), comprising:
(Claim 19 preamble) A system for generating a new organism having a target trait, comprising :one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for (Lightner ¶50: “(a) a computer
with a memory … software”) …
(Claim 20 preamble) A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device cause the electronic device to (Lightner ¶50: “(a) a computer with a memory … software”) …
receiving one or more spectrograms corresponding to each organism of a set of organisms (Lightner ¶280: “Hyper spectral data was collected for each of the samples by remote sensing.” Lightner discloses: "Multi- or hyper-spectral data was collected for the plots by remote sensing imaging from which X-block calibration data can be extracted" (¶344). Lightner further teaches: "Spectra from the plant canopies were collected in digitized form and stored" (¶281 ).);
generating, from the one or more spectrograms, a plurality of spectrogram attributes characterizing wavelengths for each organism of the set of organisms (Lightner discloses: " An example of remote sensing data is a remote sensed image of the plant or plants, which can be converted by well-known methods into a spectrogram. The spectrogram can be sampled at a number of wavelengths to provide a matrix of values correlated to wavelength" (¶165). Lightner further teaches: “data suitably include spectra of a plurality of wavelengths from one of more of visible (VIS), infrared (IR), near infrared (NIR), or ultraviolet (UV) light … The spectra may be reflectance or absorbance; absorbance spectra are considered especially suitable. In some examples, the spectra are hyper spectral data” (¶135).);
reducing a dimensionality of the plurality of spectrogram attributes to obtain a set of spectral variables, wherein a number of spectral variables in the set of spectral variables is smaller than a number of spectrogram attributes in the plurality of spectrogram attributes (Lightner discloses: "A PCA method can be used to reduce the dimensionality of a large number of interrelated variables (absorption intensities at different wavelengths) while retaining information that distinguishes one component from another … The new set of variables is ordered such that the first few retain most of the variation present in the original set” (¶181).);
selecting a spectral variable of interest from the set of spectral variables (Lightner discloses: "Predictions can also be made from a regression of the scores against the traits of interest. Calibration models based on scores is called a principal component regression model." (¶186));
receiving observed trait values for the target trait from the set of organisms (Lightner discloses: "analyzing the spectroscopic data to quantitatively measure one or more characteristics (e.g., phenotypic variables) of the one or more progeny plants " (¶133)
and "Desired traits that may be transferred through backcross conversion include, but are not limited to, waxy starch, sterility (nuclear and cytoplasmic), fertility restoration, grain color (white), nutritional enhancements, drought resistance, enhanced nitrogen utilization efficiency, altered nitrogen responsiveness, altered fatty acid profile, increased digestibility, low phytate, industrial enhancements, disease resistance (bacterial, fungal or viral), insect resistance, herbicide resistance and yield enhancements " (¶306).);
determining a first influence metric for the spectral variable of interest based on variable values for the spectral variable of interest and the observed trait values for the target trait (Lightner discloses: “analyzing the spectroscopic data to quantitatively measure one or more phenotypic variables of the one or more progeny plants; assessing proximity of the progeny plant or plants to the desired parental line by reference to the quantitative measure of the one or more phenotypic variables” (¶150) “Predictions can also be made from a regression of the scores against the traits of interest. Calibration models based on scores is called a principal component regression mode” (¶186) Regression analysis was performed to determine the relationship between the principal components and the phenotypic traits. Lightner: “Results show very strong correlation between the actual and predicted as shown by the regression coefficients” (¶273) shows correlation coefficients were calculated between the principal component and the disease resistance trait (¶306). Lightner
further teaches: "The regression equation for chlorophyll consists of a set of terms comprising a regression coefficient as found by PLS-R, and corresponding absorbance value at each of the spectral points … chlorophyll content may be indicative of other things, such as phenotype, health, vitality, yield, etc." (¶¶272-273).);
based on the determination, generating a new organism with the target trait (Lightner discloses: "Backcrossing can be used to transfer a specific desirable trait from one
line… for the desired trait to be transferred from the non-recurrent parent" (¶299) and "Typically after about four or more backcross generations with selection for the desired trait, the progeny will contain essentially all genes of the recurrent parent except for the genes controlling the desired trait " (¶300). Lightner further teaches: "Progeny are selfed and selected so that the newly developed inbred has many of the attributes of the recurrent parent and yet several of the desired attributes of the non-recurrent parent “ (¶301).).
Lightner discloses all of the subject matter as described above except for specifically teaching “determining a variable-to-variable model configured to predict values for the spectral variable of interest based on one or more other spectral variables of the set of spectral variables;
determining substitute variable values for the spectral variable of interest using the variable-to-variable model” and “determining a second influence metric for the spectral variable of interest based on the substitute variable values for the spectral variable of interest and the observed trait values for the target trait; determining that the spectral variable of interest is a causal variable by comparing between the first influence metric and the second influence metric.”
However, Candes in the same field of endeavor teaches determining a variable-to-variable model configured to predict values for the spectral variable of interest based on one or more other spectral variables of the set of spectral variables (Candes “
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” In the Model-X procedure, the construction of the substitute value
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requires knowledge of the conditional distribution (the V2V model),
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. This distribution dictates the predicted relationship of
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to its neighbors (
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) that the substitute variable must mimic);
determining substitute variable values for the spectral variable of interest using the variable-to-variable model (The suitable variable values are the knockoff variables “
X
~
j
” which are constructed probabilistically. Candes: “Correct inference in such a broad setting is achieved by constructing knockoff variables probabilistically instead of geometrically” (Summary, p. 551). Candes: “The random variables
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~
1
…
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~
p
are MX knockoffs for
X
1
…
X
p
if and
only if, for any j ∈ {1,… ,p}, the pair (Xj, ˜Xj) is exchangeable conditionally on all the other
variables and their knockoffs (and, of course, ˜X
⊥
Y|X)” (§3.4.1, p. 563). This ensures the knockoff variables
X
~
) (the substitute values) are statistically exchangeable with the original values X.) and
determining a second influence metric for the spectral variable of interest based on the substitute variable values for the spectral variable of interest and the observed trait values for the target trait (Candes discloses the second influence metric “(
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~
)
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” is the feature statistic (or importance metric) obtained by swapping the original variable X and the subsite variable
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(p. 560));
determining that the spectral variable of interest is a causal variable by comparing between the first influence metric and the second influence metric (Candes discloses the comparison between the first influence metric (Z, for the original variable X) and the second influence metric (
Z
~
, for the substitute variable
X
~
)
is defied by the knockoff test statistic W, “W=Zj –
Z
~
j” which is a threshold to determine causality (variable selection) (p. 561).).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Lightner and Candes before the effective filing date of the claimed invention. The motivation for this combination of references would have been to overcome the inherent risk of false correlation and overfitting cause by high collinearity in Lightner’s spectral data. Candes provides the highly robust statistical solution (Model-X Knockoffs) specifically designed to discriminate true causal variables from noise in high-dimensional datasets. Applying this causality filter to Lightner’s spectral variables predictably yields the desired outcome of an accurate and stable plant trait prediction model.
Claim 2.
The combination of Lightner and Candes discloses the method of Claim 1, wherein the set of organisms comprises organisms at a seed stage (Lightner " The estimation or prediction may be used to select for or against a plant, seed, or condition. " (¶132)).
Claim 3.
The combination of Lightner and Candes discloses the method of Claim 1, wherein the set of organisms comprises organisms at a seedling stage (Lightner " The predictions generated by the methods may be used for selection of a plant or its seed.” (¶149)).
Claim 4.
The combination of Lightner and Candes discloses the method of Claim 1, wherein the one or more spectrograms are received from a spectrometer (Lightner ¶¶192, 324 spectroscopy).
Claim 5.
The combination of Lightner and Candes discloses the method of Claim 1, wherein the one or more spectrograms are received from a camera (Lightner ¶104 “imaging spectroscopy is similar to an image produced by a digital camera”).
Claim 6.
The combination of Lightner and Candes discloses the method of Claim 5, wherein the camera is a drone camera (Lightner ¶241 “to several tens of meters above the ground on a cherry picker or crane, to one-hundred or more meters via a plane, helicopter, or even a satellite, using a hyper-spectral digital imaging system or other specta-gathering devices.”).
Claim 7.
The combination of Lightner and Candes discloses the method of Claim 1, wherein the wavelengths comprise near-infrared wavelengths (Lightner ¶242 “near infrared (NIR)”).
Claim 8.
The combination of Lightner and Candes discloses the method of Claim 1, wherein the plurality of spectrogram attributes comprise at least one of: an absorption, a reflectance, a transmission, or an emission at a wavelength (Lightner “include spectra from one or more wavelengths from the visible light spectrum, from the infrared spectrum, the near-infrared spectrum" (¶112)).
Claim 9.
The combination of Lightner and Candes discloses the method of Claim 1, wherein reducing the dimensionality comprises generating one or more wavelets from the plurality of spectrogram attributes (Lightner "Martens and Naes 1989 describes various pretreatments of data useful in model building. The identified software allows a variety of pretreatments ... mathematical treatments such as smoothing or derivatives are available in the software packages for pretreatment of the data" (¶215). Wavelet transformation is a known mathematical pretreatment technique for spectral data.).
Claim 10.
The combination of Lightner and Candes discloses the method of Claim 1, wherein reducing the dimensionality comprises removing one or more principal components from the plurality of spectrogram attributes (Lightner "PCA method can be used to reduce the dimensionality of a large number of interrelated variables ( absorption intensities at different wavelengths)" (¶181). Lightner further teaches: "PCA replaces the many variables (the individual wavelengths of multi-spectral or hyper spectral spectrum range) with new variables" (¶184)).
Claim 11.
The combination of Lightner and Candes discloses the method of Claim 1, wherein reducing the dimensionality comprises fitting a regression to one or more spectrogram attributes (Lightner teaches regression to one or more spectrogram attributes as a dimensionality reduction technique (¶¶287-290).).
Claim 12.
The combination of Lightner and Candes discloses the method of Claim 1, wherein reducing the dimensionality comprises determining a summary statistic of one or more spectrogram attributes (Lightner discloses: "These reflectance readings at 1.2 nm increments collected over a visible and near infrared wavelength range for each image of each sample canopy can be each stored. Optionally, the reflectance readings for each wavelength for all sample images can be averaged into one hyper-spectral plot" (¶243). Averaging is a summary statistic.).
Claim 13.
The combination of Lightner and Candes discloses the method of Claim 1, wherein determining the variable-to-variable model comprises fitting a regression to one or more spectrogram attributes (Candes teaches the conditional distribution (the V2V model),
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(
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|
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-
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,
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:
j
-
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)
. (p. 563). Candes’s summary on p. 1 discloses “although we provide preliminary experimental evidence that our procedure is robust to unknown or estimated distributions.” Estimating a conditional distribution in statistics often involves a regression model. Lightner discloses regression analysis was performed to determine the relationship between the principal components and the phenotypic traits (¶287) ).
Claim 14.
The combination of Lightner and Candes discloses the method of Claim 1, wherein the variable-to-variable model is configured to generate a distribution of substitute variable values based on the one or more other spectral variables (Candes “Correct inference in such a broad setting is achieved by constructing knockoff variables probabilistically instead of geometrically” (p. 551, Summary). Knockoff variables (sub values) are created via probabilistic process (sampling ), meaning they are generated from a distribution.
“
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, where,
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is a conditional distribution.).
Claim 15.
The combination of Lightner and Candes discloses the method of Claim 1, wherein the first influence metric and the second influence metric are regression coefficients (Lightner discloses regression analysis was performed to determine the relationship between the principal components and the phenotypic traits (¶287) and "correlation coefficients" (¶268) where, the regression coefficient quantifies the strength of association.).
Claim 16.
The combination of Lightner and Candes discloses the method of Claim 1, further comprising determining a target value for the causal spectral variable based on the target trait and generating the new organism based on the target value (Lightner discloses: Plants identified as having favorable spectral signatures associated with disease resistance were selected for breeding (¶432) and "Selection of progeny containing the trait of interest is accomplished by direct selection for a trait associated with a dominant allele" (¶307). This involves determining target spectral values associated with desired trait values.).
Claim 17.
The combination of Lightner and Candes discloses the method of Claim 1, wherein generating the new organism comprises breeding one or more organisms from the set of organisms based on the determination (Lightner teaches: "A plant can be selected for further use in a breeding program based on the predicted constituent from one or more of the models" (¶432) and "These plants may then be grown and self-pollinated, backcrossed, and/or outcrossed" (¶119)).
Claim 18.
The combination of Lightner and Candes discloses the method of Claim 1, wherein generating the new organism comprises altering one or more organisms from the set of organisms based on the determination (Lightner discloses: "The plant cells and/or tissue that have been transformed may be grown into plants" (¶119) and discusses "transgenic trait" applications (¶120). Lightner further teaches: "Such traits include – but are not limited to – insect resistance, com rootworm resistance, herbicide resistance, drought tolerance …" (¶120), indicating that organisms are altered (transformed) to generate new organisms with target traits. Additionally, Lightner discusses: “transformation" as a plant breeding technique (¶440).).
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 Ross Varndell whose telephone number is (571)270-1922. The examiner can normally be reached M-F, 9-5 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, O’Neal Mistry can be reached at (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ross Varndell/Primary Examiner, Art Unit 2674