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 .
DETAILED ACTION
This action is responsive to the following communication: Preliminary Amendment filed Jan. 15, 2025.
Claims 1-27 are pending in the case. Claims 1, 2 and 18 are independent claims.
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.
Claims 1-27 are rejected under 35 U.S.C. 103 as being unpatentable over Hazebroek et al. (hereinafter Hazebroek) U.S. Patent Publication No. 2012/0119080 in view of Jia (hereinafter Jia) U.S. Patent Publication No. 2023/0139567.
With respect to independent claim 1, Hazebroek teaches a method for predicting a phenotype of interest for at least one plant (see e.g., Abstract and Para [5]-[9][12]-“predicting the development of a phenotype or trait of interest in an independent plant that was not used to establish the unbiased model of the invention. In one embodiment, the unbiased models of the invention are applied to the metabolic profile of an immature, independent plant in order to predict the development of a phenotype or trait of interest in the plant. In another such embodiment, immature plants are selected based on their predicted develop of a phenotype or trait of interest.”), said method comprising:
obtaining one or more data profiles from at least two groups of training plants (see e.g., Para [10]-[18] and Claim 1 -“establishing an unbiased model using the metabolic profile and phenotypic profile of at least two groups of plants, said method comprising: a) characterizing the phenotypic profiles of said at least two groups of plants, wherein said at least two groups of plants have different phenotypes, or wherein said at least two groups of plants are grown under different environmental conditions”),
wherein said at least two groups of training plants have different identified phenotypes for a phenotype of interest (see e.g., Para [10] Claim 1 - “two groups of plants have different phenotypes, or wherein said at least two groups of plants are grown under different environmental conditions”),
wherein the at least two groups of training plants are grown under the same conditions (see e.g., Para [10] Claim 1 - “two groups of plants have different phenotypes, or wherein said at least two groups of plants are grown under different environmental conditions” It is obvious that the two groups of plants are grown under same conditions when they have different phenotypes.);
predicting the phenotype of interest for at least one test plant by inputting a data profile from a test plant into the established supervised learning model to predict a phenotype for the phenotype of interest for the at least one test plant (see e.g., Para [9]-[26][127]-“to establish unbiased models to predict a phenotype or trait of interest in an independent, immature plant.”).
Hazebroek does not expressly show using the one or more data profiles to establish a supervised learning model for predicting a phenotype of interest. However, Hazebroek expressly teaches metabolic profile and phenotypic profile as input data (see e.g. claim 1). Furthermore, Jia teaches similar features (see e.g., Para [46] [53] [55] [60] [62] [67]-“ Supervised learning is a method that may be used to train a model … an untrained model may be trained using labeled training inputs, i.e., training inputs with known outputs, such as reconstructed multi-omics or multi-modal data with observed phenotype data for at least one phenotype of interest. The training inputs may be provided to an untrained model to generate a predicted output, such as a predicted phenotype for a trait.”). Both Hazebroek and Jia are directed to phenotypes prediction methods. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Hazebroek and Jia in front of them to modify the system of Hazebroek to include the above feature. The motivation to combine Hazebroek and Jia comes from Jia. Jia discloses the motivation to create a supervised learning model for prediction so that the prediction performance can be improved (see e.g. paras [4][5]). This motivation for combination also applies to the remaining claims which depend on this combination.
With respect to in dependent claim 2, the modified Hazebroek teaches a method for establishing a supervised learning model (see e.g., Jia Para [46] [53] [55] [60] [62] [67] and discussion above with respect to claim 1) using the data profile of at least two groups of training plants, said method comprising:
characterizing one or more data profiles of at least two groups of training plants, wherein said at least two groups of training plants have different identified phenotypes for a phenotype of interest (see e.g., Para [10]-[18] and Claim 1 -“establishing an unbiased model using the metabolic profile and phenotypic profile of at least two groups of plants, said method comprising: a) characterizing the phenotypic profiles of said at least two groups of plants, wherein said at least two groups of plants have different phenotypes, or wherein said at least two groups of plants are grown under different environmental conditions”),
wherein the at least two groups of training plants are grown under the same conditions (see e.g., Para [10] Claim 1 - “two groups of plants have different phenotypes, or wherein said at least two groups of plants are grown under different environmental conditions” It is obvious that the two groups of plants are grown under same conditions when they have different phenotypes.); and
establishing a supervised learning model using as input the one or more data profiles from the at least two groups of training plants, whereby the model predicts a phenotype for the phenotype of interest based on the one or more data profiles (see e.g., Jia Para [46] [53] [55] [60] [62] [67]-“ Supervised learning is a method that may be used to train a model … an untrained model may be trained using labeled training inputs, i.e., training inputs with known outputs, such as reconstructed multi-omics or multi-modal data with observed phenotype data for at least one phenotype of interest. The training inputs may be provided to an untrained model to generate a predicted output, such as a predicted phenotype for a trait.” Also see discussion above with respect to claim 1).
With respect to in dependent claim 3, the modified Hazebroek teaches growing the at least two groups of training plants under the same non-stress conditions or same stress conditions (see e.g., Para [10] Claim 1 - “two groups of plants have different phenotypes, or wherein said at least two groups of plants are grown under different environmental conditions” It is obvious that the two groups of plants are grown under same conditions when they have different phenotypes. There is no limitation regarding the type of conditions).
With respect to in dependent claim 4, the modified Hazebroek teaches the one or more data profiles comprises genomic data profiles, transcriptomic data profiles, proteomic data profiles, metabolomic data profiles, spectral data profiles, or phenotypic data profiles (see e.g., Jia Para [46][62] – “the generated latent representations are invariant to the selection of particular genomic, exomic, epigenomic, transcriptomic, proteomic, metabolomic, hyperspectral, or phenomic association features.”).
With respect to dependent claim 5, the modified Hazebroek teaches the supervised learning model is a regression or classification model (see e.g., Jia Para [57]–[61]).
With respect to dependent claim 6, the modified Hazebroek teaches selecting the at least one test plant based on the predicted phenotype for the phenotype of interest (see e.g., Jia Para [69]-[72]).
With respect to dependent claim 7, the modified Hazebroek teaches growing the selected at least one test plant in a plant growing environment (see e.g., Jia Para [74] -“the methods may also include growing the selected plants or a part thereof in a plant growing environment, such as a greenhouse, a laboratory, a field, or any other suitable environment. The one or more organisms, such as one or more plants or microorganisms, including populations or one or more members thereof, that are predicted to have at least one desired phenotype of interest may be crossed with another plant or animal.”).
With respect to dependent claim 8, the modified Hazebroek teaches the phenotype of interest is an agronomic trait of interest (see e.g., Jia Para [72] Claim 5).
With respect to dependent claim 9, the modified Hazebroek teaches the phenotype of interest comprises disease resistance, drought tolerance, standability, yield, heat tolerance, cold tolerance, salinity tolerance, metal tolerance, herbicide tolerance, improved water use efficiency, nitrogen utilization, nitrogen fixation, pest resistance, or herbivore resistance (see e.g., Jia Para [72][73]).
With respect to dependent claim 10, the modified Hazebroek teaches the data profiles comprise genomic data profiles, transcriptomic data profiles, proteomic data profiles, metabolomic data profiles, spectral data profiles, or phenotypic data profiles (see e.g., Jia Para [44]-[48][66]-“ Examples of omics data includes but is not limited to genomic, exomic, epigenomic, transcriptomic, proteomic, metabolomic, or phenomic data, such as hyperspectral data.”).
With respect to dependent claim 11, the modified Hazebroek teaches predicting a phenotype for the phenotype of interest for at least one plant by inputting a data profile from a test plant into the established supervised learning model to predict a phenotype for the phenotype of interest for the test plant (see e.g., Jia Para [60]-[65] – “the data, such as reconstructed input data from the training population and observed phenotype data for at least one phenotype of interest from the training population, is inputted into a model that is trained using supervised learning for use in predicting a phenotype of interest for an organism, such as a plant or microorganism. In some aspects, reconstructed input data for the testing population serves as the input data for predicting at least one or more phenotypes of interest for an organism in a trained supervised learning model”).
With respect to dependent claim 12, the modified Hazebroek teaches selecting the at least one test plant based on the predicted phenotype for the phenotype of interest (see e.g., Jia Para [73] – “The methods may include selecting one or more members of the testing population having a desired predicted value for the phenotype of interest. In some examples, the one or more selected plants are predicted to exhibit an improved or increased desirable phenotype of interest, such increased yield, increased drought resistance, or improved standability, as compared to a control plant or control plant population.”).
With respect to dependent claim 13, the modified Hazebroek teaches growing the selected at least one test plant in a plant growing environment (see e.g., Jia Para [74]- “Accordingly, the methods may also include growing the selected plants or a part thereof in a plant growing environment, such as a greenhouse, a laboratory, a field, or any other suitable environment.”).
With respect to dependent claim 14, the modified Hazebroek teaches the data profile of the test plant and the data profiles of the at least two training groups of plants are the same type of data profiles (see e.g., Jia Para [50] [66]–“Accordingly, the methods may also include growing the selected plants or a part thereof in a plant growing environment, such as a greenhouse, a laboratory, a field, or any other suitable environment.”).
With respect to dependent claim 15, the modified Hazebroek teaches the supervised learning model is established using multivariate analysis of the one or more data profiles (see e.g., Claim 1).
With respect to dependent claim 16, the modified Hazebroek teaches the supervised learning model is established using multivariate analysis of the one or more data profiles to relate the one or more data profiles to phenotypic data profiles (see e.g., Claim 1 – Partial-least-squares calibration based on metabolic and phenotypic profile data).
With respect to dependent claim 17, the modified Hazebroek teaches the supervised learning model's performance for phenotype prediction is evaluated using ROC (Receiver Operating Characteristics) analysis and AUC (Area Under The Curve) values (The examiner notes that the claim feature is well-known in the art).
With respect to independent claim 18, the modified Hazebroek teaches a system for use in predicting a phenotype of interest for a plant, the system comprising: one or more servers, each of the one or more server storing plant data profiles; and a computing device communicatively coupled to the one or more servers, the computing device comprising: a memory; and one or more processors configured to: obtain data profiles for two groups of training plants, wherein the data profiles from the two groups of plants have different identified phenotypes for a phenotype of interest (see e.g., Para [10]-[18] and Claim 1 -“establishing an unbiased model using the metabolic profile and phenotypic profile of at least two groups of plants, said method comprising: a) characterizing the phenotypic profiles of said at least two groups of plants, wherein said at least two groups of plants have different phenotypes, or wherein said at least two groups of plants are grown under different environmental conditions”), wherein the data profiles are obtained from the at least two groups of plants grown under the same conditions (see e.g., Para [10] Claim 1 - “two groups of plants have different phenotypes, or wherein said at least two groups of plants are grown under different environmental conditions” It is obvious that the two groups of plants are grown under same conditions when they have different phenotypes.); analyze or learn phenotype prediction from the data profiles using a supervised learning model (see e.g., discussion above with respect to claim 1 and Jia Para [46] [53] [55] [60] [62] [67]-“ Supervised learning is a method that may be used to train a model … an untrained model may be trained using labeled training inputs, i.e., training inputs with known outputs, such as reconstructed multi-omics or multi-modal data with observed phenotype data for at least one phenotype of interest. The training inputs may be provided to an untrained model to generate a predicted output, such as a predicted phenotype for a trait.”); obtain a data profile for a test plant; and predict the phenotype of a phenotype of interest for the test plant (see e.g., Jia Para [60]-[65] – “the data, such as reconstructed input data from the training population and observed phenotype data for at least one phenotype of interest from the training population, is inputted into a model that is trained using supervised learning for use in predicting a phenotype of interest for an organism, such as a plant or microorganism. In some aspects, reconstructed input data for the testing population serves as the input data for predicting at least one or more phenotypes of interest for an organism in a trained supervised learning model”).
With respect to dependent claim 19, the modified Hazebroek teaches the phenotype of interest comprises disease resistance, drought tolerance, standability, yield, heat tolerance, cold tolerance, salinity tolerance, metal tolerance, herbicide tolerance, improved water use efficiency, nitrogen utilization, nitrogen fixation, pest resistance, or herbivore resistance (see e.g., Jia Para [68][69]).
With respect to dependent claim 20, the modified Hazebroek teaches the data profile of the test plant and the data profiles of the at least two training groups of plants are the same type of data profiles (see e.g., Jia Para [50] [66]–“Accordingly, the methods may also include growing the selected plants or a part thereof in a plant growing environment, such as a greenhouse, a laboratory, a field, or any other suitable environment.”), wherein the data profiles comprise genomic data profiles, transcriptomic data profiles, proteomic data profiles, metabolomic data profiles, spectral data profiles, or phenotypic data profiles (see e.g., Jia Para [46][62] – “the generated latent representations are invariant to the selection of particular genomic, exomic, epigenomic, transcriptomic, proteomic, metabolomic, hyperspectral, or phenomic association features.”).
With respect to dependent claim 21, the modified Hazebroek teaches growing the at least two groups of training plants under the same non-stress conditions or same stress conditions (see e.g., Para [10] Claim 1 - “two groups of plants have different phenotypes, or wherein said at least two groups of plants are grown under different environmental conditions” It is obvious that the two groups of plants are grown under same conditions when they have different phenotypes. There is no limitation regarding the type of conditions).
With respect to dependent claim 22, the modified Hazebroek teaches the supervised learning model is a regression or classification model (see e.g., Jia Para [57]–[61]).
With respect to dependent claim 23, the modified Hazebroek teaches the phenotype of interest is an agronomic trait of interest (see e.g., Jia Para [72] Claim 5).
With respect to dependent claim 24, the modified Hazebroek teaches the phenotype of interest comprises disease resistance, drought tolerance, standability, yield, heat tolerance, cold tolerance, salinity tolerance, metal tolerance, herbicide tolerance, improved water use efficiency, nitrogen utilization, nitrogen fixation, pest resistance, or herbivore resistance (see e.g., Jia Para [72][73]).
With respect to dependent claim 25, the modified Hazebroek teaches the supervised learning model is established using multivariate analysis of the one or more data profiles (see e.g., Claim 1).
With respect to dependent claim 26, the modified Hazebroek teaches the supervised learning model is established using multivariate analysis of the one or more data profiles to relate the one or more data profiles to phenotypic data profiles (see e.g., Claim 1 – Partial-least-squares calibration based on metabolic and phenotypic profile data).
With respect to dependent claim 27, the modified Hazebroek teaches the supervised learning model is established using multivariate analysis of the one or more data profiles to relate the one or more data profiles to phenotypic data profiles (see e.g., Claim 1 – Partial-least-squares calibration based on metabolic and phenotypic profile data).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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