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 .
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.
Claim status
Claims 1-11, 15-17, 20, 31-34 and 37 are pending. Claims 1-5, 7, 10-11, 15, 17, 20, 31 and 33 are amended. Claims 12-14, 18-19, 21-30 and 35-36 are canceled. Claims 1, 11 and 37 are independent claims. Claims 1-11, 15-17, 20, 31-34 and 37 are examined below.
Priority
As detailed on the 08/29/2022 filing receipt, this application claims priority to as early as 11/06/2019.
Information Disclosure Statement
No Information Disclosure Statement has been provided.
Drawings
The drawings filed 5/04/2022 are accepted.
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 1-11, 15-17, 20, 31-34 and 37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis of claims in Step 1.
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Independent claim 1 is directed to a 101 process, here a "method," with process steps such as "receiving…, applying…"
Independent claim 11 is directed to a 101 machine or manufacture, here a "system," with non-transitory elements such as "hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions."
Independent claim 37 is directed to a 101 process, here a " method for remote sensing of stomatal conductance in a plant" with process steps such as "receiving…"
[Step 1: claims 1-11, 15-17, 20, 31-34 and 37: YES]
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Mental processes recited include:
Claims 1 and 11 recite: "…to predict a stomatal conductance value for said target plant." Predicting is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claims 2 and 11 recite: "…labels associated with stomatal conductance in each of said plants, wherein said spectral data samples in said training set are labeled with said labels." Labeling is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 3 recites: "…measuring reflected light from a canopy of said plant." Measuring is an act of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claims 5 and 15 recite: "…preprocessing step configured for reducing a number of wavelengths in each of said spectral data samples." Reducing a number of wavelengths is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claims 6 and 16 recite: "…preprocessing comprises at least one of: box-car averaging, removal of outlier spectra, applying standard normal variate (SNV) analysis, base-line correction, normalization to the maximum peak within each spectrum, and scaling." Preprocessing is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claims 7 and 17 recite: "... performing a feature selection stage to select an optimal subset of wavelengths from said reduced number of wavelengths, wherein said training set comprises only said optimal subset of spectral bands from each of said spectral data samples" Selecting is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 8 recites: "...wherein said feature selection stage is performed using a regression tree algorithm." Selecting and using a regression tree algorithm are acts of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 9 recites: "wherein said regression tree algorithm is a random forest algorithm with pruning." Using a regression tree algorithm with pruning is an act of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 37 recites: "applying a random forest regression tree algorithm to said spectral data samples, to identify a subset of said spectral wavelengths, based on a spectral wavelength importance measure, wherein said random forest regression tree algorithm comprises pruning associated with at least one of: (i) a total number of decision trees; (ii) a constant value of samples within a single node of each of said decision trees; and (iii) a maximum depth of said regression tree… and predicting a stomatal conductance value for said target plant, based on said spectral data associated with said subset of spectral wavelengths in said spectral data sample." Using a regression tree algorithm with pruning, identifying a subset of said spectral wavelengths and predicting are acts of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Mathematical concepts recited include:
Claims 6 and 16 recite: "preprocessing comprises at least one of: box-car averaging, removal of outlier spectra, applying standard normal variate (SNV) analysis, base-line correction, normalization to the maximum peak within each spectrum, and scaling" are mathematical concepts and/or formulas.
Claim 8 recites: "feature selection stage is performed using a regression tree algorithm." Regression tree algorithm is a mathematical concept and/or formula.
Claim 9 recites: "wherein said regression tree algorithm is a random forest algorithm with pruning" Random forest algorithm is a mathematical concept and/or formula.
Claim 37 recites: "applying a random forest regression tree algorithm to said spectral data samples, to identify a subset of said spectral wavelengths, based on a spectral wavelength importance measure, wherein said random forest regression tree algorithm comprises pruning associated with at least one of: (i) a total number of decision trees; (ii) a constant value of samples within a single node of each of said decision trees; and (iii) a maximum depth of said regression tree… and predicting a stomatal conductance value for said target plant, based on said spectral data associated with said subset of spectral wavelengths in said spectral data sample." The claim limitations are mathematical concept and/or formulas.
Claims 1-9, 11, 15-17 and 37 include claim elements that are involved with acts of evaluating, analyzing, observing and judging data as indicated above. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2) subsection III). Although, claim 11 recites performing the method as part of a method executed on a computer, there are no additional limitations to indicate that anything other than a generic computer is required. However, merely requiring that the steps are carried out with a generic computer does not negate the mental nature of these steps and equates rather to merely using a computer as a tool to perform the mental process. Therefore, under the broadest reasonable interpretation, the indicated claims above can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "Mental processes" grouping of abstract ideas.
Claims 6, 8-9, 16 and 37 recite mathematical concepts and formulas as discussed above. The regression tree and random forest algorithm and the preprocessing of data with box-car averaging, removal of outlier spectra, applying standard normal variate (SNV) analysis, base-line correction, normalization to the maximum peak within each spectrum, and scaling are mathematical concepts and/or formulas that falls under the “mathematical concepts” grouping of abstract ideas.
As such, claims 1-11, 15-17, 20, 31-34 and 37 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional elements that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claim 1 recites “…receiving by a trained machine learning model, as input at an inference stage, a target spectral data sample.”
Claim 2 recites “…receiving, as input at a training stage, a training set comprising:(i) a plurality of spectral data samples wherein each of said spectral data samples represent spectral reflectance from a plant.”
Claim 3 recites “wherein said spectral data samples are obtained by measuring reflected light from a canopy of said plant.”
Claim 4 recites “wherein said spectral data samples are obtained by remote sensing techniques.”
Claims 5 and 15 recite “preprocessing step configured for reducing a number of wavelengths in each of said spectral data samples.”
Claims 7 and 17 recite “wherein said training set comprises only said optimal subset of spectral bands from each of said spectral data samples.”
Claim 11 recites "hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions" and "receive, as input, a plurality of spectral data samples, wherein each of said spectral data samples represents spectral reflectance from a plant; at a training stage, train a machine learning model on a training set comprising: (i) said spectral data sample, and (ii) labels associated with stomatal conductance in each of said plants wherein said spectral data samples in said training set are labeled with said labels…"
Claim 37 recites: "receiving, as input, a plurality of spectral data samples, wherein each of said spectral data samples represents spectral reflectance from a plant in a set of spectral wavelengths..." and "receiving a target spectral data sample associated with a target plant…"
The elements of claims 1-5, 7, 11, 15, 17 and 37 as indicated above equate to insignificant extra solutional activities of data gathering. Data gathering serves as input to the recited judicial exception in the claims. Claim 11 recite "hardware processor" and "a non-transitory computer-readable storage medium having stored thereon program instructions," which equate to generic computer components. Claim 11 invoke the computer components merely as tools to execute the abstract idea. The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. (see MPEP 2106.05(f)). Additionally, the listed additional elements are mere instructions to apply an exception because they recite no more than an idea of a solution or outcome and does not recite a technological solution to a technological problem. (See MPEP 2106.05(f)(1)). As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1-11, 15-17, 20, 31-34 and 37 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claim 1 recites “…receiving by a trained machine learning model, as input at an inference stage, a target spectral data sample.”
Claim 2 recites “…receiving, as input at a training stage, a training set comprising:(i) a plurality of spectral data samples wherein each of said spectral data samples represent spectral reflectance from a plant.”
Claim 3 recites “wherein said spectral data samples are obtained by measuring reflected light from a canopy of said plant.”
Claim 4 recites “wherein said spectral data samples are obtained by remote sensing techniques.”
Claims 5 and 15 recite “preprocessing step configured for reducing a number of wavelengths in each of said spectral data samples.”
Claims 7 and 17 recite “wherein said training set comprises only said optimal subset of spectral bands from each of said spectral data samples.”
Claim 11 recites "hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions" and "receive, as input, a plurality of spectral data samples, wherein each of said spectral data samples represents spectral reflectance from a plant; at a training stage, train a machine learning model on a training set comprising: (i) said spectral data sample, and (ii) labels associated with stomatal conductance in each of said plants wherein said spectral data samples in said training set are labeled with said labels…"
Claim 37 recites: "receiving, as input, a plurality of spectral data samples, wherein each of said spectral data samples represents spectral reflectance from a plant in a set of spectral wavelengths..." and "receiving a target spectral data sample associated with a target plant…"
The additional elements indicated above do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. The limitations equate to mere data gathering activities, which are insignificant extra solutional activities. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. (see MPEP 2106.05(g)). Also, limitations that equate to mere data gathering and outputting via generic computer components, such as receiving data at a computer or outputting data, amount to insignificant extra-solution activity as set forth by the courts in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and OIP Techs., Inc, v, Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Also, the additional elements of claim 11 includes storing and retrieving information in memory. Storing and retrieving information in memory were identified by the courts as well-understood, routine and conventional in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Also, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more as identified by the courts in Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-11, 15-17, 20, 31-34 and 37 are not patent eligible.
Claim Rejections - 35 USC § 102
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.
Claims 1-7, 10 and 33-34 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jarolmasjed ("Proximal hyperspectral sensing of stomatal conductance to monitor the efficacy of exogenous abscisic acid applications in apple trees." Crop protection 109 (2018): 42-50., published 2018; as cited on the attached Notice of References 892 form).
Regarding independent claim 1, Jarolmasjed teaches the claim limitation of receiving by a trained machine learning model, as input at an inference stage, a target spectral data sample with Figure 2 (Page 44). Fig. 2 is a flowchart that explains the processing steps of visible near-infrared (Vis-NIR) reflectance spectra. Fig. 2 depicts reflectance spectra data as inputs to linear and quadratic support vector machine algorithms (QSVM and LSVM) and partial least squares regression (PLSR).
Jarolmasjed teaches the claim limitation of wherein said spectral data samples represents spectral reflectance from a target plant with “Vis-NIR reflectance data were collected using a spectroradiometer (SVC HR-1024i, Spectra Vista Corporation, NY, USA) with a working wavelength of 350–2500nm. The resolution at 700, 1500 and 2100nm, is ≤3.5, ≤9.5 and ≤6.5nm, respectively. Three mature leaves exposed to sunlight were selected from different shoots in each tree, and data were captured using a leaf clip probe (LC-RP PRO, Spectra Vista Corporation, NY, USA). In total, 45 spectra from ABA-treated and un treated (control) tree leaves (total of 45 leaves) were collected. In addition to the proximal Vis-NIR spectral data, images from a modified multispectral digital imager (Canon ELPH110 HS, NJ, USA) and a thermal infrared imager (Tau 2 640, FLIR® Systems, OR, USA) were acquired.” (Page 44, col. 1, para. 2).
Jarolmasjed teaches the claim limitation of applying said trained machine learning model to said target spectral data sample associated with said target plant, to predict a stomatal conductance value for said target plant with “The data analysis was performed using Matlab® software, Statistics and Machine Learning toolboxes (Mathworks, Natick, MA). The Vis-NIR reflectance spectra were normalized and binned by averaging every 10nm spectral interval (Sankaran et al., 2011) prior to further processing and analysis. Partial least square regression (PLSR), linear support vector machine (LSVM), and quadratic support vector machine (QSVM) algorithms were utilized for classification. PLSR is a multi variate analysis that is able to predict a set of dependent variables from a very large set of independent variables (i.e., predictors). This method is useful to predict the canopy status using Vis-NIR spectral signature as the predictor. The method includes partial least square analysis and multiple linear regression with combined features. PLSR extracts latent variables for prediction purposes (Abdi, 2010).” (page 44, col. 2, para. 2) and with “The stomatal conductance prediction was performed using Vis-NIR reflectance spectra and the selected spectral features using PLSR.” (page 45, col. 1, para. 2).
Regarding claim 2, Jarolmasjed teaches the claim limitation of receiving, as input at a training stage, a training set comprising: (i) a plurality of spectral data samples wherein each of said spectral data samples represent spectral reflectance from a plant, and (ii) labels associated with stomatal conductance in each of said plants, wherein said spectral data samples in said training set are labeled with said labels with “The dataset was separated into training (for model development) and testing (for in dependent validation of the developed model) datasets with a ratio of 3:1.” (page 44, col. 2, para. 2) and with Fig. 2 (page 44). Fig. 2 is a flowchart that explains the processing steps of visible near-infrared (Vis-NIR) reflectance spectra.
Jarolmasjed teaches labels associated with stomatal conductance with “Stomatal conductance was measured immediately prior to sensor based measurements in the same location. Measurements were made using a Decagon SC-15 handheld porometer (Meter Group Inc., WA, USA) between 09:00 and 11:00 a.m. on sunny days when the photo synthetically active radiation was between 1200 and 1500μmolm−2 s−1 on two sun-exposed leaves that were approximately 1.5m from the ground. The porometer used in this study considers the air relative humidity and temperature in the calibration process. For leaf measurement, the sensor obtains leaf humidity and estimates the stomatal conductance according to the difference between relative humidity in two conductance elements inside the sensor. Air temperature during measurements was between 18 and 20°C (AgWeatherNet at Washington State University). In this study, the stomatal conductance measurements were compared to the same day visible-near infrared reflectance data of the treated trees.” (Section 2.2, Ground reference measurement).
Regarding claim 3, Jarolmasjed teaches the claim limitation of wherein said spectral data samples are obtained by measuring reflected light from a canopy of said plant with “Vis-NIR reflectance data were collected using a spectroradiometer (SVC HR-1024i, Spectra Vista Corporation, NY, USA) with a working wavelength of 350–2500nm. The resolution at 700, 1500 and 2100nm, is ≤3.5, ≤9.5 and ≤6.5nm, respectively. Three mature leaves exposed to sunlight were selected from different shoots in each tree, and data were captured using a leaf clip probe (LC-RP PRO, Spectra Vista Corporation, NY, USA). In total, 45 spectra from ABA-treated and un treated (control) tree leaves (total of 45 leaves) were collected. In ad dition to the proximal Vis-NIR spectral data, images from a modified multispectral digital imager (Canon ELPH110 HS, NJ, USA) and a thermal infrared imager (Tau 2 640, FLIR® Systems, OR, USA) were acquired. The imagers were mounted on an agricultural utility vehicle (John Deere Gator™ XUV590i, John Deere, IL, USA) connected to a retractable mast (FM50-25, Floatagraph Technologies, CA, USA). The distance between the imagers and trees was approximately 7m. During data collection, the camera was always parallel to the ground surface, and images were acquired from the top of the tree canopies.” (page 44, col. 1, para. 2).
Regarding claim 4, Jarolmasjed teaches the claim limitation of wherein said spectral data samples are obtained by remote sensing techniques with “Proximal and remote sensing techniques such as visible near-infrared (Vis-NIR) spectroscopy and imaging have been used to evaluate biotic stress status.” (Page 43, col. 1, para. 4).
Regarding claim 5, Jarolmasjed teaches the claim limitation of a preprocessing step configured for reducing a number of wavelengths in each of said spectral data samples with “SRA and RFT were applied to the dataset that showed the highest classification accuracy (3 DAT, ABA-Application-2, Table 1) to reduce 217 spectral features acquired from the processed raw data (after normalization and binning) to about 5 spectral features. The spectral features selected from these two models are summarized in Table 2. One set of three spectral features (960, 1140, 1150nm; commonly selected spectral bands from two methods), and another set of five spectral features (580, 730, 960, 1140, 1150nm; with the inclusion of green and red-edge spectral bands from SRA technique) were selected.” (Page 46, col. 2, para. 2).
Regarding claim 6, Jarolmasjed teaches the claim limitation of wherein said preprocessing comprises at least one of: box-car averaging, removal of outlier spectra, applying standard normal variate (SNV) analysis, base-line correction, normalization to the maximum peak within each spectrum, and scaling with “Reference panel reflects 99% of the incident light (expected digital number=255) in each of the spectral bands. If different, the digital numbers are corrected based on the correction factor calculated from the image digital number. Each pixel of the image is multiplied by the correction factor (ratio of 255/digital number of reference panel in the image). This correction normalizes the images for the changing sunlight conditions. Following this correction, GNDVI images were generated. The soil background and leaf shadows were eliminated with a combi nation of k-means clustering (Al Bashish et al., 2011) and thresholding methods (Bulanon et al., 2001).” (Page 44, col. 2, para. 1).
Regarding claim 7, Jarolmasjed teaches the claim limitation of performing a feature selection stage to select an optimal subset of wavelengths from said reduced number of wavelengths, wherein said training set comprises only said optimal subset of spectral bands from each of said spectral data samples with “To reduce the data dimensionality, two feature selection methods, stepwise regression analysis (SRA) and rank features technique (RFT) were used. The bands that were selected by both methods were identified and validated using classification al gorithms. Two additional bands selected by SRA were included and classification algorithms were assessed once more. Fig. 2 outlines the data processing steps used during feature selection and classification processes.” (Page 45, col. 1, para. 2) and with Figure 2 (page 44).
Regarding claim 10, Jarolmasjed teaches the claim limitation of wherein said stomatal conductance is indicative of a water stress status in said target plant with Fig. 7 (page 48). Fig. 7 caption reads Stomatal conductance (SC) and green normalized difference vegetation index (GNDVI) per crop water stress index (CWSI) values at 1 and 3DAT after the ABA Application-2. Bars indicate the standard deviation of the means. A t-test was conducted (α=0.05) for each treatment, the same letter within each dataset shows treatments that were not significantly different. (page 48).
Regarding claim 33, Jarolmasjed teaches the claim limitation of wherein the spectral data comprises: (i) spectral data in a spectral band comprising wavelengths from 1087-1273 nm; and (ii) spectral data in at least one spectral band selected from the group of spectral bands comprising wavelengths from: 673 nm-785 nm, 800 nm-844 nm, 891 nm-1025 nm, and 1341 nm-1661 nm with “The spectral features selected from these two models are summarized in Table 2. One set of three spectral features (960, 1140, 1150nm; commonly selected spectral bands from two methods), and another set of five spectral features (580, 730, 960, 1140, 1150nm; with the inclusion of green and red-edge spectral bands from SRA technique) were selected. In other studies, the wavelengths close to the included green and red edge are suggested to be representative of plant responses to physio logical stresses.” (page 46, col. 2, para. 2) and Table 2 (page 46).
Regarding claim 34, Jarolmasjed teaches the claim limitation of wherein said spectral reflectance data is received from an imaging module comprising a set of imaging sensors, each configured to capture spectral reflectance in only one spectral band selected from the group of spectral bands comprising wavelengths from: 673-785 nm, 800-844 nm, 891-1025 nm, 1087-1273 nm, and 1341-1661 nm with “Vis-NIR reflectance data were collected using a spectroradiometer (SVC HR-1024i, Spectra Vista Corporation, NY, USA) with a working wavelength of 350–2500nm. The resolution at 700, 1500 and 2100nm, is ≤3.5, ≤9.5 and ≤6.5nm, respectively. Three mature leaves exposed to sunlight were selected from different shoots in each tree, and data were captured using a leaf clip probe (LC-RP PRO, Spectra Vista Corporation, NY, USA). In total, 45 spectra from ABA-treated and un treated (control) tree leaves (total of 45 leaves) were collected. In addition to the proximal Vis-NIR spectral data, images from a modified multispectral digital imager (Canon ELPH110 HS, NJ, USA) and a thermal infrared imager (Tau 2 640, FLIR® Systems, OR, USA) were acquired. The imagers were mounted on an agricultural utility vehicle (John Deere Gator™ XUV590i, John Deere, IL, USA) connected to a retractable mast (FM50-25, Floatagraph Technologies, CA, USA). The distance between the imagers and trees was approximately 7m. During data collection, the camera was always parallel to the ground surface, and images were acquired from the top of the tree canopies.” (page 44, col. 1, para. 1) and “The three bands captured in the multispectral images were green (G), blue (B), and near infrared (NIR, 680–800nm) with a resolution of 4608×3456. The thermal imager captured 8-bit images with 327,680 pixels and resolution of 640×512. The imaging sensors were connected to a triggering device and were manually triggered for simultaneous image acquisition. Images were stored in an on-board se cure digital (SD) card and post-processed.” (page 44, col. 1, para. 2).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Jarolmasjed ("Proximal hyperspectral sensing of stomatal conductance to monitor the efficacy of exogenous abscisic acid applications in apple trees." Crop protection 109 (2018): 42-50., published 2018; as cited on the attached Notice of References 892 form) as applied to claims 1-7, 10 and 33-34 above in view of Menze ("A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data." BMC bioinformatics 10.1 (2009): 213.; as cited on the attached Notice of References 892 form).
Jarolmasjed is applied to claims 1-7, 10 and 33-34 as discussed above.
Jarolmasjed does not teach wherein said feature selection stage is performed using a regression tree algorithm of claim 8. However, these limitations are taught by Menze.
Regarding claim 8, Menze teaches the claim limitation of wherein said feature selection stage is performed using a regression tree algorithm with “We propose to combine the best of both approaches, and evaluated the joint use of a feature selection based on a recursive feature elimination using the Gini importance of random forests' together with regularized classification methods on spectral data sets from medical diagnostics, chemotaxonomy, biomedical analytics, food science, and synthetically modified spectral data. Here, a feature selection using the Gini feature importance with a regularized classification by discriminant partial least squares regression performed as well as or better than a filtering according to different univariate statistical tests, or using regression coefficients in a backward feature elimination. It outperformed the direct application of the random forest classifier, or the direct application of the regularized classifiers on the full set of features.” (Abstract, Results) and with “The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data…” (Abstract, Conclusion).
It would have been prima facia obvious to combine the teachings of Jarolmasjed and Menze to arrive at the claimed invention. A person of ordinary skill in the art would have been motivated to modify the method of Jarolmasjed to include a random forest for feature selection as taught by Menze because Menze’s method provides superior means for measuring feature relevance on spectral data (Abstract, Conclusion). Menze’s method would allow for the advantage of selecting the most relevant features. Furthermore, there would have been a reasonable expectation of success, since Jarolmasjed and Menze teach methods that are involved with feature selection.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Jarolmasjed ("Proximal hyperspectral sensing of stomatal conductance to monitor the efficacy of exogenous abscisic acid applications in apple trees." Crop protection 109 (2018): 42-50., published 2018; as cited on the attached Notice of References 892 form) as applied to claims 1-7, 10 and 33-34 above in view of Menze ("A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data." BMC bioinformatics 10.1 (2009): 213.; as cited on the attached Notice of References 892 form) as applied to claim 8 above and further in view of Kulkarni ("Pruning of random forest classifiers: A survey and future directions." 2012 International Conference on Data Science & Engineering (ICDSE). IEEE, 2012.; as cited on the attached Notice of References 892 form).
Jarolmasjed is applied to claims 1-7, 10 and 33-34 and Jarolmasjed and Menze is applied to claim 8 as discussed above.
Jarolmasjed does not teach wherein said regression tree algorithm is a random forest algorithm with pruning of claim 9. However, these limitations are taught by Kulkarni.
Regarding claim 9, Kulkarni teaches the claim limitation of wherein said regression tree algorithm is a random forest algorithm with pruning with “For effective learning and classification of Random Forest, there is need for reducing number of trees (Pruning) in Random Forest.” (Abstract); “They have taken wrapper approach for classifier selection, and used Genetic Algorithm for selecting decision trees to be included in the forest. The selection procedure is done for fixed sizes of subsets as 50, 100, 150 and 200. They concluded that a dynamic algorithm can be designed by a joint measure of maximizing strength and minimizing correlation” (page 67, col. 1, para. 1); and Table 1. Table 1 is A Comparison Chart For Pruning Approaches Of Random Forest (Page 68).
It would have been prima facia obvious to combine the teachings of Jarolmasjed, Menze and Kulkarni to arrive at the claimed invention. A person of ordinary skill in the art would have been motivated to modify the method of Jarolmasjed and Menze to include a random forest with pruning as taught by Kulkarni for effective learning and classification of Random Forest. Furthermore, there would have been a reasonable expectation of success, since Jarolmasjed and Kulkarni teach methods that are involved with feature selection and Meze and Kulkarni teach methods that pertain to random forests.
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 11, 15-17, 20, 31-32 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Jarolmasjed ("Proximal hyperspectral sensing of stomatal conductance to monitor the efficacy of exogenous abscisic acid applications in apple trees." Crop protection 109 (2018): 42-50., published 2018; as cited on the attached Notice of References 892 form) in view of Menze ("A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data." BMC bioinformatics 10.1 (2009): 213.; as cited on the attached Notice of References 892 form).
Regarding independent claim 11, Jarolmasjed teaches the claim limitation of receive, as input, a plurality of spectral data samples with Figure 2 (Page 44). Fig. 2 is a flowchart that explains the processing steps of visible near-infrared (Vis-NIR) reflectance spectra. Fig. 2 depicts reflectance spectra data as inputs to linear and quadratic support vector machine algorithms (QSVM and LSVM) and partial least squares regression (PLSR).
Jarolmasjed teaches the claim limitation of wherein said spectral data samples represents spectral reflectance from a plant with “Vis-NIR reflectance data were collected using a spectroradiometer (SVC HR-1024i, Spectra Vista Corporation, NY, USA) with a working wavelength of 350–2500nm. The resolution at 700, 1500 and 2100nm, is ≤3.5, ≤9.5 and ≤6.5nm, respectively. Three mature leaves exposed to sunlight were selected from different shoots in each tree, and data were captured using a leaf clip probe (LC-RP PRO, Spectra Vista Corporation, NY, USA). In total, 45 spectra from ABA-treated and un treated (control) tree leaves (total of 45 leaves) were collected. In addition to the proximal Vis-NIR spectral data, images from a modified multispectral digital imager (Canon ELPH110 HS, NJ, USA) and a thermal infrared imager (Tau 2 640, FLIR® Systems, OR, USA) were acquired.” (Page 44, col. 1, para. 2).
Jarolmasjed teaches the claim limitation of at a training stage, a training set comprising: (i) said spectral data samples, and (ii) labels associated with stomatal conductance in each of said plants wherein said spectral data samples in said training set are labeled with said labels with “The dataset was separated into training (for model development) and testing (for in dependent validation of the developed model) datasets with a ratio of 3:1.” (page 44, col. 2, para. 2) and with Fig. 2 (page 44). Fig. 2 is a flowchart that explains the processing steps of visible near-infrared (Vis-NIR) reflectance spectra.
Jarolmasjed teaches the claim limitation of at an inference stage, apply said machine learning model to a target spectral data sample associated with a target plant, to predict a stomatal conductance value for said target plant with “The data analysis was performed using Matlab® software, Statistics and Machine Learning toolboxes (Mathworks, Natick, MA). The Vis-NIR reflectance spectra were normalized and binned by averaging every 10nm spectral interval (Sankaran et al., 2011) prior to further processing and analysis. Partial least square regression (PLSR), linear support vector machine (LSVM), and quadratic support vector machine (QSVM) algorithms were utilized for classification. PLSR is a multi variate analysis that is able to predict a set of dependent variables from a very large set of independent variables (i.e., predictors). This method is useful to predict the canopy status using Vis-NIR spectral signature as the predictor. The method includes partial least square analysis and multiple linear regression with combined features. PLSR extracts latent variables for prediction purposes (Abdi, 2010).” (page 44, col. 2, para. 2) and with “The stomatal conductance prediction was performed using Vis-NIR reflectance spectra and the selected spectral features using PLSR.” (page 45, col. 1, para. 2).
Jarolmasjed does not explicitly teach A system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor in claim 11. However, Menze teaches this limitation with “The table reports the runtime for the different feature selection and classification approaches, and the different data sets (on a 2 GHz personal computer with 2 GB memory).” (Table 4 caption, Page 13).
It would have been prima facia obvious to combine the teachings of Jarolmasjed and Menze to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art at the time of the invention to use the generic computer system and hardware as taught by Menze to automatically process spectral data and train machine learning models to efficiently and quickly analyze data. There would have been a reasonable expectation of success, since Jarolmasjed and Menze teach methods that are involved with feature selection and spectra data.
Regarding claim 15, Jarolmasjed teaches the claim limitation of a preprocessing step configured for reducing a number of wavelengths in each of said spectral data samples with “SRA and RFT were applied to the dataset that showed the highest classification accuracy (3 DAT, ABA-Application-2, Table 1) to reduce 217 spectral features acquired from the processed raw data (after normalization and binning) to about 5 spectral features. The spectral features selected from these two models are summarized in Table 2. One set of three spectral features (960, 1140, 1150nm; commonly selected spectral bands from two methods), and another set of five spectral features (580, 730, 960, 1140, 1150nm; with the inclusion of green and red-edge spectral bands from SRA technique) were selected.” (Page 46, col. 2, para. 2).
Regarding claim 16, Jarolmasjed teaches the claim limitation of wherein said preprocessing comprises at least one of: box-car averaging, removal of outlier spectra, applying standard normal variate (SNV) analysis, base-line correction, normalization to the maximum peak within each spectrum, and scaling with “Reference panel reflects 99% of the incident light (expected digital number=255) in each of the spectral bands. If different, the digital numbers are corrected based on the correction factor calculated from the image digital number. Each pixel of the image is multiplied by the correction factor (ratio of 255/digital number of reference panel in the image). This correction normalizes the images for the changing sunlight conditions. Following this correction, GNDVI images were generated. The soil background and leaf shadows were eliminated with a combi nation of k-means clustering (Al Bashish et al., 2011) and thresholding methods (Bulanon et al., 2001).” (Page 44, col. 2, para. 1).
Regarding claim 17, Jarolmasjed teaches the claim limitation of performing a feature selection stage to select an optimal subset of wavelengths from said reduced number of wavelengths, wherein said training set comprises only said optimal subset of spectral bands from each of said spectral data samples with “To reduce the data dimensionality, two feature selection methods, stepwise regression analysis (SRA) and rank features technique (RFT) were used. The bands that were selected by both methods were identified and validated using classification al gorithms. Two additional bands selected by SRA were included and classification algorithms were assessed once more. Fig. 2 outlines the data processing steps used during feature selection and classification processes.” (Page 45, col. 1, para. 2) and with Figure 2 (page 44).
Regarding claim 20, Jarolmasjed teaches the claim limitation of wherein said stomatal conductance is indicative of a water stress status in said target plant with Fig. 7 (page 48). Fig. 7 caption reads Stomatal conductance (SC) and green normalized difference vegetation index (GNDVI) per crop water stress index (CWSI) values at 1 and 3DAT after the ABA Application-2. Bars indicate the standard deviation of the means. A t-test was conducted (α=0.05) for each treatment, the same letter within each dataset shows treatments that were not significantly different. (page 48).
Regarding claim 31, Jarolmasjed teaches the claim limitation of wherein the spectral data comprises: (i) spectral data in a spectral band comprising wavelengths from 1087-1273 nm; and (ii) spectral data in at least one spectral band selected from the group of spectral bands comprising wavelengths from: 673 nm-785 nm, 800 nm-844 nm, 891 nm-1025 nm, and 1341 nm-1661 nm with “The spectral features selected from these two models are summarized in Table 2. One set of three spectral features (960, 1140, 1150nm; commonly selected spectral bands from two methods), and another set of five spectral features (580, 730, 960, 1140, 1150nm; with the inclusion of green and red-edge spectral bands from SRA technique) were selected. In other studies, the wavelengths close to the included green and red edge are suggested to be representative of plant responses to physio logical stresses.” (page 46, col. 2, para. 2) and Table 2 (page 46).
Regarding claim 32, Jarolmasjed teaches the claim limitation of wherein said spectral reflectance data is received from an imaging module comprising a set of imaging sensors, each configured to capture spectral reflectance in only one spectral band selected from the group of spectral bands comprising wavelengths from: 673-785 nm, 800-844 nm, 891-1025 nm, 1087-1273 nm, and 1341-1661 nm with “Vis-NIR reflectance data were collected using a spectroradiometer (SVC HR-1024i, Spectra Vista Corporation, NY, USA) with a working wavelength of 350–2500nm. The resolution at 700, 1500 and 2100nm, is ≤3.5, ≤9.5 and ≤6.5nm, respectively. Three mature leaves exposed to sunlight were selected from different shoots in each tree, and data were captured using a leaf clip probe (LC-RP PRO, Spectra Vista Corporation, NY, USA). In total, 45 spectra from ABA-treated and un treated (control) tree leaves (total of 45 leaves) were collected. In addition to the proximal Vis-NIR spectral data, images from a modified multispectral digital imager (Canon ELPH110 HS, NJ, USA) and a thermal infrared imager (Tau 2 640, FLIR® Systems, OR, USA) were acquired. The imagers were mounted on an agricultural utility vehicle (John Deere Gator™ XUV590i, John Deere, IL, USA) connected to a retractable mast (FM50-25, Floatagraph Technologies, CA, USA). The distance between the imagers and trees was approximately 7m. During data collection, the camera was always parallel to the ground surface, and images were acquired from the top of the tree canopies.” (page 44, col. 1, para. 1) and “The three bands captured in the multispectral images were green (G), blue (B), and near infrared (NIR, 680–800nm) with a resolution of 4608×3456. The thermal imager captured 8-bit images with 327,680 pixels and resolution of 640×512. The imaging sensors were connected to a triggering device and were manually triggered for simultaneous image acquisition. Images were stored in an on-board se cure digital (SD) card and post-processed.” (page 44, col. 1, para. 2).
Regarding claim 33, Jarolmasjed teaches the claim limitation of wherein the spectral data comprises: (i) spectral data in a spectral band comprising wavelengths from 1087-1273 nm; and (ii) spectral data in at least one spectral band selected from the group of spectral bands comprising wavelengths from: 673 nm-785 nm, 800 nm-844 nm, 891 nm-1025 nm, and 1341 nm-1661 nm with “The spectral features selected from these two models are summarized in Table 2. One set of three spectral features (960, 1140, 1150nm; commonly selected spectral bands from two methods), and another set of five spectral features (580, 730, 960, 1140, 1150nm; with the inclusion of green and red-edge spectral bands from SRA technique) were selected. In other studies, the wavelengths close to the included green and red edge are suggested to be representative of plant responses to physio logical stresses.” (page 46, col. 2, para. 2) and Table 2 (page 46).
Regarding claim 34, Jarolmasjed teaches the claim limitation of wherein said spectral reflectance data is received from an imaging module comprising a set of imaging sensors, each configured to capture spectral reflectance in only one spectral band selected from the group of spectral bands comprising wavelengths from: 673-785 nm, 800-844 nm, 891-1025 nm, 1087-1273 nm, and 1341-1661 nm with “Vis-NIR reflectance data were collected using a spectroradiometer (SVC HR-1024i, Spectra Vista Corporation, NY, USA) with a working wavelength of 350–2500nm. The resolution at 700, 1500 and 2100nm, is ≤3.5, ≤9.5 and ≤6.5nm, respectively. Three mature leaves exposed to sunlight were selected from different shoots in each tree, and data were captured using a leaf clip probe (LC-RP PRO, Spectra Vista Corporation, NY, USA). In total, 45 spectra from ABA-treated and un treated (control) tree leaves (total of 45 leaves) were collected. In addition to the proximal Vis-NIR spectral data, images from a modified multispectral digital imager (Canon ELPH110 HS, NJ, USA) and a thermal infrared imager (Tau 2 640, FLIR® Systems, OR, USA) were acquired. The imagers were mounted on an agricultural utility vehicle (John Deere Gator™ XUV590i, John Deere, IL, USA) connected to a retractable mast (FM50-25, Floatagraph Technologies, CA, USA). The distance between the imagers and trees was approximately 7m. During data collection, the camera was always parallel to the ground surface, and images were acquired from the top of the tree canopies.” (page 44, col. 1, para. 1) and “The three bands captured in the multispectral images were green (G), blue (B), and near infrared (NIR, 680–800nm) with a resolution of 4608×3456. The thermal imager captured 8-bit images with 327,680 pixels and resolution of 640×512. The imaging sensors were connected to a triggering device and were manually triggered for simultaneous image acquisition. Images were stored in an on-board se cure digital (SD) card and post-processed.” (page 44, col. 1, para. 2).
Claim 37 is rejected under 35 U.S.C. 103 as being unpatentable over Jarolmasjed ("Proximal hyperspectral sensing of stomatal conductance to monitor the efficacy of exogenous abscisic acid applications in apple trees." Crop protection 109 (2018): 42-50., published 2018; as cited on the attached Notice of References 892 form) in view of Menze ("A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data." BMC bioinformatics 10.1 (2009): 213.; as cited on the attached Notice of References 892 form) and Kulkarni ("Pruning of random forest classifiers: A survey and future directions." 2012 International Conference on Data Science & Engineering (ICDSE). IEEE, 2012.; as cited on the attached Notice of References 892 form).
Regarding independent claim 37, Jarolmasjed teaches the claim limitation of receiving, as input, a plurality of spectral data samples with Figure 2 (Page 44). Fig. 2 is a flowchart that explains the processing steps of visible near-infrared (Vis-NIR) reflectance spectra. Fig. 2 depicts reflectance spectra data as inputs to linear and quadratic support vector machine algorithms (QSVM and LSVM) and partial least squares regression (PLSR).
Jarolmasjed teaches the claim limitation of wherein said spectral data samples represents spectral reflectance from a plant in a set of spectral wavelengths with “Vis-NIR reflectance data were collected using a spectroradiometer (SVC HR-1024i, Spectra Vista Corporation, NY, USA) with a working wavelength of 350–2500nm. The resolution at 700, 1500 and 2100nm, is ≤3.5, ≤9.5 and ≤6.5nm, respectively. Three mature leaves exposed to sunlight were selected from different shoots in each tree, and data were captured using a leaf clip probe (LC-RP PRO, Spectra Vista Corporation, NY, USA). In total, 45 spectra from ABA-treated and un treated (control) tree leaves (total of 45 leaves) were collected. In addition to the proximal Vis-NIR spectral data, images from a modified multispectral digital imager (Canon ELPH110 HS, NJ, USA) and a thermal infrared imager (Tau 2 640, FLIR® Systems, OR, USA) were acquired.” (Page 44, col. 1, para. 2).
Jarolmasjed teaches the claim limitation of receiving a target spectral data sample associated with a target plant with “Vis-NIR reflectance data were collected using a spectroradiometer (SVC HR-1024i, Spectra Vista Corporation, NY, USA) with a working wavelength of 350–2500nm. The resolution at 700, 1500 and 2100nm, is ≤3.5, ≤9.5 and ≤6.5nm, respectively. Three mature leaves exposed to sunlight were selected from different shoots in each tree, and data were captured using a leaf clip probe (LC-RP PRO, Spectra Vista Corporation, NY, USA). In total, 45 spectra from ABA-treated and un treated (control) tree leaves (total of 45 leaves) were collected. In addition to the proximal Vis-NIR spectral data, images from a modified multispectral digital imager (Canon ELPH110 HS, NJ, USA) and a thermal infrared imager (Tau 2 640, FLIR® Systems, OR, USA) were acquired.” (Page 44, col. 1, para. 2).
Jarolmasjed teaches the claim limitation of and predicting a stomatal conductance value for said target plant, based on said spectral data associated with said subset of spectral wavelengths in said spectral data sample with “The stomatal conductance prediction was performed using Vis-NIR reflectance spectra and the selected spectral features using PLSR.” (page 45, col. 1, para. 2).
Jarolmasjed does not teach applying a random forest regression tree algorithm to said spectral data samples, to identify a subset of said spectral wavelengths, based on a spectral wavelength importance measure and wherein said random forest regression tree algorithm comprises pruning associated with at least one of: (i) a total number of decision trees; (ii) a constant value of samples within a single node of each of said decision trees; and (iii) a maximum depth of said regression tree of claim 37. However, these limitations are taught by Menze and Kulkarni.
Menze teaches the claim limitation of applying a random forest regression tree algorithm to said spectral data samples, to identify a subset of said spectral wavelengths, based on a spectral wavelength importance measure with “We propose to combine the best of both approaches, and evaluated the joint use of a feature selection based on a recursive feature elimination using the Gini importance of random forests' together with regularized classification methods on spectral data sets from medical diagnostics, chemotaxonomy, biomedical analytics, food science, and synthetically modified spectral data. Here, a feature selection using the Gini feature importance with a regularized classification by discriminant partial least squares regression performed as well as or better than a filtering according to different univariate statistical tests, or using regression coefficients in a backward feature elimination. It outperformed the direct application of the random forest classifier, or the direct application of the regularized classifiers on the full set of features” (Abstract, Results) and “The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data…” (Abstract, Conclusion).
Kulkarni teaches the claim limitation of wherein said random forest regression tree algorithm comprises pruning associated with at least one of: (i) a total number of decision trees; (ii) a constant value of samples within a single node of each of said decision trees; and (iii) a maximum depth of said regression tree with “For effective learning and classification of Random Forest, there is need for reducing number of trees (Pruning) in Random Forest.” (Abstract); “They have taken wrapper approach for classifier selection, and used Genetic Algorithm for selecting decision trees to be included in the forest. The selection procedure is done for fixed sizes of subsets as 50, 100, 150 and 200. They concluded that a dynamic algorithm can be designed by a joint measure of maximizing strength and minimizing correlation” (page 67, col. 1, para. 1); and Table 1. Table 1 is A Comparison Chart For Pruning Approaches Of Random Forest (Page 68) and it includes limiting a number of trees in RF.
It would have been prima facia obvious to combine the teachings of Jarolmasjed, Menze and Kulkarni to arrive at the claimed invention. A person of ordinary skill in the art would have been motivated to modify the method of Jarolmasjed to apply a random forest regression tree algorithm to said spectral data samples as taught by Menze for the advantage of selecting the most relevant features because Menze’s method provides superior means for measuring feature relevance on spectral data (Abstract, Conclusion). A person of ordinary skill in the art would have also been motivated to modify the method of Jarolmasjed and Menze to include a random forest with pruning as taught by Kulkarni for effective learning and classification of Random Forest. Furthermore, there would have been a reasonable expectation of success, since Jarolmasjed and Kulkarni teach methods that are involved with feature selection and Meze and Kulkarni teach methods that pertain to random forests.
Conclusion
No claims are allowed.
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/K.K./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686