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 .
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 03/20/2024 and 06/05/2024. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 11-18, and 21 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. A method for detecting diseases in one or more plants, the method comprising:
receiving reflectance signal data for a plant;
identifying a plurality of signal components of the reflectance signal data;
selecting signal components from the plurality of signal components, the selected signal components having a variance satisfying a variance threshold, wherein the variance is between particular signal components of reflectance signal data for a non-diseased plant of a plant species and corresponding signal components of reflectance signal data for a diseased plant of the plant species;
generating reduced-spectrum frequency data based at least in part on the reflectance signal data and the selected signal components;
generating a reflectance signature for the plant based at least in part on the reduced-spectrum frequency data; and
determining a disease state of the plant based at least in part on the reflectance signature.
11. A system for detecting diseases in one or more plants by:
receiving reflectance signal data for a plant, identifying a plurality of signal components of the reflectance signal data;
selecting, from the plurality of signal components, signal components having a variance satisfying a variance threshold;
generating reduced-spectrum frequency data based at least in part on the reflectance signal data and the selected signal components;
generating a reflectance signature for the plant based at least in part on the reduced-spectrum frequency data; and
determining a disease state of the plant based at least in part on the reflectance signature, wherein the variance is between particular signal components of reflectance signal data for a non-diseased plant and corresponding signal components of reflectance signal data for a diseased plant.
21. A computer program product for detecting diseases in one or more plants, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:
receive reflectance signal data for a plant;
identify a plurality of signal components of the reflectance signal data;
select signal components from the plurality of signal components, the selected signal components having a variance satisfying a variance threshold, wherein the variance is between particular signal components of reflectance signal data for a non-diseased plant of a plant species and corresponding signal components of reflectance signal data for a diseased plant of the plant species;
generate reduced-spectrum frequency data based at least in part on the reflectance signal data and the selected signal components;
generate a reflectance signature for the plant based at least in part on the reduced-spectrum frequency data; and
an executable portion configured to determine a disease state of the plant based at least in part on the reflectance signature.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claims 1, 11, and 21 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a step of receiving reflectance signal data is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). The additional limitation of Claim 21 of computer program product is interpreted under broadest reasonable interpretation to be a generic computer element. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2-8 and 12-18 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
Claims 8 and 18 detail the additional limitation of “the system performs one or more automated state-based actions according to the disease state of the plant”. Performing an automated state based action is considered to be mere instructions to apply an exception because the claim is only reciting the idea of a solution or outcome (i.e. perform an action) without detailing how the solution is accomplished. A recited in the MPEP, 2106.05(f), the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide signification more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Claims 9-10 and 19-20 are not rejected under 35 U.S.C. 101 as the additional limitation of Claims 9 and 19 detail “wherein the one or more automated state-based actions comprises updating an area-of-interest map to indicate the disease state of the plant positioned in an area-of-interest and presenting the area-of-interest map via displays of one or more user devices” which represents an additional limitation that integrates the judicial exception into a practical application. Claim 10 is dependent on Claim 9 and Claim 20 is dependent on Claim 19.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 11-17, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hariharan (Jeanette Hariharan, et. al., “The Basis for Development of a Foundational
Biomarker Reflectance Signature Database System for Plant Cell Identification, Disease Detection, and Classification Purposes”, 03/12/2020, IEEE, https://ieeexplore.ieee.org/document/9031170).
In regards to Claims 1, 11, and 21, Hariharan teaches “receiving reflectance signal data for a plant (“Five scans per leaf (five locations per leaf) were taken for each healthy, Lw, and deficient leaf samples. These samples were collected between 350 and 2500 nm utilizing a spectrometer (SVC HR-1024, Spectra Vista Cooperation, NY) with 1.3- nm average spectral resolution and 4° fields of view in laboratory conditions. For data analysis, only the spectral range of 400–970 nm was used. Two halogen light sources were used to create optimal conditions for performing the scans and reducing errors. The SVC device was situated so that the lens was 50 cm above the sample pointing down at it. Spectral signatures were calibrated with a barium sulphate standard reflectance panel (Spectral Reflectance Target, CSTM-SRT-99 100, Spectra Vista Cooperation, NY) before and immediately after every 8 samples measurements. Black fabric was utilized as background” – Section II, Subsection B “Spectral Data Collection”, Sub-sub-section I “Spectral data collection of avocado”, Page 0882; “Hyperspectral data was collected by using a UAV (DJI Matrice 600, Pro Hexacopter) and the same hyperspectral camera, Resonon Pika L 2.4.The UAV-based imaging system includes (i) a Resonon Pika L 2.4 hyperspectral camera (Spectronon Pro, Resonon, Bozeman, MT); (ii) visible-near infrared (V-NIR) objective lenses for the Pika L camera with a focal length of 23 mm, field of view (FOV) of 13.1 degrees, and instantaneous field of view (IFOV) of 0.52 mrad; and (iii) a global positioning system (GPS) and the inertial measurement unit IMU (DJI) flight control system for multi-rotor aircraft, to record sensor position and orientation. Data was collected at 30 m above the ground with a speed of 1.5s/h. The positions of the infected trees were known (leaves were collected and identified by PCR). The maps and images were analyzed by the Spectronon software after hyperspectral data were acquired. Calibration corrections were performed using Resonon hyperspectral data analysis software (Spectronon Pro, Resonon, Bozeman, MT). Georectification and radiometric correction plugins, from the Spectronon Pro software, were used to correct the GPS/IMU and the radiometric data, respectively. The regions of interest were selected manually (and randomly) for each tree, and 20 spectral scans were performed to ensure that the entire canopy was covered spectrally. The regions of interest were then exported as a text file. Pixel-based reflectance data was mixed for each class.” - Section II, Subsection B “Spectral Data Collection”, Sub-sub-section 2 “Outdoor Hyperspectral Data Collection”, Page 0882);
identifying a plurality of signal components of the reflectance signal data (“The first step in the data enhancement process was performing a Standard Normal transformation of the reflectance data, The Standard Normal transformation was used to provide preservation of data integrity, and restructure the data into a reasonable population domain. The Standard Normal transformation used is given by its probability density function of (1)” – Section II, Subsequence C, Sub-sub-section 1 Normalization Data analysis – Page 0882; Divided differences will reveal with higher resolution where inflection points, local minima, and maxima occur in healthy data samples, and how these points vary with the various categories of disease/deficient data sets that are tested. These variants prove to be unique to the signature attributes and are distinguished in the multivariate analysis and characteristic polynomial fit process later developed in this work. Considering how these higher-order data functions correlate at regions of interest in the spectrum allow for the categorization of plants into their respective states (i.e., healthy, deficient, diseased). Methods for second order forward differencing were applied previously [17] for enhancing spectra for the avocado data. In order to reduce the effects of numerical differentiation noise sensitivity, an interpolating polynomial is estimated prior to numerical differentiation. In the case for the citrus data, a five point centered difference formula using Stirling’s formula of divided differences was used to approximate the third order polynomial associated with each state of the hyperspectral data. - Section II, Subsequence C, Sub-sub-section 2 Higher-Order Spectral Analysis – Pages 0882-0883; The interpolating polynomial provided the necessary smoothing of the data such that it was straight forward to apply a derivative formula to the interpolated data. A Newton’s second order divided difference approximation was used to provide accuracy for not only the interior points, but also for the endpoints. The interior points were found by differentiating the three point formula and setting = −ℎ, = , = +ℎ. To achieve (ℎ) approximations for the second derivatives at the endpoints, a four point formula was used. These methods are derived in the literature [19] and are given for our calculations in Table 1 – Page 0883);
selecting signal components from the plurality of signal components, the selected signal components having a variance satisfying a variance threshold, wherein the variance is between particular signal components of reflectance signal data for a non-diseased plant of a plant species and corresponding signal components of reflectance signal data for a diseased plant of the plant species (The formal process of defining spectrums for healthy plants, the detection of diseased species by stochastic analysis based on these spectrums, and classification established through variation of defined signature of healthy verses diseased plant specimens is the premise for this paper. By using multivariate analysis with K-means clustering and applying an optimal orthogonal basis vector for classification, we are able to distinguish between infected and healthy citrus plants. The multivariate approach uses several spectral bands, with the X-variate matrix based on twenty wavebands. The cross-covariance matrix is derived through variance ( ) and covariance ( ) of the X-variate matrix. The eigenvectors are used to distinguish the modal components associated with greatest variance between infected and healthy plant species. We obtain the X-variate matrix, for leaf reflectance at varying wavelengths: X. We can represent this matrix in vector form as X = [x1 , x2 ,… xn ]. The data for the reflection coefficients are given by the matrix elements and represent data in the wavebands from 488 - 569 nm. The cross-covariance matrix is then obtained from cov(xi, xj)= E[(xi-xbari )(xj-xbarj)T ] = sigma2xixj, Where sigma is the covariance of the x independent variates. The KLE is an optimal transformation along all orthogonal component vectors. The KLE process determines a formal decorrelation of signal energy into a redistribution that weighs more heavily the components of highest energy contribution for a particular system. Therefore, the KLE realizes the lowest order model for L that adequately describes the main functional contributors to the equilibrium of a system, in our case, a plant reflectance spectrum which relates to its cellular dynamics, Section II, Subsection C, Sub-Sub-Section 4 Multi-Variate Analysis, Pages 0883-0884);
generating reduced-spectrum frequency data based at least in part on the reflectance signal data and the selected signal components (An approximation for the plant cell reflectance can be mathematically modeled by applying basic signal processing techniques from hyperspectral data, or other sensing devices. Signatures of average reflectance for specific plants can be found as well as variations caused by disease and other environmental factors. The novelty that we address in this paper is that of data significance and data reduction, to provide a feature extraction analysis that most accurately represents the frequency spectrum of a plant, and variations of that signature, caused by disease, and/or other possible stressors. Large sequences that represent a signal usually contain aspects of the signal that are irrelevant, noise-injected and erroneous. Methods to eliminate the singularities and irrelevant discriminant nodes are undertaken in a lean operation after Fourier decompositon is applied. The time domain signal is extracted and reduced series frequency and phase representation (signature) is verified as a feature identification method to be used for classification and diagnostic purposes. – Section D Page 0884);
generating a reflectance signature for the plant based at least in part on the reduced-spectrum frequency data (Having found a way to estimate the frequency spectrum of the plant reflectance signature, it is desirable to accurately extract the feature frequencies that best describe the signature. We consider how the eigenvalues from the KLE effects the dimensional reduction in the Fourier domain. The greatest eigenvalue (by spectral decomposition) is significant in the sense that it relates to the component of greatest energy level of the signal sequence, whether that sequence be developed by a Taylor series or transcendental function expansion. Relating this to our estimate of the cross-covariance matrix (6)… These energy frequencies are associated with the eigenvalues of highest to lowest magnitude ( , … ) of the cross covariance matrix as found by the linear regression approximation found in (10). Projecting these onto the Fourier domain space, the frequency components associated with these higher ranked eigenvalue variables (frequencies, in our case), are mapped onto the frequency domain. The sequence is then truncated such that the frequencies associated with the highest signal energy are used to approximate the truncated series. – Section 2 “KLE Eigenvalues Association”, Page 0885); and
determining a disease state of the plant based at least in part on the reflectance signature (Section III details the results on pages 885-887, with page 0886 detailing the morphological effects of the disease changes the spectrum in the 40-120 ps region with equations 22 representing healthy, equation 23 representing early stage and equation 24 representing asymptomatic).”
In regards to Claims 2 and 12, Hariharan discloses the claimed invention as detailed above. Hariharan further teaches “generating a signature database configured to describe one or more average reflectance signatures for a plant species associated with the plant based at least in part on the reflectance signature for the plant, each of the one or more average reflectance signatures being associated with a particular disease state of the plant species (We have shown in this paper a novel approach to finding optimized frequency spectrums (i.e. signatures) of plants, and how these spectrums vary when various diseases and nutrient deficiencies are present. The method uses multivariate analysis, in particular, KLE to define the highest absolute value of the eigenvectors responsible for the fundamental reflection pattern of the cell and how these patterns are interrupted and changed by disease and malnutrition effects. The application of KLE and spectral decomposition to define the principle eigenvalues of the cross-covariance matrix, played a major role in developing a series truncation process in the frequency domain. Realizing the value of this concept, a relationship between the effective eigenvalues and the primary frequency component transformation process, has allowed us to develop spectral identification features or biomarkers that can be used as healthy plant and disease signatures for classification and diagnostic purposes. A spectral dictionary or database for classification purposes of diseases in plants is also the basic premise set forth in this work. Generating a database of healthy and disease spectra or signatures could be used for diagnostics, based on the underlying principles of optical reflection theory. These signature databases can be useful in disease determination since it would utilize less invasive methods and only rests on the principle of understanding frequency signature components of both disease and healthy specimens. Matched filtering, correlation, convolution and neural network principles could then be readily applied for classification, based on biomarker signature. - Section IV “Conclusions”, Page 0887).”
In regards to Claims 3 and 13, Hariharan discloses the claimed invention as detailed above. Hariharan further teaches “wherein the one or more average reflectance signatures include healthy plant signatures, asymptomatic plant signatures, early disease stage plant signatures, late disease stage plant signatures, and/or nutrient-deficient plant signatures (equations 22 representing healthy, equation 23 representing early stage and equation 24 representing asymptomatic – Page 0886).”
In regards to Claims 4 and 14, Hariharan discloses the claimed invention as detailed above. Hariharan further teaches “wherein the reflectance signature comprises at least one of a truncated frequency series associated with the reflectance signal data, one or more power spectral density magnitudes associated with the selected signal components, or one or more phases associated with the selected signal components (truncated series expansion using the method of Fourier Transform – Page 0884; e energy frequencies are associated with the eigenvalues of highest to lowest magnitude ( , … ) of the cross covariance matrix as found by the linear regression approximation found in (10). Projecting these onto the Fourier domain space, the frequency components associated with these higher ranked eigenvalue variables (frequencies, in our case), are mapped onto the frequency domain. The sequence is then truncated such that the frequencies associated with the highest signal energy are used to approximate the truncated series - Page 0085).”
In regards to Claims 5 and 15, Hariharan discloses the claimed invention as detailed above. Hariharan further teaches “wherein the selected signal components are identified based at least in part on using Karhunen-Loeve Expansion as a kernel function for Mercer's Theorem (The KLE is an optimal transformation along all orthogonal component vectors. The KLE process determines a formal decorrelation of signal energy into a redistribution that weighs more heavily the components of highest energy contribution for a particular system. Therefore, the KLE realizes the lowest order model for L that adequately describes the main functional contributors to the equilibrium of a system, in our case, a plant reflectance spectrum which relates to its cellular dynamics. A KLE estimate of the covariance matrix is approximated with equation 8, then spectral decomposition theorem with equation 9, and variance with eigenvector equation with equation 10. - Pages 0883-0884).”
In regards to Claims 6 and 16, Hariharan discloses the claimed invention as detailed above. Hariharan further teaches “wherein the disease state of the plant is determined based at least in part on providing the reflectance signature to one or more machine learning models configured to predict the disease state of a plant when provided with an input reflectance signature (Generating a database of healthy and disease spectra or signatures could be used for diagnostics, based on the underlying principles of optical reflection theory. These signature databases can be useful in disease determination since it would utilize less invasive methods and only rests on the principle of understanding frequency signature components of both disease and healthy specimens. Matched filtering, correlation, convolution and neural network principles could then be readily applied for classification, based on biomarker signature. – Page 0887).”
In regards to Claims 7 and 17, Hariharan discloses the claimed invention as detailed above. Hariharan further teaches “wherein the one or more machine learning models comprise a clustering model, a bivariance correlation model, and/or a classification model (Generating a database of healthy and disease spectra or signatures could be used for diagnostics, based on the underlying principles of optical reflection theory. These signature databases can be useful in disease determination since it would utilize less invasive methods and only rests on the principle of understanding frequency signature components of both disease and healthy specimens. Matched filtering, correlation, convolution and neural network principles could then be readily applied for classification, based on biomarker signature. – Page 0887).”
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 8-10 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hariharan as applied to claims 1 and 11 above, and further in view of Gui (US20200134392).
In regards to Claims 8 and 18, Hariharan discloses the claimed invention as detailed above. Hariharan is silent with regards to the language of “performing one or more automated state-based actions according to the disease state of the plant.”
Gui teaches “performing one or more automated state-based actions according to the disease state of the plant (For each position of the sliding window, the server 170 can be programmed to apply the first digital model 804 to the portion of the resized image within the sliding window to obtain a classification corresponding to the healthy condition, one of the corn diseases having relatively small symptoms, or the collection of corn diseases having relatively large symptoms. For example, the portions 806 are classified into CR, EYE, SR, GLS-Early, or ND, and the portions 812 are classified into an other diseases (OD) class - [0134]; In some embodiments, the server 170 can be programmed to map each portion of the resized image extracted by the sliding window back into a region of the new image 802 . The server 170 is programmed to further prepare a prediction map for the new image 802 where each mapped region is shown with an indicator of the corresponding classification - [0135]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hariharan to incorporate the teaching of Gui to perform the action of updating a map with details towards the diseased state. By updating a map with disease information of plants, this is an improvement to the tracking and detection of diseases in plants.
In regards to Claims 9 and 19, Hariharan in view of Gui discloses the claimed invention as detailed above. Hariharan is silent with regards to the language of “wherein the one or more automated state-based actions comprises updating an area-of-interest map to indicate the disease state of the plant positioned in an area-of-interest and presenting the area-of-interest map via displays of one or more user devices.”
Gui teaches “wherein the one or more automated state-based actions comprises updating an area-of-interest map to indicate the disease state of the plant positioned in an area-of-interest and presenting the area-of-interest map via displays of one or more user devices (For each position of the sliding window, the server 170 can be programmed to apply the first digital model 804 to the portion of the resized image within the sliding window to obtain a classification corresponding to the healthy condition, one of the corn diseases having relatively small symptoms, or the collection of corn diseases having relatively large symptoms. For example, the portions 806 are classified into CR, EYE, SR, GLS-Early, or ND, and the portions 812 are classified into an other diseases (OD) class - [0134]; In some embodiments, the server 170 can be programmed to map each portion of the resized image extracted by the sliding window back into a region of the new image 802 . The server 170 is programmed to further prepare a prediction map for the new image 802 where each mapped region is shown with an indicator of the corresponding classification - [0135]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hariharan to incorporate the teaching of Gui to perform the action of updating a map with details towards the diseased state. By updating a map with disease information of plants, this is an improvement to the tracking and detection of diseases in plants.
In regards to Claims 10 and 20, Hariharan in view of Gui discloses the claimed invention as detailed above. Hariharan further teaches “wherein the reflectance signal data for the plant was collected via a sensing platform comprising one or more data collection devices configured to acquire reflectance signal data of the plant within an area of interest, the one or more data collection devices being configured to record position and orientation data associated with the collected reflectance signal data (Hyperspectral data was collected by using a UAV (DJI Matrice 600, Pro Hexacopter) and the same hyperspectral camera, Resonon Pika L 2.4.The UAV-based imaging system includes (i) a Resonon Pika L 2.4 hyperspectral camera (Spectronon Pro, Resonon, Bozeman, MT); (ii) visible-near infrared (V-NIR) objective lenses for the Pika L camera with a focal length of 23 mm, field of view (FOV) of 13.1 degrees, and instantaneous field of view (IFOV) of 0.52 mrad; and (iii) a global positioning system (GPS) and the inertial measurement unit IMU (DJI) flight control system for multi-rotor aircraft, to record sensor position and orientation. - [0082]).”
Conclusion
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/YOSSEF KORANG-BEHESHTI/ Primary Examiner, Art Unit 2857
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