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 .
Claim Status
Claims 1-20 are pending and are examined on the merits.
Priority
As detailed on the 11/22/2023 filing receipt, this application was filed 04/26/2023, and there is no claim to earlier priority. Accordingly, the effective filing date of the claimed invention is 04/26/2023. At this point in examination, all claims have been interpreted as being accorded this priority date. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/26/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the list of cited references was considered in full by the examiner. A signed copy of the corresponding 1449 form has been included with this Office action.
Drawings
The drawings filed 04/26/2023 are accepted.
Specification
The specification filed 04/26/2023 is accepted.
Claim rejection - 35 USC§ 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 6, 9, 14, 16, and 19 recite “… the generated satellite image data is substantially similar to the real satellite image data”. The term “substantially” in said claims is a term of relative or vague degree or form of association, neither defined in the specification nor having a well-known and sufficiently particular definition in the art and in the instant context. (MPEP 2173.05(b) pertains.). As such, the recitation of “substantially” renders the claim indefinite. It is not clear what are the metes and bounds of the claimed subject matter. Specifically, the degree of similarity is not clear in said claims.
Claims depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim(s) 1-8 and 16-20 being representative) is directed to a system and (claim(s) 9-15 being representative) is directed to a method. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claim(s) 1-20 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
Claims 1, 9, and 16 recite identifying real satellite image data of the crop growth location, wherein the real satellite image data comprises a plurality of spectral bands; the limitation “identifying”, given the plain meaning of identifying, encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of identifying based on known information/image data.
Claims 1, 9, and 16 further recite analyzing the real satellite image data to determine time-series measurements of the real satellite image data, wherein a time-series measurement comprises an observation of a spectral band of the real satellite image data over a period of time; the limitation “analyzing”, given the plain meaning of analyzing, encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of analyze known information/image data.
Claims 1, 9, and 16 further recite calculating a leaf area index for the plurality of spectral bands, wherein the leaf area index is based on the time-series measurements; the limitation calculating an index is considered a mathematical calculation, and as such, falls into mathematical concepts groupings of abstract ideas.
Claims 1, 9, and 16 further recite determining simulated canopy reflectance parameters based on the real satellite image data and the simulated satellite image data; the limitation determining parameters involves mathematical calculation/ estimating parameters based on known data, as disclosed in specification [0013] and [0043]: “… the agricultural analysis application estimates initial input parameters and provides the estimated input parameters as input into the crop growth simulator.” See also, claim 4: the canopy reflectance simulator is based on the PROSAIL model/ mathematical algorithms. As such, said limitation is considered a mathematical calculation/mathematical process. Said limitation also encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of determining based on known information/image data.
Claims 1, 9, and 16 further recite determining simulated crop growth input parameters, wherein the simulated crop growth input parameters are based on the simulated canopy reflectance parameters and simulated output of the crop growth simulator; the imitation determining input parameters, given the plain meaning of “determining”, encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of determining based on known information/ simulated canopy reflectance parameters and simulated output of the crop growth simulator.
Claims 4, 12, and 18 recite the canopy reflectance simulator is based on the PROSAIL model/mathematical model (mathematical process).
Claims 5, 13, and 18 recite crop growth simulator is based on the WOFOST model/mathematical model (mathematical process).
Claims 6, 14, and 19 recite that the data assimilation module is configured to adjust input into the canopy reflectance simulator until the generated satellite image data is substantially similar to the real satellite image data (mental process of adjusting data).
Claims 7, 15, and 20 recite that the data assimilation module is further configured to adjust input into the crop growth simulator until the simulated crop growth output parameters correspond to at least one of the target crop growth parameters (mental process of adjusting data).
Claims 2, 3, 10-11, and 17 provide further information about the abstract ideas.
The identified claims recite a law of nature, a natural phenomenon (product of nature) and/or fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the reasons set forth above. See MPEP 2106.04 (a)(2) III and MPEP 2106.04 (b) I. Therefore, claims are directed to one or more judicial exception(s) and require further analysis in Prong Two. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claim(s) 1-20 include the following.
Claim1 recites a system comprising: a processor; and memory storing an agriculture analysis application.
Claims 1, 19, and 16 recite providing the leaf area index for the plurality of spectral bands as input into a canopy reflectance simulator, wherein the canopy reflectance simulator is configured to generate simulated satellite image data; generating, via the canopy reflectance simulator, simulated satellite image data based on the leaf area index for the plurality of spectral bands.
Claims 6, 14, and 19 recites providing the simulated satellite image data in the real satellite image data as input into a data assimilation module
Claims 7, 15, and 20 recites providing simulated crop growth output parameters and the simulated canopy reflectance parameters as input into the data assimilation module.
Claim 16 recites a computer-readable storage medium comprising an agriculture analysis application that, when executed by a processor.
The additional elements of a system comprising a processor, a memory and a computer-readable storage medium comprising application are generic computer components and/or processes. There are no limitations that indicate that the processor or memory in the system require anything other than generic computing systems. The courts have found 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. See MPEP 2106.05(f).
Furthermore, the additional elements of providing data/inputting and generating data/outputting amount to necessary data gathering and outputting. The courts have found the limitations that amount to necessary data gathering and outputting are insignificant extra-solution activity that do not integrate a recited judicial exception into a practical application in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and O/P Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (see MPEP 2106.05(g)).
Therefore, the additionally recited elements amount to insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application.
MPEP 2106.04(d). I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
In Step 2A, Prong 1 above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In Step 2B below, any remaining steps and/or elements are therefore in addition to the identified JE(s). Any such additional steps and additional elements are further discussed in Step 2B.
Here in Step 2A, Prong 2, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
At this point in examination, it is not yet the case that any of the Step 2A, Prong 2 considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
In conclusion regarding Prong 2, claims 1-15 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. An inventive concept cannot be furnished by an abstract idea itself. See MPEP § 2106.05.
The additional elements of claim(s) 1-20 include the following.
Claim1 recites a system comprising: a processor; and memory storing an agriculture analysis application.
Claims 1, 19, and 16 recite providing the leaf area index for the plurality of spectral bands as input into a canopy reflectance simulator, wherein the canopy reflectance simulator is configured to generate simulated satellite image data; generating, via the canopy reflectance simulator, simulated satellite image data based on the leaf area index for the plurality of spectral bands.
Claims 6, 14, and 19 recites providing the simulated satellite image data in the real satellite image data as input into a data assimilation module
Claims 7, 15, and 20 recites providing simulated crop growth output parameters and the simulated canopy reflectance parameters as input into the data assimilation module.
Claim 16 recites a computer-readable storage medium comprising an agriculture analysis application that, when executed by a processor.
The additional elements of a system comprising a processor, a memory and a computer-readable storage medium comprising application are conventional computer components and/or processes. The courts have found 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 provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TU Communications LLC v. AV Auto, LLC, 823 F.3d 607,613,118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Furthermore, the additional elements of providing data/inputting and generating data/outputting amount to necessary data gathering and outputting. The courts have found the limitations that amount to necessary data gathering and outputting are insignificant extra-solution activity that do not amount to significantly more (see MPEP 2106.05(g)).
Therefore, the additional element is not sufficient to amount to significantly more than the judicial exception.
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sinah (Estimation of leaf area index using PROSAIL based LUT inversion, MLRA GPR and empirical models: Case study of tropical deciduous forest plantation, North India, International Journal of Applied Earth Observation and Geoinformation, Date: April 2020, pages 2-11; as cited in the attached form 892), in view of Li (Assimilating leaf area index of three typical types of subtropical forest in China from MODIS time series data based on the integrated ensemble Kalman filter and PROSAIL model, ISPRS Journal of Photogrammetry and Remote Sensing 126 (2017) pages: 68–78; as cited in the attached form 892).
Regarding claims 1, 9, and 16, the recited receiving target crop growth parameters and a crop growth location, is taught as, the study area between 29°05′30′' N -29°15′50′' N latitude and 79°20′30′ E -79°28′40′' E longitude; where crop growh parameters are site-specific biophysical and biochemical parameters (Sinha: pg. 2, col. 2, para. 4; Fig. 1; abstract; Table 1).
The recited analyzing the real satellite image data to determine time-series measurements of the real satellite image data, wherein a time-series measurement comprises an observation of a spectral band of the real satellite image data over a period of time, is taught as, using two identical satellites, Sentinel-2A, and Sentinel-2B with 13 spectral bands, with a spatial resolution of 10 m, 20 m, and 60 m. The field of view is 290 mm and revisit time is every five days under the same viewing angle; Sentinel data pre-processing (Sinah: pg. 4, col. 1, para. 2).
The recited calculating a leaf area index for the plurality of spectral bands based on the time-series measurements, is taught as, LAI value from each hemispherical photograph was computed by applying appropriate sky and leaf filters in Plant Canopy Analysis System software. For each ESU, LAI values of three SSUs were averaged and the mean LAI value per ESU was obtained (Sinah: pg. 4, col. 1, first para.)
The recited providing the leaf area index for the plurality of spectral bands as input into a canopy reflectance simulator, wherein the canopy reflectance simulator is configured to generate simulated satellite image data; generating, via the canopy reflectance simulator, simulated satellite image data based on the leaf area index for the plurality of spectral bands, is taught as, parameterizing PROSAIL model based on the available value ranges of the respective biochemical and biophysical variables of the leaf as well as geometrical parameters (solar zenith angle, view azimuth angle and view observer angle) obtained from the field (Table 1); PROSAIL model uses generated combinations of input parameters for the forward run to simulates canopy reflectance. Simulated canopy reflectance was synchronizing with the Sentinel-2 reflectance for model inversion (i.e. LUT-inversion) (Sinah: pg. 4, col. 2, para. 1, subsection: 2.4.2.).
The recited computing system comprising a processor and memory, is inherently taught by Li by teaching simulations/PROSAIL, for example, computer simulations, and machine learning algorithm (Sinah: Pg. 4, and pg. 5, col. 1, first para.)
Sinah does not teach the limitations of determining simulated canopy reflectance parameters based on the real satellite image data and the simulated satellite image data, wherein the simulated canopy reflectance parameters correspond to inputs into the canopy reflectance simulator that generate simulated satellite image data that is substantially similar to the real satellite data. This limitation is taught by Li.
Li assimilates leaf area index of three typical types of subtropical forest in China from MODIS time series data based on the integrated ensemble Kalman filter and PROSAIL model (Li: abstract). Li further teaches dynamically measuring LAI from real remote sensor data, simulating canopy reflectance using PROSAIL model analyzing three parameters (leaf structure, carotenoids, hot parameter) optimizing the model by comparing the modeled and measured data (for example, substantially similar to real data); LAI assimilation of time-series of remote sensing data (pg. 70, co. 2, para. 3- pg. 72; Fig. 2).
Further regarding limitations of determining simulated crop growth input parameters based on the simulated canopy reflectance parameters and simulated output of the crop growth simulator, that when provided as input into a crop growth simulator, cause the crop growth simulator to generate simulated crop growth output parameters that correspond to at least one of the target crop growth parameters, wherein the simulated crop growth input parameters are based on the simulated canopy reflectance parameters and simulated output of the crop growth simulator.
Li teaches simulating yields by coupling MODIS-derived LAIs and WOFOST crop model as evidenced by Zhao (Li: pg. 69, col. 1, para. 1). Li as evidenced by Zhao teaches putting the EnKF in a probabilistic framework, and built a Pywofost model on the WOFOST model with assimilating observations (remote sensing observations or observed data); coupling the WOFOST modeling and remote sensing information; The Pywofost model is designed to make simulations at three levels. Level 1 (Level 1) is retained for regular functions commonly seen in a WOFOST crop model. Level 2 (Level 2) is set for the expression of WOFOST input parameters in the form of ensemble, with the output being the ensemble cor responding to the modeling results. The final modeling results are the averages of elements ensembles. Level 3 (Level 3) is able to couple the WOFOST modeling and remote sensing information. Level 1 and Level 2 have an input and output setting that is identical to the one employed by the WOFOST model. At Level 2, parameter ensembles are randomly generated in line with input data. Level 3 has more input and output items, including observations for assimilation, uncertainty ensembles for observations and model parameters (input), state variables, simulation errors, observation errors, and the Kalman gains (output), expressed in the form of ensembles. The final modeling results would be the averages stemmed from the ensembles (Zhao: pg. 32, col. 2, last para. - pg. 33, col. 1).
Regarding claims 2, 10, and 17, the recited target crop growth parameters comprising at least one of a crop yield, biomass, or water usage, is taught as, coupling remote sensing data and a crop model based on a sequential data assimilation (EnKF) method to estimate maize growth and yield in the northeastern China (Li as evidenced by Zhao: abstract; pg. 32, col. 1, para. 2).
Regarding claims 3 and 11, the recited plurality of spectral bands comprising visible blue-green (475-575 nm), visible orange-red (580-680 nm), and visible red to near-infrared (690-830 nm), is taught as, choosing the best wavelength and cost-function to minimize residual for spectral fitting using LUT- inversion; where LAI sensitive wavebands and VIs of Sentinel-2. Wavebands located at 740nm, 783nm, 842nm, 865nm, and 2190nm showed the highest sensitivity to LAI (Fig. 4). Red edge bands (at 705nm, 740nm, and 783nm) along with NIR bands (at 842 nm and 865nm) showed a stronger correlation with SWIR bands (2190nm). (Sinah: pg. 2, col. 2, para. 2; pg. 5, col. 2, para. 1; Fig. 4).
Regarding claims 4, 12, and 18, the recited canopy reflectance simulator based on the PROSAIL model, is taught as, Using PROSAIL as the canopy reflectance model (Sinah: pg. 4, col. 1, subsection: 2.4)
Regarding claims 5, 13, and 18, the recited crop growth simulator is based on the WOFOST model, is taught as, assimilating crop model (PyWOFOST) which has coupled remote sensing information and a crop model (WOFOST) with the EnKF method (Li as evidenced by Zhao: Zhao: pg. 32, col. 2, para. 1).
Regarding claims 6 , 14, and 19, the recited providing the simulated satellite image data in the real satellite image data as input into a data assimilation module, wherein the data assimilation module is configured to adjust input into the canopy reflectance simulator until the generated satellite image data is substantially similar to the real satellite image data when an entire time-series measurement of the simulated satellite image data in a first spectral band matches an entire time-series measurement of the real satellite image data in a corresponding spectral band, is taught as, assimilating LAI dynamics based on an integrated ensemble Kalman filter using MODIS LAI data, MODIS reflectance data, and canopy reflectance data modeled by PROSAIL, which analyzes model errors according to the Monte Carlo theory and statistical methods to obtain high-accuracy time series LAIs correlates strongly with statistical yields (Li: abstract; pg. 69, col. 1, para. 1).
Regarding claims 7, 15, and 20, the recited providing simulated crop growth output parameters and the simulated canopy reflectance parameters as input into the data assimilation module to adjust input into the crop growth simulator until the simulated crop growth output parameters correspond to at least one of the target crop growth parameters, is taught as, coupling an LAI dynamic model and a radiative transfer model with EnKF, and updating the modeled LAI based on the difference between the modeled reflectance and MODIS reflectance using EnKF methods to provide high accuracy LAIs (Li: pg. 69, col. 1, para. 2). Additionally, said limitation, is taught as, EnKF obtains the forecast error covariance matrix between the state variables and the observed variables through ensemble forecasting, and updates the results of ensemble forecasting based on observations and covariance through the Kalman filter; Level_3 has more input and output items, including observations for assimilation, uncertainty ensembles for observations and model parameters (input), state variables, simulation errors, observation errors, and the Kalman gains (output), expressed in the form of ensembles (Li as evidenced by Zhao: Zhao: pg. 32, col. 2, para. 1; pg. 33, col.1, para. 1.; see also, Fig. 2 for using crop model parameters).
Rational for combining Sinah and Li:
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Sinah and Li, Examiner concludes that this combination represents applying known technique to a known method. Both Sinah and Li are directed to crop data analysis using remote sensing techniques. Sinah disclosed using real satellite data incuding spectral bands to estimate and simulate Leaf Area Index (LAI). In the same field of research, Li provided assimilation of real and simulated satellite data and using a crop growth model based on simulated reflectance and crop growth for the purpose of predicting crop yields. Combining LAI simulation of Sinah with assimilation and crop growth modeling of Li would have allowed estimating the biophysical chemistry characteristics of crops and using them as inputs to run ecosystem-process-based. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining these methods and this combination would have been expected to have improved the accuracy of the remote sensing data and crop growth parameter selection. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sinah, in view of Li, as applied to Claims 1-7, and 9-20 above, and further in view of Englard (US20220309595A1; as cited in the attached 892 form).
Claim 8 depends on claim 1. Limitations of claim 1 is taught in the above rejections.
Regarding claim 8, Sinah and Li do not teach that the target crop growth parameters are based on a persona. This limitation is taught by Englard.
Englard teaches a system and method for managing and operating a closed-loop agricultural-origin-product manufacturing supply chain network including includes: collecting agricultural data from multiple sources relating to multiple growing-plots of crops; collecting environmental data relating to the multiple growing-plots; collecting operational data with regard to intended utilization of the crops at a manufacturing facility; identifying a particular growing-plot; correlating among agricultural data related to the particular growing-plot, and environmental data related to the particular growing-plot, and operational data related to intended utilization of crops from the particular growing-plot (Englard: abstract; claim 1).
Englard further teaches that the data is satellite data [0016]; the collected an analyzed data are crop growth parameters [0013]; The system interfaces with different personas or entities in the network as needed, and facilitates their on-going collaboration [0004]; Multiple personas or entities may access or interact with the system and/or may provide data and/or may obtain data, via a User Experience module [0021]; personas or entities 101, such as the farmer(s) 102, the manufacturer operational unit 103 or agriculture procurement & sourcing department, and the manufacturer managerial unit 104; and enables such entities to request, obtain and consume insights, dashboard-based analytics, and reports via a real-time Analytics and Reports Dashboard/Interface [0040].
Rational for combining Sinah, Li, and Englard:
Applying the KSR standard to Sinah, Li, and Englard, Examiner concludes that this combination represents applying known technique to a known method. Sinah, Li, and Englard are directed to crop data analysis using remote sensing techniques. Sinah and Li disclosed using real satellite data including spectral bands to estimate and simulate Leaf Area Index (LAI) and assimilation of real and simulated satellite data and using a crop growth model based on simulated reflectance and crop growth for the purpose of predicting crop yields. In the same field of research, Englard disclosed an agricultural managing and monitoring platform having different personas or entities in the network to access or interact with the system. Combining LAI simulation and assimilation and crop growth modeling of Sinah and Li with agricultural platform of Englard would have allowed setting user-defined biophysical chemistry characteristics of crops tailoring growth models to specific parameter or region. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining these methods and this combination would have been expected to have improved the accuracy of the remote sensing data and crop growth parameter selection. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GHAZAL SABOUR whose telephone number is (703)756-1289. The examiner can normally be reached M-F 7:30-5:00.
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/G.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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