Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Rejections made under 35 U.S.C. 112(b) have been withdrawn.
Applicant’s arguments, see Remarks, filed 06/02/2026, with respect to 35 U.S.C. 101 have been fully considered and are persuasive. The rejections of claims 1-12 and 14-16 made under 35 U.S.C. 101 have been withdrawn.
Applicant’s arguments with respect to the prior art rejections of claims 1-5, 8-12, and 14-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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On pages 12-13, Applicant argues,
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While examiner substantially agrees with the first and third points, examiner asserts the second point is taught by Kim.
Examiner notes the claim does not require steps for identifying the primary producer by combining morphological feature (shapes) with spectral features NDVI and NDWI indices) as argued by applicant, but merely requires an association made between both the shapes and calculated indices with the primary producers in the images. Kim, pg. 9, lines 28-35, teaches classifying trees (the primary producers) in satellite images based on their “color, shape, size, leaf, fruit, tree trunk, etc.” This classification constitutes an association being made between a predetermined shape and a primary producer in the satellite image. Kim, pg. 10, lines 1-30, further teaches subsequently calculating the NDVI for those classified trees. This process can reasonably be interpreted as a form of associating the extracted color features (NDVI) with the primary producers of the satellite images. The primary producer is therefore associated with both its shape features and its NDVI feature, fully satisfying the claim language.
Examiner notes the combination of Kim in view of Wang and further in view of Koch outlined below teaches the missing elements of claim 1.
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On pages 13-14, Applicant argues
Examiner notes the new grounds of rejection made under the combination of Kim in view of Wang and further in view of Koch renders obvious the claimed invention according to claim 1. Specifically, Wang teaches the missing element of “computing, based on each top-down image, colour features comprising a normalized difference vegetation index and a normalized difference water index”. Wang, pgs. 13 and 14, lines 10-36 and 1-14, respectively, teaches implementation of specific classification rules that utilize both NDVI and NDWI measurements. One of ordinary skill in the art could have easily integrated Wang’s classification rules with the teachings of Kim, as Kim already computes NDVI values and performs tree classification. The combination would modify Kim’s tree classification to include NDWI calculation alongside the NDVI values for more robust decision-making.
This combination directly results in the “specific claimed algorithm” argued by the applicant, as it provides a clear method for classifying trees based on an association with both measured NDVI and NDWI values. One of ordinary skill would have been motivated to combine these references to better separate trees from other components in the image, thereby improving overall classification accuracy. (see 35 U.S.C. 103 rejection below for additional details).
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On pages 14-15 applicant argues,
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
As argued above, combining Kim in view of Wang results in a system which implements a specific method for monitoring carbon capture of primary producers from a series of top-down images, which utilizes associations of shape and color features (NDVI and NDWI) with primary producers to compute and monitor carbon capture over time. Specifically, Kim pgs. 13 and 14, lines 33-36 and 1-8, respectively, teaches predicting future NDVI values and corresponding carbon capture. This process can reasonably be interpreted as “monitoring… an evolution of the carbon capture indicator over time”, as required by claim 1. Thus, the combination of Kim in view of Wang teaches all the limitations of claim 1 aside from “outputting an alert signal if a corresponding variation over time is outside a predetermined range.” Koch, columns 31 and 32, lines 44-67 and 1-2, respectively, teaches implementing an event threshold applied to environmental data, such as carbon dioxide levels, to alert users when monitored data falls outside the predetermined range. It would have been obvious to one of ordinary skill in the art to have modified the system of Kim in view of Wang to include the alerting logic of Koch. The motivation for doing so would have been to provide users with event notifications instead of required them to manually interpret the monitored data over time. One of ordinary skill could have easily integrated Koch’s event thresholding with the system of Kim in view of Wang, as the monitored carbon capture data from Kim in view of Wang is the exact input required for such an alerting function. Thus, the combination is not an improper stitch, but a predictable addition of standard user-interface features to a carbon capture monitoring system.
Claim Interpretation
Note that according to the Federal Circuit’s 2004 Superguide v. DirecTV decision, “at least one of
… and … “ requires at least one instance of each and every item listed. Claim 5 contain such limitations,
however, the specification supports a disjunctive interpretation (see “and/or” in paragraph 66). For examination purposes, the limitations be interpreted under the broader disjunctive interpretation, requiring at least one instance of any of the items listed.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is: “processing unit” in claims 1 and 16.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification (see paragraph 0073) as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, 8-9, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 102502154 B1), (hereinafter Kim) in view of Wang et al. (CN 111488902 A), (hereinafter Wang) and further in view of Koch et al. (US 10768340 B2), (hereinafter Koch).
Regarding claim 1, Kim teaches a computer-implemented method (20) for assessing carbon capture in an area of interest (Kim, “The present invention relates to a system for predicting carbon dioxide capture amount, and more particularly, to derive a vegetation index from a video image of a tree derived from a satellite image, and to predict the carbon dioxide capture amount of the tree from the derived vegetation index using a satellite image.”, pg. 2, lines 4-8), the method being executed by a remote monitoring system including a knowledge database and a processing unit (Kim, “The sample tree input unit 310 may receive input of the species and age of the sample tree from a previously constructed database.”, pg. 11, lines 1-2, The method is performed using a system including a databases and a processor to receive and use the data.), said method comprising:
retrieving by the processing unit, from the knowledge database, a series of top-down images of at least part of the area of interest acquired at different acquisition dates (Kim, “First, referring to FIG. 3 (a), the image editing unit 110 captures trees in a certain area for a certain period of time according to a user setting, and the satellite image receiver 100 receives the satellite image.”, pg. 9, lines 10-12, The process is performed for different acquisition times according to a user-defined period, during which a set of satellite images are used to compute vegetation index values.);
implementing (22) by the processing unit at least one image analysis algorithm on said series of top-down images of at least part of the area of interest, to determine environmental data representative of at least one primary producer and/or at least one biotope of the area of interest, wherein said at least one image analysis algorithm comprises computing, based on each top-down image, colour features comprising a normalized difference vegetation index (Kim, “The satellite image receiving unit 100 may receive the satellite image of trees in the certain area from a satellite carrying a camera or radar and taking a picture or image of the ground surface through wireless communication. The vegetation index deriving unit 200 may derive the vegetation index of the trees in the predetermined area by checking the wavelength of the trees in the predetermined area from the satellite image.”, pg. 8, lines 6-12, “For example, the vegetation index derivation unit 200 determines the wavelength of trees in a certain area from the effective image determined by the image editing unit 110 and the tree classification unit 121 of the image analysis unit 120. The vegetation index of trees in a certain area may be derived by checking information classified by tree species and age of trees in a certain area and area information of trees in the certain area calculated by the tree quantity calculation unit 122. Here, the specific wavelength of the tree in the certain area is a red wavelength corresponding to a Normalized Difference Vegetation Index (NDVI) wavelength and a near-infrared wavelength, between red light and near-infrared rays corresponding to a Normalized Difference Red Edge (NDRE).”, pg. 10, lines 15-25, Satellite images of a target area are collected and processed to determine a normalized difference vegetation index (NDVI) for trees in the area.), and performing image recognition to associate predetermined shapes and/or predetermined combinations of shapes and the extracted colour features in the retrieved top-down image with the at least one primary producer and/or with the at least one biotope (Kim, “The tree classification unit 121 can classify trees by species and age by checking information such as color, shape, size, leaf, fruit, tree trunk, etc. of the trees in a certain area from the satellite image, and the tree quantity calculation unit 122 may calculate the area of trees in the certain area classified by the tree classification unit 121 in the satellite image, and the area of the trees in the certain area analyzed by the tree classification unit 121 and the tree quantity calculation unit 122 may be calculated. The amount of carbon dioxide capture can be predicted using tree species, age, and area information and the vegetation index to be described later, which will be described later.”, pg. 9, lines 28-35, Image recognition is performed including classifying trees (primary producer) in the satellite images based on their shape and then calculating the NDVI for those classified trees. These processes define associations made between both stored shape information and NDVI measurements for the trees in the satellite images.); and
based on the determined environmental data, computing (24) by the processing unit a carbon capture indicator representative of an estimated carbon capture potential of the area of interest (Kim, “The machine learning unit 300 may build a learning model for deriving the carbon dioxide capture amount of trees in a certain area by machine learning information on sample trees provided with the carbon dioxide capture amount and vegetation index for each tree species and age.”, pg. 8, lines 14-17, “Matching and performing machine learning, it is possible to construct the carbon dioxide capture amount prediction learning model in which the sample carbon dioxide capture amount per certain area is calculated according to the tree species, age, and vegetation index of the sample tree.”, pg. 11, lines 29-33, The NDVI along with tree species and age information are input to a machine learning model to estimate the carbon capture amount of the area.) wherein the computed carbon capture indicator is associated with a date of acquisition of each corresponding top-down image (Kim, “First, referring to FIG. 3 (a), the image editing unit 110 captures trees in a certain area for a certain period of time according to a user setting, and the satellite image receiver 100 receives the satellite image.”, pg. 9, lines 10-12, For each satellite image processed, NDVI are computed to predict a corresponding carbon capture. These satellite images include a date of acquisition allowing users to certain periods for analysis. Thus, the resulting carbon capture predictions are functionally associated with that specific date.); and
monitoring (28) by the processing unit an evolution of the carbon capture indicator over time (Kim, “Referring to FIG. 11, the vegetation index prediction unit 411 of the certain area carbon dioxide capture amount prediction unit 400 calculates a future vegetation index from the vegetation index of trees in the certain area using the vegetation index prediction learning 35 model. Predictable. The vegetation index prediction learning model can predict the vegetation index of trees in the certain area according to the weather and soil information of the certain area, and inputs the future climate and soil information of the certain area predicted from the Korea Meteorological Administration, the Forest Service, etc. The future vegetation index of the trees in the area can be predicted, and the future carbon dioxide capture amount of the certain area can be predicted using the future vegetation index of the trees in the certain area.”, pgs. 13-14, lines 33-36 and 1-8, respectively, The system predicts a future vegetation index and a corresponding future carbon capture amount, thereby predicting an evolution of carbon capture over time.).
Kim does not teach colour features comprising a normalized difference vegetation index and a normalized difference water index.
However, Wang teaches colour features comprising a normalized difference vegetation index and a normalized difference water index (Wang, “The object-oriented classification method generally includes three steps: a. using remote sensing image segmentation algorithm to create image objects; b. establishing object-oriented classification rule set; c. extracting the types of native coastal wetlands… b) Establish an object-oriented classification rule set using eCognition 8.64 software to calculate the normalized vegetation index NDVI, normalized water body index NDWI… In eCognition8.64 software, the decision tree classification method is used to extract wetland type information of native coastal wetlands. The classification decision tree is shown in Figure 2. First, according to whether Hue R:G:B=SWIR1:NIR:R>=0.22 is established, distinguish forest land from non-forest land in typical areas; second step, take NDWI<=0.48 as the judgment condition, divide non-forest land into two parts, one part It is used for the identification of artificial surfaces and water bodies, and the other part is used to extract the original coastal wetland and cultivated land; the third step, based on the latter, separates the original coastal wetland from the cultivated area, and the non-cultivated land part is used for wetland classification; the fourth step, to Hue R:G:B=SWIR1:NIR:R>=0.19 is the criterion to divide the non-cultivated area into the primary coastal herb wetland and the primary coastal shrub wetland.”, pg. 12, lines 24-35 and pg. 13, lines 1-36).
Kim teaches classifying trees from satellite images and calculating a normalized difference vegetation index (NDVI) associated with the trees to estimate carbon capture (Kim, pg. 9, lines 28-35 and pg. 11, lines 29-33). Kim does not teach computing a normalize difference water index (NDWI). Wang teaches estimating a carbon capture in wetlands, which includes trees and shrubs, by implementing classification rules utilizing both NDVI and NDWI measurements for vegetation separation in images (see above). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the tree classification of Kim to include the computation of both a NDVI and NDWI, as taught by Wang (Wang, pg. 12, lines 24-35 and pg. 13, lines 1-36). The motivation for doing so would have been to separate trees from other wetlands features with similar spectral signatures, thereby improving classification accuracy. The combination of Kim in view of Wang would implement classification rules to the satellite images using both NDVI and NDWI measurements. This process satisfies the required association of both indices with the trees in the satellite images. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine the teachings of Kim with Wang to obtain the invention as specified above.
Kim in view of Wang does not teach outputting an alert signal if a corresponding variation over time is outside a predetermined range.
However, Koch teaches outputting an alert signal if a corresponding variation over time is outside a predetermined range (Koch, “In one example, standard rainfall alert frequency may be set at a default threshold such as once per day or once per week such that rainfall data is recorded and made available to the user at the default threshold frequency. Once a rainfall rate or rainfall amount reaches an event threshold (e.g., empirically corresponding to a rainfall event), the alert frequency is changed to an event frequency (e.g., such that data is made available to the user every 10 minutes or every accumulation of an additional ½oth of an inch of rain, whichever occurs sooner)… It should be appreciated that the methods described herein for adjusting the frequency at which data is made available to the user are not limited to rainfall events and may be used to dynamically determine alert frequency for other field and weather data such as wind speed, air temperature, soil temperature, soil moisture, soil nutrient levels, solar radiation, and/or carbon dioxide levels or any other type of field data and weather data discussed herein.”, columns 31 and 32, lines 44-67 and 1-2, respectively, Event thresholding is applied to field and weather data collected over time to alert users of events, with thresholding being extendible to various environmental data including carbon dioxide levels.).
Kim in view of Wang teaches estimating carbon capture of a target area, including monitoring an evolution of carbon capture over time (Kim, pgs. 13-14, lines 33-36 and 1-8, respectively). Kim in view of Wang does not teach outputting alert signals corresponding to monitoring carbon capture. Koch teaches monitoring environmental conditions of a field over time and generating alerts for users when sensed environmental data exceeds an event threshold (see above). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Kim in view of Wang to include event thresholding alerts for carbon capture monitoring as taught by Koch (Koch, columns 31 and 32, lines 44-67 and 1-2, respectively). The motivation for doing so would have been to automatically notify users when monitored carbon levels reach dangerous level, thereby improving safety for the target area. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine the teachings of Kim in view of Wang with Koch to obtain the invention as specified in claim 1.
Regarding claim 5, Kim in view of Wang and further in view of Koch teaches the method (20) according to claim 1,wherein the carbon capture indicator is further computed based on habitat data associated with the area of interest and representative of at least one of climatic features, pedological features sediment characteristics, geological features, hydrographic features and topographic features of the area of interest (Kim, “Referring to (a) of FIG. 10, the carbon dioxide capture amount prediction system 10 may further include a climate/soil collection unit 500 that collects climate/soil information of the predetermined area. The climate/soil collection unit 500 may collect meteorological information such as 35 temperature and precipitation of the certain area and soil information such as soil color and soil structure of the certain area from the Korea Meteorological Administration and the Forest Service… Referring to (b) of FIG. 10, the machine learning unit 300 may further include a vegetation index input unit 350, a climate input unit 360, and a soil input unit 370.”, pgs. 12-13, lines 31-36 and 1-12, Geological features, namely soil characters, can be included in the carbon capture estimation.).
Regarding claim 8, Kim in view of Wang and further in view of Koch teaches the method (20) according to claim 1, further including retrieving on-site measurement data representative of at least one physical and/or chemical property of the area of interest, the carbon capture indicator being further computed based on the retrieved on-site measurement data (Kim, “Referring to (a) of FIG. 10, the carbon dioxide capture amount prediction system 10 may further include a climate/soil collection unit 500 that collects climate/soil information of the predetermined area. The climate/soil collection unit 500 may collect meteorological information such as 35 temperature and precipitation of the certain area and soil information such as soil color and soil structure of the certain area from the Korea Meteorological Administration and the Forest Service… Referring to (b) of FIG. 10, the machine learning unit 300 may further include a vegetation index input unit 350, a climate input unit 360, and a soil input unit 370.”, pgs. 12-13, lines 31-36 and 1-12, Soil is retrieved on-site and analyzed for carbon capture estimation.).
Regarding claim 9, Kim in view of Wang and further in view of Koch teaches the method (20) according to claim 1, further including predicting (26) an evolution of the carbon capture indicator over time based on the determined environmental data (Kim, “Referring to FIG. 11, the vegetation index prediction unit 411 of the certain area carbon dioxide capture amount prediction unit 400 calculates a future vegetation index from the vegetation index of trees in the certain area using the vegetation index prediction learning 35 model. Predictable. The vegetation index prediction learning model can predict the vegetation index of trees in the certain area according to the weather and soil information of the certain area, and inputs the future climate and soil information of the certain area predicted from the Korea Meteorological Administration, the Forest Service, etc. The future vegetation index of the trees in the area can be predicted, and the future carbon dioxide capture amount of the certain area can be predicted using the future vegetation index of the trees in the certain area.”, pgs. 13-14, lines 33-36 and 1-8, respectively, Using vegetation index values derived from current imagery, the system predicts a future vegetation index and a corresponding future carbon capture amount, thereby predicting an evolution of carbon capture over time.).
Claim 15 corresponds to claim 1, with the addition of a computer program comprising instructions, which when executed by a computer, cause the computer to carry out the steps of the method of claim 1. Kim in view of Wang and further in view of Koch teaches the addition of a computer program comprising instructions, which when executed by a computer, cause the computer to carry out the steps of the method of claim 1 (Kim, “Referring to FIG. 1, the carbon dioxide capture amount prediction system 10 according to an embodiment of the present invention includes a satellite image receiving unit 100, a vegetation index derivation unit 200, a machine learning unit 300, and a carbon dioxide capture amount prediction unit. (400).”, pg. 7, lines 26-29, Machine learning unit 300 includes a computer program for executing the carbon capture estimation.). As indicated in the analysis of claim 1, Kim in view of Wang and further in view of Koch teaches all the limitations according to claim 1. Therefore, claim 15 is rejected for the same reasons of obviousness as claim 1.
Claim 16 corresponds to claim 1, with the addition of a remote monitoring system (2) including a processing unit (6) configured to perform the steps of the method of claim 1. Kim in view of Wang and further in view of Koch teaches the addition of a remote monitoring system (2) including a processing unit (6) configured to perform the steps of the method of claim 1 (Kim, “Referring to FIG. 1, the carbon dioxide capture amount prediction system 10 according to an embodiment of the present invention includes a satellite image receiving unit 100, a vegetation index derivation unit 200, a machine learning unit 300, and a carbon dioxide capture amount prediction unit. (400).”, pg. 7, lines 26-29, Carbon dioxide capture amount prediction system 10 includes a processor to execute functions such as computing vegetation index, performing inference for the model, and/or estimation of carbon capture.). As indicated in the analysis of claim 1, Kim in view of Wang and further in view of Koch teaches all the limitations according to claim 1. Therefore, claim 16 is rejected for the same reasons of obviousness as claim 1.
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 102502154 B1) in view of Wang et al. (CN 111488902 A) and further in view of Koch et al. (US 10768340 B2) and Tripathi et al. (“Estimating net primary productivity in tropical forest plantations in India using satellite-driven ecosystem model”, GEOCARTO INTERNATIONAL, 2017), (hereinafter Tripathi).
Regarding claim 2, Kim in view of Wang and further in view of Koch teaches the method (20) according to claim 1. Kim in view of Wang and further in view of Koch does not teach wherein the computed carbon capture indicator is a net primary productivity of the area of interest.
However, Tripathi teaches wherein the computed carbon capture indicator is a net primary productivity of the area of interest (Tripathi, “The objective of this study was to (i) investigate the spatio-temporal patterns of NPP for the years 2009 and 2010 using CASA model in a plantation site with single and mixed species plantations”, pg. 2, 2nd full paragraph, lines 18-20, “The NASA-CASA model, widely used in various studies to estimate NPP (Potter et al. 1993; Field et al. 1995), is an aggregated representation of major ecosystem carbon and nitrogen (N) transformations and trace gas fluxes (Potter et al. 1997).”, pg. 3, 1st full paragraph, lines 1-3, “To estimate the annual NPP monthly estimates were integrated temporarily from January to December for both the years. The Annual NPP for both the years over the study area and over the major plantation types is presented in Figure 1.”, pg. 6, 2nd full paragraph, lines 1-3, see Fig. 1, The NASA-CASA model uses satellite and climate data to estimate Net Primary Productivity (NPP) for forest plantation areas. This includes generating NPP maps that indicate carbon capture by vegetation over a period of time.).
Kim in view of Wang and further in view of Koch teaches estimating a carbon capture for trees in a target area using a machine learning model applied to satellite imagery (Kim, pgs. 11 and 12, lines 14-33 and 1-18, respectively). Kim in view of Wang and further in view of Koch does not teach computing net primary productivity (NPP). Tripathi teaches estimating NPP for forest plantations using satellite data and generating NPP maps as indicators of carbon capture for vegetation (see above). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the machine learning model of Kim in view of Wang and further in view of Koch to output NPP maps as taught by Tripathi (Tripathi, pg. 6, 2nd full paragraph, lines 1-3, see Fig. 1). The motivation for doing so would have been to enable seasonal and inter-annual analysis of carbon capture for trees in the target area (as suggested by Tripathi, “The estimation of NPP is of vital importance as it provides an insight into seasonal and inter-annual variations in atmospheric CO2concentration and also on the net photosynthesis and respiration.”, pg. 1, 1st paragraph, lines 2-4). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine the teachings of Kim in view of Wang and further in view of Koch with Tripathi to obtain the invention as specified in claim 2.
Regarding claim 3, Kim in view of Wang and further in view of Koch and Tripathi teaches the method (20) according to claim 2, further comprising calculating the carbon capture potential of the area of interest based on the net primary productivity and a size of the area of interest (Tripathi, “The CASA (Carnegie-Ames-Stanford Approach) ecosystem model (NASA-CASA) – a light use efficiency (LUE)-based model – is one of the simple process models, which is robust in describing the spatio-temporal NPP patterns”, pg. 2, 2nd full paragraph, lines 1-3, “The study was carried out in a reserve forest area (29°01′-30″-29°16′40″N and 79°13′45″-79°00′31″E) of 405 km2”, pg. 2, 3rd full paragraph, lines 1-2, “The Annual NPP for both the years over the study area and over the major plantation types is presented in Figure 1.”, pg. 6, 2nd full paragraph, lines 2-3, see Fig. 1, The NPP is computed on a per-area basis to provide spatio-temporal NPP patterns. The combination of Kim in view of Tripathi would compute NPP maps over a defined spatial extend of each target area in satellite images to indicate carbon capture by vegetation over a period of time.).
Regarding claim 4, Kim in view of Wang and further in view of Koch teaches the method (20) according to claim 1, wherein the determined environmental data include at least one primary producer of the area of interest (Kim, “Therefore, the present invention is to solve the above problems of the prior art, to derive a 10 vegetation index from a video image of a tree derived from a satellite image, and to predict the carbon dioxide capture amount of the tree easily and efficiently from the derived vegetation index Its purpose is to provide a system for predicting carbon dioxide capture amount.”, pg. 3, lines 10-14).
Kim in view of Wang and further in view of Koch does not teach wherein computing the carbon capture indicator includes associating each primary producer to a corresponding expected net primary productivity.
However, Tripathi teaches wherein computing the carbon capture indicator includes associating each primary producer to a corresponding expected net primary productivity (Tripathi, “The objective of this study was to (i) investigate the spatio-temporal patterns of NPP for the years 2009 and 2010 using CASA model in a plantation site with single and mixed species plantations”, pg. 2, 2nd full paragraph, lines 18-20, “The NASA-CASA model, widely used in various studies to estimate NPP (Potter et al. 1993; Field et al. 1995), is an aggregated representation of major ecosystem carbon and nitrogen (N) transformations and trace gas fluxes (Potter et al. 1997).”, pg. 3, 1st full paragraph, lines 1-3, “To estimate the annual NPP monthly estimates were integrated temporarily from January to December for both the years. The Annual NPP for both the years over the study area and over the major plantation types is presented in Figure 1.”, pg. 6, 2nd full paragraph, lines 1-3, see Fig. 1, The NASA-CASA model uses satellite and climate data to estimate Net Primary Productivity (NPP) for forest plantation areas. This includes generating NPP maps that associate vegetation of the area with corresponding NPP values over a period of time.).
Kim in view of Wang and further in view of Koch teaches estimating a carbon capture for a primary producer, namely trees, in a target area using a machine learning model applied to satellite imagery (Kim, pgs. 11 and 12, lines 14-33 and 1-18, respectively). Kim in view of Wang and further in view of Koch does not teach associating trees with a corresponding net primary productivity (NPP). Tripathi teaches estimating NPP for forest plantations using satellite data and generating NPP maps which associates vegetation with NPP values (see above). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the machine learning model of Kim in view of Wang and further in view of Koch to output NPP maps as taught by Tripathi (Tripathi, pg. 6, 2nd full paragraph, lines 1-3, see Fig. 1), thereby associating trees with a corresponding NPP values. The motivation for doing so would have been to enable seasonal and inter-annual analysis of carbon capture for trees in the target area (as suggested by Tripathi, “The estimation of NPP is of vital importance as it provides an insight into seasonal and inter-annual variations in atmospheric CO2concentration and also on the net photosynthesis and respiration.”, pg. 1, 1st paragraph, lines 2-4). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine the teachings of Kim in view of Wang and further in view of Koch with Tripathi to obtain the invention as specified in claim 4.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 102502154 B1) in view of Wang et al. (CN 111488902 A) and further in view of Koch et al. (US 10768340 B2) and Kang et al. (US 20230401846 A1), (hereinafter Kang).
Regarding claim 10, Kim in view of Wang and further in view of Koch teaches the method (20) according to claim 1. Kim in view of Wang and further in view of Koch does not teach wherein the step (22) of implementing at least one image analysis algorithm includes detecting the presence of at least one predetermined human-made structure in the area of interest, the carbon capture indicator being further computed based on each detected human-made structure.
However, Kang teaches wherein the step (22) of implementing at least one image analysis algorithm includes detecting the presence of at least one predetermined human-made structure in the area of interest, the carbon capture indicator being further computed based on each detected human-made structure (Kang, “Referring to FIG. 1, a carbon emission management information providing system 10 is a system capable of providing carbon emission management information by calculating a greenhouse gas concentration of a first area RG1 corresponding to a company to be evaluated and analyzing a relationship between carbon emission management factors input in relation to the company to be evaluated and a change in the greenhouse gas concentration of the first area RG1.”, pg. 3, paragraph 0032, “According to an embodiment, the carbon emission management information providing server 300 may extract the first area RG1 corresponding to a company to be evaluated related to identification information (e.g., company name, business registration number, etc.) or address input by a user through the user terminal 400, and generate carbon emission management information about the company to be evaluated through a change in the greenhouse gas concentration of the first area.”, pg. 3, paragraph 0042, “The vegetation index analysis module 348 may calculate a change in vegetation index around the company to be evaluated from the satellite image data.”, pg. 4, paragraph 0076, see Figs. 3 and 4, An area corresponding to a company, including its structures, is extracted from satellite imagery to calculate greenhouse gas concentrations and changes in surrounding vegetation. This allows the system to monitor carbon emissions of the company and its environmental impact.).
Kim in view of Wang and further in view of Koch teaches detecting the presence of vegetation for a target area to estimate a carbon capture amount (Kim, pg. 10, lines 1-30). Kang teaches detecting structures of a company in satellite imagery and analyzing the effect of those structures on the carbon captured by surrounding vegetation (see above). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Kim in view of Wang and further in view of Koch to include detection and analysis of company structures as taught by Kang (Kang, pg. 3, paragraph 0042, pg. 4, paragraph 0076, see Figs. 3 and 4). The motivation for doing so would have been to account for the impact human-made structures have on carbon captured by vegetation, thereby improving the accuracy of the carbon capture estimation. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine the teachings of Kim in view of Wang and further in view of Koch with Kang to obtain the invention as specified in claim 10.
Regarding claim 11, Kim in view of Wang and further in view of Koch and Kang teaches the method (20) according to claim 10, further including predicting (26) an evolution of the carbon capture indicator over time based on the determined environmental data (Kim, “Referring to FIG. 11, the vegetation index prediction unit 411 of the certain area carbon dioxide capture amount prediction unit 400 calculates a future vegetation index from the vegetation index of trees in the certain area using the vegetation index prediction learning 35 model. Predictable. The vegetation index prediction learning model can predict the vegetation index of trees in the certain area according to the weather and soil information of the certain area, and inputs the future climate and soil information of the certain area predicted from the Korea Meteorological Administration, the Forest Service, etc. The future vegetation index of the trees in the area can be predicted, and the future carbon dioxide capture amount of the certain area can be predicted using the future vegetation index of the trees in the certain area.”, pgs. 13-14, lines 33-36 and 1-8, respectively, Using vegetation index values derived from current imagery, the system predicts a future vegetation index and a corresponding future carbon capture amount, thereby predicting an evolution of carbon capture over time.).
Regarding claim 12, Kim in view of Wang and further in view of Koch and Kang teaches the method (20) according to claim 11, wherein the evolution of the carbon capture indicator over time is further predicted based on each detected human-made structure (Kim, “The future vegetation index of the trees in the area can be predicted, and the future carbon dioxide capture amount of the certain area can be predicted using the future vegetation index of the trees in the certain area.”, pg. 14, lines 5-8, The combination of Kim in view of Kang would consider the presence of human-made structures when performing both current and future carbon capture estimations.).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 102502154 B1) Kim et al. (KR 102502154 B1) in view of Wang et al. (CN 111488902 A) and further in view of Koch et al. (US 10768340 B2) and Chen et al. (TW I573099 B), (hereinafter Chen).
Regarding claim 14, Kim in view of Wang and further in view of Koch teaches the method (20) according to claim 1. Kim in view of Wang and further in view of Koch does not teach further comprising: implementing at least one image analysis algorithm on at least one top-down image of at least one neighbouring area adjacent to the area of interest, to determine environmental data representative of at least one primary producer and/or at least one biotope of each neighbouring area; and for each neighbouring area, computing (30) a carbon capture indicator representative of an estimated carbon capture potential of said neighbouring area, based on the determined environmental data of said neighbouring area and on the computed carbon capture indicator of the area of interest.
However, Chen teaches further comprising: implementing at least one image analysis algorithm on at least one top-down image of at least one neighbouring area adjacent to the area of interest, to determine environmental data representative of at least one primary producer and/or at least one biotope of each neighbouring area; and for each neighbouring area, computing (30) a carbon capture indicator representative of an estimated carbon capture potential of said neighbouring area, based on the determined environmental data of said neighbouring area and on the computed carbon capture indicator of the area of interest (Chen, “The calculation range is divided into a plurality of monitoring areas. In the monitoring lens starting step, the starting selection condition is that the setting position of the monitoring lens and the centroid position are located in the same one of the plurality of monitoring areas. In this way, not only the number of unnecessary environmental images can be reduced, but also the area with high flooding probability can be monitored, which has the effects of improving resource utilization rate and water level monitoring effectiveness. After performing the monitoring lens starting step, an extended starting step is further performed, and the extended starting step is to treat the monitoring area where the centroid position is located as a reference area. The plurality of monitoring areas adjacent to the reference area are regarded as a plurality of extended areas, and at least one of the monitoring lenses disposed in the plurality of extended areas is activated to capture and generate the environmental image.”, pg. 4, lines 14-25, Images are captured and processed for a target region based on centroid tracking. Based on this analysis, images from adjacent regions are captured and processed using the image processing methods.).
Kim in view of Wang and further in view of Koch teaches using satellite imagery of a target area to determine environmental data for estimating a carbon capture (Kim, pg. 10, lines 15-25, pg. 8, lines 14-17, pg. 11, lines 29-33). Kim in view of Wang and further in view of Koch does not teach extending this estimation to neighboring areas. Chen teaches applying image processing and analysis performed for a reference region to adjacent regions (see above). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the carbon capture estimation of Kim in view of Wang and further in view of Koch to be applied to adjacent regions as taught by Chen (Chen, pg. 4, lines 14-25). The motivation for doing so would have been to extend carbon capture estimation from a single target area by applying the same estimation process to adjacent areas, thereby increasing the coverage of carbon capture estimation. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine the teachings of Kim in view of Wang and further in view of Koch with Chen to obtain the invention as specified in claim 14.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CONNOR L HANSEN/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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