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 Interpretation
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 claim 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(s) 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 recite 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 § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 is 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.
Claim 3 recites “calculating the carbon capture potential of the area of interest…” which lacks antecedent basis. It is unclear if the limitation “the carbon capture potential” is meant to refer to the previously recited “estimated carbon capture potential” or if it is meant to indicate a new element. For example, in claim 1 “a carbon capture indicator representative of an estimated carbon capture potential” does not include calculating a carbon capture potential, but instead merely serves as a representation of the carbon capture indicator. Thus, it is unclear what carbon capture potential is being calculated. For examination purposes, the limitation of the claim will be interpreted as “calculating a carbon capture potential of the area of interest”.
Claim 5 recites “habitat data associated with the area of interest and representative of climatic features, pedological features and/or sediment characteristics, geological features, hydrographic features and/or topographic features of the area of interest.”, which is indefinite. It is unclear what the listing of elements is requiring. For example, it is unclear if the recited “and/or” applies to all listed elements or only to certain elements, and whether the elements are intended to be required individually, or in groups. Thus, one of ordinary skill in the art would not be able to ascertain the scope of the claimed “habitat data”. For examination purposes, the limitation will be interpreted broadly to mean that the habitat data is representative of any one or more of the recited elements.
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 1-12 and 14-16 are rejected under 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of analyzing environmental data from images to estimate carbon capture potential.
The claim recites: “A computer-implemented method (20) for assessing carbon capture in an area of interest, the method comprising: implementing (22) at least one image analysis algorithm on at least one top-down image 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; and based on the determined environmental data, computing (24) a carbon capture indicator representative of an estimated carbon capture potential of the area of interest.”
The limitations, as drafted, are processes that, under their broadest reasonable interpretation,
cover performance of the limitation in the human mind. A person can visually assess images of an area to identify environmental information for estimating a carbon capture potential. For example, the person can analyze characteristics (e.g., type, quantity, and distribution) of organisms and/or biotopes captured in the images and, based on those characteristics, make an inference regarding the amount of carbon captured by the area.
The judicial exception is not integrated into a practical application. For example, the claim
recites the additional elements, “a computer-implemented method…” and “implementing at least one image analysis algorithm on at least one top-down image of at least part of the area of interest”. These additional elements are recited at a high level of generality such that they amount to using a generic computer-implanted image analysis algorithm. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial expectation. As discussed above with respect to integration of the abstract idea
into a practical application, the additional elements are recited at a high-level of generality. It is therefore a judicial exception that is not integrated into a practical application, and does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This claim is not patent eligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can estimate carbon output as a net for a group of carbon producers, such as specific organisms. This claim is not patent eligible.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can estimate the carbon capture potential using a net carbon output for a group of carbon producers, such as specific organisms. This claim is not patent eligible.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can identify environmental information corresponding to carbon producers, such as specific organism present in the image, and estimate carbon output as a net productivity for the organism. This claim is not patent eligible.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can estimate carbon capture based on additional habitat data. This claim is not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claim recites additional elements recited at a high
level of generality such that they amount to merely computing common index values. Accordingly, these
additional elements do not integrate the abstract idea into a practical application because they do not
impose any meaningful limits on practicing the abstract idea. This claim is not patent eligible.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can evaluate a series of images, taken at different acquisition dates, to identify environment information for estimation carbon capture potential. This claim is not patent eligible.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can estimate carbon capture based on additional on-site measurement data. This claim is not patent eligible.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can make inferences regarding the evolution of carbon capture over time, based on the identified environment information. This claim is not patent eligible.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can further identify human-made structures from the images and estimate carbon capture potential based on their presence. This claim is not patent eligible.
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can make inferences regarding the evolution of carbon capture over time, based on the identified environment information. This claim is not patent eligible.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can further identify human-made structures from the images and estimate carbon capture potential based on their presence. This claim is not patent eligible.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation
of the same abstract idea identified in the analysis of claim 1. For example, the person can repeat the carbon capture estimation process on images of neighboring areas. This claim is not patent eligible.
Claim 15 is rejected under 35 U.S.C. 101 because the claim recites additional elements recited at a high
level of generality such that they amount to merely a computer program. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. This claim is not patent eligible.
Claim 16 contains elements found analogous to claim 1, with the addition of “a remote monitoring system including a processing unit”. This additional element is recited at a high level of generality such that it amounts to a generic computer. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, claim 16 is similarly rejected under 35 U.S.C. 101. This claim is not patent eligible.
Claim 15 additionally recites “A computer program comprising instructions…”. The broadest reasonable interpretation of computer program includes software. Software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. Thus, a claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category: processes, machines, manufactures or compositions of matter (See MPEP 2106.03). Therefore, claim 15 is rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5, 7-9, 15 and 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim et al. (KR 102502154 B1), (hereinafter Kim).
Regarding claim 1, 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 comprising:
implementing (22) at least one image analysis algorithm on at least one top-down image 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 (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 vegetation index values for trees in the area.); and
based on the determined environmental data, computing (24) 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 vegetation index values along with tree species and age information are input to a machine learning model to estimate the carbon capture amount of the area.).
Regarding claim 5, Kim 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 climatic features, pedological features and/or sediment characteristics, geological features, hydrographic features and/or 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 7, Kim teaches the method (20) according to claim 1, wherein the environmental data is further determined based on a series of top-down images 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.).
Regarding claim 8, Kim 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 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 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 teaches all the limitations according to claim 1. Therefore, claim 15 is rejected for the same reason 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 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 teaches all the limitations according to claim 1. Therefore, claim 16 is rejected for the same reason as claim 1.
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 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 102502154 B1) in view of 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 teaches the method (20) according to claim 1. Kim 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 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 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 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 with Tripathi to obtain the invention as specified in claim 2.
Regarding claim 3, Kim in view of 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 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 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 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 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 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 with Tripathi to obtain the invention as specified in claim 4.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 102502154 B1) in view of Gao (“NDWI – A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space”, REMOTE SENS. ENVIRON, 1996).
Regarding claim 6, Kim teaches the method (20) according to claim 1, wherein the step (22) of implementing at least one image analysis algorithm includes computing, based on each top-down image, a normalized difference vegetation index, the environmental data being determined based on the computed normalized difference vegetation index (Kim, “For example, the vegetation index derivation unit 200 determines the wavelength of trees in 15 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… Here, the specific wavelength of the tree in the certain area is a red wavelength corresponding to a Normalized Difference Vegetation Index (NDVI)”, pg. 10, lines 15-25).
Kim does not teach computing a normalized difference water index, the environmental data being determined based on the computed normalized difference vegetation index and normalized difference water index.
However, Gao teaches computing a normalized difference water index, the environmental data being determined based on the computed normalized difference vegetation index and normalized difference water index (Gao, “The normalized difference water index (NDWI) proposed here uses two near-IR channels; one centered approximately at 0.86 µm, and the other at 1.24 µm.”, pg. 258, 2nd column, 1st full paragraph, lines 1-3, see Eq. 1, “A new vegetation index, the normalized difference water index (NDWI), is proposed for remote sensing of vegetation liquid water from space… NDWI is a measure of liquid water molecules in vegetation canopies that interacted with the incoming solar radiation. It is less sensitive to atmospheric scattering effects than NDVI.” pg. 264, 2nd column, 3rd paragraph, lines 1-10, see Figs. 11(b) and 14(b), A Normalized difference water index (NDWI) is computed alongside the normalized difference vegetation index to enable remote sensing of vegetation. NDWI provides information about vegetation liquid water content that is complementary to NDVI.).
Kim teaches determining environmental data for a target area based on computing a normalize difference vegetation index (Kim, pg. 10, lines 15-25). Kim does not teach computing a normalize difference water index. Gao teaches computing both a normalize difference vegetation index and normalized difference water index for remote sensing of 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 environmental data of Kim to include a computed normalize difference water index as taught by Gao (pg. 258, 2nd column, 1st full paragraph, lines 1-3, see Eq. 1, pg. 264, 2nd column, 3rd paragraph, lines 1-10, see Figs. 11(b) and 14(b)). The motivation for doing so would have been to monitor additional environmental features, such as vegetation water status, thereby improving the environmental analysis of 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 with Gao to obtain the invention as specified in claim 6.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 102502154 B1) in view of Kang et al. (US 20230401846 A1), (hereinafter Kang).
Regarding claim 10, Kim teaches the method (20) according to claim 1. Kim 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 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 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 with Kang to obtain the invention as specified in claim 10.
Regarding claim 11, Kim in view of 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 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 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 102502154 B1) in view of Koch et al. (US 10768340 B2), (hereinafter Koch).
Regarding claim 13, Kim teaches the method (20) according to claim 1, wherein at least one of the determined environmental data and 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, The process is performed for a set of satellite images with a date of acquisition according to a selection by a user.), the method further including monitoring (28) an evolution of the environmental data and/or 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 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 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 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 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 with Koch to obtain the invention as specified in claim 13.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 102502154 B1) in view of Chen et al. (TW I573099 B), (hereinafter Chen).
Regarding claim 14, Kim teaches the method (20) according to claim 1. Kim 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 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 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 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 with Chen to obtain the invention as specified in claim 14.
Conclusion
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/CONNOR L HANSEN/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672