DETAILED ACTION
This communication is a first Office Action Non-Final rejection on the merits. Claims 1-19 as originally filed are currently pending and considered below.
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 .
Status of Claims
This Non-Final Office action is in response to the application filed on July 02, 2024. Claims 1-19 are pending.
Priority
Application 18/762,497 was filed on July 02, 2024 and claims priority to the provisional application 63/524,768 filed July 03, 2023.
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.
Claims 1-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, 10, and 19 recite the limitation of “transforming the plurality of satellite images and the plurality of drone images of the first entity or the second entity from the satellite source database”. The claim initially recites that drone images are of the second entity. As currently written, the claim then later refers to the images being in relation to the first entity, which makes it unclear whether any drone images actually correspond to the first entity, since no such images were previously recited. This creates confusion within the claim and should be clarified to distinguish which images apply to which entity. Clarification/correction is needed.
Claim 1,10, and 19, line 6 recites “the plurality of drone images”. This limitation does not have proper antecedent basis as “the plurality of drone images” was not recited prior to this. The examiner suggests correcting it to “a plurality of drone images” for a proper introduction. Clarification/correction is needed.
Claim 1, 10, and 19, line 6 recites “the land cover”. This limitation does not have proper antecedent basis as “the land cover” was not recited prior to this. The examiner suggests correcting it to “a land cover” for a proper introduction. Clarification/correction is needed.
Claim 1,10, and 19, line 13 recites “the date when the image is captured”. This limitation does not have proper antecedent basis as “the date when the image is captured” was not recited prior to this. The examiner suggests correcting it to “a date when the image is captured” for a proper introduction. Clarification/correction is needed.
Claim 1, 10, and 19, line 21 recites “the required to offset”. This limitation does not have proper antecedent basis as “the required offset” was not recited prior to this. The examiner suggests correcting it to “a required offset” for a proper introduction. Additionally, the claim does not specify what is being offset. The phrase “required to offset” is vague without a clear object. Is it referring to a “required offset amount”, “a required adjustment”, or something else? It also is unclear what exactly is being estimated. The claim should explicitly state what is required and what is being estimated to avoid ambiguity. Clarification/correction is needed.
Claim 8 and 17 recites “the accuracy of carbon sequestration level calculations”. This limitation does not have proper antecedent basis as “the accuracy of carbon sequestration level calculations” was not recited prior to this. The examiner suggests correcting it to “an accuracy of carbon sequestration level calculations” for a proper introduction. Clarification/correction is needed.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1‐9 are directed to a method (process), Claims 10-18 are directed to a non-transitory computer readable storage medium storing (machine/apparatus), and Claim 19 is directed to a system (machine). Thus, these claims fall within one of the four statutory categories of invention. (Step 1: YES).
For step 2A, the Examiner has identified independent method Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent claim 10 and 19. Claim 1, as exemplary is recited below, isolating the abstract idea from the additional elements, wherein the abstract idea is set in bold:
A processor-implemented method for calculating emissions and measuring carbon sequestration levels, comprising: registering, by a user device, a first entity associated with an amount of carbon emission and a second entity associated with an amount of carbon absorption to create an account in a blockchain-based system using user details; obtaining a plurality of satellite images and the plurality of drone images of the land cover of the second entity from a satellite source database; monitoring the plurality of satellite images and the plurality of drone images of the land cover of the second entity by creating land coordinates to calculate a carbon sequestration level associated with the second entity using an Artificial Model (AI) model; training the AI model using an algorithm by categorizing the carbon sequestration level with known species of the second entity, wherein the known tree species comprises the plurality of drone images and the date when the image is captured; transforming the plurality of satellite images and the plurality of drone images of the first entity or the second entity from the satellite source database into data usable by a carbon predictor module; obtaining a carbon footprint based on the amount of carbon emitted by the first entity by inputting consumption details of the first entity into the carbon predictor module using a trained AI model; generating a carbon credit based on the satellite image of the second entity and the trained AI model in the carbon predictor module by estimating the required to offset the amount of carbon emitted by the first entity; converting the carbon credits into Non-Fungible Tokens (NFTs) using blockchain technology; providing the NFTs to the second entity as a representation of their carbon credits; and securing the carbon credits after the transfer to the first entity and second entity in the blockchain using smart contracts.
The above bolded limitations recite the abstract idea of calculating emissions and measuring carbon sequestration levels. These limitations under its broadest reasonable interpretation, covers certain methods of organizing human activity (i.e. commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations as well as fundamental economic principles) but for the recitation of generic computer components. That is, other than reciting a system implemented by a data processor (computer) the claimed invention amounts to the abstract idea stated above. For example, for the related computer components, this claim encompasses actions that could conventionally be performed by environmental analysis, sustainability officers, or even carbon credit brokers manually as part of carbon accounting and trading process. This progress can be done manually through paper and pencil. Additionally, calculating emissions and measuring carbon sequestration levels is considered a fundamental economic principle because it involves managing and quantifying environmental resources, which are basic methods of organizing human activity related to economic and regulatory decision-making. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions between parties, but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. The mere nominal recitation of a “processor”, “user device”, “an account in a blockchain-based system”, “a satellite source database”, “an Artificial Model (AI) model”, “training the AI model”, “a carbon predictor module”, “into Non-Fungible Tokens (NFTs) using blockchain technology”, and “the blockchain using smart contracts”, do not take the claim out of the methods of organizing human interactions grouping. Thus, claims 1, 10, and 19 recites an abstract idea. (Step 2A- Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application (2nd prong of eligibility test for step 2A). In particular, Claim 1 recites additional elements of “processor”, “user device”, “an account in a blockchain-based system”, “a satellite source database”, “an Artificial Model (AI) model”, “training the AI model”, “a carbon predictor module”, “into Non-Fungible Tokens (NFTs) using blockchain technology”, and “the blockchain using smart contracts”. Claim 10 recites the same additional elements of Claim 1 with the addition of “one or more non-transitory computer readable storage mediums”. Claim 19 recites the same additional elements of Claim 1 with the addition of “a memory”. These additional elements are all considered nothing more than generic computing devices to perform generic communicating functions such as storing data and instructions, transmitting and receiving data between computers. The claims also include additional elements that are considered nothing more a general link to blockchain, Artificial Intelligence, and training because there is no recitation of specifics of how this additional element is being used. These elements are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of communicating data between users) such that they amount no more than mere instructions to apply the exception using a generic computer component in technological environment. See MPEP 2106.05(f) and (h). Accordingly, these additional elements (combination of computer, the use of AI, and the use blockchain) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are recited at a high level of generality when considered both individually and as a whole. Thus, Claims 1, 10, and 19 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
For step 2B, the claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not amount to more than simply instructing one to practice the abstract idea by using generic computer components to carry out the steps that define the abstract idea, as discussed above. This does not render the claims as being eligible. See MPEP 2106.05(f). The additional elements of using computer, AI, training, and blockchain when considered both individually and as an ordered combination did not add significantly more to the abstract idea because they were simply applying the abstract idea using generic computer components, AI, training, and blockchain. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (See MPEP 2106.05(g)). Accordingly, these additional elements, do not change the outcome of the analysis, and claims 1, 10, and 19 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Claims 2-3, 5, and 11-12 recite limitations that further define the same abstract idea of independent claims to include wherein the first entity's carbon emissions are categorized into (i) direct emissions from sources owned or controlled by the first entity, (ii) indirect emissions from purchased electricity, heat, or steam consumed by the first entity and (iii) other indirect emissions resulting from activities but arising from sources not owned or controlled by the first entity, categorizing the carbon sequestration level based on the type and age of the tree species identified in the satellite images, transforms the plurality of satellite images and the plurality of drone images into data usable for carbon footprint analysis by employing a computational core based on algorithms processing activity data using chosen emission factors. The dependent claims 2-3, 5, and 11-13 do not include any new additional elements and therefore are considered patent ineligible for the reasons given above.
Claim 4 and 13 recite limitations that further define the same abstract idea of independent claims to include sing a dataset that includes historical carbon sequestration data and corresponding satellite images. In addition, claim 4 recite the additional element “the AI model is trained” which is considered nothing more than a general link to Artificial Intelligence because there is no recitation of specifics of how this additional element is being used. See MPEP 2106.05(f) and (h) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. Therefore claim 4 and 13 are patent ineligible.
Claims 6, 9, 15, and 18 recite limitations that further define the same abstract idea of independent claims to include verifying authenticity and integrity of the NFTs representing carbon credits through cryptographic methods and wherein the second entity's carbon sequestration level is monitored periodically to update the carbon credits based on real-time satellite imagery and a drone imagery. The dependent claims 6, 9, 15 and 18 do not include any new additional elements and therefore are considered patent ineligible for the reasons given above.
Claims 7-8 and 16-17 recite limitations that further define the same abstract idea of independent claims to include automatically executed based on predefined conditions related to carbon emission and sequestration levels, weather data and land use changes to enhance the accuracy of carbon sequestration level calculations. In addition, claim 7-8 and 16-17 recite the additional element “the smart contracts”, “blockchain”, and “AI model “which are considered nothing more than a general link to blockchain technology and Artificial Intelligence because there is no recitation of specifics of how this additional element is being used. See MPEP 2106.05(f) and (h) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. Therefore claim 4 are patent ineligible.
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, 3-4, 7-9, 10, 12-13, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Stolt et al. (US 20240354851) in view of Sidhu et al. (US 20230059038).
With regards to Claim 1, Stolt et al. teaches processor-implemented method for calculating emissions and measuring carbon sequestration levels, comprising (See Abstract & FIG 1):
a first entity and a second entity associated with an amount of carbon absorption to create an account in a blockchain-based system using user details; (See [0020]- In practice this may have the meaning that a buyer will pay a producer for sequestrating an agreed quantity of CO2 in for a specific geographic area, for example, by cultivating a crop or plant. Also See [0041]- Typically, the geographic area is property of a producer of the carbon dioxide sequestration. The producer may also be the seller of the carbon dioxide sequestration. Also See [0010]-[0011]- providing a distributed blockchain ledger; storing at least a portion of the distributed blockchain ledger on a plurality of computing devices;. Also See [0080]- FIG. 2 further includes a producer (205), such as a farmer, and a buyer (206) of a carbon dioxide sequestration.)
*Examiner is interpreting the producer disclosed by the prior art to equate to the second entity as recited in the claim language and the buyer to equate to the first entity as recited in the claim.
obtaining a plurality of satellite images and the plurality of drone images of the land cover of the second entity from a satellite source database; (See [0013]-obtaining at least a first image, such as a first satellite image, of the geographic area before the sequestration. Also See [0044]- The images may be based on other imaging technologies, such as infrared imaging, ultraviolet imaging, radar images, spectral imaging, or LIDAR technology. The images may be captured by, for example, airborne or spaceborne vehicles. Also See [0022]- Moreover, more than two images may be used for the estimation. The geographic area may also be continuously monitored. Satellite imaging technology may be combined with a model of biomass change to estimate the sequestered quantity of CO2.)
*Examiner notes that the prior art discloses images captured by both satellite and drone which sufficiently satisfies the claim language, as the claims do not specifically require the actual act of capturing images by satellites or drones, but only that the images be from a satellite or drone sources.
monitoring the plurality of satellite images and the plurality of drone images of the land cover of the second entity by creating land coordinates to calculate a carbon sequestration level associated with the second entity using an Artificial Model (AI) model; (See [0044], Also See [0022]- Moreover, more than two images may be used for the estimation. The geographic area may also be continuously monitored. Satellite imaging technology may be combined with a model of biomass change to estimate the sequestered quantity of CO2. Also See [0055]-[0057]-satellite check of GPS coordinates of the geographic area; satellite check GPS coordinates matched with land register (ownership of land), which is matched with the producer; satellite check of the geographic area/GPS coordinates in the past and the boarders of the field (has the land changed in size or form indicating change in ownership of the land, deforestation, or non-sustainable land use e.g. next to lakes or water with the risk of erosion); Also See [0045]- The method may involve using a machine learning model trained to translate changes in time of satellite images to sequestrate carbon dioxide.)
training the AI model using an algorithm by categorizing the carbon sequestration level with known species of the second entity, wherein the known tree species comprises the plurality of drone images and the date when the image is captured; (See [0044]- The images may be captured by, for example, airborne or spaceborne vehicles. Also See [0045]- The method may involve using a machine learning model trained to translate changes in time of satellite images to sequestrate carbon dioxide. The process of training such a model may be based on other technologies and real ground measurements. It is also possible to integrate detailed land cover information with ground observations of forest inventories. Also See [0046]- The estimation of carbon dioxide sequestration may involve use of satellite observation tools and indexes, such as the Landsat Normalized Difference Vegetation Index (NDVI). As an example, a tree stem may be estimated to have a mass 2-3 times greater than the mass of the crown. The figure typically depends on a number of parameters, such as species, age, etc. In this case, such a model can be used to estimate a total biomass, wherein the estimation is based on images and the model. Each plant type may associated with a factor. Based on an identification, or other information based on which the system is aware of the planet type in the area, the system can estimate the biomass and carbon dioxide sequestration. The method may comprise obtaining predetermined data about average biomass and dry weight per species. The method may further comprise above/below biomass ratio. This varies between species and may be accounted for. Also See [0060]- trained models for recognizing plant species in an image of a plant exist. The method may further comprise the step of computing the vegetation density based on the obtained images. Also See [0048]- Based on differences in vegetation for the satellite images at different points in time, the method may estimate a relatively rough sequestration.)
transforming the plurality of satellite images and the plurality of drone images of the first entity or the second entity from the satellite source database into data usable by a carbon predictor module; (See [0044]- The images may be captured by, for example, airborne or spaceborne vehicles. Also See [0022]- The step of determining whether the carbon dioxide sequestration has been effectuated may comprise an estimation, which may include a calculation, of the quantity of CO2 sequestered between the first and second satellite images. Moreover, more than two images may be used for the estimation. The geographic area may also be continuously monitored. Satellite imaging technology may be combined with a model of biomass change to estimate the sequestered quantity of CO2. Also See [0050]- The method may comprise the step of continuously, or by sampling, over a period of time, estimating a total quantity of sequestrated carbon dioxide based on satellite images of the geographic area.)
generating a carbon credit based on the satellite image of the second entity and the trained AI model in the carbon predictor module by estimating the required to offset the amount of carbon emitted by the first entity; (See [0041]- A transaction within the context of the present application may comprise a carbon dioxide token assigned to a carbon dioxide sequestration associated with a geographic area be a transaction of a carbon credit. A defined targeted quantity of carbon dioxide is exchanged against a quantity of compensation. A token may be seen as a digital representation of an asset, in this case of a certain quantity of carbon dioxide sequestration The token may comprise a targeted quantity of carbon dioxide and a quantity of compensation for the targeted quantity of carbon dioxide. More specifically, the token may be associated with a CO2 sequestration associated with a specific geographic area. Preferably, the CO2 token comprises a targeted quantity of CO2. The token of carbon dioxide may be associated with a buyer of the carbon dioxide sequestration and a producer of the carbon dioxide sequestration. Typically, the geographic area is property of a producer of the carbon dioxide sequestration. The producer may also be the seller of the carbon.)
*Examiner is interpreting the buyer disclosed by the prior art to equate to the first entity as recited in the claim language.
converting the carbon credits into Non-Fungible Tokens (NFTs) using blockchain technology; (See [0041]- A transaction within the context of the present application may comprise a carbon dioxide token assigned to a carbon dioxide sequestration associated with a geographic area be a transaction of a carbon credit. A token may be seen as a digital representation of an asset, in this case of a certain quantity of carbon dioxide sequestration. The token may be issued as a non-fungible token (NFT), as it refers to its capacity of not being exchanged in between them as of equal value. Accordingly, the presently disclosed method may comprise the step of creating a non-fungible token based on an estimated carbon dioxide sequestration between the first image and the second image.)
providing the NFTs to the second entity as a representation of their carbon credits; (See [0040]- Accordingly, the presently disclosed method may comprise the step of creating a non-fungible token based on an estimated carbon dioxide sequestration between the first image and the second image. A token is, preferably, tradeable. The token may comprise a targeted quantity of carbon dioxide and a quantity of compensation for the targeted quantity of carbon dioxide. More specifically, the token may be associated with a CO2 sequestration associated with a specific geographic area. Preferably, the CO2 token comprises a targeted quantity of CO2. The token of carbon dioxide may be associated with a buyer of the carbon dioxide sequestration and a producer of the carbon dioxide sequestration. Typically, the geographic area is property of a producer of the carbon dioxide sequestration. The producer may also be the seller of the carbon dioxide sequestration.)
securing the carbon credits after the transfer to the first entity and second entity in the blockchain using smart contracts (See [0023]- the system may further automatically trigger a payment for the carbon dioxide token and update the transaction of carbon dioxide in the distributed blockchain ledger on the plurality of computing devices. The system can thereby be said to link a transaction both to a plurality of computing devices through a distributed blockchain ledger and to a physical satellite imaging-based verification of the CO2 sequestration. The payment may be made in the form of a non-fungible token against digital currencies. The payment may be administrated by means of a smart contract. A smart contract is a computer program or transaction protocol which is intended to automatically execute, control or document an event. Also See [0089]-[0090]- The smart contract may automatically draw the money from the buyer's crypto wallet to become a “value representation”, which is placed on the blockchain. When the terms of the smart contract are fulfilled, the “value representation” may be moved to the producer's crypto wallet. From there the “value representation” can be converted to fiat currency. At the fulfillment of the smart contract, the system and method may automatically push a CO2 binding certificate to the buyer with all data traceable to the specific producer, the land and quantity of CO2 offset. All data may be provided by the blockchain.)
Stolt et al. teaches a first entity but does not teach registering, by a user device, a first entity associated with an amount of carbon emission and obtaining a carbon footprint based on the amount of carbon emitted by the first entity by inputting consumption details of the first entity into the carbon predictor module using a trained AI model. However, Sidhu et al. teaches:
registering, by a user device, a first entity associated with an amount of carbon emission (See [0029]- The system 100 for optimizing carbon emissions from online streaming platforms can analyze streaming patterns of a user that registers with the system and provides permission for the system to have access to the user's historical streaming data. Also See [0033]- Referring to block 1 of the method depicted in FIG. 1, the method may begin with, in response to receiving permission from a user for data collection, registering users with the system 100 for optimizing carbon emissions from online streaming platforms.)
obtaining a carbon footprint based on the amount of carbon emitted by the first entity by inputting consumption details of the first entity into the carbon predictor module using a trained AI model; (See [0040]- training the regressor model can include machine learning employing artificial neural networks. Referring to FIG. 2, the training of the regressor model is used to forecast a user's carbon footprint in the in the upcoming month that results from their usage of the streaming services of the streaming service provider 50. Also See [0069]- Using the data collected at block 2, and the regressor model at block 4, in combination with machine learning, the system 100 can forecast the carbon footprint of the user in the coming month at block 5. The input to the system is the streaming history of the user 10. The history of the user can include data such as the number of account users, e.g., secondary users, that are streaming for the parent streaming account, i.e., primary user.)
Stolt et al. and Sidhu et al. are both considered to be analogous to the claimed invention because they are in the same field of calculating carbon footprint and estimating carbon sequestration. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Stolt et al. reference to further include registering, by a user device, a first entity associated with an amount of carbon emission and obtaining a carbon footprint based on the amount of carbon emitted by the first entity by inputting consumption details of the first entity into the carbon predictor module using a trained AI model as taught by Sidhu et al. This is desirable such that it provides a system that can not only track carbon emission for online streaming, but also provides optimization of the streaming process to reduce the carbon footprint. (See Sidhu, [0021]).
In regards to Claim 10, the Stolt-Sidhu combination teaches the claimed invention similar to Claim 1 with the addition of:
Stolt et al. teaches:
One or more non-transitory computer readable storage mediums storing one or more sequences of instructions, which when executed by one or more processors, causes a processor implemented method for calculating emissions and measuring carbon sequestration levels, comprising: (See [0082]- a computer program that provides a front-end application in the form of a user interface. The system and method may be provided in a web-based provided on an Internet browser on, for example, a computer or mobile phone. The computer program may run on any suitable processing unit, including servers and cloud-based solutions. Also See Claim 38 “A non-transitory storage medium comprising a computer program product having instructions embodied thereon, the computer program product, when executed by a computing device or system, causes the computing device or system to process carbon dioxide or carbon dioxide equivalent related transactions, by”)
In regards to Claim 19, the Stolt-Sidhu combination teaches the claimed invention similar to Claim 1 with the addition of:
Stolt et al. teaches:
A system for calculating emissions and measuring carbon sequestration levels, said system comprising: (See [0008]- the disclosure relates to a method and a system for processing carbon dioxide related transactions by earth observation biomass calculations. Also See [0023]- According to one embodiment of the presently disclosed method and system, an estimation of a total quantity of sequestrated carbon dioxide based on satellite images of the geographic area.)
a memory that stores a set of instructions; (See [0081]-The system may further comprise a memory, for example, random access memory (RAM), and a secondary memory, such as a read-only memory (ROM).)
a processor that executes the set of instructions and is configured to: (See [0081]-The processing unit may be any type of processor device, for example, an type of special purpose or a general-purpose microprocessor device The processing unit may be a single processor or multiple processors.)
In regards to Claim 3 and 12, the Stolt-Sidhu combination teaches the claimed invention as recited in the independent claim above.
Stolt et al. teaches:
categorizing the carbon sequestration level based on the type and age of the tree species identified in the satellite images (See [0049]- This may include comparing specific details and colors in the images. It may also comprise the step of categorizing the land in the entire geographic area or parts of the geographic area. Also See [0046]- The figure typically depends on a number of parameters, such as species age etc. In this case, such a model can be used to estimate a total biomass, wherein the estimation is based on images and the model. Each plant type may associated with a factor. Based on an identification, or other information based on which the system is aware of the planet type in the area, the system can estimate the biomass and carbon dioxide sequestration.).
In regards to Claim 4 and 13, the Stolt-Sidhu combination teaches the claimed invention as recited in the independent claim above.
Stolt et al. teaches:
wherein the AI model is trained using a dataset that includes historical carbon sequestration data and corresponding satellite images (See [0045]- The method may involve using a machine learning model trained to translate changes in time of satellite images to sequestrate carbon dioxide. The process of training such a model may be based on other technologies and real ground measurements. It is also possible to integrate detailed land cover information with ground observations of forest inventories. In one embodiment the method comprises the step of analyzing the satellite images using a machine learning model trained to translate changes in time of satellite images to sequestrated carbon dioxide. The machine learning model may be trained to translate changes in time of satellite images to sequestrated carbon dioxide for a specific geographic area.).
In regards to Claim 7 and 16, the Stolt-Sidhu combination teaches the claimed invention as recited in the independent claim above.
Stolt et al. teaches:
wherein the smart contracts used to secure the carbon credits in the blockchain are automatically executed based on predefined conditions related to carbon emission and sequestration levels (See [0059]- The payment may be administrated by means of a smart contract. A smart contract is a computer program or transaction protocol which is intended to automatically execute, control or document an event. A smart contract may be seen as programs stored on a blockchain that run when predetermined conditions are met. Smart contracts may comprise “if-then” statements written into code on a blockchain. The plurality of computing devices may execute a task when predetermined conditions have been met and verified, in the present disclosure when the carbon dioxide sequestration has been effectuated. The blockchain may then be updated when the transaction is completed. Also See [0084]- The method or system can then calculate an expected CO2 sequestration. The method and system may the create a carbon dioxide sequestration associated with a geographic area, which is added to the smart contract.).
In regards to Claim 8 and 17, the Stolt-Sidhu combination teaches the claimed invention as recited in the independent claim above.
Stolt et al. teaches:
wherein the AI model incorporates weather data and land use changes to enhance the accuracy of carbon sequestration level calculations (See [0045]- The machine learning model may be trained to translate changes in time of satellite images to sequestrated carbon dioxide for a specific geographic area. Also See [0050]- The method may comprise the step of continuously, or by sampling, over a period of time, estimating a total quantity of sequestrated carbon dioxide based on satellite images of the geographic area. The total quantity of sequestrated carbon dioxide may be estimated for a period of time…The estimation may include temporal and spatial monitoring. Also See [0057]-[0058]-satellite check of the geographic area/GPS coordinates in the past and the boarders of the field (has the land changed in size or form indicating change in ownership of the land, deforestation, or non-sustainable land use e.g. next to lakes or water with the risk of erosion); satellite check for assessing biomass per area unit over time; control of normal growing season for food and CO2 crops. Also See [0053]- The method may comprise further validations, including, but not limited to, satellite data validating that the land has not changed over time, including changes related to field size, deforestation and ownership.).
*Examiner is interpreting the “temporal monitoring” disclosed by the prior art to equate weather data recited in the claim language. The examiner notes that temporal monitoring can be interpreted to include collection of weather data, since weather data inherently involves repeated measurements of atmospheric conditions over time.
In regards to Claim 9 and 18, the Stolt-Sidhu combination teaches the claimed invention as recited in the independent claim above.
Stolt et al. teaches:
wherein the second entity's carbon sequestration level is monitored periodically to update the carbon credits based on real-time satellite imagery and a drone imagery (See [0022]- Moreover, more than two images may be used for the estimation. The geographic area may also be continuously monitored. Satellite imaging technology may be combined with a model of biomass change to estimate the sequestered quantity of CO2. The sequestered quantity of CO2 may be determined and calculated based on individual species' CO2 binding capacity and environmental conditions. Also See [0044]- The method may capture satellite images continuously or with fixed intervals and continuously estimate how much carbon dioxide that has been sequestrated. The invention is not limited to satellite imaging).
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Stolt et al. (US 20240354851) in view of Sidhu et al. (US 20230059038), further in view of Dilip et al. (US 20160055596).
In regards to Claim 2 and 11, the Stolt-Sidhu combination teaches the claimed invention as recited in the independent claim above.
Stolt et al. teaches a first entity but does not teach wherein the first entity's carbon emissions are categorized into (i) direct emissions from sources owned or controlled by the first entity, (ii) indirect emissions from purchased electricity, heat, or steam consumed by the first entity and (iii) other indirect emissions resulting from activities but arising from sources not owned or controlled by the first entity.
Dilip et al. teaches:
wherein the first entity's carbon emissions are categorized into (i) direct emissions from sources owned or controlled by the first entity, (ii) indirect emissions from purchased electricity, heat, or steam consumed by the first entity and (iii) other indirect emissions resulting from activities but arising from sources not owned or controlled by the first entity (See [0007]- The carbon emissions management application categorizes the real time emission data 101 from the detectors as scope 1 emission as defined by the greenhouse gas (GHG) protocol. The real time emission data 101 comprises, for example, data of greenhouse gases (GHG), for example, carbon dioxide (CO2). The carbon emissions management application converts the GHG emission data into carbon and carbon dioxide equivalents for reporting purposes. The GHG protocol categorizes the direct and indirect emissions into three broad scopes, that is, scope 1 emission, scope 2 emissions, and scope 3 emissions. The scope 1 emissions comprise direct GHG emissions from the detectors. The scope 2 emissions comprise indirect GHG emissions resulting from consumption of purchased electricity, heat, steam, etc. The carbon score for scope 2 emissions are calculated, for example, based on the amount of purchased electricity. The scope 3 emissions comprise other indirect emissions resulting from, for example, extraction and production of purchased materials and fuels, transport-related activities in automobiles not owned or controlled by a reporting entity, electricity-related activities such as transmission and distribution losses not covered under scope 2 emissions, outsourced activities, waste disposal, etc.)
Stolt et al., Sidhu et al., and Dilip et al. are all considered to be analogous to the claimed invention because they are in the same field of calculating carbon footprint and estimating carbon sequestration. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Stolt-Sidhu combination to further include wherein the first entity's carbon emissions are categorized into (i) direct emissions from sources owned or controlled by the first entity, (ii) indirect emissions from purchased electricity, heat, or steam consumed by the first entity and (iii) other indirect emissions resulting from activities but arising from sources not owned or controlled by the first entity as taught by Dilip et al. This is desirable such that it provides a carbon emissions management application performs analytics using the real time emission data obtained from the detectors via the communication protocol, scope 2 emissions, and scope 3 emissions and generates analytical reports associated with the emission data. (See Dilip, [0036]).
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Stolt et al. (US 20240354851) in view of Sidhu et al. (US 20230059038), further in view of Mohite et al. (US 20240331142).
In regards to Claim 5, the Stolt-Sidhu combination teaches the claimed invention as recited in the independent claim above.
Stolt et al. teaches
wherein the carbon predictor module transforms the plurality of satellite images and the plurality of drone images into data usable for carbon footprint analysis (See [0044]- The images may be captured by, for example, airborne or spaceborne vehicles. Also See [0022]- The step of determining whether the carbon dioxide sequestration has been effectuated may comprise an estimation, which may include a calculation, of the quantity of CO2 sequestered between the first and second satellite images. Moreover, more than two images may be used for the estimation. The geographic area may also be continuously monitored. Satellite imaging technology may be combined with a model of biomass change to estimate the sequestered quantity of CO2. Also See [0050]- The method may comprise the step of continuously, or by sampling, over a period of time, estimating a total quantity of sequestrated carbon dioxide based on satellite images of the geographic area.)
However, the Stolt-Sidhu combination does not teach by employing a computational core based on algorithms processing activity data using chosen emission factors.
Mohite et al. teaches:
by employing a computational core based on algorithms processing activity data using chosen emission factors (See [0007]- from the first image, using the image analysis technique and a set of trained machine learning models; determining a pixel-level tillage depth prescription map, for each geo-tagged field, based on the pixel-level soil bulk density, the pixel-level soil porosity, and the pixel-level hydraulic conductivity, of the pertaining geo-tagged field; calculating the carbon emission due to fuel and a needed tillage depth, for each geo-tagged field, based on the fuel quantity required and the pixel-level tillage depth prescription map pertaining to each geo-tagged field, using the emission factor look-up table; estimating the soil organic carbon released due to the needed tillage depth; calculating precise emission of carbon. Also See [0076]- The emission factor look-up table includes reference value of carbon emission factors that may be emitted for different fuel types and different fuel quantities consumed by different tillage implements used for the tillage operation. The average value of the carbon emission factor is determined based on the fuel type.).
Stolt et al., Sidhu et al., and Mohite et al. are all considered to be analogous to the claimed invention because they are in the same field of calculating carbon footprint and estimating carbon sequestration. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Stolt-Sidhu combination to further include by employing a computational core based on algorithms processing activity data using chosen emission factors as taught by Mohite et al. This is desirable such that it provides precise estimation of carbon emission due to tillage operations by accounting not only based on the tillage operation detection, but also the type of implement used for tillage and depth of tillage with the use of satellite image data, which effectively helps in precise estimation of carbon emission due to tillage operations. (See Mohite, [0026])
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Stolt et al. (US 20240354851) in view of Sidhu et al. (US 20230059038), further in view of Spagnolo et al. (US20230418982).
In regards to Claim 6 and 15, the Stolt-Sidhu combination teaches the claimed invention as recited in the independent claim above.
Stolt et al. teaches:
verifying authenticity and integrity of the NFTs representing carbon credits (See [0067]-Embodiments of the presently disclosed method and a system for processing carbon dioxide related transactions may comprise verifications related to tokens, including a quality control that limiting features of a given token is not broken, for example, that an organic crops cannot generate more than a certain predefined or calculated amount of biomass per area unit. If the amount of biomass exceed the predefined or calculated amount of biomass, the method may indicate a break agreement. The method may further comprise random triggering of manual inspection of the geographic area.).
However, the Stolt-Sidhu combination does not teach the verifying authenticity and integrity of the NFTs through cryptographic methods. Spagnolo et al. teaches:
cryptographic methods (See [0119]- First, authentication logic 708 of the CMS authenticates the app logic 702 on the CMS. This can entail authenticating the consumer app logic 702 and its invocation request 704 based on a cryptographic authentication method, such as a token authentication method or shared secret based authentication method.)
Stolt et al., Sidhu et al., and Spagnolo et al. are all considered to be analogous to the claimed invention because they are in the same field of calculating carbon footprint and estimating carbon sequestration. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Stolt-Sidhu combination to further include cryptographic methods as taught by Spagnolo et al. This is desirable such that it provides an authorization that is user-defined for the particular space environment, so that even if the app successfully authenticates to the CMS, the app still cannot invoke an action by another app unless specifically authorized to do so in the context of the space environment. (See Spagnolo, [0119])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Garg et al. (US 20250272955) discloses a system and method for determining the carbon footprint of a land area based upon emissions of GHGs and carbon sequestration of ABGs. The system makes use of satellite images, public-source emissions data and AGB data to produce a comprehensive carbon footprint metric for a designated land area.
Kang et al. (US 20230401846) discloses a method of providing carbon emission management information, the method comprising extracting, by a carbon emission management information providing server, an area corresponding to a company to be evaluated from satellite image data of the company to be evaluated, calculating, by the carbon emission management information providing server, a greenhouse gas concentration of the area corresponding to the company to be evaluated from the satellite image data.
All sources listed above are relevant to the disclosed and claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAA WADIE HUSSEIN whose telephone number is 571-270-1748. The examiner can normally be reached M-F: 8:00-5:00.
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