DETAILED ACTION
This communication is a Final Office Action rejection on the merits. Claims 1-6, 8-19, and 21-22 are currently pending and have been addressed 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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 09/12/2024, 01/20/2025, 02/10/2025, 07/31/2025, 09/03/2025, 10/06/2025, 10/28/2025, 02/10/2026, 02/20/2026, and 02/25/2026 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Response to Arguments
Applicant's arguments filed on 03/25/2026 (related to the 103 Rejection) have been fully considered but are moot in view of new grounds of rejection. Applicant's amendments necessitated the new ground(s) of rejection presented in this Office action. Rejection based on a newly cited reference(s) follows.
Applicant's arguments filed on 03/25/2026 (related to the 101 Rejection) have been fully considered but they are not persuasive.
Applicant states, on pages 13-21, that like Claim 2 of Example 46 of the Subject Matter Eligibility Examples, Applicant respectfully submits that the recitations of independent claims 1, 11, and 17 integrate the alleged abstract idea of monitoring sustainability alerts into the practical application of managing operations of certain devices to improve sustainability operations. For example, independent claim 1 recites, inter alia, "send one or more commands to the one or more devices to adjust the one or more respective operations according to the alternate sustainability action plan, wherein the one or more devices correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region of the earth, adjusting one or more utility operations within one or more buildings, or both, wherein the one or more devices are configured to adjust the one or more respective operations in response to receiving the one or more commands." (Emphasis added.) Further, the devices of claim 1, "are associated with controlling a flow of hydrocarbons from a subsurface region of the earth via one or more pumps, one or more wellheads, one or more artificial lifts, or any combination thereof," as recited by claim 1.
Accordingly, Applicant submits that enabling the control of one or more devices based on the one or more sustainability action plans is analogous to the patent-eligible limitation of Claim 2 of Example 46 of the Subject Matter Eligibility Examples. Indeed, the amended independent claims are integrated into a practical application of a "computationally efficient manner to monitor, track, project, and adjust sustainability parameters associated with operations throughout an enterprise." (Emphasis added.) Application, paragraph [0040].
Furthermore, Applicant respectfully submits that even assuming, arguendo, that amended independent claims 1, 11, and 17 recite a judicial exception, the amended independent claims are integrated into a practical application of "action plans provided by the engineering workflow systems 78 may provide operational recommendations to reduce GHG emissions for the facility operations, the production operations, or both." Application, paragraph [0119]. In particular, the "sustainability platform system 72 may send commands to equipment (e.g., lights, pumps, wellheads, artificial lifts), such as via IoT devices 44, to adjust operations based on the recommended action plan 90 to improve the sustainability parameters associated with the enterprise." Application, paragraph [0061]. Accordingly, even if the claims recite a judicial exception, independent claims 1, 11, and 17 are directed to a practical application, and therefore are directed to patent-eligible subject matter.
For at least these reasons, Applicant respectfully submits that independent claims 1, 11, and 17 are subject matter eligible under 35 U.S.C. § 101. Accordingly, Applicant respectfully requests withdrawal of the rejection of independent claims 1, 11, and 17 and their dependent claims under 35 U.S.C. § 101.
Examiner respectfully disagrees with Applicant. Although multiple Eligibility Examples were provided by the Applicant. Examiner notes that the example most similar to the amended claims is Example 46, Claim 2 of the Subject Matter Eligibility Examples.
These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “managing personal behavior.” In this case, “sending one or more commands in response to the currently action plan effectiveness” is just describing concepts related to following rules or instructions (e.g., provide an optimization of properties or utility operations in order to achieve sustainability target data). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The main functions of the additional elements recited in claim 1 are merely used to: collect data (e.g. historical sustainability action plans associated with improving one or more sustainability parameters of the enterprise via one or more abatement technologies), analyze the data (e.g. simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the updated carbon credit data to generate simulated sustainability parameters), and display certain results of the collection and analysis (e.g. present the simulated alternate sustainability parameters of the alternate sustainability action plan). Those are functions that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). The last step of “sending one or more commands to the one or more devices to adjust the one or more respective operations according to the alternate sustainability action plan, wherein the one or more devices correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region of the earth, adjusting one or more utility operations within one or more buildings, or both, wherein the one or more devices are configured to adjust the one or more respective operations in response to receiving the one or more commands” does not provide any meaningful limits of how the properties or utility operations are controlled in order to reduce the carbon emissions (see MPEP 2106.04(d) & 2106.05(f)). As stated in the specification, the utility operations may include to adjust lighting operations (see Applicant’s specification, Paragraph 0036). In this case, the “adjusting” step is not particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the “adjusting” step does not integrate the abstract idea into a practical application. (see MPEP 2106.04(d)). In contrast, Example 46 specifies an effective amount of supplemental salt and minerals mixed with feed when the analysis results for the animal indicate that the animal is exhibiting an aberrant behavioral pattern indicative of grass tetany. Also, the step of “updating carbon credit data” is considered a well-understood, routine, and conventional function since it's just “receiving or transmitting data over a network” and “performing repetitive calculations” (see MPEP 2106.05(d)).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Thus, the claim is not patent eligible.
Independent claims 11 and 17 recite similar features and therefore are rejected for the same reasons as independent claim 1. Claims 2-6, 8-10, 12-16, 18-19, and 21-22 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claims 1, 11, and 17.
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 1-6, 8-19, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to an apparatus which is a statutory category.
Step 2A, Prong One - Claim 1 recites: An enterprise system comprising: to perform one or more respective operations of an enterprise, wherein the one or more respective operations of the enterprise are associated with controlling a flow of hydrocarbons from a subsurface region of the earth via one or more pumps, one or more wellheads, one or more artificial lifts, or any combination thereof; and a system, wherein the system is configured to: obtain a sustainability model representative of a state of operations of the enterprise; obtain a currently implemented sustainability action plan associated with improving one or more sustainability parameters of the enterprise via one or more abatement technologies; determine that updated carbon credit data associated with the one or more respective operations, the sustainability model, or both is available; receive the updated carbon credit data; simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the updated carbon credit data to generate simulated sustainability parameters; determine whether the effect of the currently implemented sustainability action plan is effective based on a comparison of the simulated sustainability parameters to sustainability target data; in response to determining that the effect of the currently implemented sustainability action plan is effective, send one or more commands to maintain the one or more respective operations according to the currently implemented sustainability action plan; in response to determining that the effect of the currently implemented sustainability action plan is not effective, identify one or more engineering workflow systems of a plurality of engineering workflow systems based on the one or more sustainability parameters, and wherein the one or more engineering workflow systems are identified by: broadcasting a request indicative of the one or more sustainability parameters to the plurality of engineering workflow systems; and querying a list of abatement technologies associated with each of the plurality of engineering workflow systems based on the one or more sustainability parameters, wherein each abatement technology of the list corresponds to a device associated with the one or more respective operations of the enterprise, an operational parameter for an additional device associated with the one or more respective operations of the enterprise, or both; receive one or more responses from the one or more engineering workflow systems, wherein the one or more engineering workflow systems are configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the one or more sustainability parameters; update one or more portions of the sustainability model based on the one or more responses; obtain an alternate sustainability action plan based on the updated one or more portions of the sustainability model; and send one or more commands to adjust the one or more respective operations according to the alternate sustainability action plan, wherein correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region of the earth, adjusting one or more utility operations within one or more buildings, or both, wherein the one or more devices are configured to adjust the one or more respective operations in response to receiving the one or more commands. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “managing personal behavior.” In this case, “sending one or more commands in response to the currently action plan effectiveness” is just describing concepts related to following rules or instructions (e.g., provide an optimization of properties or utility operations in order to achieve sustainability target data). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: one or more devices; and a sustainability platform system; wherein each of the plurality of engineering workflow systems corresponds to a separate computing device relative to the computing system and is configured to independently analyze the sustainability model; querying a database.
The device is merely used to: perform various analysis operations; and send one or more commands (Paragraph 0008 & 0056). The sustainability platform system is merely used to: obtain a sustainability model representative of a state of operations of the enterprise and a current sustainability action plan associated with improving one or more sustainability parameters of the enterprise; receive updated carbon credit data; simulate an effect of the current sustainability action plan on the sustainability parameters based on the updated carbon credit data; and determine whether the current sustainability action plan is effective based on a comparison of the simulated sustainability parameters to sustainability target data (Paragraph 0008). The separate computing device is merely used to independently analyze data and produce outputs. Each engineering workflow system may thus send queries for information or data to the sustainability platform system, which may serve as data intermediary to assist each engineering workflow system in retrieving relevant information to allow the respective engineering workflow system to perform its analysis. In the same manner, the sustainability platform system may query one or more engineering workflow systems to retrieve solutions, analysis, recommendations, or the like to determine action plans to improve sustainability parameters (Paragraph 0070). The database is merely used to store solutions, analysis, recommendations, or the like to determine action plans to improve sustainability parameters (Paragraph 0070 & 0123). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “one or more devices,” “sustainability platform system,” “separate computing device,” and “database” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Also, the sustainability platform system merely used to gather data (e.g., carbon credit data and sustainability parameters). This is considered “insignificant extra-solution activity” since is just “mere data gathering” to use it for a simulation analysis (MPEP 2106.05g). Accordingly, alone and in combination, 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. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of simulating an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the updated carbon credit data. The specification shows that the device is merely used to: perform various analysis operations; and send one or more commands (Paragraph 0008 & 0056). The sustainability platform system is merely used to: obtain a sustainability model representative of a state of operations of the enterprise and a current sustainability action plan associated with improving one or more sustainability parameters of the enterprise; receive updated carbon credit data; simulate an effect of the current sustainability action plan on the sustainability parameters based on the updated carbon credit data; and determine whether the current sustainability action plan is effective based on a comparison of the simulated sustainability parameters to sustainability target data (Paragraph 0008). The separate computing device is merely used to independently analyze data and produce outputs. Each engineering workflow system may thus send queries for information or data to the sustainability platform system, which may serve as data intermediary to assist each engineering workflow system in retrieving relevant information to allow the respective engineering workflow system to perform its analysis. In the same manner, the sustainability platform system may query one or more engineering workflow systems to retrieve solutions, analysis, recommendations, or the like to determine action plans to improve sustainability parameters (Paragraph 0070). The database is merely used to store solutions, analysis, recommendations, or the like to determine action plans to improve sustainability parameters (Paragraph 0070 & 0123). Also, the “updating carbon credit data” step is considered a well-understood, routing, and conventional function since it’s just “receiving or transmitting data over a network” and “performing repetitive calculations” (MPEP 2106.05(d)). Further, the “sending” step is considered a conventional computer function of “receiving or transmitting data over a network” (MPEP 2106.05d). Examiner notes that although the one or more commands may be used to implement the action plan (Paragraph 0036, adjusting lighting operations), the claim does not provide any details of how the action plan is implemented (MPEP 2106.05(f), idea of a solution or outcome). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 11 is directed to a method at step 1, which is a statutory category. Claim 11 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 11 further recites “computing system” – which is treated as just an explicit “processor/computer” for executing the operations and is treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, the claim is ineligible.
Independent claim 17 is directed to an article of manufacture at step 1, which is a statutory category. Claim 17 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 17 further recites “machine-readable medium” and “one or more processors” – which are treated as just an explicit “processor/computer” for executing/storing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, the claim is ineligible.
Dependent claims 2-5, and 16 are not directed to any additional claim elements. Rather, these claims offer additional functions of elements found in the independent claims and addressed above - such as wherein the computing system is configured to: simulate additional parameters based on the updated carbon credit data (e.g., economic data and sustainability parameters), wherein the simulated economic data comprises estimated expenditures associated with carbon credits; and determine the sustainability target data based on the updated carbon credit data, wherein the sustainability target data comprises changes from previous sustainability target data based on the updated carbon credit data. In this case, the sustainability platform system merely used to gather additional data over time (e.g., updated carbon credit data). At Step 2A, this is considered “insignificant extra-solution activity” since is just “mere data gathering” to use it for a simulation analysis (MPEP 2106.05g). At Step 2B, the step of using “updated carbon credit data” is considered a well-understood, routing, and conventional function since it’s just “performing repetitive calculations” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 6, 9-10, 13-15, and 18 are not directed to any additional claim elements. Rather, these claims offer additional functions of elements found in the independent claims and addressed above - such as wherein the computing system is configured to: in response to determining that the effect of the currently implemented sustainability action plan is not effective: provide an indication to supplement or supplant the one or more devices according to the alternate sustainability action plan. In this case, the sustainability platform system merely used to gather additional data over time (e.g., updated carbon credit data, updated sustainability parameters, updated state of operations, adjusted economic constraints, and effectiveness of the current implementation). At Step 2A, this is considered “insignificant extra-solution activity” since is just “mere data gathering” to use it for a simulation analysis (MPEP 2106.05g). At Step 2B, the step of using “tracking updated data and effectiveness of the current action plan” is considered a well-understood, routing, and conventional function since it’s just “performing repetitive calculations” (MPEP 2106.05(d)). Also, although the one or more commands may be used to implement the action plan (Paragraph 0169), the claim does not provide any details of how the action plan is implemented (MPEP 2106.05(f), idea of a solution or outcome). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 8, 12, and 19 are not directed to any additional claim elements. Rather, these claims offer additional functions of elements found in the independent claims and addressed above - such as wherein the computing system is configured to determine that the updated carbon credit data is available by: querying one or more data sources for the updated carbon credit data; and determining that the updated carbon credit data has changed by at least a threshold amount compared to previous carbon credit data. In this case, the sustainability platform system merely used to gather/query additional data over time (e.g., updated carbon credit data). At Step 2A, this is considered “insignificant extra-solution activity” or “field of use” since is just “mere data gathering” to use it for a simulation analysis, but the querying technology is not improved (MPEP 2106.05g & MPEP 2106.05h). At Step 2B, the step of using “updated carbon credit data” is considered a well-understood, routing, and conventional function since it’s just “receiving or transmitting data over a network” and “performing repetitive calculations” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 21-22 are directed to additional elements such as: a graphical user interface. The graphical user interface is merely used to solicits inputs from a user regarding various parts of the enterprise operations (Paragraph 0068). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Also, the step of “simulating an alternate effect based on updated carbon credit data” is considered a well-understood, routing, and conventional function since it’s just “receiving or transmitting data over a network” and “performing repetitive calculations” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Natarajan et al. (US 2024/0272133 A1), in view of Debs (US 2024/0003244 A1).
Regarding claim 1 (Currently Amended), Natarajan et al. discloses an enterprise system comprising: one or more devices configured to perform one or more respective operations of an enterprise, wherein the one or more respective operations of the enterprise are associated with controlling a flow of hydrocarbons from a subsurface region of the earth via one or more pumps, one or more wellheads, one or more artificial lifts, or any combination thereof (Paragraph 0046, The plant 102 may, for example, be a processing plant that receives and processes ingredients as inputs to create a final product, such as a hydrocarbon processing plant. The plant 102 may generate waste gasses. In various embodiments, waste gasses may be released to atmosphere, such as through a stack 104. Alternatively, waste gases may be flared when being released to atmosphere. Additionally, or alternatively, flaring and venting of gases may occur at locations other than a stack 104. For example, smaller quantities of gases at other locations may be released or may unintentionally leak into the atmosphere. In some embodiments, locations other than a stack 104 where gases may be vented and/or flared and/or where gases may unintentionally leak may include well heads, safety release valves, pipe headers, and/or the like. These other locations may also be observed, measured, analyzed by, and/or the like by the one or more sensors 120; Paragraph 0047, The plant 102 in some embodiments includes any number of individual processing units. The processing units may each embody an asset of the plant 102 that performs a particular function during operation of the plant 102. For example in the example context of a hydrocarbon processing plant, a refinery plant, a drilling plant, and/or a fracking plant embodying the plant 102, the processing units may include one or more crude processing units, a hydrotreating units, isomerization units, vapor recovery units, catalytic cracking units, aromatics reduction units, visbreaker units, storage tank units, blender units, pump units, flash venting units, compressor units, cooler units (e.g., air cooler units), sensor units, flare units (e.g., the stack 104), and/or the like that perform a particular operation for transforming, storing, and/or otherwise handling one or more input ingredient(s); It can be noted that the claim language is written in alternative form. The limitation taught by Natarajan et al. is based on “one or more wellheads and one or more pumps of a hydrocarbon processing plant):
and a sustainability platform system comprising a computing system, wherein the computing system is configured to: obtain a sustainability model representative of a state of operations of the enterprise (Paragraph 0059, The one or more databases 150 may be configured to receive, store, and/or transmit data. In some embodiments, the one or more databases may be associated with sensor data received from sensors 120. The sensor data may include emissions data. In some embodiments, the sensor data may include historical sensor data as well as current and/or real-time sensor data. Additionally or alternatively, the one or more databases 150 may be associated with operations data received from the plant 102, such as from the one or more sensor units of the plant 102);
obtain a currently implemented sustainability action plan associated with improving one or more sustainability parameters of the enterprise via one or more abatement technologies (Paragraph 0059, in some embodiments, the one or more databases 150 may be associated with and/or configured to store historical, current (e.g., real-time), and/or planned or projected (e.g., for the future) operational data (e.g., including sensor data, operating conditions data, operating capacity data, and/or operating mode data) for one or more plants 102, emissions data, simulated data (e.g., including simulated emissions and/or simulated operational data), production parameters, and/or emissions reduction strategy information such as emissions reduction plan);
determine that updated carbon … data associated with the one or more respective operations, the sustainability model, or both is available; receive the updated carbon … data (Paragraph 0040, embodiments provide for generating optimized emissions quantification and reporting utilizing a particular model-based framework that specifically incorporates many of the (e.g., all of the) important factors for the model to ensure accurate and improved generation of reports, for example, that includes accurate emissions quantification of one or more individual greenhouse gases and/or combined greenhouse gases. For example, in embodiments, emissions quantification for a plant may comprise emissions amount for one or more individual greenhouse gases (e.g., methane, nitrous oxide, and/or other gases). Additionally or alternatively, in embodiments, emissions quantification for a plant may comprise emissions amount for multiple individual greenhouse gases emitted by the plant and may be represented, for example, in terms of carbon dioxide equivalent (e.g., a total amount of carbon dioxide determined based on the multiple greenhouse gases emissions amount); Paragraph 0041, One or more embodiments of the present disclosure utilize a reconciliation model specially configured to reconcile (e.g., continuously, periodically, predetermined intervals, and/or the like), for a given greenhouse gas, emissions data obtained from various data sources and/or associated with various measurement/estimation techniques. By doing so, example embodiments of the present disclosure improve accuracy of emissions quantification and reporting—for example, by utilizing a specially configured reconciliation model that reconciles emissions data from various sources while effectively capturing the various factors associated with the emissions data. This in turn ensures, as non-limiting examples, that operational and/or physical changes made (e.g., to measuring devices, to a plant, control system, and/or the like) in response to generated emissions quantification and/or report are not erroneous);
simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the updated carbon … data to generate simulated sustainability parameters (Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples);
determine whether the effect of the currently implemented sustainability action plan is effective based on a comparison of the simulated sustainability parameters to sustainability target data (Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0100, In some embodiments, machine learning technique(s) may be employed to determine/generate the weights for the reconciliation model. For example, in some embodiments, the reconciliation model may be trained based on a training dataset to determine/generate the weights. The training dataset may comprise historical emissions data, historical operational data, and/or simulated data; Paragraph 0101, At operation 306, an apparatus (such as, but not limited to, the apparatus 200 or circuitry thereof as described above in connection with FIG. 2) updates the reconciliation model. In some embodiments, updating the reconciliation model comprises updating the weights for the reconciliation model. In some embodiments, the weights may be updated based at least in part on trends with respect to the effect of the optimized emissions quantification over a period of time);
in response to determining that the effect of the currently implemented sustainability action plan is effective, send one or more commands to the one or more devices to maintain the one or more respective operations according to the currently implemented sustainability action plan (Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Examiner notes that when the current plan is meeting the emission goal, then no action is taken by the system);
in response to determining that the effect of the currently implemented sustainability action plan is not effective, identify one or more engineering workflow systems of a plurality of engineering workflow systems based on the one or more sustainability parameters (Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions), wherein each of the plurality of engineering workflow systems corresponds to a separate computing device relative to the computing system and is configured to independently analyze the sustainability model (Paragraph 0045, One or more of the sensors 120 may generate and/or transmit sensor data across a network 130 to an emissions quantification system 140. The emissions quantification system 140 may be electronically and/or communicatively coupled to one or more operational systems, for example one or more of the plants 102, one or more databases 150, and one or more user devices 160), and wherein the one or more engineering workflow systems are identified by: broadcasting a request indicative of the one or more sustainability parameters to the plurality of engineering workflow systems; and querying a database comprising a list of abatement technologies associated with each of the plurality of engineering workflow systems based on the one or more sustainability parameters, wherein each abatement technology of the list corresponds to a device associated with the one or more respective operations of the enterprise, an operational parameter for an additional device associated with the one or more respective operations of the enterprise, or both (Paragraph 0041, One or more embodiments of the present disclosure utilize a reconciliation model specially configured to reconcile (e.g., continuously, periodically, predetermined intervals, and/or the like), for a given greenhouse gas, emissions data obtained from various data sources and/or associated with various measurement/estimation techniques. By doing so, example embodiments of the present disclosure improve accuracy of emissions quantification and reporting—for example, by utilizing a specially configured reconciliation model that reconciles emissions data from various sources while effectively capturing the various factors associated with the emissions data. This in turn ensures, as non-limiting examples, that operational and/or physical changes made (e.g., to measuring devices, to a plant, control system, and/or the like) in response to generated emissions quantification and/or report are not erroneous; Paragraph 0059, The one or more databases 150 may be configured to receive, store, and/or transmit data. In some embodiments, the one or more databases may be associated with sensor data received from sensors 120. The sensor data may include emissions data. In some embodiments, the sensor data may include historical sensor data as well as current and/or real-time sensor data. Additionally or alternatively, the one or more databases 150 may be associated with operations data received from the plant 102, such as from the one or more sensor units of the plant 102. For example, in some embodiments, the one or more databases 150 may be associated with and/or configured to store historical, current (e.g., real-time), and/or planned or projected (e.g., for the future) operational data (e.g., including sensor data, operating conditions data, operating capacity data, and/or operating mode data) for one or more plants 102, emissions data, simulated data (e.g., including simulated emissions and/or simulated operational data), production parameters, and/or emissions reduction strategy information such as emissions reduction plan. In some embodiments a process model may be generated based at least in part on the operations data and may be incorporated into the reconciliation model. In some embodiments, the one or more databases 150 store data associated with multiple individual plant(s), for example multiple plants associated with the same enterprise entity but located in different geographic locations across the world);
receive one or more responses from the one or more engineering workflow systems, wherein the one or more engineering workflow systems are configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the one or more sustainability parameters (Paragraph 0085, The historical operational data may comprise sensor data, including operating conditions data and/or emissions data generated via the one or more sensors 120. The sensor data may include sensor data collected over relatively long periods of time such as one or more years as well as current sensor data (e.g., collected in real time). For example, operating conditions data of the historical operational data 350 may include a timestamp indicating an instance of time when each detection or measurement was taken along with sensor data (e.g., sensor values) indicative of the emissions amount and/or operating conditions at that instance of time such as temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the plant 102 or of the components of the plant 102 themselves, to list a few examples; Paragraph 0087, The historical production parameters may be combined with, may incorporate, and/or may include references to information and/or data that is determined and/or calculated during the past operation of the plant 102 and/or one or more particular assets, sensors, and/or components thereof and then stored in association with the historical production parameters such as, for example, historical sensor data, including historical operating conditions data and/or the historical emissions data; As stated in Paragraph 0141 of Applicant’s specification, the abatement technology may include particular actions and/or solutions for abating or reducing the negative sustainability parameters to improve the overall sustainability of the enterprise operations. Therefore, based on broadest reasonable interpretation in light of the specification, Natarajan discloses “one or more responses” since the system can receive optimal operating conditions of various components/assets of the plant in order to reduce emissions);
update one or more portions of the sustainability model based on the one or more responses; obtain an alternate sustainability action plan based on the updated one or more portions of the sustainability model (Paragraph 0089, The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples; Paragraph 0090, In some embodiments, the historical operational data, historical emissions data, and simulated data are input into the training process of the reconciliation model to train the model to generate the optimized emissions quantification. In some embodiments, a product of the model training are trained model weights that are used by the prediction process of the reconciliation model. In some embodiments, after an initial training, further training data (e.g., subsequently received and/or generated historical operational data, historical emissions data, simulated data) may be input to the training process of the reconciliation model, periodically or on an on-going basis, to refine and update the model);
and send one or more commands to the one or more devices to adjust the one or more respective operations according to the alternate sustainability action plan, wherein the one or more devices correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region of the earth, adjusting one or more utility operations within one or more buildings, or both, wherein the one or more devices are configured to adjust the one or more respective operations in response to receiving the one or more commands (Paragraph 0046, The plant 102 may, for example, be a processing plant that receives and processes ingredients as inputs to create a final product, such as a hydrocarbon processing plant. The plant 102 may generate waste gasses. In various embodiments, waste gasses may be released to atmosphere, such as through a stack 104. Alternatively, waste gases may be flared when being released to atmosphere. Additionally, or alternatively, flaring and venting of gases may occur at locations other than a stack 104. For example, smaller quantities of gases at other locations may be released or may unintentionally leak into the atmosphere. In some embodiments, locations other than a stack 104 where gases may be vented and/or flared and/or where gases may unintentionally leak may include well heads, safety release valves, pipe headers, and/or the like. These other locations may also be observed, measured, analyzed by, and/or the like by the one or more sensors 120; Paragraph 0085, Operating conditions data of the historical operational data 350 may include a timestamp indicating an instance of time when each detection or measurement was taken along with sensor data (e.g., sensor values) indicative of the emissions amount and/or operating conditions at that instance of time such as temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the plant 102 or of the components of the plant 102 themselves, to list a few examples; Paragraph 0115, At operation 410, an apparatus (such as, but not limited to, the apparatus 200 or circuitry thereof described above in connection with FIG. 2) initiates the performance of one or more prediction-based actions based at least in part on the optimized emissions quantification. In some embodiments, the apparatus outputs the optimized emissions quantification for use in automatically configuring/reconfiguring operation one or more sensors, component(s), and/or assets of the corresponding one or more plants, based at least in part on the generated optimized emissions quantification).
Although Natarajan et al. discloses to receive updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses determine that updated carbon credit data associated with the one or more respective operations, the sustainability model, or both is available; receive the updated carbon credit data; … based on the updated carbon credit data to generate … sustainability parameters (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015 & 0017).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 11 (Currently Amended), Natarajan et al. discloses a method comprising (Paragraph 0003, In general, embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating optimized emissions quantification; Paragraph 0046, The plant 102 may, for example, be a processing plant that receives and processes ingredients as inputs to create a final product, such as a hydrocarbon processing plant. The plant 102 may generate waste gasses.):
obtaining, via a computing system, a sustainability model representative of a state of operations of the enterprise (Paragraph 0059, The one or more databases 150 may be configured to receive, store, and/or transmit data. In some embodiments, the one or more databases may be associated with sensor data received from sensors 120. The sensor data may include emissions data. In some embodiments, the sensor data may include historical sensor data as well as current and/or real-time sensor data. Additionally or alternatively, the one or more databases 150 may be associated with operations data received from the plant 102, such as from the one or more sensor units of the plant 102);
obtaining, via a computing system, a currently implemented sustainability action plan associated with improving one or more sustainability parameters of the enterprise via one or more abatement technologies (Paragraph 0059, in some embodiments, the one or more databases 150 may be associated with and/or configured to store historical, current (e.g., real-time), and/or planned or projected (e.g., for the future) operational data (e.g., including sensor data, operating conditions data, operating capacity data, and/or operating mode data) for one or more plants 102, emissions data, simulated data (e.g., including simulated emissions and/or simulated operational data), production parameters, and/or emissions reduction strategy information such as emissions reduction plan);
determining, via the computing system, that updated carbon … data associated with the one or more respective operations, the sustainability model, or both is available; receive the updated carbon … data (Paragraph 0040, embodiments provide for generating optimized emissions quantification and reporting utilizing a particular model-based framework that specifically incorporates many of the (e.g., all of the) important factors for the model to ensure accurate and improved generation of reports, for example, that includes accurate emissions quantification of one or more individual greenhouse gases and/or combined greenhouse gases. For example, in embodiments, emissions quantification for a plant may comprise emissions amount for one or more individual greenhouse gases (e.g., methane, nitrous oxide, and/or other gases). Additionally or alternatively, in embodiments, emissions quantification for a plant may comprise emissions amount for multiple individual greenhouse gases emitted by the plant and may be represented, for example, in terms of carbon dioxide equivalent (e.g., a total amount of carbon dioxide determined based on the multiple greenhouse gases emissions amount); Paragraph 0041, One or more embodiments of the present disclosure utilize a reconciliation model specially configured to reconcile (e.g., continuously, periodically, predetermined intervals, and/or the like), for a given greenhouse gas, emissions data obtained from various data sources and/or associated with various measurement/estimation techniques. By doing so, example embodiments of the present disclosure improve accuracy of emissions quantification and reporting—for example, by utilizing a specially configured reconciliation model that reconciles emissions data from various sources while effectively capturing the various factors associated with the emissions data. This in turn ensures, as non-limiting examples, that operational and/or physical changes made (e.g., to measuring devices, to a plant, control system, and/or the like) in response to generated emissions quantification and/or report are not erroneous);
simulating, via the computing system, an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the updated carbon … data to generate simulated sustainability parameters (Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples);
determining, via the computing system, whether the effect of the currently implemented sustainability action plan is effective based on a comparison of the simulated sustainability parameters to sustainability target data (Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0100, In some embodiments, machine learning technique(s) may be employed to determine/generate the weights for the reconciliation model. For example, in some embodiments, the reconciliation model may be trained based on a training dataset to determine/generate the weights. The training dataset may comprise historical emissions data, historical operational data, and/or simulated data; Paragraph 0101, At operation 306, an apparatus (such as, but not limited to, the apparatus 200 or circuitry thereof as described above in connection with FIG. 2) updates the reconciliation model. In some embodiments, updating the reconciliation model comprises updating the weights for the reconciliation model. In some embodiments, the weights may be updated based at least in part on trends with respect to the effect of the optimized emissions quantification over a period of time);
in response to determining that the effect of the currently implemented sustainability action plan is effective, sending, via the computing system, one or more commands to the one or more devices to maintain the one or more respective operations according to the currently implemented sustainability action plan (Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Examiner notes that when the current plan is meeting the emission goal, then no action is taken by the system);
in response to determining that the effect of the currently implemented sustainability action plan is not effective, identifying, via the computing system, one or more engineering workflow systems of a plurality of engineering workflow systems based on the one or more sustainability parameters (Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions), wherein each of the plurality of engineering workflow systems corresponds to a separate computing device relative to the computing system and is configured to independently analyze the sustainability model (Paragraph 0045, One or more of the sensors 120 may generate and/or transmit sensor data across a network 130 to an emissions quantification system 140. The emissions quantification system 140 may be electronically and/or communicatively coupled to one or more operational systems, for example one or more of the plants 102, one or more databases 150, and one or more user devices 160), and wherein the one or more engineering workflow systems are identified by: broadcasting a request indicative of the one or more sustainability parameters to the plurality of engineering workflow systems; and querying a database comprising a list of abatement technologies associated with each of the plurality of engineering workflow systems based on the one or more sustainability parameters, wherein each abatement technology of the list corresponds to a device associated with the one or more respective operations of the enterprise, an operational parameter for an additional device associated with the one or more respective operations of the enterprise, or both (Paragraph 0041, One or more embodiments of the present disclosure utilize a reconciliation model specially configured to reconcile (e.g., continuously, periodically, predetermined intervals, and/or the like), for a given greenhouse gas, emissions data obtained from various data sources and/or associated with various measurement/estimation techniques. By doing so, example embodiments of the present disclosure improve accuracy of emissions quantification and reporting—for example, by utilizing a specially configured reconciliation model that reconciles emissions data from various sources while effectively capturing the various factors associated with the emissions data. This in turn ensures, as non-limiting examples, that operational and/or physical changes made (e.g., to measuring devices, to a plant, control system, and/or the like) in response to generated emissions quantification and/or report are not erroneous; Paragraph 0059, The one or more databases 150 may be configured to receive, store, and/or transmit data. In some embodiments, the one or more databases may be associated with sensor data received from sensors 120. The sensor data may include emissions data. In some embodiments, the sensor data may include historical sensor data as well as current and/or real-time sensor data. Additionally or alternatively, the one or more databases 150 may be associated with operations data received from the plant 102, such as from the one or more sensor units of the plant 102. For example, in some embodiments, the one or more databases 150 may be associated with and/or configured to store historical, current (e.g., real-time), and/or planned or projected (e.g., for the future) operational data (e.g., including sensor data, operating conditions data, operating capacity data, and/or operating mode data) for one or more plants 102, emissions data, simulated data (e.g., including simulated emissions and/or simulated operational data), production parameters, and/or emissions reduction strategy information such as emissions reduction plan. In some embodiments a process model may be generated based at least in part on the operations data and may be incorporated into the reconciliation model. In some embodiments, the one or more databases 150 store data associated with multiple individual plant(s), for example multiple plants associated with the same enterprise entity but located in different geographic locations across the world);
receiving, via the computing system, one or more responses from the one or more engineering workflow systems, wherein the one or more engineering workflow systems are configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the one or more sustainability parameters (Paragraph 0085, The historical operational data may comprise sensor data, including operating conditions data and/or emissions data generated via the one or more sensors 120. The sensor data may include sensor data collected over relatively long periods of time such as one or more years as well as current sensor data (e.g., collected in real time). For example, operating conditions data of the historical operational data 350 may include a timestamp indicating an instance of time when each detection or measurement was taken along with sensor data (e.g., sensor values) indicative of the emissions amount and/or operating conditions at that instance of time such as temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the plant 102 or of the components of the plant 102 themselves, to list a few examples; Paragraph 0087, The historical production parameters may be combined with, may incorporate, and/or may include references to information and/or data that is determined and/or calculated during the past operation of the plant 102 and/or one or more particular assets, sensors, and/or components thereof and then stored in association with the historical production parameters such as, for example, historical sensor data, including historical operating conditions data and/or the historical emissions data; As stated in Paragraph 0141 of Applicant’s specification, the abatement technology may include particular actions and/or solutions for abating or reducing the negative sustainability parameters to improve the overall sustainability of the enterprise operations. Therefore, based on broadest reasonable interpretation in light of the specification, Natarajan discloses “one or more responses” since the system can receive optimal operating conditions of various components/assets of the plant in order to reduce emissions);
updating, via the computing system, one or more portions of the sustainability model based on the one or more responses; obtaining, via the computing system, an alternate sustainability action plan based on the updated one or more portions of the sustainability model (Paragraph 0089, The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples; Paragraph 0090, In some embodiments, the historical operational data, historical emissions data, and simulated data are input into the training process of the reconciliation model to train the model to generate the optimized emissions quantification. In some embodiments, a product of the model training are trained model weights that are used by the prediction process of the reconciliation model. In some embodiments, after an initial training, further training data (e.g., subsequently received and/or generated historical operational data, historical emissions data, simulated data) may be input to the training process of the reconciliation model, periodically or on an on-going basis, to refine and update the model);
and sending, via the computing system, one or more commands to the one or more devices to adjust the one or more respective operations according to the alternate sustainability action plan, wherein the one or more devices correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region of the earth, adjusting one or more utility operations within one or more buildings, or both, wherein the one or more devices are configured to adjust the one or more respective operations in response to receiving the one or more commands (Paragraph 0046, The plant 102 may, for example, be a processing plant that receives and processes ingredients as inputs to create a final product, such as a hydrocarbon processing plant. The plant 102 may generate waste gasses. In various embodiments, waste gasses may be released to atmosphere, such as through a stack 104. Alternatively, waste gases may be flared when being released to atmosphere. Additionally, or alternatively, flaring and venting of gases may occur at locations other than a stack 104. For example, smaller quantities of gases at other locations may be released or may unintentionally leak into the atmosphere. In some embodiments, locations other than a stack 104 where gases may be vented and/or flared and/or where gases may unintentionally leak may include well heads, safety release valves, pipe headers, and/or the like. These other locations may also be observed, measured, analyzed by, and/or the like by the one or more sensors 120; Paragraph 0085, Operating conditions data of the historical operational data 350 may include a timestamp indicating an instance of time when each detection or measurement was taken along with sensor data (e.g., sensor values) indicative of the emissions amount and/or operating conditions at that instance of time such as temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the plant 102 or of the components of the plant 102 themselves, to list a few examples; Paragraph 0115, At operation 410, an apparatus (such as, but not limited to, the apparatus 200 or circuitry thereof described above in connection with FIG. 2) initiates the performance of one or more prediction-based actions based at least in part on the optimized emissions quantification. In some embodiments, the apparatus outputs the optimized emissions quantification for use in automatically configuring/reconfiguring operation one or more sensors, component(s), and/or assets of the corresponding one or more plants, based at least in part on the generated optimized emissions quantification).
Although Natarajan et al. discloses receiving updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses determining, via the computing system, that updated carbon credit data associated with the one or more respective operations, the sustainability model, or both is available; receiving, via the computing system, the updated carbon credit data; … based on the updated carbon credit data to generate … sustainability parameters (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015 & 0017).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 17 (Currently Amended), Natarajan et al. discloses a non-transitory, machine-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising (Paragraph 0003, In general, embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating optimized emissions quantification; Paragraph 0046, The plant 102 may, for example, be a processing plant that receives and processes ingredients as inputs to create a final product, such as a hydrocarbon processing plant. The plant 102 may generate waste gasses; Paragraph 0096, In some embodiments, the process 300 is embodied by computer program code stored on a non-transitory computer-readable storage medium of a computer program product configured for execution to perform the process as depicted and described. Alternatively or additionally, in some embodiments, the process 300 is performed by one or more specially configured computing devices, such as the apparatus 200 alone or in communication with one or more other component(s), device(s), system(s), and/or the like. In this regard, in some such embodiments, the apparatus 200 is specially configured by computer-coded instructions (e.g., computer program instructions) stored thereon, for example in the memory 204 and/or another component depicted and/or described herein and/or otherwise accessible to the apparatus 200, for performing the operations as depicted and described):
obtaining a sustainability model representative of a state of operations of the enterprise (Paragraph 0059, The one or more databases 150 may be configured to receive, store, and/or transmit data. In some embodiments, the one or more databases may be associated with sensor data received from sensors 120. The sensor data may include emissions data. In some embodiments, the sensor data may include historical sensor data as well as current and/or real-time sensor data. Additionally or alternatively, the one or more databases 150 may be associated with operations data received from the plant 102, such as from the one or more sensor units of the plant 102);
obtaining a currently implemented sustainability action plan associated with improving one or more sustainability parameters of the enterprise via one or more abatement technologies (Paragraph 0059, in some embodiments, the one or more databases 150 may be associated with and/or configured to store historical, current (e.g., real-time), and/or planned or projected (e.g., for the future) operational data (e.g., including sensor data, operating conditions data, operating capacity data, and/or operating mode data) for one or more plants 102, emissions data, simulated data (e.g., including simulated emissions and/or simulated operational data), production parameters, and/or emissions reduction strategy information such as emissions reduction plan);
determining that updated carbon … data associated with the one or more respective operations, the sustainability model, or both is available; receive the updated carbon … data (Paragraph 0040, embodiments provide for generating optimized emissions quantification and reporting utilizing a particular model-based framework that specifically incorporates many of the (e.g., all of the) important factors for the model to ensure accurate and improved generation of reports, for example, that includes accurate emissions quantification of one or more individual greenhouse gases and/or combined greenhouse gases. For example, in embodiments, emissions quantification for a plant may comprise emissions amount for one or more individual greenhouse gases (e.g., methane, nitrous oxide, and/or other gases). Additionally or alternatively, in embodiments, emissions quantification for a plant may comprise emissions amount for multiple individual greenhouse gases emitted by the plant and may be represented, for example, in terms of carbon dioxide equivalent (e.g., a total amount of carbon dioxide determined based on the multiple greenhouse gases emissions amount); Paragraph 0041, One or more embodiments of the present disclosure utilize a reconciliation model specially configured to reconcile (e.g., continuously, periodically, predetermined intervals, and/or the like), for a given greenhouse gas, emissions data obtained from various data sources and/or associated with various measurement/estimation techniques. By doing so, example embodiments of the present disclosure improve accuracy of emissions quantification and reporting—for example, by utilizing a specially configured reconciliation model that reconciles emissions data from various sources while effectively capturing the various factors associated with the emissions data. This in turn ensures, as non-limiting examples, that operational and/or physical changes made (e.g., to measuring devices, to a plant, control system, and/or the like) in response to generated emissions quantification and/or report are not erroneous);
simulating an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the updated carbon … data to generate simulated sustainability parameters (Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples);
determining whether the effect of the currently implemented sustainability action plan is effective based on a comparison of the simulated sustainability parameters to sustainability target data (Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0100, In some embodiments, machine learning technique(s) may be employed to determine/generate the weights for the reconciliation model. For example, in some embodiments, the reconciliation model may be trained based on a training dataset to determine/generate the weights. The training dataset may comprise historical emissions data, historical operational data, and/or simulated data; Paragraph 0101, At operation 306, an apparatus (such as, but not limited to, the apparatus 200 or circuitry thereof as described above in connection with FIG. 2) updates the reconciliation model. In some embodiments, updating the reconciliation model comprises updating the weights for the reconciliation model. In some embodiments, the weights may be updated based at least in part on trends with respect to the effect of the optimized emissions quantification over a period of time);
in response to determining that the effect of the currently implemented sustainability action plan is effective, sending, via the computing system, one or more commands to the one or more devices to maintain the one or more respective operations according to the currently implemented sustainability action plan (Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Examiner notes that when the current plan is meeting the emission goal, then no action is taken by the system);
in response to determining that the effect of the currently implemented sustainability action plan is not effective, identifying, via the computing system, one or more engineering workflow systems of a plurality of engineering workflow systems based on the one or more sustainability parameters (Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions), wherein each of the plurality of engineering workflow systems corresponds to a separate computing device relative to the computing system and is configured to independently analyze the sustainability model (Paragraph 0045, One or more of the sensors 120 may generate and/or transmit sensor data across a network 130 to an emissions quantification system 140. The emissions quantification system 140 may be electronically and/or communicatively coupled to one or more operational systems, for example one or more of the plants 102, one or more databases 150, and one or more user devices 160), and wherein the one or more engineering workflow systems are identified by: broadcasting a request indicative of the one or more sustainability parameters to the plurality of engineering workflow systems; and querying a database comprising a list of abatement technologies associated with each of the plurality of engineering workflow systems based on the one or more sustainability parameters, wherein each abatement technology of the list corresponds to a device associated with the one or more respective operations of the enterprise, an operational parameter for an additional device associated with the one or more respective operations of the enterprise, or both (Paragraph 0041, One or more embodiments of the present disclosure utilize a reconciliation model specially configured to reconcile (e.g., continuously, periodically, predetermined intervals, and/or the like), for a given greenhouse gas, emissions data obtained from various data sources and/or associated with various measurement/estimation techniques. By doing so, example embodiments of the present disclosure improve accuracy of emissions quantification and reporting—for example, by utilizing a specially configured reconciliation model that reconciles emissions data from various sources while effectively capturing the various factors associated with the emissions data. This in turn ensures, as non-limiting examples, that operational and/or physical changes made (e.g., to measuring devices, to a plant, control system, and/or the like) in response to generated emissions quantification and/or report are not erroneous; Paragraph 0059, The one or more databases 150 may be configured to receive, store, and/or transmit data. In some embodiments, the one or more databases may be associated with sensor data received from sensors 120. The sensor data may include emissions data. In some embodiments, the sensor data may include historical sensor data as well as current and/or real-time sensor data. Additionally or alternatively, the one or more databases 150 may be associated with operations data received from the plant 102, such as from the one or more sensor units of the plant 102. For example, in some embodiments, the one or more databases 150 may be associated with and/or configured to store historical, current (e.g., real-time), and/or planned or projected (e.g., for the future) operational data (e.g., including sensor data, operating conditions data, operating capacity data, and/or operating mode data) for one or more plants 102, emissions data, simulated data (e.g., including simulated emissions and/or simulated operational data), production parameters, and/or emissions reduction strategy information such as emissions reduction plan. In some embodiments a process model may be generated based at least in part on the operations data and may be incorporated into the reconciliation model. In some embodiments, the one or more databases 150 store data associated with multiple individual plant(s), for example multiple plants associated with the same enterprise entity but located in different geographic locations across the world);
receiving one or more responses from the one or more engineering workflow systems, wherein the one or more engineering workflow systems are configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the one or more sustainability parameters (Paragraph 0085, The historical operational data may comprise sensor data, including operating conditions data and/or emissions data generated via the one or more sensors 120. The sensor data may include sensor data collected over relatively long periods of time such as one or more years as well as current sensor data (e.g., collected in real time). For example, operating conditions data of the historical operational data 350 may include a timestamp indicating an instance of time when each detection or measurement was taken along with sensor data (e.g., sensor values) indicative of the emissions amount and/or operating conditions at that instance of time such as temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the plant 102 or of the components of the plant 102 themselves, to list a few examples; Paragraph 0087, The historical production parameters may be combined with, may incorporate, and/or may include references to information and/or data that is determined and/or calculated during the past operation of the plant 102 and/or one or more particular assets, sensors, and/or components thereof and then stored in association with the historical production parameters such as, for example, historical sensor data, including historical operating conditions data and/or the historical emissions data; As stated in Paragraph 0141 of Applicant’s specification, the abatement technology may include particular actions and/or solutions for abating or reducing the negative sustainability parameters to improve the overall sustainability of the enterprise operations. Therefore, based on broadest reasonable interpretation in light of the specification, Natarajan discloses “one or more responses” since the system can receive optimal operating conditions of various components/assets of the plant in order to reduce emissions);
updating one or more portions of the sustainability model based on the one or more responses; obtaining an alternate sustainability action plan based on the updated one or more portions of the sustainability model (Paragraph 0089, The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples; Paragraph 0090, In some embodiments, the historical operational data, historical emissions data, and simulated data are input into the training process of the reconciliation model to train the model to generate the optimized emissions quantification. In some embodiments, a product of the model training are trained model weights that are used by the prediction process of the reconciliation model. In some embodiments, after an initial training, further training data (e.g., subsequently received and/or generated historical operational data, historical emissions data, simulated data) may be input to the training process of the reconciliation model, periodically or on an on-going basis, to refine and update the model);
simulating an alternate effect of the alternate sustainability action plan on the one or more sustainability parameters over a period of time based on the updated carbon credit data to generate simulated alternate sustainability parameters; determining whether the alternate effect of the alternate sustainability action plan is effective based on a comparison of the simulated alternate sustainability parameters to sustainability target data; ranking the alternate sustainability action plan alongside the currently implemented sustainability action plan based on the simulated alternate sustainability parameters and the simulated sustainability parameters; (Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples; Examiner interprets “emissions quantification” as the “ranking”);
presenting the simulated alternate sustainability parameters of the alternate sustainability action plan and the simulated sustainability parameters of the currently implemented sustainability action plan to a user via a graphical user interface for selection of a sustainability action plan (Paragraph 0115, At operation 410, an apparatus (such as, but not limited to, the apparatus 200 or circuitry thereof described above in connection with FIG. 2) initiates the performance of one or more prediction-based actions based at least in part on the optimized emissions quantification. In some embodiments, initiating the performance of one or more prediction-based actions comprises generating an emissions report based on the optimized emissions quantification. In some embodiments, initiating the one or more prediction-based actions comprises outputting the optimized emissions quantification. In some embodiments, the apparatus outputs the optimized emissions quantification via a display of the apparatus, for example by causing rendering of user interface (e.g., output user interface) via the apparatus. Additionally or alternatively, in some embodiments, the apparatus outputs the optimized emissions quantification via at least one transmission to a client device to cause the client device to cause rendering of a user interface including or otherwise associated with the optimized emissions quantification. Additionally or alternatively, in some embodiments, the apparatus outputs the optimized emissions quantification for subsequent downstream processing. In some embodiments, the apparatus outputs the optimized emissions quantification by transmitting the optimized emissions quantification for use and/or further processing by an external device, system, and/or the like).
and sending one or more commands to the one or more devices to adjust the one or more respective operations according to the alternate sustainability action plan, wherein the one or more devices correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region of the earth, adjusting one or more utility operations within one or more buildings, or both, wherein the one or more devices are configured to adjust the one or more respective operations in response to receiving the one or more commands (Paragraph 0046, The plant 102 may, for example, be a processing plant that receives and processes ingredients as inputs to create a final product, such as a hydrocarbon processing plant. The plant 102 may generate waste gasses. In various embodiments, waste gasses may be released to atmosphere, such as through a stack 104. Alternatively, waste gases may be flared when being released to atmosphere. Additionally, or alternatively, flaring and venting of gases may occur at locations other than a stack 104. For example, smaller quantities of gases at other locations may be released or may unintentionally leak into the atmosphere. In some embodiments, locations other than a stack 104 where gases may be vented and/or flared and/or where gases may unintentionally leak may include well heads, safety release valves, pipe headers, and/or the like. These other locations may also be observed, measured, analyzed by, and/or the like by the one or more sensors 120; Paragraph 0085, Operating conditions data of the historical operational data 350 may include a timestamp indicating an instance of time when each detection or measurement was taken along with sensor data (e.g., sensor values) indicative of the emissions amount and/or operating conditions at that instance of time such as temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the plant 102 or of the components of the plant 102 themselves, to list a few examples; Paragraph 0115, At operation 410, an apparatus (such as, but not limited to, the apparatus 200 or circuitry thereof described above in connection with FIG. 2) initiates the performance of one or more prediction-based actions based at least in part on the optimized emissions quantification. In some embodiments, the apparatus outputs the optimized emissions quantification for use in automatically configuring/reconfiguring operation one or more sensors, component(s), and/or assets of the corresponding one or more plants, based at least in part on the generated optimized emissions quantification).
Although Natarajan et al. discloses receiving updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses determining that updated carbon credit data associated with the one or more respective operations, the sustainability model, or both is available; receiving the updated carbon credit data; … based on the updated carbon credit data to generate … sustainability parameters (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015 & 0017).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2 (Currently Amended), which is dependent of claim 1, the combination of Natarajan et al. and Debs discloses all the limitations in claim 1. Natarajan et al. further discloses wherein the computing system is configured to simulate the effect of the currently implemented sustainability action plan by: simulating … data associated with the sustainability model based on the updated carbon … data; and comparing the simulated economic data to … target data (Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples).
Although Natarajan et al. discloses to receive updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses … economic data associated with the sustainability model based on the updated carbon credit data; and comparing the … economic data to economic target data (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; Paragraph 0050, A database device for recording potential carbon credit sources 610. An analytical processor device 609 is used for analyzing and identifying high-quality emission reduction opportunities. A qualitative processor device 610 is used for preparing an assessment of high-quality emission reduction opportunities factors. A blockchain platform connectivity device 616 is used for marketing carbon credit opportunities. The identification of the high-quality emission reduction opportunities includes the stakeholders 618, gases being emitted, and the type of facility for the environmental projects 620; Paragraph 0058, In one embodiment, a database can be used for recording potential carbon credit sources 640, GPS location, stakeholders, and identification of GHG emissions compile the data. Carbon offset user #2 700 and carbon offset user #3 702 can check the carbon credit registry platform 626 and purchase the credits they need on the blockchain platform 626; Paragraph 0063, FIG. 9 shows for illustrative purposes only an example of filtering out the greatest emission reduction opportunities of one embodiment. FIG. 9 shows an environmental project funnel to filter out the greatest emission reduction opportunities 900. The funnel analyses factors that lead to high emission reduction at economical costs and low risk; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015, 0017, & 0020).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3 (Currently Amended), which is dependent of claim 2, the combination of Natarajan et al. and Debs discloses all the limitations in claim 2. Natarajan et al. further discloses wherein the simulated … data comprises estimated [emissions] associated with carbon … for the currently implemented sustainability action plan, and wherein the carbon … modify the simulated sustainability parameters, the sustainability target data, or both (Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples).
Although Natarajan et al. discloses to receive updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses wherein the … economic data comprises estimated expenditures associated with carbon credits for the currently implemented sustainability action plan, and wherein the carbon credits modify the … sustainability parameters, the sustainability target data, or both (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; Paragraph 0050, A database device for recording potential carbon credit sources 610. An analytical processor device 609 is used for analyzing and identifying high-quality emission reduction opportunities. A qualitative processor device 610 is used for preparing an assessment of high-quality emission reduction opportunities factors. A blockchain platform connectivity device 616 is used for marketing carbon credit opportunities. The identification of the high-quality emission reduction opportunities includes the stakeholders 618, gases being emitted, and the type of facility for the environmental projects 620; Paragraph 0058, In one embodiment, a database can be used for recording potential carbon credit sources 640, GPS location, stakeholders, and identification of GHG emissions compile the data. Carbon offset user #2 700 and carbon offset user #3 702 can check the carbon credit registry platform 626 and purchase the credits they need on the blockchain platform 626; Paragraph 0063, FIG. 9 shows for illustrative purposes only an example of filtering out the greatest emission reduction opportunities of one embodiment. FIG. 9 shows an environmental project funnel to filter out the greatest emission reduction opportunities 900. The funnel analyses factors that lead to high emission reduction at economical costs and low risk; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015, 0017, & 0020).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 4 (Currently Amended), which is dependent of claim 1, the combination of Natarajan et al. and Debs discloses all the limitations in claim 1. Natarajan et al. further discloses wherein the computing system is configured to determine the sustainability target data based on the updated carbon … data, wherein the sustainability target data comprises changes from previous sustainability target data based on the updated carbon … data (Paragraph 0040, embodiments provide for generating optimized emissions quantification and reporting utilizing a particular model-based framework that specifically incorporates many of the (e.g., all of the) important factors for the model to ensure accurate and improved generation of reports, for example, that includes accurate emissions quantification of one or more individual greenhouse gases and/or combined greenhouse gases. For example, in embodiments, emissions quantification for a plant may comprise emissions amount for one or more individual greenhouse gases (e.g., methane, nitrous oxide, and/or other gases). Additionally or alternatively, in embodiments, emissions quantification for a plant may comprise emissions amount for multiple individual greenhouse gases emitted by the plant and may be represented, for example, in terms of carbon dioxide equivalent (e.g., a total amount of carbon dioxide determined based on the multiple greenhouse gases emissions amount); Paragraph 0041, One or more embodiments of the present disclosure utilize a reconciliation model specially configured to reconcile (e.g., continuously, periodically, predetermined intervals, and/or the like), for a given greenhouse gas, emissions data obtained from various data sources and/or associated with various measurement/estimation techniques. By doing so, example embodiments of the present disclosure improve accuracy of emissions quantification and reporting—for example, by utilizing a specially configured reconciliation model that reconciles emissions data from various sources while effectively capturing the various factors associated with the emissions data. This in turn ensures, as non-limiting examples, that operational and/or physical changes made (e.g., to measuring devices, to a plant, control system, and/or the like) in response to generated emissions quantification and/or report are not erroneous).
Although Natarajan et al. discloses to receive updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses to … based on the updated carbon credit data (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; Paragraph 0050, A database device for recording potential carbon credit sources 610. An analytical processor device 609 is used for analyzing and identifying high-quality emission reduction opportunities. A qualitative processor device 610 is used for preparing an assessment of high-quality emission reduction opportunities factors. A blockchain platform connectivity device 616 is used for marketing carbon credit opportunities. The identification of the high-quality emission reduction opportunities includes the stakeholders 618, gases being emitted, and the type of facility for the environmental projects 620; Paragraph 0058, In one embodiment, a database can be used for recording potential carbon credit sources 640, GPS location, stakeholders, and identification of GHG emissions compile the data. Carbon offset user #2 700 and carbon offset user #3 702 can check the carbon credit registry platform 626 and purchase the credits they need on the blockchain platform 626; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015 & 0017).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 5 (Original), which is dependent of claim 1, the combination of Natarajan et al. and Debs discloses all the limitations in claim 1. Natarajan et al. further discloses wherein the sustainability target data comprises one or more threshold limits corresponding to the one or more sustainability parameters, one or more ranges of the one or more sustainability parameters, or both, and wherein the one or more sustainability parameters comprise a carbon footprint of the one or more devices, a water usage of the one or more devices, a waste output of the one or more devices, a greenhouse gas emission of the one or more devices, or any combination thereof (Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; It can be noted that the claim language is written in alternative form. The limitation taught by Natarajan et al. is based on “a greenhouse gas emission of the one or more devices").
Regarding claims 6 and 14 (Currently Amended), which are dependent of claims 1 and 11, the combination of Natarajan et al. and Debs discloses all the limitations in claims 1 and 11. Natarajan et al. further discloses wherein the computing system is configured to, in response to determining that the effect of the currently implemented sustainability action plan is not effective, provide an indication to supplement or supplant the one or more devices with one or more other devices according to the alternate sustainability action plan ((Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0115, At operation 410, an apparatus (such as, but not limited to, the apparatus 200 or circuitry thereof described above in connection with FIG. 2) initiates the performance of one or more prediction-based actions based at least in part on the optimized emissions quantification. In some embodiments, the apparatus outputs the optimized emissions quantification for use in automatically configuring/reconfiguring operation one or more sensors, component(s), and/or assets of the corresponding one or more plants, based at least in part on the generated optimized emissions quantification).
Regarding claims 8 and 19 (Currently Amended), which are dependent of claims 1 and 17, the combination of Natarajan et al. and Debs discloses all the limitations in claims 1 and 17. Natarajan et al. further discloses wherein the computing system is configured to determine that the updated carbon … data is available by: querying one or more data sources for the updated carbon … data; and determining that the updated carbon … data has changed by at least a threshold amount compared to previous carbon credit data (Paragraph 0040, embodiments provide for generating optimized emissions quantification and reporting utilizing a particular model-based framework that specifically incorporates many of the (e.g., all of the) important factors for the model to ensure accurate and improved generation of reports, for example, that includes accurate emissions quantification of one or more individual greenhouse gases and/or combined greenhouse gases. For example, in embodiments, emissions quantification for a plant may comprise emissions amount for one or more individual greenhouse gases (e.g., methane, nitrous oxide, and/or other gases). Additionally or alternatively, in embodiments, emissions quantification for a plant may comprise emissions amount for multiple individual greenhouse gases emitted by the plant and may be represented, for example, in terms of carbon dioxide equivalent (e.g., a total amount of carbon dioxide determined based on the multiple greenhouse gases emissions amount); Paragraph 0041, One or more embodiments of the present disclosure utilize a reconciliation model specially configured to reconcile (e.g., continuously, periodically, predetermined intervals, and/or the like), for a given greenhouse gas, emissions data obtained from various data sources and/or associated with various measurement/estimation techniques. By doing so, example embodiments of the present disclosure improve accuracy of emissions quantification and reporting—for example, by utilizing a specially configured reconciliation model that reconciles emissions data from various sources while effectively capturing the various factors associated with the emissions data. This in turn ensures, as non-limiting examples, that operational and/or physical changes made (e.g., to measuring devices, to a plant, control system, and/or the like) in response to generated emissions quantification and/or report are not erroneous).
Although Natarajan et al. discloses to receive updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs wherein the computing system is configured to determine that the updated carbon credit data is available by: querying one or more data sources for the updated carbon credit data; and determining that the updated carbon credit data has changed … (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; Paragraph 0050, A database device for recording potential carbon credit sources 610. An analytical processor device 609 is used for analyzing and identifying high-quality emission reduction opportunities. A qualitative processor device 610 is used for preparing an assessment of high-quality emission reduction opportunities factors. A blockchain platform connectivity device 616 is used for marketing carbon credit opportunities. The identification of the high-quality emission reduction opportunities includes the stakeholders 618, gases being emitted, and the type of facility for the environmental projects 620; Paragraph 0058, In one embodiment, a database can be used for recording potential carbon credit sources 640, GPS location, stakeholders, and identification of GHG emissions compile the data. Carbon offset user #2 700 and carbon offset user #3 702 can check the carbon credit registry platform 626 and purchase the credits they need on the blockchain platform 626; Paragraph 0085, In the context of SDG points, the use of carbon credits may be one way to incentivize and reward emission reduction projects that contribute to SDGs. The specific calculation of SDG points may depend on the framework or program being used, but generally, the number of SDG points may increase based on the number of carbon credits generated by the project).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9 (Currently Amended), which is dependent of claim 1, the combination of Natarajan et al. and Debs discloses all the limitations in claim 1. Natarajan et al. further discloses wherein the computing system is configured to obtain the sustainability model by: receiving enterprise data indicative of the state of operations of a portion of the enterprise associated with a geographical region, wherein the state of operations of the portion of the enterprise comprises a listing of the one or more devices and emissions data associated with the one or more devices (Paragraph 0037, The noted greenhouse gas emissions amount can be calculated at various levels ranging from an enterprise level (e.g., across an entire organization) to an individual asset level; Paragraph 0049, Additionally or alternatively, in some embodiments, the plant 102 itself is associated with a determinable location. The determinable location of the plant 102 in some embodiments represents an absolute position (e.g., GPS coordinates, latitude and longitude locations, an address, and/or the like) or a relative position of the plant (e.g., an identifier representing the location of the plant 102 as compared to one or more other plants, an enterprise headquarters, or general description in the world for example based at least in part on continent, state, or other definable region). In some embodiments, the plant 102 includes or otherwise is associated with a location sensor and/or software-driven location services that provide the location data corresponding to the plant 102. In other embodiments, the location of the plant 102 is stored and/or otherwise determinable to one or more systems, for example including the emissions quantification system 140; Paragraph 0059, in some embodiments, the one or more databases 150 may be associated with and/or configured to store historical, current (e.g., real-time), and/or planned or projected (e.g., for the future) operational data (e.g., including sensor data, operating conditions data, operating capacity data, and/or operating mode data) for one or more plants 102, emissions data, simulated data (e.g., including simulated emissions and/or simulated operational data), production parameters, and/or emissions reduction strategy information such as emissions reduction plan);
and generating the sustainability model based on the enterprise data (Paragraph 0059, in some embodiments, the one or more databases 150 may be associated with and/or configured to store historical, current (e.g., real-time), and/or planned or projected (e.g., for the future) operational data (e.g., including sensor data, operating conditions data, operating capacity data, and/or operating mode data) for one or more plants 102, emissions data, simulated data (e.g., including simulated emissions and/or simulated operational data), production parameters, and/or emissions reduction strategy information such as emissions reduction plan).
Regarding claims 10, 15, and 18 (Currently Amended), which are dependent of claims 9, 11, and 18, the combination of Natarajan et al. and Debs discloses all the limitations in claims 9, 11, and 18. Natarajan et al. further discloses wherein the computing system is configured to: update the sustainability model based on the updated carbon … data, wherein the updated sustainability model comprises … constraints associated with carbon … for augmenting the sustainability parameters of the enterprise; and simulate the effect of the currently implemented sustainability action plan on the one or more sustainability parameters based on the updated sustainability model (Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples).
Although Natarajan et al. discloses to receive updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses to wherein the sustainability platform system is configured to: update the sustainability model based on the updated carbon credit data, wherein the updated sustainability model comprises adjusted economic constraints associated with carbon credits for augmenting the sustainability parameters of the enterprise (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; Paragraph 0050, A database device for recording potential carbon credit sources 610. An analytical processor device 609 is used for analyzing and identifying high-quality emission reduction opportunities. A qualitative processor device 610 is used for preparing an assessment of high-quality emission reduction opportunities factors. A blockchain platform connectivity device 616 is used for marketing carbon credit opportunities. The identification of the high-quality emission reduction opportunities includes the stakeholders 618, gases being emitted, and the type of facility for the environmental projects 620; Paragraph 0058, In one embodiment, a database can be used for recording potential carbon credit sources 640, GPS location, stakeholders, and identification of GHG emissions compile the data. Carbon offset user #2 700 and carbon offset user #3 702 can check the carbon credit registry platform 626 and purchase the credits they need on the blockchain platform 626; Paragraph 0063, FIG. 9 shows for illustrative purposes only an example of filtering out the greatest emission reduction opportunities of one embodiment. FIG. 9 shows an environmental project funnel to filter out the greatest emission reduction opportunities 900. The funnel analyses factors that lead to high emission reduction at economical costs and low risk; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015, 0017, & 0020);
and [quantifying] the effect of the currently implemented sustainability action plan on the one or more sustainability parameters based on the updated sustainability model (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; Paragraph 0052, Calculating the value in points for high-quality emission reduction opportunities involves several variables that need to be considered. Some of the variables include GHG emissions, implementation costs, technical feasibility, environmental and social co-benefits, and additionality. GHG emissions include reduction potential that is the amount of greenhouse gas emissions that can be avoided or reduced through the implementation of the opportunity. This variable can be valued based on the current market price of carbon, which is typically determined through the trading of carbon credits on various carbon markets; Paragraph 0063, FIG. 9 shows for illustrative purposes only an example of filtering out the greatest emission reduction opportunities of one embodiment. FIG. 9 shows an environmental project funnel to filter out the greatest emission reduction opportunities 900. The funnel analyses factors that lead to high emission reduction at economical costs and low risk; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015, 0017, & 0020).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 12 (Original), which is dependent of claim 11, the combination of Natarajan et al. and Debs discloses all the limitations in claim 11. Natarajan et al. further discloses determining, via the computing system, the sustainability target data based on the updated carbon … data, wherein the sustainability target data comprises updated [emissions] data relative to previous sustainability target data based on the updated carbon … data, wherein the updated … data comprises estimated [emissions] associated with carbon … the currently implemented sustainability action plan, and wherein the carbon … modify the simulated sustainability parameters, the sustainability target data, or both, and wherein the sustainability target data comprises one or more threshold limits of the one or more sustainability parameters, one or more ranges of the one or more sustainability parameters, or both, and wherein the one or more sustainability parameters comprise a carbon footprint of the one or more devices, a water usage of the one or more devices, a waste output of the one or more devices, a greenhouse gas emission of the one or more devices, or any combination thereof (Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0041, One or more embodiments of the present disclosure utilize a reconciliation model specially configured to reconcile (e.g., continuously, periodically, predetermined intervals, and/or the like), for a given greenhouse gas, emissions data obtained from various data sources and/or associated with various measurement/estimation techniques. By doing so, example embodiments of the present disclosure improve accuracy of emissions quantification and reporting—for example, by utilizing a specially configured reconciliation model that reconciles emissions data from various sources while effectively capturing the various factors associated with the emissions data. This in turn ensures, as non-limiting examples, that operational and/or physical changes made (e.g., to measuring devices, to a plant, control system, and/or the like) in response to generated emissions quantification and/or report are not erroneous; It can be noted that the claim language is written in alternative form. The limitation taught by Natarajan et al. is based on “a greenhouse gas emission of the one or more devices").
Although Natarajan et al. discloses to receive updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses determining, via the computing system, the sustainability target data based on the updated carbon credit data, wherein the sustainability target data comprises updated economic data relative to previous sustainability target data based on the updated carbon credit data, wherein the updated economic data comprises estimated expenditures associated with carbon credits the currently implemented sustainability action plan, and wherein the carbon credits modify the … sustainability parameters, the sustainability target data, or both, and wherein the sustainability target data comprises one or more threshold limits of the one or more sustainability parameters, one or more ranges of the one or more sustainability parameters, or both, and wherein the one or more sustainability parameters comprise a carbon footprint of the one or more devices, a water usage of the one or more devices, a waste output of the one or more devices, a greenhouse gas emission of the one or more devices, or any combination thereof (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; Paragraph 0050, A database device for recording potential carbon credit sources 610. An analytical processor device 609 is used for analyzing and identifying high-quality emission reduction opportunities. A qualitative processor device 610 is used for preparing an assessment of high-quality emission reduction opportunities factors. A blockchain platform connectivity device 616 is used for marketing carbon credit opportunities. The identification of the high-quality emission reduction opportunities includes the stakeholders 618, gases being emitted, and the type of facility for the environmental projects 620; Paragraph 0058, In one embodiment, a database can be used for recording potential carbon credit sources 640, GPS location, stakeholders, and identification of GHG emissions compile the data. Carbon offset user #2 700 and carbon offset user #3 702 can check the carbon credit registry platform 626 and purchase the credits they need on the blockchain platform 626; Paragraph 0063, FIG. 9 shows for illustrative purposes only an example of filtering out the greatest emission reduction opportunities of one embodiment. FIG. 9 shows an environmental project funnel to filter out the greatest emission reduction opportunities 900. The funnel analyses factors that lead to high emission reduction at economical costs and low risk; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015, 0017, & 0020).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 13 (Currently Amended), which is dependent of claim 11, the combination of Natarajan et al. and Debs discloses all the limitations in claim 11. Natarajan et al. further discloses wherein obtaining the currently implemented sustainability action plan comprises: identifying one or more additional engineering workflow systems of the computing system that correlate with the sustainability model and are associated with the one or more sustainability parameters (Paragraph 0038, It is necessary for enterprises to accurately track and report their plant emissions in order to ensure that their plant(s) are meeting the various milestones (e.g., near-term and long-term emissions goals; Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions);
and generating, via the one or more additional engineering workflow systems, the currently implemented sustainability action plan based on an estimated improvement to at least one sustainability parameter of the one or more sustainability parameters, relative to previous sustainability target data, based on the sustainability model (Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220).
Regarding claim 16 (Original), which is dependent of claim 11, the combination of Natarajan et al. and Debs discloses all the limitations in claim 11. Natarajan et al. further discloses wherein simulating the effect of the currently implemented sustainability action plan by: simulating [emissions] data associated with the sustainability model based on the updated carbon … data, wherein the simulated economic data comprises estimated expenditures associated with carbon … the currently implemented sustainability action plan, and wherein the carbon … modify the simulated sustainability parameters, the sustainability target data, or both; and comparing the simulated [emissions] data to [emission] target data (Paragraph 0040, Embodiments of the present disclosure provide for generating efficient and accurate greenhouse emissions quantification and reporting to enable more accurate and efficient tracking and reporting of greenhouse gas emissions, which in turn enables efficient and effective greenhouse gas emissions reduction measures and facilitates achievement of emissions goal(s), for example, embodying prediction-based actions; Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples).
Although Natarajan et al. discloses to receive updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses to wherein … the effect of the currently implemented sustainability action plan by: … economic data associated with the sustainability model based on the updated carbon credit data, wherein the … economic data comprises estimated expenditures associated with carbon credits the currently implemented sustainability action plan, and wherein the carbon credits modify the … sustainability parameters, the sustainability target data, or both; and comparing the … economic data to economic target data (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; Paragraph 0050, A database device for recording potential carbon credit sources 610. An analytical processor device 609 is used for analyzing and identifying high-quality emission reduction opportunities. A qualitative processor device 610 is used for preparing an assessment of high-quality emission reduction opportunities factors. A blockchain platform connectivity device 616 is used for marketing carbon credit opportunities. The identification of the high-quality emission reduction opportunities includes the stakeholders 618, gases being emitted, and the type of facility for the environmental projects 620; Paragraph 0058, In one embodiment, a database can be used for recording potential carbon credit sources 640, GPS location, stakeholders, and identification of GHG emissions compile the data. Carbon offset user #2 700 and carbon offset user #3 702 can check the carbon credit registry platform 626 and purchase the credits they need on the blockchain platform 626; Paragraph 0063, FIG. 9 shows for illustrative purposes only an example of filtering out the greatest emission reduction opportunities of one embodiment. FIG. 9 shows an environmental project funnel to filter out the greatest emission reduction opportunities 900. The funnel analyses factors that lead to high emission reduction at economical costs and low risk; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015, 0017, & 0020).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 21 (New), which is dependent of claim 1, the combination of Natarajan et al. and Debs discloses all the limitations in claim 1. Natarajan et al. further discloses wherein the computing system is configured to: simulate an alternate effect of the alternate sustainability action plan on the one or more sustainability parameters over a period of time based on the updated carbon … data to generate simulated alternate sustainability parameters; and determine whether the alternate effect of the alternate sustainability action plan is effective based on a comparison of the simulated alternate sustainability parameters to sustainability target data (Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples).
Although Natarajan et al. discloses to receive updated carbon data indicating whether the previous action plan was effective (e.g., capturing the various factors associated with the emissions data), Natarajan et al. does not specifically disclose wherein the effectiveness of the plan is stored as an updated carbon credit data (e.g., each credit is represented as one ton of emission reduced).
However, Debs discloses wherein the computing system is configured to: … an alternate effect of the alternate sustainability action plan on the one or more sustainability parameters over a period of time based on the updated carbon credit data to generate … alternate sustainability parameters; … (Paragraph 0049, FIG. 6 shows a block diagram of an overview flow chart identifying high-quality emission reduction opportunities of one embodiment. FIG. 6 shows a carbon credit opportunities network platform 502. The carbon credit opportunities network platform 502 includes databases 600 for recording high-quality emission reduction opportunities data. The computer 602 with a carbon credit mobile application 604 receives, transmits, and processes high-quality emission reduction opportunities data. Machine learning device 606 software tools are used to update the data in the evolving Voluntary Carbon Market. A tracking device 607 is used for tracking potential carbon credit sources remediation potential status and emissions chemicals; See provisional application # 63/357,343, filed on 06/30/22, Paragraphs 0015 & 0017).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the enterprise system used to simulate an effect of the currently implemented sustainability action plan on the one or more sustainability parameters over a period of time based on the carbon data to generate simulated sustainability parameters of the invention of Natarajan et al. to further specify wherein the effect of the currently implemented sustainability action is stored as carbon credit data (e.g., each credit is represented as one ton of emission reduced) of the invention of Debs because doing so would allow the enterprise system to record high-quality emission reduction opportunities data in a carbon credit opportunities network platform (see Debs, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 22 (New), which is dependent of claim 1, the combination of Natarajan et al. and Debs discloses all the limitations in claim 1. Natarajan et al. further discloses wherein the computing system is configured to: rank the alternate sustainability action plan alongside the currently implemented sustainability action plan based on the simulated alternate sustainability parameters and the simulated sustainability parameters (Paragraph 0089, The simulated data may comprise simulated emissions data, simulated production parameters, and/or simulated operating conditions data. The simulated emissions data may indicate estimated emissions for a particular gas that would result from operation of the plant 102 and/or one or more particular assets and/or components thereof according to one or more sets of simulated production parameters. In some embodiments, the simulated emissions data may be generated or caused to be generated by the reconciliation circuitry 218 and/or the AI and machine learning circuitry 220. The simulated emissions data may be generated by a simulation process configured to receive (e.g., via user input) a virtual representation of one or more plant 102 and/or one or more particular assets and/or components thereof and simulated production parameters and, based on the received virtual representation and simulated production parameters, output simulated data representing results of operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated emissions data representing estimated emissions of a particular gas (e.g., methane) that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters. The simulated data output by the simulation process may comprise simulated operating conditions data representing estimated operating conditions that would result from the operation of the plant 102 and/or one or more particular assets and/or components thereof according to the simulated production parameters, such as estimated temperature, pressure, flow rate, and/or composition of materials moving through and/or being processed within or by various components of the system or of the components themselves, to list a few examples; Examiner interprets “emissions quantification” as the “ranking”);
and present the simulated alternate sustainability parameters of the alternate sustainability action plan and the simulated sustainability parameters of the currently implemented sustainability action plan to a user via a graphical user interface for selection of a sustainability action plan (Paragraph 0115, At operation 410, an apparatus (such as, but not limited to, the apparatus 200 or circuitry thereof described above in connection with FIG. 2) initiates the performance of one or more prediction-based actions based at least in part on the optimized emissions quantification. In some embodiments, initiating the performance of one or more prediction-based actions comprises generating an emissions report based on the optimized emissions quantification. In some embodiments, initiating the one or more prediction-based actions comprises outputting the optimized emissions quantification. In some embodiments, the apparatus outputs the optimized emissions quantification via a display of the apparatus, for example by causing rendering of user interface (e.g., output user interface) via the apparatus. Additionally or alternatively, in some embodiments, the apparatus outputs the optimized emissions quantification via at least one transmission to a client device to cause the client device to cause rendering of a user interface including or otherwise associated with the optimized emissions quantification. Additionally or alternatively, in some embodiments, the apparatus outputs the optimized emissions quantification for subsequent downstream processing. In some embodiments, the apparatus outputs the optimized emissions quantification by transmitting the optimized emissions quantification for use and/or further processing by an external device, system, and/or the like).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Pattanavekin et al. (US 2025/0086660 A1) – discloses a mechanism for the verification of stored real-time greenhouse gas emission data using a verification unit. The verification step or mechanism ensures compliance with emission reporting standards. Further, GGEMS 104 implements the steps of transforming or converting verified real-time greenhouse emission data into standardized carbon credit tokens using a converting unit. The tokenization enables standardized trading and exchange of carbon credits. Further, GGEMS 104 implements the steps of generation of real-time visualization of greenhouse gas emissions on a user interface. The visualization includes real-time greenhouse gas emission reports that enable monitoring of emission trends and patterns (see at least Paragraph 0037).
Pomerantz et al. (US 2024/0403892 A1) – discloses a method that includes providing one or more recommendations relating to greenhouse gas emission reduction goals to a customer. The method also includes receiving one or more greenhouse gas emission reduction goals from the customer. The one or more greenhouse gas emission reduction goals relate to a reduction in greenhouse gas emissions at one or more oil and gas worksites associated with the customer. The method also includes determining a greenhouse gas emission reduction plan to reduce the greenhouse gas emissions at the one or more oil and gas worksites to achieve the one or more greenhouse gas emission reduction goals. The method further includes performing work to implement the greenhouse gas emission reduction plan (see at least Abstract).
Tetteh (Tetteh, E.K., Amankwa, M.O. and Yeboah, C., 2021. Emerging carbon abatement technologies to mitigate energy-carbon footprint-a review. Cleaner Materials, 2, p.100020) - discloses carbon abatement technologies (see at least 6. Carbon abatement technologies).
Majundar et al. (US 2014/0279830 A1) – discloses data validation, and delta change identification. Source to target mapping (not necessary if an ETL tool is used) Integration component 150 will use the metadata to validate data, identify changes, generate the necessary SQL, and run the actual SQL with data binding to do the actual updates (see at least Paragraph 0030).
Ibanez et al. (US 2024/0311733 A1) – discloses a recommendation engine can receive a request from a graphical user interface (GUI) characterizing reduction of a carbon footprint and/or an increase in power consumption, as well as a budget. In response to the request, the recommendation engine can recommend a plan and corresponding operations at the given site to reduce the carbon footprint based on data associated with the given site. Furthermore, the recommendation engine can recommend the plan and corresponding operations based on data associated with other sites and extrapolated to the given site. The plan can include adjusting demand response at the given site, adding an energy source (e.g., solar panels), replacing energy consuming equipment (e.g., lighting), adding or modifying a virtual power purchasing agreement, and purchasing carbon offset credits. The recommended plan can be provided to the GUI. In some examples, the recommendation engine can automatically instantiate the recommended plan, or some portion thereof (see at least Paragraph 0012).
Kaufman (EP 2,244,140 A2) – discloses a simulation tool e.g. simulation processor has a model component (130) that produces a virtual model of industrial process (110) based on sustainability data. A portion of the sustainability data itemizes an energy impact of the industrial process as a function of one or more distinct subsets. A simulation component (140) assembles a dynamic simulation including the virtual model of industrial process (see at least Abstract).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJORIE PUJOLS-CRUZ whose telephone number is (571)272-4668. The examiner can normally be reached Mon-Thru 7:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia H Munson can be reached at (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.P./Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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