DETAILED ACTION
This communication is a Final Office Action rejection on the merits. Claims 1-20 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 .
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, 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 03/23/2026 (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 03/23/2026 (related to the 101 Rejection) have been fully considered but they are not persuasive.
Applicant states, on pages 12-18, that like Claim 2 of Example 46 of the Subject Matter Eligibility Examples, Applicant respectfully submits that the recitations of independent claims 1, 10, 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 one 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].
Even if the Examiner determines that independent claims 1, 10, and 17 are directed to a judicial exception and do not incorporate the alleged judicial exception into a practical application, independent claims 1, 10, and 17 recite an inventive concept. Indeed, as described in more detail herein, independent claims 1, 10, and 17 include limitations, or combinations of limitations, that are not well-understood, routine, conventional activity in the field. As such, Applicant respectfully submits that the claims recite an inventive concept under Step 2B and are directed to patentable subject matter.
For at least these reasons, Applicant respectfully submits that independent claims 1, 10, and 17 are subject matter eligible under 35 US.C. § 101. Accordingly, Applicant respectfully requests withdrawal of the rejection of independent claims 1, 10, 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 … according to the one or more sustainability action plans” is just describing concepts related to following rules or instructions (e.g., generating the one or more sustainability action plans based on the one or more responses). 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. a list of abatement technologies associated with each of the plurality of engineering workflow systems based on the one or more sustainability parameters), analyze the data (e.g. determine a respective authority level for each of the one or more input data sources, determine uncertainty in the input data, and generate the one or more sustainability action plans based on the one or more responses), and display certain results of the collection and analysis (e.g. present the each sustainability action plan of the one or more sustainability action plans with a respective confidence value associated with an expected effectiveness in improving the one or more sustainability parameters determined based on the confidence parameters). 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 one 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, “querying a database” is considered “field of use” since it's just used to receive information for generating a plan, but the technology is not improved (see MPEP 2106.05(h)).
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 10 and 17 recite similar features and therefore are rejected for the same reasons as independent claim 1. Claims 2-9, 11-16, and 18-20 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claims 1, 10, 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 1-20 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 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 to: obtain input data; determine a respective authority level for each of the one or more input data; determine uncertainty data associated with the input data based on the respective authority level for each of the one or more input data sources; determine confidence parameters associated with the input data based on the uncertainty data; generate one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data and the confidence parameters, wherein generating the one or more sustainability action plans comprises: identifying one or more engineering workflow systems of a plurality of engineering workflow systems based on the one or more sustainability parameters, to independently analyze a sustainability model representative of a state of operations of the enterprise, 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; 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; generating the one or more sustainability action plans based on the one or more responses; and presenting each sustainability action plan of the one or more sustainability action plans with a respective confidence value associated with an expected effectiveness in improving the one or more sustainability parameters determined based on the confidence parameters; determine a selection of one sustainability action plan of the one or more sustainability action plans based on the respective confidence value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan, send one or more commands to adjust the one or more respective operations according to the one 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 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 … according to the one or more sustainability action plans” is just describing concepts related to following rules or instructions (e.g., generating the one or more sustainability action plans based on the one or more responses). 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 comprising a computing system; one or more input data sources; wherein each of the plurality of engineering workflow systems corresponds to a separate computing device relative to the computing system; querying a database; and a graphical user interface.
The device is merely used to: perform various analysis operations; and receive one or more commands (Paragraph 0008 & 0056). The sustainability platform system comprising a computing system is merely used to: determine uncertainty data associated with the input data based on the respective authority level for each of the input data sources; determine confidence parameters associated with the input data based on the uncertainty data; generate one or more sustainability action plans for improving the sustainability parameters of the enterprise based on the input data stored in the database; and send one or more commands to adjust their respective operations according to the one or more sustainability action plans (Paragraph 0008). The data source is merely input data regarding measured sustainability parameters, policies for sustainability programs (Paragraph 0056). The separate computing device is merely used to independently analyze data and produce outputs (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). The graphical user interface is merely used to receive inputs from a user regarding various parts of the enterprise operations (Paragraph 0069). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “device,” “sustainability platform system,” “data source,” “separate computing device,” “database,” and “graphical user interface” 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., input data from one or more input data sources). This is considered “insignificant extra-solution activity” since is just “mere data gathering” to use it for a sustainability 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 generating one or more sustainability action plans for improving one or more sustainability parameters of the enterprise. The specification shows that the device is merely used to: perform various analysis operations; and receive one or more commands (Paragraph 0008 & 0056). The sustainability platform system is merely used to: determine uncertainty data associated with the input data based on the respective authority level for each of the input data sources; determine confidence parameters associated with the input data based on the uncertainty data; generate one or more sustainability action plans for improving the sustainability parameters of the enterprise based on the input data stored in the database; and send one or more commands to adjust their respective operations according to the one or more sustainability action plans (Paragraph 0008). The data source is merely input data regarding measured sustainability parameters, policies for sustainability programs (Paragraph 0056). The separate computing device is merely used to independently analyze data and produce outputs (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). The graphical user interface is merely used to receive inputs from a user regarding various parts of the enterprise operations (Paragraph 0069). Also, the step of “sending one or more commands” 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 0061), 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 10 is directed to a method at step 1, which is a statutory category. Claim 10 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 10 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 2-6, 8, 11-13, and 19-20 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 the respective authority level for each of the one or more input data sources based on a perceived reliability of each of the one or more input data sources; determine the uncertainty data based on the respective authority level for each of the one or more input data sources and obtained uncertainty data in data values of the input data from the one or more input data sources; wherein the obtained uncertainty data comprises ranges in the data values of the input data based on one or more confidence intervals defined in the input data; wherein the data values comprise forecasted values for associated with a future time period, and the confidence parameters are based on an uncertainty in the future time period; and wherein the confidence parameters are based on a sentiment analysis of a context of the input data. In this case, the main functions are merely used to: collect data (e.g., input data from one or more data sources) and analyze the data (e.g., confidence parameters of the input data). 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 (MPEP 2106.05h) Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 7-8, 14, and 18 are directed to an additional element such as: a large language model or machine learning algorithm. The database is further used to store input data (Paragraph 0187). The large language model or machine learning algorithm is merely used to query and/or scrape one or more input data sources, wherein the one or more input data sources comprise government regulatory websites, social media websites, news publication websites, product catalogs corresponding to the one or more devices, or any combination thereof (Paragraphs 0182-0183). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). In this case, the claim does not provide any details about how the large language model or machine learning algorithm operates (e.g., how is trained to query a specific type of data). See 2024 AI Guidance, example 47, claim 2. At Step 2B, the step of “receiving and storing data” is considered a well-understood, routing, and conventional function since it’s just “storing information in a memory” and “receiving or transmitting data over a network” (MPEP 2106.05d). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 9 and 15-16 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 sustainability platform system is configured to: determine at least one abatement technology estimated to improve the one or more sustainability parameters based on the input data and a sustainability model representative of a state of operations of the enterprise, 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; generate the one or more sustainability action plans based on the at least one abatement technology; simulate an effect of the one or more sustainability action plans on the one or more sustainability parameters over a period of time based on the input data to generate one or more simulated sustainability parameters; and in response to determining that the one or more simulated sustainability parameters are within one or more thresholds, sending the one or more commands to the one or more devices. In this case, the main functions are merely used to: collect data (e.g., sustainability parameters) and analyze the data (e.g., determine at least one abatement technology estimated to improve the one or more sustainability parameters by simulating an effect of the one or more plans). 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 (MPEP 2106.05h). 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.
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 1-5, 9-13, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Shi (US 2020/0372588 A1), in view of Natarajan et al. (US 2024/0272133 A1).
Regarding claim 1 (Currently Amended), Shi discloses an enterprise system comprising, … (Paragraph 0002, The present invention generally relates to the field of artificial intelligence. In particular, the present invention is directed to methods and systems for machine-learning for prediction grid carbon emissions):
one or more devices configured to perform one or more respective operations of an enterprise (Paragraph 0086, Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module);
and a sustainability platform system comprising a computing system, wherein the computing system is configured to (Figure 1, item 104, computing device; Paragraph 0035, For example, a facility may monitor and control energy resources, a sustainability team may monitor and track emission impacts, an operations team may set operational preferences and monitor the system, and the like. Data produced as described herein may also be accessible to the public to view real-time energy and emission data, for instance to raise public awareness and engage sustainability initiatives):
obtain input data from one or more input data sources (Paragraph 0036, Referring now to FIG. 2, a block diagram illustrates an exemplary embodiment indicating data flow in system 100 in a non-limiting embodiment. Data 200 may be received, for instance, as described above from one or more local grid monitoring devices; both real-time and historical data may be received); …
determine confidence parameters associated with the input data based on the uncertainty data (Paragraph 0045; Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like; As stated in Paragraph 0185 of Applicant’s specification, the confidence parameters to the input data may be based on the values of the input data);
generate one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data and the confidence parameters (Paragraph 0085, Systems and methods described herein integrate real-time emissions with energy and sustainability management, to determine how to estimate real-time carbon emissions and learn grid carbon intensity models, how to leverage real-time data to improve sustainability, how to make real-time control decisions under uncertainties of the ambient environment and user behaviors, and the like by uncovering hitherto arcane information about real-time carbon signals from a grid and proposed technology aims, to achieve economics and sustainability objectives simultaneously. Use of machine-learning and real-time data collection, coupled with optimization programs to generate recommended courses of action, enables previously unavailable clarity regarding impacts of various decisions and optimal courses of action to be taken in managing a power grid), wherein generating the one or more sustainability action plans comprises:
identifying one or more engineering workflow systems of a plurality of engineering workflow systems based on the one or more sustainability parameters, 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 a sustainability model representative of a state of operations of the enterprise, 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; 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; generating the one or more sustainability action plans based on the one or more responses (Paragraph 0033, Still referring to FIG. 1, results of machine-learning processes and/or other processes as described below to calculate carbon intensity and/or rate of change thereof may be stored in a carbon intensity datastore 148. Carbon intensity datastore 148 may be implemented in any manner suitable for implementation of power quantities datastore 124 as described above. Process may alternatively or additionally be provided to a client device 156 operated by an end-user such as without limitation a manager who makes energy management decisions, a green technology company, a company attempting to achieve a carbon offset or other environmental mandate, and/or a policy maker. Results may be transmitted via a client interface 152, which may perform one or more optimization, recommendation and/or forecasting outputs in textual and/or graphical form. Results may alternatively or additionally be communicated using an API, for instance as described in further detail below; Paragraph 0034, In operation, and still referring to FIG. 1, computing device 104 may fetch grid and/or market data from one or more local grid monitoring devices, such as without limitation generation fuel mix, marginal fuel type, system demand, locational marginal prices (LMPs), historical emissions, and emission data from a reporting service 136 such as the EPA. Estimation process 144 and/or forecast and/or models may be built and/or trained using historical data. Real-time grid data from local grid monitoring devices' web services may be ingested into computer and sent to models and/or processes to estimate and predict grid carbon intensities. Results may be stored and then served, for instance using a REST web service according to the RESTful web service protocol generated using the representational state transfer (REST) architectural style, for other applications to access grid carbon intensity data. Models and/or machine-learning processes may be updated and/or validated by benchmarking with ground truth, defined for the purposes of this disclosure as ex-post emission data to ensure model accuracy and reliability; such data may be received, without limitation, from reporting services 136, which may, for instance, provide emission data some period of time, such as a year or more, after real time or batch processes have process outputs; Paragraph 0072, Still referring to FIG. 4, computing device 104 may generate one or more power output recommendations for a local grid operator, power-consuming entity, or the like. Such recommendations may aid such users in handling intrinsic uncertainty and randomness of an ambient environment. Real-time decisions may include deciding control actions for different types of energy resources. Objectives of such recommendation processes may include minimizing emissions while maximizing economic benefits. In an embodiment, computing device 104 a power output recommendation minimizing carbon output. This may be accomplished, without limitation, by using machine-learning models and/or calculations as described above to determine likely carbon intensity resulting from various power consumption choices; possible selections of power source and/or proportions of power generated thereby may be used as variables in a mathematical expression such as a loss function as described in further detail below. Such mathematical expression may be iteratively modified to minimize a carbon intensity output);
and presenting each sustainability action plan of the one or more sustainability action plans with a respective confidence value associated with an expected effectiveness in improving the one or more sustainability parameters determined based on the confidence parameters via a graphical user interface (Paragraph 0036, Real-time data streams may be as inputs continuously fed to models, such as real-time models 220 for production of current values such as current carbon intensity and/or cumulative past carbon tonnage and forecast models 224 used for predicting future carbon intensity, past or future carbon tonnage, past or future avoided carbon tonnage, and/or costs; models may generate outputs that are sent to an optimization engine 228. Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like. All of outputs and results may be stored in an analytical data store 232, which may be implemented in any manner suitable for implementation of power quantities datastore 124 as described above and may be accessed via an interface 236 such as a user interface and/or API; Paragraph 0045; Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like);
determine a selection of one sustainability action plan of the one or more sustainability action plans based on the respective confidence value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan (Paragraph 0033, Process may alternatively or additionally be provided to a client device 156 operated by an end-user such as without limitation a manager who makes energy management decisions, a green technology company, a company attempting to achieve a carbon offset or other environmental mandate, and/or a policy maker. Results may be transmitted via a client interface 152, which may perform one or more optimization, recommendation and/or forecasting outputs in textual and/or graphical form. Results may alternatively or additionally be communicated using an API, for instance as described in further detail below. Client interface 152 may provide a two-way communication interface with client devices 156, including without limitation by means of graphical user interfaces, industry communications protocols such as Modbus, BACnet, IEC 61850, TCP/IP, other proprietary protocols, and/or an API), send one or more commands to the one or more devices to adjust the one or more respective operations according to the one sustainability action plan, …, 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 0036, Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like; Paragraph 0076, In an embodiment, and still referring to FIG. 4, both real-time and forecasted signals may be sent to optimization engine. The outputs of optimization engine may include dispatches sent to control energy resources; Paragraph 0085, Use of machine-learning and real-time data collection, coupled with optimization programs to generate recommended courses of action, enables previously unavailable clarity regarding impacts of various decisions and optimal courses of action to be taken in managing a power grid).
Although Shi discloses to generate one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data and the confidence parameters (e.g., determine a degree of uncertainty in received data, which may be represented as a confidence interval), Shi does not specifically disclose to determine a respective authority level for each of the one or more input data sources (e.g., based on the input data source such as government or social media, see Paragraph 0189 of Applicant’s specification) and wherein the enterprise is a hydrocarbon enterprise.
However, 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 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 input data from one or more input data sources (Paragraph 0042, In example embodiments, optimized greenhouse gas emissions quantification for a plant, for example, for individual greenhouse gases or combined greenhouse gases, is generated based on emissions data obtained from a variety of data sources. Additionally, in example embodiments, optimized emissions quantification is generated based on historical data describing how a system or plant has operated in the past, past emissions amount for the system or plant, and/or projected production parameters for determining and/or describing how a plant or operational system will be operated for a given period of time. In one example, historical greenhouse gas emissions measurements corresponding to past operation of a system under certain operating conditions and/or production parameters may be used to reconcile and/or predict greenhouse gas emissions amount for current or future operation of the same system under the same or similar operating conditions and/or production parameters);
determine a respective authority level for each of the one or more input data sources; determine uncertainty data associated with the input data based on the respective authority level for each of the one or more input data sources (Paragraph 0080, In some embodiments, the reconciliation circuitry 218 utilizes a reconciliation model embodying a weighted least square-based objective function that comprises weights indicative of the reliability of that portion of the emissions data. In some embodiments, the weights reflect the accuracy of the respective portion of the input emissions data. For example, in some embodiments, emissions data obtained from a given source-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type. In some embodiments, emissions data obtained from a site-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type; Examiner interprets the weight assigned to each data source as the authority level); …;
generate one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data and the [accuracy/reliability of the input data], wherein generating the one or more sustainability action plans comprises: identifying 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 a sustainability model representative of a state of operations of the enterprise (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);
generating the one or more sustainability action plans based on the one or more responses; and presenting each sustainability action plan of the one or more sustainability action plans … improving the one or more sustainability parameters determined based on the [reliable/accurate] parameters via a graphical user interface; determine a selection of one sustainability action plan of the one or more sustainability action plans based on the respective [reliable/accurate] value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan (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, 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), send one or more commands to the one or more devices to adjust the one or more respective operations according to the one 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).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system, wherein input data is analyzed to determine uncertainty data of the invention of Shi to further incorporate other ways to analyze the uncertainty of the input data (e.g., based on the respective authority level for each of the one or more input data sources) of the invention of Natarajan et al. because doing so would allow the system to associate a given source to a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type (see Natarajan et al., Paragraph 0080). 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 10 (Currently Amended), Shi discloses a method comprising (Paragraph 0002, The present invention generally relates to the field of artificial intelligence. In particular, the present invention is directed to methods and systems for machine-learning for prediction grid carbon emissions):
obtaining, via a computer system, input data from one or more input data sources (Figure 1, item 104, computing device; Paragraph 0036, Referring now to FIG. 2, a block diagram illustrates an exemplary embodiment indicating data flow in system 100 in a non-limiting embodiment. Data 200 may be received, for instance, as described above from one or more local grid monitoring devices; both real-time and historical data may be received); …
determining, via the computer system, confidence parameters associated with the input data based on the uncertainty data (Figure 1, item 104, computing device; Paragraph 0045; Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like; As stated in Paragraph 0185 of Applicant’s specification, the confidence parameters to the input data may be based on the values of the input data);
generating, via the computer system, one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data (Paragraph 0085, Systems and methods described herein integrate real-time emissions with energy and sustainability management, to determine how to estimate real-time carbon emissions and learn grid carbon intensity models, how to leverage real-time data to improve sustainability, how to make real-time control decisions under uncertainties of the ambient environment and user behaviors, and the like by uncovering hitherto arcane information about real-time carbon signals from a grid and proposed technology aims, to achieve economics and sustainability objectives simultaneously. Use of machine-learning and real-time data collection, coupled with optimization programs to generate recommended courses of action, enables previously unavailable clarity regarding impacts of various decisions and optimal courses of action to be taken in managing a power grid), wherein generating the one or more sustainability action plans comprises:
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, 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 a sustainability model representative of a state of operations of the enterprise, 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; 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; and generating, via the computing system, the one or more sustainability action plans based on the one or more responses (Paragraph 0033, Still referring to FIG. 1, results of machine-learning processes and/or other processes as described below to calculate carbon intensity and/or rate of change thereof may be stored in a carbon intensity datastore 148. Carbon intensity datastore 148 may be implemented in any manner suitable for implementation of power quantities datastore 124 as described above. Process may alternatively or additionally be provided to a client device 156 operated by an end-user such as without limitation a manager who makes energy management decisions, a green technology company, a company attempting to achieve a carbon offset or other environmental mandate, and/or a policy maker. Results may be transmitted via a client interface 152, which may perform one or more optimization, recommendation and/or forecasting outputs in textual and/or graphical form. Results may alternatively or additionally be communicated using an API, for instance as described in further detail below; Paragraph 0034, In operation, and still referring to FIG. 1, computing device 104 may fetch grid and/or market data from one or more local grid monitoring devices, such as without limitation generation fuel mix, marginal fuel type, system demand, locational marginal prices (LMPs), historical emissions, and emission data from a reporting service 136 such as the EPA. Estimation process 144 and/or forecast and/or models may be built and/or trained using historical data. Real-time grid data from local grid monitoring devices' web services may be ingested into computer and sent to models and/or processes to estimate and predict grid carbon intensities. Results may be stored and then served, for instance using a REST web service according to the RESTful web service protocol generated using the representational state transfer (REST) architectural style, for other applications to access grid carbon intensity data. Models and/or machine-learning processes may be updated and/or validated by benchmarking with ground truth, defined for the purposes of this disclosure as ex-post emission data to ensure model accuracy and reliability; such data may be received, without limitation, from reporting services 136, which may, for instance, provide emission data some period of time, such as a year or more, after real time or batch processes have process outputs; Paragraph 0072, Still referring to FIG. 4, computing device 104 may generate one or more power output recommendations for a local grid operator, power-consuming entity, or the like. Such recommendations may aid such users in handling intrinsic uncertainty and randomness of an ambient environment. Real-time decisions may include deciding control actions for different types of energy resources. Objectives of such recommendation processes may include minimizing emissions while maximizing economic benefits. In an embodiment, computing device 104 a power output recommendation minimizing carbon output. This may be accomplished, without limitation, by using machine-learning models and/or calculations as described above to determine likely carbon intensity resulting from various power consumption choices; possible selections of power source and/or proportions of power generated thereby may be used as variables in a mathematical expression such as a loss function as described in further detail below. Such mathematical expression may be iteratively modified to minimize a carbon intensity output);
and presenting each sustainability action plan of the one or more sustainability action plans with a respective confidence value associated with an expected effectiveness in improving the one or more sustainability parameters determined based on the confidence parameters via a graphical user interface (Paragraph 0036, Real-time data streams may be as inputs continuously fed to models, such as real-time models 220 for production of current values such as current carbon intensity and/or cumulative past carbon tonnage and forecast models 224 used for predicting future carbon intensity, past or future carbon tonnage, past or future avoided carbon tonnage, and/or costs; models may generate outputs that are sent to an optimization engine 228. Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like. All of outputs and results may be stored in an analytical data store 232, which may be implemented in any manner suitable for implementation of power quantities datastore 124 as described above and may be accessed via an interface 236 such as a user interface and/or API; Paragraph 0045; Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like);
determining a selection of one sustainability action plan of the one or more sustainability action plans based on the respective confidence value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan (Paragraph 0033, Process may alternatively or additionally be provided to a client device 156 operated by an end-user such as without limitation a manager who makes energy management decisions, a green technology company, a company attempting to achieve a carbon offset or other environmental mandate, and/or a policy maker. Results may be transmitted via a client interface 152, which may perform one or more optimization, recommendation and/or forecasting outputs in textual and/or graphical form. Results may alternatively or additionally be communicated using an API, for instance as described in further detail below. Client interface 152 may provide a two-way communication interface with client devices 156, including without limitation by means of graphical user interfaces, industry communications protocols such as Modbus, BACnet, IEC 61850, TCP/IP, other proprietary protocols, and/or an API), 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 one sustainability action plan, …, 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 0036, Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like; Paragraph 0076, In an embodiment, and still referring to FIG. 4, both real-time and forecasted signals may be sent to optimization engine. The outputs of optimization engine may include dispatches sent to control energy resources; Paragraph 0085, Use of machine-learning and real-time data collection, coupled with optimization programs to generate recommended courses of action, enables previously unavailable clarity regarding impacts of various decisions and optimal courses of action to be taken in managing a power grid).
Although Shi discloses to generate one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data and the confidence parameters (e.g., determine a degree of uncertainty in received data, which may be represented as a confidence interval), Shi does not specifically disclose to determine a respective authority level for each of the one or more input data sources (e.g., based on the input data source such as government or social media, see Paragraph 0189 of Applicant’s specification) and wherein the enterprise is a hydrocarbon enterprise.
However, Natarajan et al. discloses a method comprising (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);
obtaining, via the computing system, input data from one or more input data sources (Paragraph 0042, In example embodiments, optimized greenhouse gas emissions quantification for a plant, for example, for individual greenhouse gases or combined greenhouse gases, is generated based on emissions data obtained from a variety of data sources. Additionally, in example embodiments, optimized emissions quantification is generated based on historical data describing how a system or plant has operated in the past, past emissions amount for the system or plant, and/or projected production parameters for determining and/or describing how a plant or operational system will be operated for a given period of time. In one example, historical greenhouse gas emissions measurements corresponding to past operation of a system under certain operating conditions and/or production parameters may be used to reconcile and/or predict greenhouse gas emissions amount for current or future operation of the same system under the same or similar operating conditions and/or production parameters);
determining, via the computing system, a respective authority level for each of the one or more input data sources; determine uncertainty data associated with the input data based on the respective authority level for each of the one or more input data sources (Paragraph 0080, In some embodiments, the reconciliation circuitry 218 utilizes a reconciliation model embodying a weighted least square-based objective function that comprises weights indicative of the reliability of that portion of the emissions data. In some embodiments, the weights reflect the accuracy of the respective portion of the input emissions data. For example, in some embodiments, emissions data obtained from a given source-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type. In some embodiments, emissions data obtained from a site-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type; Examiner interprets the weight assigned to each data source as the authority level); …;
generating, via the computing system, one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data, wherein generating the one or more sustainability action plans comprises: 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 a sustainability model representative of a state of operations of the enterprise (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);
and generating, via the computing system, the one or more sustainability action plans based on the one or more responses; and presenting each sustainability action plan of the one or more sustainability action plans … improving the one or more sustainability parameters determined based on the [reliable/accurate] parameters via a graphical user interface; determining a selection of one sustainability action plan of the one or more sustainability action plans based on the respective [reliable/accurate] value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan (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, 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), 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 one 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).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system, wherein input data is analyzed to determine uncertainty data of the invention of Shi to further incorporate other ways to analyze the uncertainty of the input data (e.g., based on the respective authority level for each of the one or more input data sources) of the invention of Natarajan et al. because doing so would allow the system to associate a given source to a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type (see Natarajan et al., Paragraph 0080). 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), Shi 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 0087, Such software may be a computer program product that employs a machine-readable storage medium. A machine-readable storage medium may be any medium that is capable of storing and/or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and/or embodiments described herein):
obtaining input data from one or more input data sources (Figure 1, item 104, computing device; Paragraph 0036, Referring now to FIG. 2, a block diagram illustrates an exemplary embodiment indicating data flow in system 100 in a non-limiting embodiment. Data 200 may be received, for instance, as described above from one or more local grid monitoring devices; both real-time and historical data may be received); …
determining confidence parameters associated with the input data based on the uncertainty data (Figure 1, item 104, computing device; Paragraph 0045; Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like; As stated in Paragraph 0185 of Applicant’s specification, the confidence parameters to the input data may be based on the values of the input data);
generating one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data (Paragraph 0085, Systems and methods described herein integrate real-time emissions with energy and sustainability management, to determine how to estimate real-time carbon emissions and learn grid carbon intensity models, how to leverage real-time data to improve sustainability, how to make real-time control decisions under uncertainties of the ambient environment and user behaviors, and the like by uncovering hitherto arcane information about real-time carbon signals from a grid and proposed technology aims, to achieve economics and sustainability objectives simultaneously. Use of machine-learning and real-time data collection, coupled with optimization programs to generate recommended courses of action, enables previously unavailable clarity regarding impacts of various decisions and optimal courses of action to be taken in managing a power grid), wherein generating the one or more sustainability action plans comprises:
identifying one or more engineering workflow systems of a plurality of engineering workflow systems based on the one or more sustainability parameters, 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 a sustainability model representative of a state of operations of the enterprise, 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; 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; generating the one or more sustainability action plans based on the one or more responses (Paragraph 0033, Still referring to FIG. 1, results of machine-learning processes and/or other processes as described below to calculate carbon intensity and/or rate of change thereof may be stored in a carbon intensity datastore 148. Carbon intensity datastore 148 may be implemented in any manner suitable for implementation of power quantities datastore 124 as described above. Process may alternatively or additionally be provided to a client device 156 operated by an end-user such as without limitation a manager who makes energy management decisions, a green technology company, a company attempting to achieve a carbon offset or other environmental mandate, and/or a policy maker. Results may be transmitted via a client interface 152, which may perform one or more optimization, recommendation and/or forecasting outputs in textual and/or graphical form. Results may alternatively or additionally be communicated using an API, for instance as described in further detail below; Paragraph 0034, In operation, and still referring to FIG. 1, computing device 104 may fetch grid and/or market data from one or more local grid monitoring devices, such as without limitation generation fuel mix, marginal fuel type, system demand, locational marginal prices (LMPs), historical emissions, and emission data from a reporting service 136 such as the EPA. Estimation process 144 and/or forecast and/or models may be built and/or trained using historical data. Real-time grid data from local grid monitoring devices' web services may be ingested into computer and sent to models and/or processes to estimate and predict grid carbon intensities. Results may be stored and then served, for instance using a REST web service according to the RESTful web service protocol generated using the representational state transfer (REST) architectural style, for other applications to access grid carbon intensity data. Models and/or machine-learning processes may be updated and/or validated by benchmarking with ground truth, defined for the purposes of this disclosure as ex-post emission data to ensure model accuracy and reliability; such data may be received, without limitation, from reporting services 136, which may, for instance, provide emission data some period of time, such as a year or more, after real time or batch processes have process outputs; Paragraph 0072, Still referring to FIG. 4, computing device 104 may generate one or more power output recommendations for a local grid operator, power-consuming entity, or the like. Such recommendations may aid such users in handling intrinsic uncertainty and randomness of an ambient environment. Real-time decisions may include deciding control actions for different types of energy resources. Objectives of such recommendation processes may include minimizing emissions while maximizing economic benefits. In an embodiment, computing device 104 a power output recommendation minimizing carbon output. This may be accomplished, without limitation, by using machine-learning models and/or calculations as described above to determine likely carbon intensity resulting from various power consumption choices; possible selections of power source and/or proportions of power generated thereby may be used as variables in a mathematical expression such as a loss function as described in further detail below. Such mathematical expression may be iteratively modified to minimize a carbon intensity output);
and presenting each sustainability action plan of the one or more sustainability action plans with a respective confidence value associated with an expected effectiveness in improving the one or more sustainability parameters determined based on the confidence parameters via a graphical user interface (Paragraph 0036, Real-time data streams may be as inputs continuously fed to models, such as real-time models 220 for production of current values such as current carbon intensity and/or cumulative past carbon tonnage and forecast models 224 used for predicting future carbon intensity, past or future carbon tonnage, past or future avoided carbon tonnage, and/or costs; models may generate outputs that are sent to an optimization engine 228. Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like. All of outputs and results may be stored in an analytical data store 232, which may be implemented in any manner suitable for implementation of power quantities datastore 124 as described above and may be accessed via an interface 236 such as a user interface and/or API; Paragraph 0045; Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like);
determining a selection of one sustainability action plan of the one or more sustainability action plans based on the respective confidence value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan (Paragraph 0033, Process may alternatively or additionally be provided to a client device 156 operated by an end-user such as without limitation a manager who makes energy management decisions, a green technology company, a company attempting to achieve a carbon offset or other environmental mandate, and/or a policy maker. Results may be transmitted via a client interface 152, which may perform one or more optimization, recommendation and/or forecasting outputs in textual and/or graphical form. Results may alternatively or additionally be communicated using an API, for instance as described in further detail below. Client interface 152 may provide a two-way communication interface with client devices 156, including without limitation by means of graphical user interfaces, industry communications protocols such as Modbus, BACnet, IEC 61850, TCP/IP, other proprietary protocols, and/or an API), sending one or more commands to the one or more devices to adjust the one or more respective operations according to the one sustainability action plan, …, 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 0036, Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like; Paragraph 0076, In an embodiment, and still referring to FIG. 4, both real-time and forecasted signals may be sent to optimization engine. The outputs of optimization engine may include dispatches sent to control energy resources; Paragraph 0085, Use of machine-learning and real-time data collection, coupled with optimization programs to generate recommended courses of action, enables previously unavailable clarity regarding impacts of various decisions and optimal courses of action to be taken in managing a power grid).
Although Shi discloses to generate one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data and the confidence parameters (e.g., determine a degree of uncertainty in received data, which may be represented as a confidence interval), Shi does not specifically disclose to determine a respective authority level for each of the one or more input data sources (e.g., based on the input data source such as government or social media, see Paragraph 0189 of Applicant’s specification) and wherein the enterprise is a hydrocarbon enterprise.
However, 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 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; Paragraph 0096, Specifically, FIG. 3 illustrates an example computer-implemented process 300. 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 input data from one or more input data sources (Paragraph 0042, In example embodiments, optimized greenhouse gas emissions quantification for a plant, for example, for individual greenhouse gases or combined greenhouse gases, is generated based on emissions data obtained from a variety of data sources. Additionally, in example embodiments, optimized emissions quantification is generated based on historical data describing how a system or plant has operated in the past, past emissions amount for the system or plant, and/or projected production parameters for determining and/or describing how a plant or operational system will be operated for a given period of time. In one example, historical greenhouse gas emissions measurements corresponding to past operation of a system under certain operating conditions and/or production parameters may be used to reconcile and/or predict greenhouse gas emissions amount for current or future operation of the same system under the same or similar operating conditions and/or production parameters);
determining a respective authority level for each of the one or more input data sources; determine uncertainty data associated with the input data based on the respective authority level for each of the one or more input data sources (Paragraph 0080, In some embodiments, the reconciliation circuitry 218 utilizes a reconciliation model embodying a weighted least square-based objective function that comprises weights indicative of the reliability of that portion of the emissions data. In some embodiments, the weights reflect the accuracy of the respective portion of the input emissions data. For example, in some embodiments, emissions data obtained from a given source-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type. In some embodiments, emissions data obtained from a site-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type; Examiner interprets the weight assigned to each data source as the authority level); …;
generating one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data and the [accuracy/reliability of the input data], wherein generating the one or more sustainability action plans comprises: identifying 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 a sustainability model representative of a state of operations of the enterprise (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);
and generating the one or more sustainability action plans based on the one or more responses; and presenting each sustainability action plan of the one or more sustainability action plans … improving the one or more sustainability parameters determined based on the [reliable/accurate] parameters via a graphical user interface; determine a selection of one sustainability action plan of the one or more sustainability action plans based on the respective [reliable/accurate] value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan (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, 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), sending one or more commands to the one or more devices to adjust the one or more respective operations according to the one 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).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system, wherein input data is analyzed to determine uncertainty data of the invention of Shi to further incorporate other ways to analyze the uncertainty of the input data (e.g., based on the respective authority level for each of the one or more input data sources) of the invention of Natarajan et al. because doing so would allow the system to associate a given source to a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type (see Natarajan et al., Paragraph 0080). 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 claims 2 and 13 (Currently Amended), which are dependent of claims 1 and 10, the combination of Shi and Natarajan et al. discloses all the limitations in claims 1 and 10. Although Shi discloses to determine confidence parameters associated with the input data based on the uncertainty data (e.g., determine a degree of uncertainty in received data, which may be represented as a confidence interval), Shi does not specifically disclose to determine a respective authority level for each of the one or more input data sources (e.g., based on the input data source such as government or social media, see Paragraph 0189 of Applicant’s specification).
However, Natarajan et al. further discloses wherein the computing system is configured to determine the respective authority level for each of the one or more input data sources based on a perceived reliability of each of the one or more input data sources (Paragraph 0080, In some embodiments, the reconciliation circuitry 218 utilizes a reconciliation model embodying a weighted least square-based objective function that comprises weights indicative of the reliability of that portion of the emissions data. In some embodiments, the weights reflect the accuracy of the respective portion of the input emissions data. For example, in some embodiments, emissions data obtained from a given source-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type. In some embodiments, emissions data obtained from a site-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type; Examiner interprets the weight assigned to each data source as the authority level).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system, wherein input data is analyzed to determine uncertainty data of the invention of Shi to further incorporate other ways to analyze the uncertainty of the input data (e.g., based on the respective authority level for each of the one or more input data sources) of the invention of Natarajan et al. because doing so would allow the system to associate a given source to a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type (see Natarajan et al., Paragraph 0080). 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 claims 3 and 11 (Currently Amended), which are dependent of claims 1 and 10, the combination of Shi and Natarajan et al. discloses all the limitations in claims 1 and 10. Shi further discloses wherein the computing platform system is configured to determine the uncertainty data … for each of the one or more input data sources and obtained uncertainty data in data values of the input data from the one or more input data sources (Paragraph 0045; Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like).
Although Shi discloses to determine confidence parameters associated with the input data based on the uncertainty data (e.g., determine a degree of uncertainty in received data, which may be represented as a confidence interval), Shi does not specifically disclose to determine a respective authority level for each of the one or more input data sources (e.g., based on the input data source such as government or social media, see Paragraph 0189 of Applicant’s specification).
However, Natarajan et al. further discloses … based on the respective authority level for each of the one or more input data sources … (Paragraph 0080, In some embodiments, the reconciliation circuitry 218 utilizes a reconciliation model embodying a weighted least square-based objective function that comprises weights indicative of the reliability of that portion of the emissions data. In some embodiments, the weights reflect the accuracy of the respective portion of the input emissions data. For example, in some embodiments, emissions data obtained from a given source-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type. In some embodiments, emissions data obtained from a site-based sensor may correspond to a portion of the input emissions data and may be associated with a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type; Examiner interprets the weight assigned to each data source as the authority level).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system, wherein input data is analyzed to determine uncertainty data of the invention of Shi to further incorporate other ways to analyze the uncertainty of the input data (e.g., based on the respective authority level for each of the one or more input data sources) of the invention of Natarajan et al. because doing so would allow the system to associate a given source to a weight indicative of the reliability of the respective emissions data based at least in part on associated emissions source category and/or sensor type (see Natarajan et al., Paragraph 0080). 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 (Original), which is dependent of claim 3, the combination of Shi and Natarajan et al. discloses all the limitations in claim 3. Shi further discloses wherein the obtained uncertainty data comprises ranges in the data values of the input data based on one or more confidence intervals defined in the input data (Paragraph 0045, Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like. Degree of uncertainty may be calculated, without limitation, using historical data; for instance, a given local grid and/or a power generator within the grid may be determined using historical data, results of audits by reporting agencies, or the like to fall within a given range of possible values about a reported value).
Regarding claim 5 (Currently Amended), which is dependent of claim 3, the combination of Shi and Natarajan et al. discloses all the limitations in claim 3. Shi further discloses wherein the data values comprise forecasted values associated with a future time period, and the confidence parameters are based on an uncertainty in the future time period (Paragraph 0018, In embodiments, artificial intelligence and machine-learning methods are used to estimate real-time emission impacts based on grid data. Deep learning for real-time/online optimization and control may be used to minimize emission impacts while maximizing efficiency benefits under uncertainties of the ambient environment and user behaviors; Paragraph 0034, Estimation process 144 and/or forecast and/or models may be built and/or trained using historical data; Paragraph 0045, Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like).
Regarding claim 9 (Currently Amended), which is dependent of claim 1, the combination of Shi and Natarajan et al. discloses all the limitations in claim 1. Shi further discloses wherein the computing system is configured to generate the one or more sustainability action plans by: determining at least one abatement technology estimated to improve the one or more sustainability parameters based on the input data and a sustainability model representative of a state of operations of the enterprise, 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; and generating the one or more sustainability action plans based on the at least one abatement technology (Paragraph 0085, Systems and methods described herein integrate real-time emissions with energy and sustainability management, to determine how to estimate real-time carbon emissions and learn grid carbon intensity models, how to leverage real-time data to improve sustainability, how to make real-time control decisions under uncertainties of the ambient environment and user behaviors, and the like by uncovering hitherto arcane information about real-time carbon signals from a grid and proposed technology aims, to achieve economics and sustainability objectives simultaneously. Use of machine-learning and real-time data collection, coupled with optimization programs to generate recommended courses of action, enables previously unavailable clarity regarding impacts of various decisions and optimal courses of action to be taken in managing a power grid; It can be noted that the claim language is written in alternative form. The limitation taught by Shi is based on “wherein the one or more sustainability parameters comprise a carbon footprint of the one or more devices").
Regarding claim 12 (Original), which is dependent of claim 11, the combination of Shi and Natarajan et al. discloses all the limitations in claim 11. Shi further discloses wherein the obtained uncertainty data comprises ranges in the data values of the input data based on one or more confidence intervals defined in the input data (Figure 1, item 104, computing device; Paragraph 0045; Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like).
Regarding claim 15 (Original), which is dependent of claim 10, the combination of Shi and Natarajan et al. discloses all the limitations in claim 10. Shi further discloses wherein generating the one or more sustainability action plans comprises: a determining at least one abatement technology estimated to improve the one or more sustainability parameters based on the input data and a sustainability model of the enterprise, wherein the sustainability model is representative of a state of operations of the enterprise; and generating the one or more sustainability action plans based on the at least one abatement technology (Paragraph 0085, Systems and methods described herein integrate real-time emissions with energy and sustainability management, to determine how to estimate real-time carbon emissions and learn grid carbon intensity models, how to leverage real-time data to improve sustainability, how to make real-time control decisions under uncertainties of the ambient environment and user behaviors, and the like by uncovering hitherto arcane information about real-time carbon signals from a grid and proposed technology aims, to achieve economics and sustainability objectives simultaneously. Use of machine-learning and real-time data collection, coupled with optimization programs to generate recommended courses of action, enables previously unavailable clarity regarding impacts of various decisions and optimal courses of action to be taken in managing a power grid).
Regarding claim 16 (Original), which is dependent of claim 10, the combination of Shi and Natarajan et al. discloses all the limitations in claim 10. Shi further discloses generating, via the computing system, the sustainability model based on the input data; simulating, via the computing system, an effect of the one or more sustainability action plans on the one or more sustainability parameters over a period of time based on the input data to generate one or more simulated sustainability parameters; and … (Paragraph 0082, In a practical example, a control problem as described above was simulated using 2017 real data to compare the results of three strategies: 1) minimization of demand charge only; 2) minimization of carbon emissions only; and 3) co-optimization of demand charge with carbon emissions).
Although Shi discloses simulating an effect of the one or more sustainability action plans on the one or more sustainability parameters over a period of time based on the input data to generate one or more simulated sustainability parameters (Paragraph 0082), Shi does not specifically disclose sending one or more commands to the one or more devices in response to determining that the one or more simulated sustainability parameters are within one or more thresholds.
However, Natarajan et al. discloses generating, via the computing system, the sustainability model based on the input data; simulating, via the computing system, an effect of the one or more sustainability action plans on the one or more sustainability parameters over a period of time based on the input data to generate one or more simulated sustainability parameters; and in response to determining that the one or more simulated sustainability parameters are within one or more thresholds (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), sending, via the computing system, the one or more commands to the one or more devices (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).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system used for simulating an effect of the one or more sustainability action plans of the invention of Shi to further incorporate sending one or more commands to the one or more devices in response to determining that the one or more simulated sustainability parameters are within one or more threshold of the invention of Natarajan et al. because doing so would allow the system to automatically configure/reconfigure operation of 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 (see Natarajan et al., Paragraph 0115). 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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Shi (US 2020/0372588 A1), in view of Natarajan et al. (US 2024/0272133 A1), in further view of Kaushansky et al. (US 2008/0215607 A1).
Regarding claim 6 (Original), which is dependent of claim 1, the combination of Shi and Natarajan et al. discloses all the limitations in claim 1. Although Shi discloses to determine confidence parameters associated with the input data based on the uncertainty data (e.g., determine a degree of uncertainty in received data, which may be represented as a confidence interval), Shi does not specifically disclose wherein the confidence parameters are based on a sentiment analysis of a context of the input data.
However, Kaushansky et al. discloses wherein the confidence parameters are based on a sentiment analysis of a context of the input data (Paragraph 0028, It is often the case where it is desirable to collect information from blogs where authors are more likely to provide content on two or more subjects and to provide indications of their opinions or their positive/negative sentiments toward such topics; Paragraph 0058, A first cluster may be Cluster 1 (Label: environment) with the following significant terms/phrases: energy oil global gas warming environment power change fuel earth climate environmental waste carbon green planet need water solar electric; Paragraph 0074, Embodiments of the tribe analysis tool may also perform a semantic analysis of each message to determine attributes of the speech itself. For example, an attribute might indicate a message thread to which the message belongs (e.g., a numerical thread II) or a text thread name). Also, attributes might indicate semantic characteristics that can be implied from the text. For example, an attribute of the speech might indicate whether the tone of the speech is positive or negative. In some embodiments, the analysis tool uses statistical models to determine a confidence level for an implied attribute. A low confidence level will indicate that the attribute is less likely to be accurate).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system, wherein input data is analyzed to determine confidence parameters associated with the input data (e.g., input values) of the invention of Shi to further incorporate wherein the confidence parameters are based on a sentiment analysis of a context of the input data of the invention of Kaushansky et al. because doing so would allow the system to use statistical models to determine a confidence level for an implied attribute (see Kaushansky et al., Paragraph 0074). 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.
Claims 7-8, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Shi (US 2020/0372588 A1), in view of Natarajan et al. (US 2024/0272133 A1), in further view of Raees et al. (US 2024/0362404 A1).
Regarding claim 7 (Currently Amended), which is dependent of claim 1, the combination of Shi and Natarajan et al. discloses all the limitations in claim 1. Shi further discloses wherein the computing system is configured to obtain the input data from the one or more input data sources by: querying one or more databases of the one or more input data sources for the input data; … (Paragraph 0031, Machine-learning algorithms and/or other processes may sort training data according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis; Paragraph 0034, In operation, and still referring to FIG. 1, computing device 104 may fetch grid and/or market data from one or more local grid monitoring devices, such as without limitation generation fuel mix, marginal fuel type, system demand, locational marginal prices (LMPs), historical emissions, and emission data from a reporting service 136 such as the EPA).
Shi discloses to obtain the input data from the one or more input data sources by: querying one or more databases of the one or more input data sources for the input data. Although Shi further discloses a machine learning to categorize the data, Shi does not specifically disclose wherein the machine learning is used to scrape the input data from the one or more input data sources.
However, Raees et al. discloses scraping the input data from the one or more input data sources via a large language model machine learning algorithm; or both (Paragraph 0049, In some embodiments, the present invention can acquire data for any selected topic(s) of interest from any of a plurality of sources using an automated report/news article generator system 202, which can acquire data using, for example, a web crawler, web scraper, database or direct web search using Natural Language Processing (NLP), Natural Language Understanding (NLU), etc., in accordance with aspects of the present invention).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system, wherein the input data is received from the one or more input data sources of the invention of Shi to further incorporate wherein the data is received by scraping the input data from the one or more input data sources via a machine learning algorithm of the invention of Raees et al. because doing so would allow the system to receive data using a direct web search using Natural Language Processing (NLP) (see Raees et al., 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 8 (Original), which is dependent of claim 7, the combination of Shi, Natarajan et al., and Raees et al. discloses all the limitations in claim 7. Shi further discloses wherein the one or more input data sources comprise government regulatory websites, social media websites, news publication websites, product catalogs corresponding to the one or more devices, or any combination thereof (Paragraph 0034, In operation, and still referring to FIG. 1, computing device 104 may fetch grid and/or market data from one or more local grid monitoring devices, such as without limitation generation fuel mix, marginal fuel type, system demand, locational marginal prices (LMPs), historical emissions, and emission data from a reporting service 136 such as the EPA; It can be noted that the claim language is written in alternative form. The limitation taught by Shi is based on “government regulatory websites").
Regarding claims 14 and 18 (Original), which are dependent of claims 10 and 17, the combination of Shi and Natarajan et al. discloses all the limitations in claims 10 and 17. Shi further discloses wherein the sustainability platform system is configured to obtain the input data from the one or more input data sources by: querying one or more databases of the one or more input data sources for the input data, wherein the one or more input data sources comprise government regulatory websites, social media websites, news publication websites, product catalogs corresponding to the one or more devices, or any combination thereof; … (Paragraph 0031, Machine-learning algorithms and/or other processes may sort training data according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis; Paragraph 0034, In operation, and still referring to FIG. 1, computing device 104 may fetch grid and/or market data from one or more local grid monitoring devices, such as without limitation generation fuel mix, marginal fuel type, system demand, locational marginal prices (LMPs), historical emissions, and emission data from a reporting service 136 such as the EPA).
Shi discloses to obtain the input data from the one or more input data sources by: querying one or more databases of the one or more input data sources for the input data. Although Shi further discloses a machine learning to categorize the data, Shi does not specifically disclose wherein the machine learning is used to scrape the input data from the one or more input data sources.
However, Raees et al. discloses scraping the input data from the one or more input data sources via a large language model machine learning algorithm; or both (Paragraph 0049, In some embodiments, the present invention can acquire data for any selected topic(s) of interest from any of a plurality of sources using an automated report/news article generator system 202, which can acquire data using, for example, a web crawler, web scraper, database or direct web search using Natural Language Processing (NLP), Natural Language Understanding (NLU), etc., in accordance with aspects of the present invention).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system, wherein the input data is received from the one or more input data sources of the invention of Shi to further incorporate wherein the data is received by scraping the input data from the one or more input data sources via a machine learning algorithm of the invention of Raees et al. because doing so would allow the system to receive data using a direct web search using Natural Language Processing (NLP) (see Raees et al., 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.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shi (US 2020/0372588 A1), in view of Natarajan et al. (US 2024/0272133 A1), in further view of Raees et al. (US 2024/0362404 A1) and Crane et al. (US 2015/0363730 A1).
Regarding claim 19 (Original), which is dependent of claim 18, the combination of Shi and Natarajan et al. discloses all the limitations in claim 18. Shi further discloses wherein the operations comprise determining the uncertainty data … for each of the one or more input data sources and obtained uncertainty data in data values of the input data from the one or more input data sources, … (Paragraph 0045; Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like).
Although Shi discloses to determine confidence parameters associated with the input data based on the uncertainty data (e.g., determine a degree of uncertainty in received data, which may be represented as a confidence interval), Shi does not specifically disclose to determine a respective authority level for each of the one or more input data sources (e.g., based on the input data source such as government or social media, see Paragraph 0189 of Applicant’s specification).
However, Crane et al. further discloses … based on the respective authority level for each of the one or more input data sources …, wherein the government regulatory websites comprise a higher authority level than the social media websites regarding regulation data of the input data (Paragraph 0054, each source can be weighted differently depending upon reliability, and the threshold can be a threshold score. For example, the threshold score can be ten. The threshold score can be satisfied when a news source weighted with a score of four, a government source weighted with a score of five, and one hundred Twitter sources each weighed with a score of 0.02 all report an SCD event; Examiner interprets the weight assigned to each data source as the authority level).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the sustainability platform system, wherein input data is analyzed to determine uncertainty data of the invention of Shi to further incorporate other ways to analyze the uncertainty of the input data (e.g., based on the respective authority level for each of the one or more input data sources) of the invention of Crane et al. because doing so would allow the system to weight each source differently depending upon reliability (see Crane et al., Paragraph 0054). 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 20 (Currently Amended), which is dependent of claim 19, the combination of Shi, Nararajan et al, Raees et al., and Crane et al. discloses all the limitations in claim 19. Shi further discloses wherein the data values comprise forecasted values associated with a future time period, and the confidence parameters are based on an uncertainty in the future time period (Paragraph 0018, In embodiments, artificial intelligence and machine-learning methods are used to estimate real-time emission impacts based on grid data. Deep learning for real-time/online optimization and control may be used to minimize emission impacts while maximizing efficiency benefits under uncertainties of the ambient environment and user behaviors; Paragraph 0034, Estimation process 144 and/or forecast and/or models may be built and/or trained using historical data; Paragraph 0045, Still referring to FIG. 4, computing device 104 may perform one or more data quality analysis steps, such as without limitation determining a degree of uncertainty in received data, which may be represented as a confidence interval, a degree of potential deviation from a reported value, or the like).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Zhu et al. (US 2023/0089850 A1) – discloses media sentiment detection module 118 is configured to scrape social media, news articles, online discussion groups, etc. for discussions about specific products or manufactures as they relate to environmental impact components. For example, an online forum discussing a particular products environmental friendly image may suggest one or more positive environmental impact components. Conversely, negative discussions may correlate to one or more negative environmental impact components (see at least Paragraph 0024).
Freier et al. (US 2023/0289911 A1) – discloses a carbon emissions management system that can operate based on measuring a carbon footprint for an individual, organization, or nation, where measuring the carbon footprint can be based on different types of carbon accounting techniques (e.g., greenhouse emissions assessment, a life cycle assessment). The carbon emissions management system can support developing a strategy to reduce the carbon footprint, for example, via carbon offsetting, carbon capture, better process management, energy efficient, and technological developments (see at least Paragraph 0017).
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).
Chang (TW-202309816-A) – discloses a technical field of carbon emission trading, in particular to a management method based on the transformation of a specific quantification mechanism for carbon emission reduction behavior under blockchain technology into digital currency (see at least Abstract).
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).
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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