The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Notice to Applicant
In response to the communication received on 07/30/2025, the following is a Non-Final Office Action for Application No. 19284852.
Status of Claims
Claims 1-20 are pending.
Drawings
The applicant’s drawings submitted on 07/30/2025 are acceptable for examination purposes.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 02/10/2026, 02/20/2026, and 07/21/2026 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of applicant’s claim for priority based on: 19284852 filed 07/30/2025 is a Continuation of 18733951 , filed 06/05/2024 ,now U.S. Patent # 12387151 and having 1 RCE-type filing therein; 18733951 Claims Priority from Provisional Application 63471174 , filed 06/05/2023.
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 as directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, the claims fall within statutory class of process or machine or manufacture. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
An enterprise system comprising: one or more facility level devices of an enterprise, wherein the one or more facility level devices comprise one or more mechanical devices that are controlled to adjust one or more respective operations of the one or more facility level devices; and a sustainability platform system comprising one or more processors and one or more non- transitory memories comprising instructions that, when executed by the one or more processors, cause the sustainability platform system to: obtain a sustainability action plan associated with improving one or more sustainability parameters of the enterprise, wherein the sustainability platform system obtains the sustainability action plan by: identifying one or more engineering workflow systems of a plurality of engineering workflow systems, wherein each of the plurality of engineering workflow systems corresponds to a separate computing device, and wherein the one or more engineering workflow systems are identified by based on: 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, wherein each abatement technology of the list corresponds to performing an operation to modify at least one sustainability parameter of the one or more sustainability parameters; 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; and determining, via the identified one or more engineering workflow systems, the sustainability action plan associated with improving at least one sustainability parameter of the one or more sustainability parameters based on enterprise data indicative of a state of operations of the enterprise; identify one or more data sources relevant for measuring the one or more sustainability parameters of the sustainability action plan, wherein the one or more data sources comprises one or more sensors of the one or more facility level devices, the one or more sensors configured to measure current operational parameters of the one or more facility level devices, wherein the current operational parameters correspond to current sustainability parameters; identify missing data of the one or more data sources or one or more missing data sources of the one or more data sources, wherein the one or more missing data sources comprise the missing data; simulate the missing data relevant for measuring the one or more sustainability parameters of the sustainability action plan and unavailable for use by the sustainability platform system to generate simulated sustainability data; determine whether the sustainability action plan causes the one or more sustainability parameters to be within one or more target thresholds, one or more target ranges, or both based on the simulated sustainability data; and in response to determining that the sustainability action plan causes the one or more sustainability parameters to be within one or more target thresholds, one or more target ranges, or both, send one or more commands to the one or more facility level devices associated with the enterprise to control the one or more facility level devices to adjust the one or more respective operations according to the sustainability action plan to effect a change in the current sustainability parameters of the one or more facility level devices.
[or]
A method comprising: obtaining, via a computing system, a sustainability action plan associated with improving one or more sustainability parameters of an enterprise, wherein the sustainability action plan is obtained by: identifying one or more engineering workflow systems of a plurality of engineering workflow systems, wherein each of the plurality of engineering workflow systems corresponds to a separate computing device relative to the computing system, and wherein the one or more engineering workflow systems are identified by based on: 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, wherein each abatement technology of the list corresponds to performing an operation to modify at least one sustainability parameter of the one or more sustainability parameters; 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; and determining, via the identified one or more engineering workflow systems, the sustainability action plan associated with improving at least one sustainability parameter of the one or more sustainability parameters based on enterprise data indicative of a state of operations of the enterprise; identifying, via the computing system, one or more data sources relevant for measuring the one or more sustainability parameters of the sustainability action plan, wherein the one or more data sources comprises one or more sensors of one or more devices of the enterprise, the one or more sensors configured to measure current operational parameters of the one or more devices, wherein the one or more devices comprise one or more mechanical devices that are controlled to adjust one or more respective operations of the one or more devices, and wherein the current operational parameters correspond to current sustainability parameters; identifying, via the computing system, missing data of the one or more data sources or one or more missing data sources of the one or more data sources, wherein the one or more missing data sources comprise the missing data; simulating, via the computing system, the missing data relevant for measuring the one or more sustainability parameters of the sustainability action plan and unavailable for use by the computing system to generate simulated sustainability data; determining, via the computing system, whether the sustainability action plan causes the one or more sustainability parameters to be within one or more target thresholds, one or more target ranges, or both based on the simulated sustainability data; and in response to determining that the sustainability action plan causes the one or more sustainability parameters to be within one or more target thresholds, one or more target ranges, or both, sending, via the computing system, one or more commands to the one or more devices associated with the enterprise to control the one or more devices to adjust the one or more respective operations according to the sustainability action plan to effect a change in the current sustainability parameters of the one or more devices.
[or]
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: obtaining a sustainability action plan associated with improving one or more sustainability parameters of an enterprise, wherein the sustainability action plan is obtained by: identifying one or more engineering workflow systems of a plurality of engineering workflow systems, wherein each of the plurality of engineering workflow systems corresponds to a separate computing device, and wherein the one or more engineering workflow systems are identified by based on: 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, wherein each abatement technology of the list corresponds to performing an operation to modify at least one sustainability parameter of the one or more sustainability parameters; 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; and determining, via the identified one or more engineering workflow systems, the sustainability action plan associated with improving at least one sustainability parameter of the one or more sustainability parameters based on enterprise data indicative of a state of operations of the enterprise; identifying one or more data sources relevant for measuring the one or more sustainability parameters of the sustainability action plan, wherein the one or more data sources comprises one or more sensors of one or more devices of the enterprise, the one or more sensors configured to measure current operational parameters of the one or more devices, wherein the one or more devices comprise one or more mechanical devices that are controlled to adjust one or more respective operations of the one or more devices, and wherein the current operational parameters correspond to current sustainability parameters; identifying missing data of the one or more data sources or one or more missing data sources of the one or more data sources, wherein the one or more missing data sources comprise the missing data; simulating the missing data relevant for measuring the one or more sustainability parameters of the sustainability action plan and unavailable for use to generate simulated sustainability data; determining whether the sustainability action plan causes the one or more sustainability parameters to be within one or more target thresholds, one or more target ranges, or both based on the simulated sustainability data; and in response to determining that the sustainability action plan causes the one or more sustainability parameters to be within one or more target thresholds, one or more target ranges, or both, sending one or more commands to the one or more devices associated with the enterprise to control the one or more devices to adjust the one or more respective operations according to the sustainability action plan to effect a change in the current sustainability parameters of the one or more devices.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The sensor, processor and/or memory medium is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic sensor, processor and/or memory medium limitation is no more than mere instructions to apply the exception using a generic computer component. Further, sending one or more commands to the one or more devices associated with the enterprise by a sensor, processor and/or memory medium is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The machine-learned model is used to generally apply the abstract idea without placing any limits on how the machine-learned model functions. Rather, these limitations only recite the outcome of the functions and do not include any details about how the functions via the machine-learned model are accomplished. See MPEP 2106.05(f). Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: sensor, processor and memory medium. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. The additional element of a machine-learned model is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Further, sending one or more commands to one or more devices associated with the enterprise by a sensor, processor and/or memory medium is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0179 wherein processor may include discrete electronic components couplet to programmable logic devices. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements other than the addressed machine learned model that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed 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. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jayan et al. (US 20220344934 A1) hereinafter referred to as Jayan in view of Pomerantz et al. (WO 2023033832 A1) hereinafter referred to as Pomerantz.
Jayan teaches:
Claim 1. An enterprise system comprising:
one or more facility level devices of an enterprise, wherein the one or more facility level devices comprise one or more mechanical devices that are controlled to adjust one or more respective operations of the one or more facility level devices (¶0041 The sensors 140 may include various types of sensors that are configured to measure parameters associated with the first facility 154. For example, the sensors 140 may include identifier (ID) sensors (e.g., ID scanners, badge readers, or the like), temperature sensors, pressure sensors, light sensors, sound sensors, proximity sensors, and the like. The cameras 142 may include one or more cameras or image capture devices that are located at the first facility 154. For example, the cameras 142 may include security cameras, web cameras or other telecommunication devices, facial recognition cameras, or the like. The IoT devices 144 may include one or more IoT devices such as sensors or other measuring devices that monitor aspects of or support distributed operations at the first facility 154. For example, the IoT devices 144 may include remote sensors, measuring devices, smart devices, hubs, smart controllers, and the like. The sensors 146, the cameras 148, and the IoT devices 150 may include similar devices to the sensors 140, the cameras 142, and the IoT devices 144, respectively, that are located at the second facility 156. ¶0095 The recommended actions may include multiple types of actions, such as the generation actions 810, the REC actions 812, the planting actions 814, other actions, or a combination thereof. The generation actions 810 may include one or more actions to invest in renewable energy generation at the one or more facilities or to acquire energy from renewable energy resource suppliers. For example, the generation actions 810 may include purchasing and installing solar panels or wind turbines at the one or more facilities, purchasing electricity from an electric grid supplied by hydroelectric power, or the like. The REC actions 812 may include one or more actions to purchase RECs or otherwise invest in investments or organizations that financially or otherwise support the use, investment in, education regarding, or the like, for renewable energy resources. For example, the REC actions 812 may include recommendations to purchase various quantities of RECs based to achieve a target emissions offset. Additionally or alternatively, the REC actions 812 may include any actions with respect to a government or regulatory body to satisfy a renewable energy regulation. The planting actions 814 may include actions to plant trees, grasses, or other vegetation that convert CO.sub.2 into oxygen in order to offset CO.sub.2 emissions. For example, the planting actions 814 may include recommendations to plant and maintain various numbers of trees to achieve a target emissions offset. Any of the generation actions 810, the REC actions 812, the planting actions 814, or a combination thereof, may be automatically initiated or performed by a system or displayed to a user to enable manual performance of the actions, as described above with reference to FIG. 1.); and
a sustainability platform system comprising one or more processors and one or more non- transitory memories comprising instructions that, when executed by the one or more processors, cause the sustainability platform system to: obtain a sustainability action plan associated with improving one or more sustainability parameters of the enterprise, wherein the sustainability platform system obtains the sustainability action plan by (¶0134 If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. The processes of a method or algorithm disclosed herein may be implemented in a processor-executable software module which may reside on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that may be enabled to transfer a computer program from one place to another. A storage media may be any available media that may be accessed by a computer. By way of example, and not limitation, such computer-readable media can include random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Also, any connection may be properly termed a computer-readable medium.):
identifying one or more engineering workflow systems of a plurality of engineering workflow systems, wherein each of the plurality of engineering workflow systems corresponds to a separate computing device, and wherein the one or more engineering workflow systems are identified by based on: 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, wherein each abatement technology of the list corresponds to performing an operation to modify at least one sustainability parameter of the one or more sustainability parameters (¶0092 The context data 802 may indicate the context for which the offset actions are to be recommended. For example, the context data 802 may indicate selected energy resources that are to be acquired for use by an organization to power one or more facilities, forecasted energy demand corresponding to the one or more facilities, location data, government or regulation data, other context information, or a combination thereof. The emission data 804 may indicate an environmental impact corresponding to use of various energy resources. For example, the emission data 804 may indicate a quantity of CO.sub.2 (or another compound or chemical) emissions expected to result from use of particular energy resources, such as non-renewable energy resources such as coal, oil, natural gas, etc. The emission data 804 may also indicate an environmental offset (e.g., a positive environmental impact) expected to result from use of other particular energy resources. For example, the emission data 804 may indicate quantities of CO.sub.2 emissions that are offset by use of renewable/sustainable energy resources, such as wind power, solar power, hydroelectric power, etc. As a particular example, the emissions data 804 may indicate a generation offset 820 corresponding to renewable energy generation (“G.sub.e”), an REC offset 822 corresponding to purchase of RECs (“R.sub.e”), and a planting offset 824 corresponding to planting trees (“P.sub.C”), in tons of CO.sub.2. ¶0094 The recommendation engine 800 may select the one or more recommended actions based on the context data 802, the emission data 804, the cost data 806, and the constraint data 808 to recommend one or more actions that offset the environmental impact associated with selected energy resources, particularly non-renewable/non-sustainable energy resources.);
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 (¶0011 In a particular aspect, a method for forecasting energy demand using machine learning includes determining, by one or more processors, a forecasted occupancy corresponding to one or more facilities of an organization based at least in part on sensor data received from one or more sensors located at the one or more facilities. The method also includes providing, by the one or more processors, first input data to a first set of one or more machine learning (ML) models to generate a forecasted energy demand corresponding to the one or more facilities. The first input data is based on the forecasted occupancy and historical energy demand data corresponding to the one or more facilities. The first set of ML models is configured to forecast energy demand based on energy data and occupancy data. The method includes providing, by the one or more processors, second input data to a second set of one or more ML models to select energy resources to be used at the one or more facilities to satisfy the forecasted energy demand. The second input data is based on the forecasted energy demand and available energy resources information. The second set of ML models is configured to select energy sources based on energy demand forecasts and energy source information. The method further includes outputting, by the one or more processors, an energy plan that is based on the selected energy resources. ¶0039 The recommendations actions may include actions with respect to the organization or the one or more facilities (e.g., investing in renewable energy resource generation, replacing equipment with newer, more energy-efficient models, and the like), actions with respect to the environment (e.g., planting trees), actions with respect to government or regulatory agencies (e.g., purchasing renewable energy certificates (RECs)), other actions, or a combination thereof. In some implementations, the action recommendation engine 128 may include or access the third set of ML models 134 to output the recommended actions. For example, the third set of ML models 134 may be trained based on environmental impact data, costs of the multiple preset actions, target costs, target environmental impacts, and the like, to recommend one or more actions based on input energy resources. Additional details of recommending actions using ML models are described below with reference to FIG. 8.); and
determining, via the identified one or more engineering workflow systems, the sustainability action plan associated with improving at least one sustainability parameter of the one or more sustainability parameters based on enterprise data indicative of a state of operations of the enterprise (¶0038 The resource selection engine 126 is configured to select one or more energy resources to be acquired to satisfy a forecasted energy demand corresponding to the one or more facilities. For example, the resource selection engine 126 may select one or more energy resources that are provided by the energy suppliers 152 to be acquired (e.g., purchased) to satisfy the forecasted energy demand corresponding to the one or more facilities. The selected energy resources may include renewable and non-renewable energy resources in sufficient quantities to satisfy the forecasted energy demand and, optionally, to satisfy one or more constraints such as cost, renewable energy resource use, or the like. In some implementations, the resource selection engine 126 may include or access the second set of ML models 132 to select the energy resources. For example, the second set of ML models 132 may be trained based on energy resource information such as cost, available quantities, type (e.g., renewable/sustainable or non-renewable/non-sustainable), target costs, target ratios of different types of energy resources, or the like, to select energy resources from available energy resources to satisfy forecasted energy demands. Additional details of selecting energy resources using ML models are described below with reference to FIG. 5. ¶0039 The action recommendation engine 128 is configured to output one or more recommended actions for reducing or compensating for a negative environmental impact corresponding to use of energy resources selected by the resource selection engine 126. For example, the action recommendation engine 128 may select one or more recommended actions from multiple preset actions that reduce negative environmental impacts. The recommendations actions may include actions with respect to the organization or the one or more facilities (e.g., investing in renewable energy resource generation, replacing equipment with newer, more energy-efficient models, and the like), actions with respect to the environment (e.g., planting trees), actions with respect to government or regulatory agencies (e.g., purchasing renewable energy certificates (RECs)), other actions, or a combination thereof. In some implementations, the action recommendation engine 128 may include or access the third set of ML models 134 to output the recommended actions. For example, the third set of ML models 134 may be trained based on environmental impact data, costs of the multiple preset actions, target costs, target environmental impacts, and the like, to recommend one or more actions based on input energy resources. Additional details of recommending actions using ML models are described below with reference to FIG. 8.);
identify one or more data sources relevant for measuring the one or more sustainability parameters of the sustainability action plan, wherein the one or more data sources comprises one or more sensors of the one or more facility level devices, the one or more sensors configured to measure current operational parameters of the one or more facility level devices, wherein the current operational parameters correspond to current sustainability parameters (¶0109 The method 1100 includes pre-processing data, at 1102. For example, an input dataset may be received or obtained from one or more sources, such as sensors, cameras, IoT devices, databases, servers, user devices, or the like, and the input dataset may be pre-processed to generate training data and testing data for use in building and training one or more ML models. Pre-processing the input data includes imputing missing data, at 1104. For example, if the input dataset includes periodic energy meter data (e.g., hourly, daily, weekly, etc.) for which some entries are missing or unusable, replacement values may be imputed. In some implementations, the values may be imputed using gradient descent, as further described herein with reference to FIG. 12. ¶0122 In some implementations, the selected energy resources include at least one renewable energy resource. For example, the selected energy resources 116 of FIG. 1 may include solar power, wind power, hydroelectric power, or a combination thereof. Additionally or alternatively, the one or more sensors may include identification scanners, cameras, temperature sensors, pressure sensors, or a combination thereof. For example, the sensors 140 and 146 of FIG. 1 may include ID badge scanners, cameras, temperature sensors, pressure sensors, or the like. Additionally or alternatively, the selected energy resources include at least one backup energy resource and the second set of ML models is configured to select energy resources based further on predicted outages at the one or more facilities. For example, the selected energy resources 116 of FIG. 1 include diesel for generators (or anther backup energy resource), and the second set of ML models 132 may select the selected energy resources 116 to compensate for one or more predicted outages, as further described with reference to FIG. 9.);
identify missing data of the one or more data sources or one or more missing data sources of the one or more data sources, wherein the one or more missing data sources comprise the missing data (¶0085 The method 600 includes imputing missing data, pre-processing the data, and performing feature engineering, at 604. Imputing missing data from the single dataset may be performed using extrapolation, gradient descent, or other techniques, as further described herein. Pre-processing the data may include converting the data to a common format, reducing the complexity of the data, or other pre-processing operations. ¶0102 The outage models 922 may be trained to forecast outages for the one or more facilities based on historic outage data and the like. The outage models 922 may forecast outages based on forecasted outages data included in the forecasted demand data 902 (or otherwise obtained by the analyzer 900). The analyzer 900 may aggregate (or otherwise combine) the demand forecast output by the forecast models 920 and the forecasted outages from the outage models 922 to generate a net demand 924 (e.g., a net forecasted energy demand that accounts for outages) and the energy resources selected to satisfy the net demand 924. For example, the net demand 924 may include the forecasted demand (and corresponding selected energy resources) output by the forecast models 920 adjusted based on the output of the outage models 922. To further illustrate, the net demand 924 may include a combination of a forecasted energy demand output by the forecast models 920 and a backup energy demand equal to (or based on) energy corresponding to the forecasted outages output by the outage models 922, and the net demand 924 may be satisfied by a selection of renewable and non-renewable energy resources that correspond to the output of the forecast models 920 and a selection of backup energy resources that correspond to the output of the outage models 922. ¶0109 The method 1100 includes pre-processing data, at 1102. For example, an input dataset may be received or obtained from one or more sources, such as sensors, cameras, IoT devices, databases, servers, user devices, or the like, and the input dataset may be pre-processed to generate training data and testing data for use in building and training one or more ML models. Pre-processing the input data includes imputing missing data, at 1104. For example, if the input dataset includes periodic energy meter data (e.g., hourly, daily, weekly, etc.) for which some entries are missing or unusable, replacement values may be imputed. In some implementations, the values may be imputed using gradient descent, as further described herein with reference to FIG. 12. ¶0120 In some implementations, the energy plan indicates one or more recommended actions to offset an environmental impact associated with use of the selected energy resources. For example, the one or more recommended actions may include or correspond to the recommended actions 184 of FIG. 1. In some such implementations, the method 1300 further includes initiating performance of at least one of the one or more recommended actions. ¶0114 As described above, the method 1200 supports automated filling of missing input data. For example, if input meter data is missing a particular amount (e.g., 50%, as a non-limiting example), the method 1200 may impute the missing data with a smaller Mean Squared Error (MSE) than conventional data imputing techniques.);
simulate the missing data relevant for measuring the one or more sustainability parameters of the sustainability action plan and unavailable for use by the sustainability platform system to generate simulated sustainability data (¶0085 The method 600 includes imputing missing data, pre-processing the data, and performing feature engineering, at 604. Imputing missing data from the single dataset may be performed using extrapolation, gradient descent, or other techniques, as further described herein. Pre-processing the data may include converting the data to a common format, reducing the complexity of the data, or other pre-processing operations. ¶0109 The method 1100 includes pre-processing data, at 1102. For example, an input dataset may be received or obtained from one or more sources, such as sensors, cameras, IoT devices, databases, servers, user devices, or the like, and the input dataset may be pre-processed to generate training data and testing data for use in building and training one or more ML models. Pre-processing the input data includes imputing missing data, at 1104. For example, if the input dataset includes periodic energy meter data (e.g., hourly, daily, weekly, etc.) for which some entries are missing or unusable, replacement values may be imputed. In some implementations, the values may be imputed using gradient descent, as further described herein with reference to FIG. 12. Pre-processing the input data includes estimating an energy ratio for a future time period, at 1106. ¶0112 For example, the meter data at the daily level may be aggregated, averaged, or otherwise summarized into data entries on a monthly basis (e.g., at the “monthly level”). Summarizing the meter data at the daily level may include extrapolating one or more missing or otherwise useful values to be used to convert the meter data from the daily level to the monthly level. For example, the meter data may be provided for some, but not all days of each month, and the missing days may not have a set pattern.¶0114 As described above, the method 1200 supports automated filling of missing input data. For example, if input meter data is missing a particular amount (e.g., 50%, as a non-limiting example), the method 1200 may impute the missing data with a smaller Mean Squared Error (MSE) than conventional data imputing techniques.);
determine whether the sustainability action plan causes the one or more sustainability parameters to be within one or more target thresholds, one or more target ranges, or both based on the simulated sustainability data (¶0120 the one or more recommended actions may include any of the recommended actions described herein with reference to FIGS. 1, 7, and 8. Additionally or alternatively, the method 1300 may further include comparing a net environmental impact estimate corresponding to the selected energy resources to one or more thresholds. In some such implementations, the method 1300 further includes selecting the one or more recommended actions from a plurality of actions based on the comparing. For example, the net environmental impact may be determined based on the emission data 804 of FIG. 8, and the one or more thresholds may include or correspond to the constraint data 808 of FIG. 8. Additionally or alternatively, the method 1300 may further include providing third input data to a third set of one or more ML models to select the one or more recommended actions. For example, the third set of ML models may include or correspond to the third set of ML models 134 of FIG. 1. In some such implementations, the third input data is based on the selected energy resources, environmental impact data associated with the selected energy resources, and environmental impact data associated with a plurality of actions. For example, the third input data may be based on the context data 802, the emission data 804, and the cost data 806 of FIG. 8. In some such implementations, the third set of one or more ML models is configured to select one or more actions of the plurality of actions to offset an environmental impact of energy resource use and an environmental impact of the selected one or more actions. For example, the environmental impact of energy resource use may include or correspond to the emission data 804, and the environmental impact of the selected one or more actions may include the generation offset 820, the REC offset 822, and the planting offset 824 of FIG. 8.); and
in response to determining that the sustainability action plan causes the one or more sustainability parameters to be within one or more target thresholds, one or more target ranges, or both, send one or more commands to the one or more facility level devices associated with the enterprise to control the one or more facility level devices to adjust the one or more respective operations according to the sustainability action plan to effect a change in the current sustainability parameters of the one or more facility level devices (¶0041 The sensors 140 may include various types of sensors that are configured to measure parameters associated with the first facility 154. For example, the sensors 140 may include identifier (ID) sensors (e.g., ID scanners, badge readers, or the like), temperature sensors, pressure sensors, light sensors, sound sensors, proximity sensors, and the like. The cameras 142 may include one or more cameras or image capture devices that are located at the first facility 154. For example, the cameras 142 may include security cameras, web cameras or other telecommunication devices, facial recognition cameras, or the like. The IoT devices 144 may include one or more IoT devices such as sensors or other measuring devices that monitor aspects of or support distributed operations at the first facility 154. For example, the IoT devices 144 may include remote sensors, measuring devices, smart devices, hubs, smart controllers, and the like. The sensors 146, the cameras 148, and the IoT devices 150 may include similar devices to the sensors 140, the cameras 142, and the IoT devices 144, respectively, that are located at the second facility 156.).
Although not explicitly taught by Jayan, Pomerantz teaches in the analogous art of systems for greenhouse gas emission quantification and reduction:
one or more facility level devices of an enterprise, wherein the one or more facility level devices comprise one or more mechanical devices that are controlled to adjust one or more respective operations of the one or more facility level devices [and] send one or more commands to the one or more facility level devices associated with the enterprise to control the one or more facility level devices (¶0038 As described in greater detail herein, the greenhouse gas emission quantification and reduction system 24 is configured to detect the existence of a gas emission within the oil and gas worksite 10, to quantify greenhouse gas emissions and/or gas emissions relating to flaring operations at the oil and gas worksite 10, to track trends in the greenhouse gas emissions and/or gas emissions relating to flaring operations at the oil and gas worksite 10 over time, and to facilitate the reduction of the greenhouse gas emissions and/or gas emissions relating to flaring operations at the oil and gas worksite 10. In addition, in certain embodiments, in response to detecting a particular gas emission, the greenhouse gas emission quantification and reduction system 24 may automatically send one or more control signals to one or more pieces of equipment (e.g., the flares 14, the storage tanks 16, the compressors 18 (or pumps), the processing equipment 22, the mobile platforms 26, and so forth) of the oil and gas worksite 10 such that one or more operational parameters of the one or more pieces of equipment may be automatically adjusted to minimize (e.g., mitigate) the effects of the detected gas emission. For example, if the greenhouse gas emission quantification and reduction system 24 determines that a gas emission is likely due to a particular valve opening too much, the greenhouse gas emission quantification and reduction system 24 may automatically send a control signal to the particular valve to cause the particular valve to close. ¶0049 In certain embodiments, the processor(s) 46 of the mobile platform(s) 26 may execute computer program logic to determine how to automatically (e.g., autonomously) control maneuvering equipment 52 of the respective mobile platform 26 to enable the maneuvering equipment 52 to maneuver the respective mobile platform 26 around the oil and gas worksite 10 for the purpose of repositioning its respective sensors 12 such that the sensors 12 can detect data relating to potential gas leaks at the oil and gas worksite 10. For example, in certain embodiments, a mobile platform 26 may be an unmanned aerial vehicle (e.g., a drone) and the maneuvering equipment 52 may include propellers, motors configured to rotate the propellers at specific speeds, and so forth, configured to enable the unmanned aerial vehicle to maneuver the mobile platform 26 aerially about the oil and gas worksite 10. However, in other embodiments, a mobile platform 26 may be a mobile robot and the maneuvering equipment 52 may include robotic legs, wheels, and so forth, configured to maneuver the mobile platform 26 over the ground and certain structures 20 and/or equipment of the oil and gas worksite 10. ¶0050 in certain embodiments, the greenhouse gas emission quantification and reduction system 24 may be configured to automatically send control signals to the mobile platform(s) 26 to at least partially control the maneuvering of a particular mobile platform 26 when, for example, the greenhouse gas emission quantification and reduction system 24 determines that certain data relating to a potential gas leak may be useful, and that a particular sensor 12 attached to the particular mobile platform 26 may be capable of collecting such data of interest.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the systems for greenhouse gas emission quantification and reduction of Pomerantz with the system for energy demand forecasting and sustainable energy management using machine learning of Jayan for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Jayan ¶0004 teaches that there is a need to more accurately forecast energy demand to enable an efficient and cost-effective transition to renewable energy.;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Jayan Abstract teaches systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning (ML) to forecast energy demand and to generate an energy plan for one or more facilities of an organization, and Pomerantz Abstract teaches systems and methods relating to a method that includes providing one or more recommendations relating to greenhouse gas emission reduction goals to a customer; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Jayan at least the above cited paragraphs, and Pomerantz at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the systems for greenhouse gas emission quantification and reduction of Pomerantz with the system for energy demand forecasting and sustainable energy management using machine learning of Jayan. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Jayan teaches:
Claim 2. The enterprise system of claim 1, wherein the instructions, when executed by the one or more processors, cause the sustainability platform system to obtain a sustainability model representative of a state of operations of the enterprise (¶0087 Referring to FIG. 7, an example energy plan according to one or more aspects is shown as an energy plan 700. In some implementations, the energy plan 700 may include or correspond to the energy plan 180 of FIG. 1. In some implementations, the energy plan 700 may be displayed as part of a GUI by a computing device, such as the energy forecast device 102 of FIG. 1, a user or client device, or the like. The energy plan 700 indicates information related to energy demand forecasts, selected energy resources, and environmental impact for one or more facilities of an organization. For example, the energy plan 700 may include an annual demand forecast, whether incentives are available for renewable energy use, a renewable energy forecast, a renewable energy procurement forecast, a conventional energy forecast, potential emissions, environmental offset recommendations, net emissions, total costs, other information, or a combination thereof. In some implementations, as shown in FIG. 7, the energy plan 700 may be organized by facility. In other implementations, the energy plan 700 may be organized in other manners, such as by region, by operations, by cost, or the like. ¶0105 The GUI 1000 may include multiple inputs 1002 and an energy plan based on the inputs 1002. The inputs 1002 may include multiple dynamically modifiable parameters used to generate the energy plan. In the example shown in FIG. 10, the inputs 1002 include a year input, a month input, a facility input, a total forecasted demand input, a vendor contract input, and a renewable energy input. The year input and the month input may be used to indicate a particular date or time period for which the energy plan is to be generated. The facility input may be used to indicate one or more particular facilities for which the energy plan is to be generated. The total forecasted demand input may be used to indicate a forecasted energy demand for the one or more particular facilities.).
Jayan teaches:
Claim 3. The enterprise system of claim 1, wherein the instructions, when executed by the one or more processors, cause the sustainability platform system to determine whether the simulated sustainability data and the measured current operational parameters are within a threshold (¶0051 Each of the available actions may be associated with a respective positive environmental impact, a respective cost, and the like, and such information may be used by the action recommendation engine 128 in addition to the selected energy resources 116 to select the recommended actions 184. The action recommendation engine 128 may select the recommended actions 184 to satisfy a particular criterion (e.g., environmental impact threshold) or to prioritize or optimize a particular criterion (e.g., total cost, environmental impact, etc.). For example, the action recommendation engine 128 may determine a net environmental impact estimate based on the respective environmental impacts that correspond to each of the selected energy resources 116, and the action recommendation engine 128 may compare the net environmental impact estimate to one or more thresholds. Depending on the relationship between the net environmental impact estimate and the one or more thresholds, the action recommendation engine 128 may select one or more of the available actions as the recommended actions 184. For example, if the net environmental impact estimate satisfies a first threshold, the recommended actions 184 may include purchasing RECs, and if the net environmental impact estimate satisfies a second threshold that is greater than the first threshold, the recommended actions 184 may include purchasing additional RECs and planting a particular quantity of trees.).
Jayan teaches:
Claim 4. The enterprise system of claim 2, wherein the instructions, when executed by the one or more processors, cause the sustainability platform system to obtain the sustainability model by:receiving the enterprise data indicative of the state of operations of a portion of the enterprise associated with a geographical region, wherein the state of operations of the portion of the enterprise comprises a listing of the one or more facility level devices and emissions data corresponding to the one or more facility level devices; and generating the sustainability model based on the enterprise data (¶0092 The context data 802 may indicate the context for which the offset actions are to be recommended. For example, the context data 802 may indicate selected energy resources that are to be acquired for use by an organization to power one or more facilities, forecasted energy demand corresponding to the one or more facilities, location data, government or regulation data, other context information, or a combination thereof. The emission data 804 may indicate an environmental impact corresponding to use of various energy resources. For example, the emission data 804 may indicate a quantity of CO.sub.2 (or another compound or chemical) emissions expected to result from use of particular energy resources, such as non-renewable energy resources such as coal, oil, natural gas, etc. The emission data 804 may also indicate an environmental offset (e.g., a positive environmental impact) expected to result from use of other particular energy resources. For example, the emission data 804 may indicate quantities of CO.sub.2 emissions that are offset by use of renewable/sustainable energy resources, such as wind power, solar power, hydroelectric power, etc. As a particular example, the emissions data 804 may indicate a generation offset 820 corresponding to renewable energy generation (“G.sub.e”), an REC offset 822 corresponding to purchase of RECs (“R.sub.e”), and a planting offset 824 corresponding to planting trees (“P.sub.C”), in tons of CO.sub.2.).
Jayan teaches:
Claim 5. The enterprise system of claim 2, wherein the instructions, when executed by the one or more processors, cause the sustainability platform system to: generate a sustainability report based on the enterprise data, wherein the enterprise data comprises the measured current operational parameters; and generate the sustainability model based on the sustainability report (¶0052 After the selected energy resources 116 (and optionally the recommended actions 184) are selected, the energy forecast device 102 (e.g., the processor 104) may generate an energy plan 180 based on the selected energy resources 116. The energy plan 180 may indicate the selected energy resources 116, the forecasted energy demand 114, costs corresponding to the selected energy resources 116, environmental impact information corresponding to the selected energy resources 116, any government or regulatory agency requirements, other information, or a combination thereof. An illustrative energy plan is described further herein with reference to FIG. 7. ).
Jayan teaches:
Claim 6. The enterprise system of claim 1, wherein the instructions, when executed by the one or more processors, cause the sustainability platform system to simulate the missing data via a machine learning model that correlates independent measured variables to the simulated sustainability data, by estimating the simulated sustainability data based on proxy data sources, by estimating the simulated sustainability data based on prior data associated with a time period prior to implementation of the sustainability action plan, or any combination thereof (¶0102 The outage models 922 may be trained to forecast outages for the one or more facilities based on historic outage data and the like. The outage models 922 may forecast outages based on forecasted outages data included in the forecasted demand data 902 (or otherwise obtained by the analyzer 900). The analyzer 900 may aggregate (or otherwise combine) the demand forecast output by the forecast models 920 and the forecasted outages from the outage models 922 to generate a net demand 924 (e.g., a net forecasted energy demand that accounts for outages) and the energy resources selected to satisfy the net demand 924. For example, the net demand 924 may include the forecasted demand (and corresponding selected energy resources) output by the forecast models 920 adjusted based on the output of the outage models 922. To further illustrate, the net demand 924 may include a combination of a forecasted energy demand output by the forecast models 920 and a backup energy demand equal to (or based on) energy corresponding to the forecasted outages output by the outage models 922, and the net demand 924 may be satisfied by a selection of renewable and non-renewable energy resources that correspond to the output of the forecast models 920 and a selection of backup energy resources that correspond to the output of the outage models 922. ¶0109 The method 1100 includes pre-processing data, at 1102. For example, an input dataset may be received or obtained from one or more sources, such as sensors, cameras, IoT devices, databases, servers, user devices, or the like, and the input dataset may be pre-processed to generate training data and testing data for use in building and training one or more ML models. Pre-processing the input data includes imputing missing data, at 1104. For example, if the input dataset includes periodic energy meter data (e.g., hourly, daily, weekly, etc.) for which some entries are missing or unusable, replacement values may be imputed. In some implementations, the values may be imputed using gradient descent, as further described herein with reference to FIG. 12. ¶0120 In some implementations, the energy plan indicates one or more recommended actions to offset an environmental impact associated with use of the selected energy resources. For example, the one or more recommended actions may include or correspond to the recommended actions 184 of FIG. 1. In some such implementations, the method 1300 further includes initiating performance of at least one of the one or more recommended actions. For example, the energy forecast device 102 of FIG. 1 may initiate or perform at least one of the recommended actions 184 of FIG. 1. Additionally or alternatively, the one or more recommended actions may include planting trees, reconfiguring one or more systems at the one or more facilities, ceasing operation of particular equipment at the one or more facilities, obtaining one or more RECs, or a combination thereof. For example, the one or more recommended actions may include any of the recommended actions described herein with reference to FIGS. 1, 7, and 8. Additionally or alternatively, the method 1300 may further include comparing a net environmental impact estimate corresponding to the selected energy resources to one or more thresholds. In some such implementations, the method 1300 further includes selecting the one or more recommended actions from a plurality of actions based on the comparing. For example, the net environmental impact may be determined based on the emission data 804 of FIG. 8, and the one or more thresholds may include or correspond to the constraint data 808 of FIG. 8. Additionally or alternatively, the method 1300 may further include providing third input data to a third set of one or more ML models to select the one or more recommended actions.).
Jayan teaches:
Claim 7. The enterprise system of claim 1, wherein the instructions, when executed by the one or more processors, cause the sustainability platform system to: in response to determining that the sustainability action plan does not cause the one or more sustainability parameters to be within one or more target thresholds, one or more target ranges, or both, select an alternate sustainability action plan; and send one or more second commands to the one or more facility level devices associated with the enterprise to cause the one or more facility level devices to adjust the one or more respective operations according to the alternate sustainability action plan (¶0120 In some implementations, the energy plan indicates one or more recommended actions to offset an environmental impact associated with use of the selected energy resources. For example, the one or more recommended actions may include or correspond to the recommended actions 184 of FIG. 1. In some such implementations, the method 1300 further includes initiating performance of at least one of the one or more recommended actions. For example, the energy forecast device 102 of FIG. 1 may initiate or perform at least one of the recommended actions 184 of FIG. 1. Additionally or alternatively, the one or more recommended actions may include planting trees, reconfiguring one or more systems at the one or more facilities, ceasing operation of particular equipment at the one or more facilities, obtaining one or more RECs, or a combination thereof. For example, the one or more recommended actions may include any of the recommended actions described herein with reference to FIGS. 1, 7, and 8. Additionally or alternatively, the method 1300 may further include comparing a net environmental impact estimate corresponding to the selected energy resources to one or more thresholds. In some such implementations, the method 1300 further includes selecting the one or more recommended actions from a plurality of actions based on the comparing. For example, the net environmental impact may be determined based on the emission data 804 of FIG. 8, and the one or more thresholds may include or correspond to the constraint data 808 of FIG. 8. Additionally or alternatively, the method 1300 may further include providing third input data to a third set of one or more ML models to select the one or more recommended actions.).
Jayan teaches:
Claim 8. The enterprise system of claim 7, wherein the instructions, when executed by the one or more processors, cause the sustainability platform system to generate the alternate sustainability action plan based on the simulated sustainability data (¶0120 In some implementations, the energy plan indicates one or more recommended actions to offset an environmental impact associated with use of the selected energy resources. For example, the one or more recommended actions may include or correspond to the recommended actions 184 of FIG. 1. In some such implementations, the method 1300 further includes initiating performance of at least one of the one or more recommended actions. For example, the energy forecast device 102 of FIG. 1 may initiate or perform at least one of the recommended actions 184 of FIG. 1. Additionally or alternatively, the one or more recommended actions may include planting trees, reconfiguring one or more systems at the one or more facilities, ceasing operation of particular equipment at the one or more facilities, obtaining one or more RECs, or a combination thereof. For example, the one or more recommended actions may include any of the recommended actions described herein with reference to FIGS. 1, 7, and 8. Additionally or alternatively, the method 1300 may further include comparing a net environmental impact estimate corresponding to the selected energy resources to one or more thresholds. In some such implementations, the method 1300 further includes selecting the one or more recommended actions from a plurality of actions based on the comparing. For example, the net environmental impact may be determined based on the emission data 804 of FIG. 8, and the one or more thresholds may include or correspond to the constraint data 808 of FIG. 8. Additionally or alternatively, the method 1300 may further include providing third input data to a third set of one or more ML models to select the one or more recommended actions.).
As per claims 9-15 and 16-20, the method and machine-readable medium tracks the system of claims 1-7 and 1,6, 2, 4, 7, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-7 and 1,6, 2, 4, 7 are applied to claims 9-15 and 16-20, respectively. Jayan discloses that the embodiment may be found as a system and manufacture (Figs. 1-6 and ¶0134).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURTIS GILLS whose telephone number is (571)270-3315. The examiner can normally be reached on M-F 8-5 PM.
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/KURTIS GILLS/Primary Examiner, Art Unit 3624
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