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 .
DETAILED ACTION
2. The request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for Continued Examination under 37 CFR 1.114, the fee set forth in 37 CFR 1.17(e) has been paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed 12/2/2025 has been entered. An action on the RCE follows.
Summary of claims
3. Claims 1-20 are pending,
Claims 1, 14 are amended,
Claims 1, 14 are independent claims,
Claims 1-20 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 12/2/2025, with respect to the rejection(s) of claim(s) 1-20 under 103 have been fully considered and are not persuasive.
Applicant argued on pages 9-12 that the cited references including Kumar, Cohen and Meehan did not teach the newly added limitations in claim 1, such as, “wherein the multi-optimization model is configured to minimize a current cumulative carbon output value for operations of the plurality of assets and a current cumulative impact value for operations of the plurality of assets based on the operational constraints of the processing plants.” Examiner respectfully disagrees and submits that Kumar discloses in [0061]: The system can alternatively be used to assess, monitor or predict the carbon foot-print (usage and emissions) of an enterprise on any time scale. For example, the recommendation engine 42 can employ one or more machine learning techniques that can include a variety of algorithms (e.g., supervised learning, unsupervised learning, reinforcement learning, knowledge-based learning, natural-language-based learning such as natural language generation and natural language processing, deep learning, and the like) and can access execution engines comprising software applications that enable implementation of the underlying algorithm. As is known, the machine learning techniques are trained using training data. This training data is used to modify and fine-tune the weights associated with the machine learning models, as well as record ground truth for where correct answers can be found within the data. As such, the better the training data, the more accurate and effective the machine learning model can be. The recommendation library or engine 42 processes the data in the data layer by applying one or more machine learning or artificial intelligence techniques so as to generate or extract recommendations, insights, predictions and the like from the data. The cognitive intelligence unit 34 can also employ a risk model unit 40 that includes suitable software for applying one or more risk modelling techniques to the data that model or address strategic, operational, compliance, geopolitical, and other types of risk. The wider availability of data and sophisticated analysis capabilities of the risk models makes modeling more practical. As is known, a risk model is a mathematical representation of a system that commonly includes probability distributions. The models use relevant historical data as well as relevant third party data to understand the probability of a risk event occurring and its potential severity from the input data. Please note the risk model describes mathematically modeling using plant data. In addition, Cohen discloses in [1138]-[1151]: Ability to simulate multiple programs to assess cumulative impact; Remark: This include the same KPI/normalization functionality as e.g. for benchmarking; Add additional customer's projects (could be already planned/already running) to the overall picture; Select improvement measures and estimate their potential; Select applicable improvement areas by customer's characteristics/vertical markets/domains (supply (e.g. sourcing or hedging, demand, sustainability); Select improvement measures out of selected improvement areas guided by customer's characteristics/vertical markets; Display the improvement potential for selected assets (e.g. sites, buildings, equipment) based on model calculation; Aggregate improvement potential for any selected asset hierarchy level (floor, building, campus, enterprise etc.); Ability to input specific or estimated improvement impact for customized improvement measures; Create abatement curves to provide strong visualization of scenarios; Create and display different scenarios for selected assets with different improvement measures to compare effects of different combinations; Store these scenarios; Provide cost abatement curve for a scenario and be able to compare scenarios in terms of cost reduction, capital investment, carbon reduction, risk reduction and other customizable KPI's (i.e. cost per sq ft, kWh per sq ft etc); Pull information of Gap analysis into visualization to set targets/restrictions; Please note Cohen also describes using mathematical algorithms and models to provide optimization and improvement measures. Accordingly, the cited references still read on the amended limitations.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
5. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Venki Kumar et al (US Publication 20220327538 A1, hereinafter Kumar), and in view of Henrik Cohen et al (US Publication 20190155268 A1, hereinafter Cohen), and Michael Meehan et al (US Publication 20120166616 A1, hereinafter Meehan).
As for independent claim 1, Kumar discloses: A computer-implemented method for optimizing carbon emissions associated with an operation of a processing plant (Kumar: [0011], The cognitive intelligence unit of the data analysis module can derive granular insights and help enterprises make predictions for how best to optimize resources or manage portions of or the entire emissions or carbon footprint of the enterprise, so as to help mitigate the overall environmental impact of the enterprise), the processing plant comprising a plurality of assets (Kumar: [0010], the system of the present invention enables businesses to integrate environmental data to assess the quality of assets, such as property assets, measure the overall climate exposure of the asset, and advise businesses on the financial risks associated with the asset), the computer-implemented method comprising: identifying a carbon output value associated with operating each asset of the plurality of assets of the processing plant (Kumar: [0006], monitoring and assessing the entire carbon chain or footprint of an enterprise from source to recycle or reuse. The entire carbon chain or footprint of the enterprise includes for example tracking, monitoring and assessing the carbon usage or generation associated with the raw materials that are sourced for making for example a device or a building or equipment, the activities associated with transporting the raw materials to a processing or production location, the processing of the raw materials, the activities associated with assembling or manufacturing the designed product, the activities associated with the storing, distributing, and the selling of the product to customers including the enterprise, and customers using the product. The carbon chain also includes activities associated with the enterprise (e.g., customer), such as operating their facilities, the reuse or recycling of emissions or materials, and the like; [0093], when the enterprise earns carbon credits through carbon sequestration programs and wants to retire or apply the carbon credits towards the emissions of a selected cluster (e.g., cluster 142B) located in a specific country or region, the emissions value of the carbon credits can be normalized relative to regulatory guidelines underpinning the asset class of the carbon credit); identifying an impact value associated with each asset of the plurality of assets (Kumar: [0084], The system 10 can also evaluate the impact of the different decarbonization paths that enterprises can adopt or pursue to account for emissions reduction while assessing the effectiveness of the paths to achieve the climate goals of the enterprise; [0115], determine or calculate the net impact of climate actions taken or performed by the enterprise), wherein the impact value associated with each asset comprises at least: a marginal abatement impact value associated with the asset (Kumar: [0089], the system 10 via the risk management unit 88 can determine the overall impact of climate-related risks on the balance sheet by accounting for any mitigation and abatement actions taken to lower exposure to climate-related risks as required by regulations or legal mandates established by investors, lenders, underwriters and the like); an operational impact value associated with operating the asset (Kumar: [0088], The emission intensity factor can also be used to compare the environmental impact of different fuels or activities performed by the enterprise); or an asset alteration impact value associated with the asset (Kumar: [0064], the various systems in the building can be modified so as to operate the physical facility in a more cost and environmentally friendly and efficient manner); generating an optimized set of transformation actions corresponding to the plurality of assets utilizing a multi-optimization model (Kumar: [0067], The reports can include among other things reports on green house gas emissions, green house gas optimization, carbon emission setting and optimization, risks and associated controls, transaction settlements, net-zero emissions compliance, and carbon pricing; [0070], the emissions management unit 82 can determine or calculate the emissions reduction achieved by the enterprise when utilizing different energy optimization measures, projects or programs via suitable software applications, the replacement of equipment, retrofitting equipment with advanced sensor and control capabilities, or automating command and control of the building control systems and associated IoT devices; [0078], The cognitive intelligence unit 34 of the data analysis module 16 can derive granular insights and help enterprises make predictions for how best to optimize resources or manage portions of or the entire emissions or carbon footprint of the enterprise, so as to help mitigate the overall environmental impact of the enterprise) based at least in part on (i) the carbon output value associated with operating each asset of the plurality of assets (Kumar: [0006], monitoring and assessing the entire carbon chain or footprint of an enterprise from source to recycle or reuse. The entire carbon chain or footprint of the enterprise includes for example tracking, monitoring and assessing the carbon usage or generation associated with the raw materials that are sourced for making for example a device or a building or equipment, the activities associated with transporting the raw materials to a processing or production location, the processing of the raw materials, the activities associated with assembling or manufacturing the designed product, the activities associated with the storing, distributing, and the selling of the product to customers including the enterprise, and customers using the product. The carbon chain also includes activities associated with the enterprise (e.g., customer), such as operating their facilities, the reuse or recycling of emissions or materials, and the like), and (ii) the impact value associated with each asset of the plurality of assets (Kumar: [0084], The system 10 can also evaluate the impact of the different decarbonization paths that enterprises can adopt or pursue to account for emissions reduction while assessing the effectiveness of the paths to achieve the climate goals of the enterprise; [0115], determine or calculate the net impact of climate actions taken or performed by the enterprise), wherein the multi-optimization model is configured to minimize a current cumulative carbon output value for operations of the plurality of assets and a current cumulative impact value for operations of the plurality of assets based on the operational constraints of the processing plants (Kumar: [0061], The system can alternatively be used to assess, monitor or predict the carbon foot-print (usage and emissions) of an enterprise on any time scale. For example, the recommendation engine 42 can employ one or more machine learning techniques that can include a variety of algorithms (e.g., supervised learning, unsupervised learning, reinforcement learning, knowledge-based learning, natural-language-based learning such as natural language generation and natural language processing, deep learning, and the like) and can access execution engines comprising software applications that enable implementation of the underlying algorithm. As is known, the machine learning techniques are trained using training data. This training data is used to modify and fine-tune the weights associated with the machine learning models, as well as record ground truth for where correct answers can be found within the data. As such, the better the training data, the more accurate and effective the machine learning model can be. The recommendation library or engine 42 processes the data in the data layer by applying one or more machine learning or artificial intelligence techniques so as to generate or extract recommendations, insights, predictions and the like from the data. The cognitive intelligence unit 34 can also employ a risk model unit 40 that includes suitable software for applying one or more risk modelling techniques to the data that model or address strategic, operational, compliance, geopolitical, and other types of risk. The wider availability of data and sophisticated analysis capabilities of the risk models makes modeling more practical. As is known, a risk model is a mathematical representation of a system that commonly includes probability distributions. The models use relevant historical data as well as relevant third party data to understand the probability of a risk event occurring and its potential severity from the input data. Please note the risk model describes mathematically modeling using plant data); and wherein at least one of: (i) the optimized cumulative carbon output value associated with the operations of the plurality of assets is less than a current cumulative carbon output value associated with the plurality of assets, wherein the optimized cumulative carbon output value is further based at least in part on the optimized set of transformation actions (Kumar: [0069], The emissions accounting unit 80 can also be configured to track emissions generated from their operations with the required level of granularity or specificity for deciding specific to reducing emissions through various known carbon management strategies. For example, the emissions accounting unit 80 can use the environmental data to determine the biggest emission generators in the building and then take selected emissions remediation actions (e.g., climate actions), such as for example upgrade the emission sources, initiate repair or maintenance of selected building components, and to forecast future investment needs. The climate actions can also include for example initiatives related to carbon reduction (e.g., energy efficiency), carbon removal, carbon offsetting, carbon capture and storage, and the like), or (ii) the optimized cumulative impact value associated with the operations of the plurality of assets is less than a current cumulative asset impact value associated with the plurality of assets, wherein the optimized cumulative impact value is further based at least in part on the optimized set of transformation actions (Kumar: [0069], The emissions accounting unit 80 can also be configured to track emissions generated from their operations with the required level of granularity or specificity for deciding specific to reducing emissions through various known carbon management strategies. For example, the emissions accounting unit 80 can use the environmental data to determine the biggest emission generators in the building and then take selected emissions remediation actions (e.g., climate actions), such as for example upgrade the emission sources, initiate repair or maintenance of selected building components, and to forecast future investment needs. The climate actions can also include for example initiatives related to carbon reduction (e.g., energy efficiency), carbon removal, carbon offsetting, carbon capture and storage, and the like).
Further, Kumar does not clearly disclose abatement value, in an analogous art of optimization system for carbon emission, Cohen discloses: a marginal abatement impact value associated with the asset (Cohen: [0208], Show impact of scenario and visualize through abatement curves) … wherein the multi-optimization model is configured to minimize a current cumulative carbon output value for operations of the plurality of assets and a current cumulative impact value for operations of the plurality of assets based on the operational constraints of the processing plants (Cohen: [1138]-[1151], Ability to simulate multiple programs to assess cumulative impact; Remark: This include the same KPI/normalization functionality as e.g. for benchmarking; Add additional customer's projects (could be already planned/already running) to the overall picture; Select improvement measures and estimate their potential; Select applicable improvement areas by customer's characteristics/vertical markets/domains (supply (e.g. sourcing or hedging, demand, sustainability); Select improvement measures out of selected improvement areas guided by customer's characteristics/vertical markets; Display the improvement potential for selected assets (e.g. sites, buildings, equipment) based on model calculation; Aggregate improvement potential for any selected asset hierarchy level (floor, building, campus, enterprise etc.); Ability to input specific or estimated improvement impact for customized improvement measures; Create abatement curves to provide strong visualization of scenarios; Create and display different scenarios for selected assets with different improvement measures to compare effects of different combinations; Store these scenarios; Provide cost abatement curve for a scenario and be able to compare scenarios in terms of cost reduction, capital investment, carbon reduction, risk reduction and other customizable KPI's (i.e. cost per sq ft, kWh per sq ft etc); Pull information of Gap analysis into visualization to set targets/restrictions; Please note Cohen also describes using mathematical algorithms and models to provide optimization and improvement measures);
Kumar and Cohen are analogous arts because they are in the same field of endeavor, optimization system for carbon emission. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Kumar using the teachings of Cohen to include abatement curves. It would provide Kumar’s method with enhanced capabilities of determining impact value more efficiently.
Furthermore, Kumar-Cohen discloses abatement curves (Cohen [0208]) but does not clearly disclose abatement impact value representing a cost per unit of carbon emissions reduction for the asset, Meehan discloses: a marginal abatement impact value associated with the asset, representing a cost per unit of carbon emissions reduction for the asset (Meehan: [0052], when evaluating a portfolio of energy initiatives, users may be presented with a marginal abatement cost chart, which is a graph that shows the marginal cost and abatement impact of a variety of initiatives within a portfolio)
Kumar and Cohen and Meehan are analogous arts because they are in the same field of endeavor, optimization system for carbon emission. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Kumar using the teachings of Meehan to include abatement cost data. It would provide Kumar’s method with enhanced capabilities of determining impact value more efficiently.
As for claim 2, Kumar-Cohen-Meehan discloses: further comprising identifying a carbon emissions goal value, wherein the optimized cumulative carbon output value is less than the carbon emissions goal value (Kumar: [0069], to track emissions relative to established emissions goals for a specific building, equipment, enterprise, or country; and to determine the overall emissions related liabilities of the enterprise).
As for claim 3, Kumar-Cohen-Meehan discloses: determining the carbon emissions goal value based at least in part on a geographical location of the processing plant (Kumar: [0091], the energy consumption data available from selected equipment can be scored using the different data attributes available from the location in which the equipment is operated; [0093], the term “normalization” is intended to refer to a way for the enterprise to determine the absolute emissions of one or more clusters of the enterprise after accounting for other factors that may influence the context of the emissions estimate, such as for example, the age of the underlying asset, geolocation, the particular connection or arrangement of the systems in a cluster, and the like; Cohen: [0083], Considering the location of the one or more buildings allows that current and actual situations and conditions e.g. regarding the current weather conditions at the location or regarding the current energy prices in the respective area can be taken into account).
As for claim 4, Kumar-Cohen-Meehan discloses: wherein the generating the optimized set of transformation actions further comprises minimizing the optimized cumulative impact value associated with the plurality of assets (Cohen: [0005], actions may include reducing end use, increasing efficiency, eliminating wasted energy, finding alternative energy sources and minimizing procurement costs; [0141], maximize energy efficiency, minimize utility expense and enhance the sustainability of such buildings).
As for claim 5, Kumar-Cohen-Meehan discloses: wherein the generating the optimized set of transformation actions further comprises minimizing the optimized cumulative carbon output value associated with the plurality of assets (Cohen: [0005], actions may include reducing end use, increasing efficiency, eliminating wasted energy, finding alternative energy sources and minimizing procurement costs; [0141], maximize energy efficiency, minimize utility expense and enhance the sustainability of such buildings).
As for claim 6, Kumar-Cohen-Meehan discloses: determining the optimized cumulative carbon output value associated with the plurality of assets, wherein the generating the optimized set of transformation actions is in response to the optimized cumulative carbon output value exceeding a carbon emissions goal value (Kumar: [0069], The emissions accounting unit 80 can also be configured to track emissions generated from their operations with the required level of granularity or specificity for deciding specific to reducing emissions through various known carbon management strategies. For example, the emissions accounting unit 80 can use the environmental data to determine the biggest emission generators in the building and then take selected emissions remediation actions (e.g., climate actions), such as for example upgrade the emission sources, initiate repair or maintenance of selected building components, and to forecast future investment needs. The climate actions can also include for example initiatives related to carbon reduction (e.g., energy efficiency), carbon removal, carbon offsetting, carbon capture and storage, and the like).
As for claim 7, Kumar-Cohen-Meehan discloses: wherein the generating the optimized set of transformation actions comprises generating the optimized set of transformation actions in real-time or in near real-time with the determining the optimized cumulative carbon output value associated with the plurality of assets (Kumar: [0059], The Nantum OS software application unlocks correlated trends and analyzes data from devices such as sensors in disparate building systems (including building management systems (BMS), utility and power quality meters, and access control) and combines this with data from third-party sources 38 to prescribe operational adjustments in real-time that improve building performance and tenant comfort; [0061], The various techniques enable real-time adjustments of the data received from the data layer based on various factors, including data type and how the data is used by the enterprise; [0068], The selected software modules can be configured to process data from one or more selected types of data sources 12, thus allowing the client to access granular environmental data, such as for example emissions related data, so as to derive insights across the users operations in near real time; [0102], The modular design of the smart contracts preserves, in a current version of the world state database, all data object-related attribute values provided to the blockchain, thus reflecting the current status of the data object, including all attributes and attribute values, in real time or near real time).
As for claim 8, Kumar-Cohen-Meehan discloses: wherein the optimized set of transformation actions comprises a transformation action to generate a schedule for installing, replacing, renewing, or modifying at least one asset of the plurality of assets (Kumar: [0070], the emissions management unit 82 can determine or calculate the emissions reduction achieved by the enterprise when utilizing different energy optimization measures, projects or programs via suitable software applications, the replacement of equipment, retrofitting equipment with advanced sensor and control capabilities, or automating command and control of the building control systems and associated IoT devices; [0075], The governance reporting unit 90 can employ pre-defined techniques to track and analyze the impact of the different climate actions undertaken by the enterprise to reduce their overall impact and performance leveraging the insights generated by the cognitive intelligence unit 34 for subsequent social and governance actions planning; [0093], The normalized emissions associated with the selected equipment increases the fidelity or trustworthiness of the emissions estimate for the equipment and helps determine appropriate actions, such as maintenance, retrofitting, replacement, retirement, and the like, to reduce the emissions footprint of the enterprise).
As for claim 9, Kumar-Cohen-Meehan discloses: wherein the optimized set of transformation actions comprises: a first transformation action to generate a schedule for installing, replacing, renewing, or modifying a first asset of the plurality of assets (Kumar: [0070], the emissions management unit 82 can determine or calculate the emissions reduction achieved by the enterprise when utilizing different energy optimization measures, projects or programs via suitable software applications, the replacement of equipment, retrofitting equipment with advanced sensor and control capabilities, or automating command and control of the building control systems and associated IoT devices; [0075], The governance reporting unit 90 can employ pre-defined techniques to track and analyze the impact of the different climate actions undertaken by the enterprise to reduce their overall impact and performance leveraging the insights generated by the cognitive intelligence unit 34 for subsequent social and governance actions planning; [0093], The normalized emissions associated with the selected equipment increases the fidelity or trustworthiness of the emissions estimate for the equipment and helps determine appropriate actions, such as maintenance, retrofitting, replacement, retirement, and the like, to reduce the emissions footprint of the enterprise); and a second transformation action to generate a recommendation to install, replace, renew, or modify the first asset of the plurality of assets (Kumar: [0064], The enrichment layer 22 can recommend adjustments to be made to the building operational systems so as to improve overall building efficiency and tenant comfort; [0068], The reports can be constructed so as to allow the user to view and analyze the data, such as environmental data, so as to help make decisions or to take or recommend actions in response thereto and which are related to selected system capabilities or functionalities, including for example emissions accounting, emissions management, emissions reporting, emissions trading, and risk management; Cohen: [0022], the improvement measures comprise accordingly recommending inspection of ventilator, ventilator drive, damper, damper drive or air duct section, and/or induce replacement thereof).
As for claim 10, Kumar-Cohen-Meehan discloses: wherein the optimized set of transformation actions comprises at least one of: a transformation action to generate a recommendation to install, replace, renew, or modify at least one asset of the plurality of assets (Kumar: [0064], The enrichment layer 22 can recommend adjustments to be made to the building operational systems so as to improve overall building efficiency and tenant comfort; [0068], The reports can be constructed so as to allow the user to view and analyze the data, such as environmental data, so as to help make decisions or to take or recommend actions in response thereto and which are related to selected system capabilities or functionalities, including for example emissions accounting, emissions management, emissions reporting, emissions trading, and risk management; Cohen: [0022], the improvement measures comprise accordingly recommending inspection of ventilator, ventilator drive, damper, damper drive or air duct section, and/or induce replacement thereof); a transformation action to automatically generate a work order to install, replace, renew, or modify at least one asset of the plurality of assets (Cohen: [0020], the improvement measures comprise: replacement of assets (e.g. air ducts, boilers, dampers, lamps, HVAC equipment, chillers, boilers) and/or changing the operation modus of an asset); a transformation action to convert received electricity to operate at least one asset of the plurality of assets from a non-renewable energy source to a renewable energy source (Kumar: [0013], the utilization of renewable resources); a transformation action to convert at least one asset of the plurality of assets to operate with green hydrogen (Kumar: [0067], green house gas emissions, green house gas optimization); or a transformation action to obtain a carbon credit offset for an operation of at least one asset of the plurality of assets (Kumar: [0013], These factors enable the enterprise to determine the proper areas for investment, whether through carbon offsets or credits, the utilization of renewable resources, the retrofitting of equipment, and the like).
As for claim 11, Kumar-Cohen-Meehan discloses: performing at least one transformation action of the optimized set of transformation actions on at least one asset of the plurality of assets (Kumar: [0070], the emissions management unit 82 can determine or calculate the emissions reduction achieved by the enterprise when utilizing different energy optimization measures, projects or programs via suitable software applications, the replacement of equipment, retrofitting equipment with advanced sensor and control capabilities, or automating command and control of the building control systems and associated IoT devices); and generating a time stamp of when the at least one transformation action was performed on the at least one asset of the plurality of assets (Cohen: [0635], The system must be able to track stages of problem bill resolution, to include date stamping each time the invoice is handled/by whom and provide reporting on “pending” invoices (invoices that are not yet resolved)).
As for claim 12, Kumar-Cohen-Meehan discloses: wherein generating the optimized set of transformation actions comprises generating a first scenario for the optimized set of transformation actions in real-time or in near real-time with generating a second scenario for the optimized set of transformation actions, wherein the first scenario comprises a first transformation action associated with a first asset of the plurality of assets, wherein the second scenario comprises a second transformation action, different than the first transformation action, that is associated with a second asset of the plurality of assets, and comparing the first scenario and second scenario using the multi-optimization model based on at least one of carbon output value and the impact value associated with each asset of the plurality of assets; selecting one of the first scenario or the second scenario based on the comparison and, wherein the computer-implemented method further comprises performing the first transformation action or the second transformation action, but not both (Kumar: [0070], the emissions management unit 82 can determine or calculate the emissions reduction achieved by the enterprise when utilizing different energy optimization measures, projects or programs via suitable software applications, the replacement of equipment, retrofitting equipment with advanced sensor and control capabilities, or automating command and control of the building control systems and associated IoT devices; please note different measurements may be selected as needed; Cohen: [0474], Compare objects (whole enterprises, sites, buildings, equipments with other similar objects or specific references figures regarding selected Operational efficiency KPIs (Key Performance Indicators); [0970], For selected assets, analyze performance data by selecting value types, calculating and comparing performance indicators; [1148], Create and display different scenarios for selected assets with different improvement measures to compare effects of different combinations; [1150], Provide cost abatement curve for a scenario and be able to compare scenarios in terms of cost reduction, capital investment, carbon reduction, risk reduction and other customizable KPI's (i.e. cost per sq ft, kWh per sq ft etc.); [1301], Improvement measures and performance data is directly linked to one asset and can be tracked for comparison; [1332], Cost/resources/emissions comparison; [1440], Compare the results of a series of the same implemented improvement measures with estimated results in original improvement measures in the catalogue).
As for claim 13, Kumar-Cohen-Meehan discloses: wherein generating the optimized set of transformation actions comprises generating a first scenario for the optimized set of transformation actions in real-time or in near real-time with generating a second scenario for the optimized set of transformation actions,
wherein the first scenario comprises a first transformation action associated with a first asset of the plurality of assets, wherein the second scenario comprises a second transformation action, different than the first transformation action, that is associated with the first asset of the plurality of assets, and comparing the first scenario and second scenario using the multi-optimization model based on at least one of carbon output value and the impact value associated with each asset of the plurality of assets; selecting one of the first scenario or the second scenario based on the comparison and, wherein the computer-implemented method further comprises performing the first transformation action or the second transformation action, but not both (Kumar: [0070], the emissions management unit 82 can determine or calculate the emissions reduction achieved by the enterprise when utilizing different energy optimization measures, projects or programs via suitable software applications, the replacement of equipment, retrofitting equipment with advanced sensor and control capabilities, or automating command and control of the building control systems and associated IoT devices; please note different measurements may be selected as needed; Cohen: [0474], Compare objects (whole enterprises, sites, buildings, equipments with other similar objects or specific references figures regarding selected Operational efficiency KPIs (Key Performance Indicators); [0970], For selected assets, analyze performance data by selecting value types, calculating and comparing performance indicators; [1148], Create and display different scenarios for selected assets with different improvement measures to compare effects of different combinations; [1150], Provide cost abatement curve for a scenario and be able to compare scenarios in terms of cost reduction, capital investment, carbon reduction, risk reduction and other customizable KPI's (i.e. cost per sq ft, kWh per sq ft etc.); [1301], Improvement measures and performance data is directly linked to one asset and can be tracked for comparison; [1332], Cost/resources/emissions comparison; [1440], Compare the results of a series of the same implemented improvement measures with estimated results in original improvement measures in the catalogue).
As per claim 14, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As per claim 15, it recites features that are substantially same as those features claimed by claim 2, thus the rationales for rejecting claim 2 are incorporated herein.
As per claim 16, it recites features that are substantially same as those features claimed by claim 6, thus the rationales for rejecting claim 6 are incorporated herein.
As per claim 17, it recites features that are substantially same as those features claimed by claim 9, thus the rationales for rejecting claim 9 are incorporated herein.
As per claim 18, it recites features that are substantially same as those features claimed by claim 10, thus the rationales for rejecting claim 10 are incorporated herein.
As per claim 19, it recites features that are substantially same as those features claimed by claim 11, thus the rationales for rejecting claim 11 are incorporated herein.
As per claim 20, it recites features that are substantially same as those features claimed by claim 12, thus the rationales for rejecting claim 12 are incorporated herein.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118