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37 CFR 1.114, the fee set forth in 37 CFR 1.17(e) has been paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/2/2026 has been entered. An action on the RCE follows.
Summary of claims
3. Claims 1-20 are pending,
Claims 1, 3-11, 13-14, 20 are amended,
Claims 1, 14, 20 are independent claims,
Claims 1-20 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 1/2/2026, with respect to the rejection(s) of claim(s) 1-20 under 103 have been fully considered and are not persuasive in view of new rejection ground(s).
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 Charles Howard Cella et al (US Publication 20180299878 A1, hereinafter Cella), and further in view of Ian Slagle et al (US Publication 20190130330 A1, hereinafter Slagle).
As for independent claim 1, Kumar discloses: A system, comprising: one or more processors (Kumar: [0058], The computing layer 18 can form part of the data analysis module 16 or can be a separate component therefrom. The computing layer can include selected computing hardware, such as processors, memory and storage. The data from the data sources 12 can be stored at the computing layer 18. The computing layer 18 can include relevant software applications and associated protocols for interfacing with the devices, ensuring device connectivity, the ability to process and log the data, store the data, and the like); a memory having program code stored thereon that, in execution with the at least one processor (Kumar: [0058], The computing layer 18 can form part of the data analysis module 16 or can be a separate component therefrom. The computing layer can include selected computing hardware, such as processors, memory and storage. The data from the data sources 12 can be stored at the computing layer 18. The computing layer 18 can include relevant software applications and associated protocols for interfacing with the devices, ensuring device connectivity, the ability to process and log the data, store the data, and the like), causes the system to: determine a set of emission constraints associated with emission optimization for an industrial domain related to one or more industrial processes that produce one or more industrial process products (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. The emissions management unit 82 can also determine the overall energy consumption of the enterprise and allow the integration of this enriched environmental data with third party data sources to facilitate the procurement of energy for the enterprise);
configure an emission optimization model based at least in part on the set of emission constraints and at least one other non-emission constraint (Kumar: [0048], The data associated therewith can also include any associated identifiable element in the physical, cultural, demographic, economic, political, regulatory, climatic, or technological environment that affects the survival, operations, and/or growth of any of the foregoing. The data can also concern the physical, chemical (e.g., chemicals, fluids, gases and the like), and/or biological factors that can act upon the natural and man-made structures); … apply the emission optimization model to real-time measurement data associated with the one or more industrial processes (Kumar: [0058], the data sources 12a-12n can include data that is generated by a plurality of sources or different devices or measuring devices, including for example sensors, detectors, measurement devices and the like. The measuring devices can be coupled to any suitable structure or facility to measure any selected parameter, including for example power generation, power consumption, humidity, occupancy, emissions of various fluids (e.g., liquids and gases) and the like) to determine one or more operational modifications for the one or more industrial processes that at least satisfy the set of emission constraints and optimize the at least one non-emission constraint (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); based on the emission optimization pathway, perform one or more actions associated with the one or more industrial processes (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 receive feedback data associated with the one or more work orders, and update the emission optimization model based at least in part on the feedback data (Kumar: [0059], performing operational adjustments in real-time that improve building performance; [0068], help make decisions or to take or recommend actions in response to the emissions related data in near real time; further, Kumar discloses using reinforcement learning (Kumar: [0061]), please note reinforcement learning is an optimal strategy independently by receiving feedback in the form of rewards or penalties for their actions).
Kumar discloses providing carbon emission optimization measures as required but does not clearly disclose receiving an emission optimization request, and generating an emission optimization pathway defining a sequence of operational adjustments evaluated over multiple future time intervals, in an analogous art of optimizing carbon emission system, Cella discloses: receive an emission optimization request to optimize carbon emissions related to the one or more industrial processes; and in response to the emission optimization request: (Cella: [1621], an enhanced data request value (e.g., an operator, model, optimization routine, and/or other process requests enhanced data resolution for one or more parameters); … determine the one or more operational modifications for the one or more industrial processes, wherein the determination of the one or more operational modifications comprises generating an emission optimization pathway (Cella: [1195], The information in the data set may also identify a plurality of potential pathways in a system for data collection in an industrial environment for sensor data to be delivered to a data collector) defining a sequence of operational adjustments evaluated over multiple future time intervals to satisfy the set of emission constraints and the at least one other non-emission constraint (Cella: [0220], coupling this capability to alarm with adaptive scheduling techniques for continuous monitoring and the continuous monitoring system's software adapting and adjusting the data collection sequence based on statistics, analytics, data alarms and dynamic analysis may allow the system to quickly collect dynamic spectral data on the alarming sensor very soon after the alarm sounds; [0252], adaptive scheduling techniques for continuous monitoring… multiple scheduling levels are provided. In embodiments, at the lowest level, which is continuous for the most part, all of the measurement points will be cycled through in round-robin fashion. For example, if it takes 30 seconds to acquire and process a measurement point and there are 30 points, then each point is serviced once every 15 minutes; please note scheduling techniques include scheduling for a defined time interval; [0316], The platform 100 provides real-time monitoring and predictive maintenance in many industrial environments wherein it has been shown to present a cost-savings over regularly-scheduled maintenance processes (multiple future time intervals)); … and receive feedback data associated with the one or more work orders, and update the emission optimization model based at least in part on the one or more work orders (Cella: [0011], training artificial intelligence (“AI”) models based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, yield, or impact, where the AI model operates on sensor data from an industrial environment; [0206], [0207], [0211], training a machine based on initial models created by humans that are augmented by providing feedback (such as based on measures of success), that is, Cella clearly discloses dynamically updates the emission optimization process based on the received feedback data);
Kumar and Cella 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 Cella to include receiving optimization request and dynamically updating emission models based on the received feedback data, and predicting and scheduling regular maintenance. It would provide Kumar’s system with enhanced capabilities of allowing user to manage carbon emission optimization process as desired.
Further, Kumar-Cella does not clearly disclose generating work orders, Slagle discloses: wherein the one or more actions comprise generating one or more work orders for one or more industrial assets (Slagle: [0108], Work orders are issued and tasks are initiated and worked as prioritized. Manpower and other resources are focused on their assigned task without deviation so as to minimize the total duration of the task; [0115], The MSP is configured to communicate task work orders to specific work cells 152 within the manufacturing facility 150 and to resource pools 160. Resources are then employed within the work cells 152 to accomplish the assigned tasks);
Kumar and Slagle are analogous arts because they are in the same field of endeavor, optimization system for project tasks. 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 Slagle to include generating work orders and priority tasks. It would provide Kumar’s system with enhanced capabilities of allowing user to manage tasks in a project more efficiently.
As for claim 2, Kumar-Cella discloses: capture at least a portion of the real-time measurement data via a set of sensors configured to monitor real-time emissions related to the one or more industrial processes (Kumar: [0058], the data sources 12a-12n can include data that is generated by a plurality of sources or different devices or measuring devices, including for example sensors, detectors, measurement devices and the like. The measuring devices can be coupled to any suitable structure or facility to measure any selected parameter, including for example power generation, power consumption, humidity, occupancy, emissions of various fluids (e.g., liquids and gases) and the like); and apply the emission optimization model to the portion of the real-time measurement data associated with the set of sensors (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).
As for claim 3, Kumar-Cella discloses: capture at least a portion of the real-time measurement data via a set of gas cloud imaging cameras configured to monitor real-time emissions related to the one or more industrial processes (Cella: [0997], sensors may be ultrasonic, microphone, touch, capacitive, vibration, acoustic, pressure, strain gauges, thermographic (e.g., camera), imaging (e.g., camera, laser, IR, structured light), a field detector, an EMF meter to measure an AC electromagnetic field, a gaussmeter, a motion detector, a chemical detector, a gas detector); and apply the emission optimization model to the portion of the real-time measurement data associated with the set of gas cloud imaging cameras (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).
As for claim 4, Kumar-Cella discloses: capture at least a portion of the real-time measurement data via a set of gas leak sensing devices configured to monitor for or predict gas leaks related to the one or more industrial processes (Cella: [1272], A tank pressure sensor can detect evaporative leaks in a gasoline or diesel fuel tank due to an absent gas cap, and in other tank applications such as pressurized tanks can detect how full a gaseous tank is; [1282], air leak sensors, fluid leak sensors, and lubricant leak sensors; [2046], one or more safety detectors (such as gas leak detectors); and apply the emission optimization model to the portion of the real-time measurement data associated with the set of gas leak sensing devices (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).
As for claim 5, Kumar-Cella discloses: determine market data that comprises at least one of real-time gas cost data corresponding to gas employed by the one or more industrial processes, real-time electricity cost data corresponding to the one or more industrial processes (Cella: [1003], The expert system may be seeded with a model for operation of the pipeline in a manner that results in a specified profit goal, such as indicating a given flow rate of material through the pipeline based on the current market sale price for the material and the cost of getting the material into the pipeline), carbon credit cost data available for a location associated with the industrial domain (Kumar: [0019], a carbon credit, a renewable energy credit, an emissions reduction credit, and a carbon offset), and real-time interest rate data associated with a banking system associated with the location (Kumar: [0049], As used herein the term “financial data” can include any data that is associated with or contains financial or financial related information. The financial information can include information that is presented free form or in tabular formats and is related to data associated with financial, monetary, or pecuniary interests); and apply the emission optimization model to the real-time measurement data and the market data to determine the one or more operational modifications for the one or more industrial processes that satisfy the set of emission constraints (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).
As for claim 6, Kumar-Cella discloses: determine static configuration data that comprise at least one of resource data related to resource material employed by the one or more industrial processes, costs data related to costs associated with the one or more industrial processes, enterprise data related to assets for an enterprise associated with the one or more industrial processes, and regulatory data related to regulatory incentives for a geographic location of an industrial facility associated with the one or more industrial processes (Kumar: [0048], The data associated therewith can also include any associated identifiable element in the physical, cultural, demographic, economic, political, regulatory, climatic, or technological environment that affects the survival, operations, and/or growth of any of the foregoing); and apply the emission optimization model to the real-time measurement data and the static configuration data to determine the one or more operational modifications for the one or more industrial processes that satisfy the set of emission constraints (Kumar: [0070], The emissions management unit 82 can also include suitable software for tracking carbon prices as set forth by regulatory bodies specific to a jurisdiction or by the enterprise, and facilitate the determination of carbon-related liabilities applicable to the products or services or both of the enterprise).
As for claim 7, Kumar-Cella discloses: determine the set of emission constraints based at least in part on a set of operational baseline thresholds for the one or more industrial processes (Kumar: [0113], B.sub.ai is the baseline for the natural resources consumed by the built and operated system ‘a’ at cluster i in the operational boundary of an enterprise and its suppliers).
As for claim 8, Kumar-Cella discloses: determine the set of emission constraints based at least in part on a set of internal constraints that comprise at least one of capital for an enterprise associated with the one or more industrial processes (Kumar: [0089], The method also contemplates accounting for the financing activities conducted by the enterprise to raise capital for funding different projects that can be, for example, focused on reducing the overall carbon footprint of the enterprise, track the impact of investments on the company's balance sheets, the economic outcomes, and the like, step 116B), resources available for the one or more industrial processes (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), an infrastructure for an industrial facility associated with the one or more industrial processes (Kumar: [0013], The data collection and processing system of the present invention can also be used by the enterprise to align together, and to consider in coordination with other factors, the climate strategy and goals of the enterprise, the impact on the enterprise of climate risks, such as those associated with manufacturing and infrastructure, and the overall investment strategy of the enterprise), risk tolerance rules for the one or more industrial processes (Kumar: [0010], advise businesses on the financial risks associated with the asset), and implementation timelines for achieving the emission optimization associated with the one or more industrial processes (Kumar: [0071], The emissions reporting unit 84 can employ pre-defined techniques to track and analyze the impact of the different climate actions undertaken by the enterprise to reduce their overall emissions and leverage the insights generated by the cognitive intelligence unit 34 for subsequent emissions planning; [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) .
As for claim 9, Kumar-Cella discloses: determine the set of emission constraints based at least in part on a set of external constraints that comprises regulatory information for a geographic location of an industrial facility associated with the one or more industrial processes (Kumar: [0048], The data associated therewith can also include any associated identifiable element in the physical, cultural, demographic, economic, political, regulatory, climatic, or technological environment that affects the survival, operations, and/or growth of any of the foregoing), economic information for the geographic location (Kumar: [0048], The data associated therewith can also include any associated identifiable element in the physical, cultural, demographic, economic, political, regulatory, climatic, or technological environment that affects the survival, operations, and/or growth of any of the foregoing), social information for the geographic location (Kumar: [0075], any information associated with or directed or related to the social, corporate, employee, investment, social, or environmental aspects or governance of the enterprise), technical information for an operating environment associated with the one or more industrial processes (Kumar: [0093], The normalization unit 100 can also include a regulations module 104 that can automatically apply to the data selected rules and logic associated with any selected legal, technical, regulatory or industry specific framework that is related or relevant to the data being processed thereby so as to produce regulation data), legal information for the operating environment, and environmental information for the geographic location or the operating environment (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, step 116C).
As for claim 10, Kumar-Cella discloses: determine the set of emission constraints based at least in part on user input received via a dashboard visualization rendered on a display of a user device (Kumar: [0063], The reports can include financial reports and the like. According to one practice, the reports can include, when processing environmental data, an emission history report, water usage report and other enterprise (e.g., building) specific reports, as well as provide a summary dashboard showing selected metrics or parameters, including building efficiency and the like; [0122], an input device, and an output device. Program code may be applied to input entered using the input device to perform the functions described and to generate output using the output device).
As for claim 11, Kumar-Cella discloses: transmit a control signal configured based on the one or more operational modifications to a controller associated with the one or more industrial processes (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).
As for claim 12, Kumar-Cella discloses: transmit the control signal in response to a level of risk associated with the one or more operational modifications being below a defined risk threshold (Kumar: [0073], the risk management unit 88 can determine the financial risk via a financial risk value or score associated with the enterprise based on the enriched environmental data or environmental data; [0081], the post-processing unit 24 of the system can be employed to provide reports that benchmark performance against selected financial information, such as investment portfolios and the like, in order to assess risks and portfolio level impacts).
As for claim 13, Kumar-Cella discloses: record data associated with the one or more operational modifications and related timestamp data in a decarbonization historian database (Kumar: [0062], the original data or the enriched data can be stored in a series of batches or blocks that include among other things a time stamp; [0081], The system 10 can also allow the enterprise to analyze actions taken in response to the environmental and financial data so as to estimate the specific emissions of individual decarbonization initiatives, and the post-processing unit 24 of the system can be employed to provide reports that benchmark performance against selected financial information, such as investment portfolios and the like, in order to assess risks and portfolio level impacts; [0090], The method also contemplates processing the emissions data and then determining a decarbonization strategy based on the total emissions of the enterprise, identification of the source of the emissions, and the like, step 118B. The decarbonization strategy can be compared with a suitable benchmark in order to assess progress towards emissions reduction goals, step 118C. The processed emissions data can also be used to determine the value of the decarbonization project and initiatives, step 118D; [0107], the world state database may further include additional metadata, such as a version number of the data object, a timestamp that indicates when the current version was created or updated); generate a work order notification for the one or more industrial processes based at least in part on the one or more operational modifications (Cella: [0439], Alarms from the cloud alarm module 5910 may be generated and may be sent to various devices 5920 via email, texts, or other messaging mechanisms); and in response to the one or more operational modifications being applied to the one or more industrial processes via the work order notification, update the data in the decarbonization historian database based at least in part on the work order notification, wherein the emission optimization model determines the one or more operational modifications based at least in part on the data in the decarbonization historian database (Kumar: [0107], the world state database may further include additional metadata, such as a version number of the data object, a timestamp that indicates when the current version was created or updated).
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 3, thus the rationales for rejecting claim 3 are incorporated herein.
As per claim 17, it recites features that are substantially same as those features claimed by claim 4, thus the rationales for rejecting claim 4 are incorporated herein.
As per claim 18, it recites features that are substantially same as those features claimed by claim 5, thus the rationales for rejecting claim 5 are incorporated herein.
As per claim 19, 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 20, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 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
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