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 action is responsive to the communications filed on 10/6/2025. Claims 1, 3, 5-19, 21-26 are pending in the case. Claims 1, 3, 5-19 are amended. Claims 2, 4, 20 are cancelled. Claims 21-26 are newly added. Claims 1, 24 are independent claims. Claims 1-20 are rejected.
Summary of claims
3. Claims 1, 3, 5-19, 21-26 are pending,
Claims 1, 3, 5-19 are amended,
Claims 2, 4, 20 are cancelled,
Claims 21-26 are newly added,
Claims 1, 24 are independent claims,
Claims 1, 3, 5-19, 21-26 are rejected.
Remarks
4. Since Applicant cancelled claim 4, Examiner respectfully withdraws claim objections to claims 3 and 4.
Applicant’s arguments, see Remarks, filed on 10/6/2025, with respect to the rejection(s) of claim(s) 1, 3, 5-19, 21-26 under 103 have been fully considered and are moot 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, 3, 5-19, 21-26 are rejected under 35 U.S.C. 103 as being unpatentable over Jason Ryan Cooner (US Publication 20200027096 A1, hereinafter Cooner), and in view of Takafumi Kozakai et al (US Publication 20240004373 A1, hereinafter Kozakai).
As for independent claim 1, Cooner discloses: An artificial-intelligence-based (AI-based) platform for enabling intelligent orchestration and management of power and energy ([0132], The IoT Platform currently includes such feature sets as machine-learning, artificial intelligence (AI), business analytics, integration services for connecting to the existing Enterprise computer software tier, as well as middleware servers for connecting to the Edge devices; [1497], The purpose of this disclosure is to cover using all aspects of the IoT technological advancements mentioned herein to implement IoT-based energy management systems (EMS)), the AI-based platform comprising: a set of energy-using entities ([0375], It will allow entities to track performance and progress in reducing GHG emissions, space as well as facilitate the crediting and trade of GHG emission reductions or removal enhancements); a set of edge devices ([0070], The edge devices' computation power can be used to analyse and process data, thus providing easy real time scalability; [0132], The IoT Platform currently includes such feature sets as machine-learning, artificial intelligence (AI), business analytics, integration services for connecting to the existing Enterprise computer software tier, as well as middleware servers for connecting to the Edge devices), wherein, the set of energy-using entities is at least one of linked to or governed by the set of edge devices, each edge device of the set of edge devices is configured to record carbon production of at least one entity of the set of energy-using entities (Abstract, The distributed ledger can provide records that combine the details of the carbon credits' origin, transaction history, and financial instructions associated with trading of the carbon credits via a distributed ledger system; [1500], Another implementation is to retrofit or integrate from factory the Edge hardware needed to measure, monitor and track all electrical flow from renewable resources including solar panels, wind turbines, hydroelectric setups (dams, etc.), biomass, etc. to capture the overall electrical production and transmit it to an IoT Platform via a wireless and/or wired connection), and at least one of the set of edge drives is configured to, based on the record of the carbon production, simulate a future carbon production of the at least one entity ([0101], IoT modeling and simulation (and emulation) is typically carried out at the design stage before deployment of the network. Network simulators like OPNET, NetSim and NS2 can be used to simulate IoT networks), and based on the simulated future carbon production, automatically adjust an activity associated with the future carbon production ([0457], GHG information management system processes used to process, analyze, gather, collate, transfer, correct or adjust aggregate or disaggregate and store the responsible party's GHG information; [0481], Description and justification for any adjustment to the base year GHG inventory, including application of the base year GHG inventory adjustment policy; [0486], GHG information management system processes used to gather, collate, transfer, process, analyze, correct or adjust, aggregate (or disaggregate) and store the organization's GHG information; [1344], Baseline Scenario The project baseline is a counterfactual scenario that forecasts the likely stream of emissions or removals to occur if the Project Proponent does not implement the project, i.e., the “business as usual” case. It also reflects the sum of the changes in carbon stocks (and where significant, N2O and CH4 emissions) in the carbon pools within the project boundary that would occur in the absence of the Project Activity; [1433], The project baseline is a counterfactual scenario that forecasts the likely stream of GHG emissions reductions or removals to occur if the project proponent does not implement the project, i.e., the “business as usual” case.2 Baseline calculations must be consistent with the WRI/WBCSD GHG Protocol and ISO 14064-2, and consider ACR-approved best practices).
Cooner discloses using the edge devices to analyse and process data ([0070], [0132]), please note in Cooner, edge devices are used to process and manage carbon production data, in addition, in an analogous art of carbon data management system, Kozakai discloses: wherein, the set of energy-using entities is at least one of linked to or governed by the set of edge devices, each edge device of the set of edge devices is configured to record carbon production of at least one entity of the set of energy-using entities ([0068], An edge computer (which may also be referred to as a gateway or apparatus) E900 in the physical space E100 includes a control section (Control), and the control section has a communication function (Communication), an edge analytics function (Edge Analytics), a transformation function (Transformation), a sensing function (Sense), and an actuation function (Actuation); [0072], the operation section (Operations) K113 performs feedback control for an edge computer E900 according to a result of data analysis by AI, for example. Information for the control may be any information as long as it is information for changing a control state of the edge computer E400, and may be, for example, information for causing the edge computer E900 to perform communication, analysis in the edge computer E400, state change, sensing, operation instruction, and the like. The autonomous operation section K113 may be referred to as autonomous operation control; [0073], The edge computer E900 transmits data collected from IoT devices and the like to the platform; [0081], The edge computer E900 and the platform K100 are connected via, for example, an IoT bus (IoT Bus), and the platform and the enterprise service are connected via a service bus (Service Bus));
Cooner and Kozakai are analogous arts because they are in the same field of endeavor, collect, process and manage carbon information. 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 Cooner using the teachings of Kozakai to more expressly include using edge computers to perform data process.
Claims 2, 4, 20 are cancelled
As for claim 3, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to execute a set of machine-learned algorithms trained on a training data set of carbon generation data to calculate a metric of the carbon production for the set of energy-using entities (Cooner: [0132], IoT is ultimately about the data and how it can be used to help companies improve operational efficiencies, drive new revenue streams and provide customer insights. The IoT Platform currently includes such feature sets as machine-learning, artificial intelligence (AI), business analytics, integration services for connecting to the existing Enterprise computer software tier, as well as middleware servers for connecting to the Edge devices. The Edge tier described in this disclosure will be able to encapsulate several of the feature sets contained in the IoT Platform such as AI, machine learning, and Edge devices can also be reprogrammed so that over time they can be “taught” by software and configurations created by the IoT Platform tier. This ongoing “learning cycle” may take several forms depending on where the data resides to figure out how to improve the overall system and which Edge devices need to be reconfigured or reprogrammed to carry out the “learned” behavior the system learns over time through the use of machine learning and AI techniques; [1347], Carbon Dioxide-equivalent (CO2e) Carbon dioxide equivalence (CO2e) is a metric to compare GHGs based on their global warming potential (GWP) relative to CO2 over the same timeframe).
As for claim 5, Cooner- Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to adapt a transport of data over at least one of a network or a communication system, and the adapting is based on at least one of, a congestion condition, a delay condition, a latency condition (Cooner: [1347], The IoT platform can continuously monitor the location and conditions of cargo and assets via wireless sensors and send specific alerts when management exceptions occur (delays, damages, thefts, etc.)), a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition,
a usage condition (Cooner: [0025], Usage of IoT devices for monitoring and operating infrastructure is likely to improve incident management and emergency response coordination, and quality of service, up-times and reduce costs of operation in all infrastructure related area), a market factor condition (Cooner: Abstract, Greenhouse gas emissions are capped and then markets are used to allocate the emissions among the group of regulated sources), or a user configuration condition.
As for claim 6, Cooner-Kozakai discloses: further comprising an adaptive energy digital twin that represents at least one of, an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility,
a stakeholder transportation system, a market condition, or an energy usage priority condition (Cooner: [0101], Digital Twins may also be implemented to produce updates on the status and health of an asset, based upon sensor readings integrated with a computational model of the asset).
As for claim 7, Cooner-Kozakai discloses: further comprising an adaptive energy digital twin that is configured to perform at least one of, providing a visual indicator (Cooner: [0174], One visual display indicators (green LED); [0201] Two visual display indicators (green LED—on/send, Blue—receive data)) of energy consumption by one or more energy consumers, providing an analytic indicator of energy consumption by the one or more energy consumers, filtering energy data, highlighting energy data, adjusting energy data, or generating at least one of a visual indicator or analytic indicator of energy consumption by at least one of, one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.
As for claim 8, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to perform at least one of, extracting energy-related data, detecting errors in energy-related data, correction errors in energy-related data, transforming energy-related data, converting energy-related data, normalizing energy-related data, cleansing energy-related data, parsing energy-related data, detecting patterns in energy-related data, detecting content in energy-related data, detecting objects in energy-related data,
compressing energy-related data, streaming energy-related data, filtering energy-related data, loading energy-related data, storing energy-related data, routing energy-related data, transporting energy-related data, or maintaining security of energy-related data (Cooner: [0311], the sensor pack reads the response, parses out the information which may include a unique id and/or the checksum data to verify the amount and/or contents of the data transmission).
As for claim 9, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices includes at least one of an AI-based model or an AI-based algorithm, that is trained based on a training data set, and the training data set is based on at least one of, one or more human tags, one or more labels, one or more human interactions with a hardware system, one or more human interactions with a software system, one or more outcomes, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process (Cooner: [0132], IoT is ultimately about the data and how it can be used to help companies improve operational efficiencies, drive new revenue streams and provide customer insights. The IoT Platform currently includes such feature sets as machine-learning, artificial intelligence (AI), business analytics, integration services for connecting to the existing Enterprise computer software tier, as well as middleware servers for connecting to the Edge devices. The Edge tier described in this disclosure will be able to encapsulate several of the feature sets contained in the IoT Platform such as AI, machine learning, and Edge devices can also be reprogrammed so that over time they can be “taught” by software and configurations created by the IoT Platform tier. This ongoing “learning cycle” may take several forms depending on where the data resides to figure out how to improve the overall system and which Edge devices need to be reconfigured or reprogrammed to carry out the “learned” behavior the system learns over time through the use of machine learning and AI techniques).
As for claim 10, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to orchestrate delivery of energy to one or more points of consumption, and the delivery of the energy includes at least one of,
one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more deliveries of fuel, or one or more deliveries of stored energy (Cooner: [0143], Our wireless protocol has a 4-phase commit, which guarantees packet delivery and goes well beyond the stability of platforms built on Wi-Fi, Bluetooth or Zigbee, making it ideal for mission critical applications such as healthcare; [0475], Gross indirect GHG emissions associated with the import or purchase of electricity, heat, steam or other fossil fuel-derived energy products separately for each type of GHG).
As for claim 11, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to record, in a distributed ledger, one or more energy-related events, the one or more energy-related events including at least one of, an energy purchase event, an energy sale event, a service charge associated with the energy purchase, a service charge associated with the energy sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event, a pollution production event, or a pollution abatement event (Cooner: [1510], All records, certifications, validation and verification reports should be stored in an immutable data storage facility like a Blockchain implementation).
As for claim 12, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is deployed in an off-grid environment, and the off-grid environment includes at least one of, an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system (Cooner: [0002], the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, virtual power plants, smart homes, intelligent transportation and smart cities).
As for claim 13, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to determine a change in the carbon production over a period of time based on a comparison of a current metric of the carbon production with a historical metric of the carbon production (Cooner: [0391], Validations usually rely on projections and estimates, while verification usually relies on historical information).
As for claim 14, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to determine a target for the carbon production based on a policy for the carbon production (Cooner: [0481], Description and justification for any adjustment to the base year GHG inventory, including application of the base year GHG inventory adjustment policy).
As for claim 15, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to, perform a comparison of a metric of the carbon production with a target of the carbon production, and determine a compliance of the carbon production with a policy for the carbon production based on the comparison (Cooner: [1063], ACR aims to maximize flexibility and usability for Project Proponents, while maintaining the environmental integrity and scientific rigor necessary to ensure that projects developed against its standards and methodologies are recognized as being of the highest quality, whether used for voluntary or pre-compliance early action purposes).
As for claim 16, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to determine an environmental impact of the carbon production based on a metric of the carbon production with a target of the carbon production (Cooner: [0747], Environmental impact analysis).
As for claim 17, Cooner-Kozakai discloses: wherein the carbon production are associated with a set of activities, and at least one edge device of the set of edge devices is configured to allocate at least a portion of the carbon production to at least one activity of the set of activities (Cooner: [0386], Consistency: ensure that GHG information verification and validation activities are comparable over time).
As for claim 18, Cooner-Kozakai discloses: wherein at least one edge device of the setoff edge devices is configured to associate at least one indicator with a metric of the carbon production with a target of the carbon production, and the indicator includes at least one of, a date of the carbon production, a time of the carbon production, a time period of the carbon production, a source location of the carbon production, a direction and/or speed of the conveyance of the carbon production, an impacted location of the carbon production, a physical metric of the carbon production, a chemical component of the carbon production,
a weather pattern occurring in an area that is associated with the carbon production, a wildlife population in an area that is associated with the carbon production, or a human activity that is affected by the carbon production (Cooner: [0137], using networked sensors combined with historical and real-time data on weather).
As for claim 19, Cooner-Kozakai discloses: wherein at least one edge device of the set of edge devices is configured to transmit an alert associated with the carbon production based on a comparison of a metric of the carbon production with an alert threshold associated with the carbon production (Cooner: [0335], The device must also have enough computing power for some analytics and to make real-time decisions such as turning off machine if the temperature passes a specified threshold).
As for claim 21, Cooner-Kozakai discloses: wherein the carbon production includes at least one of carbon generation or carbon emissions (Cooner: [0475], Gross indirect GHG emissions associated with the import or purchase of electricity, heat, steam or other fossil fuel-derived energy products separately for each type of GHG)).
As for claim 22, Cooner-Kozakai discloses: wherein the activity associated with the future carbon production is a current activity (Cooner: [0325], by tracking the location of devices, more context relevant information can be pushed to the device such as special offers and recommendations based current conditions).
As for claim 23, Cooner-Kozakai discloses: wherein the activity associated with the future carbon production is a future activity (Cooner: [1091], Under possible future U.S. climate regulations).
As for claim 24, 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 for claim 25, it recites features that are substantially same as those features claimed by claim 13, thus the rationales for rejecting claim 13 are incorporated herein.
As for claim 26, it recites features that are substantially same as those features claimed by claim 15, thus the rationales for rejecting claim 15 are incorporated herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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