DETAILED ACTION
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 .
Claims 1-20 are pending.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant's cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 1 is objected to because of the following informalities: The term "AI-based" in line 1 should read "artificial intelligence based (AI-based)". Appropriate correction is required.
Claim 18 is objected to because of the following informalities: The term "wherein at last one edge device" in line 3 should read “wherein at least one edge device”. Appropriate correction is required
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 (i.e., changing from AIA to pre-AIA ) 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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim(s) 1-9, 11, 13, 16, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goparaju et al, US Patent Pub US 20150301548 A1 (hereinafter Goparaju) in view of Nammouchi et al, “Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management”, Sept 2021, IEEE International Conference, pp 1-4 (hereinafter Nammouchi)
Claim 1
Goparaju teaches an AI-based platform for managing energy (Goparaju, para 108, 113 – A cognitive platform for energy management using artificial intelligence techniques.), comprising: an artificial intelligence system configured to produce at least one operating parameter for energy-related operations of a set of infrastructure assets, wherein the at least one operating parameter is based on a data set of energy-related data associated with the set of infrastructure assets (Goparaju, para 25 – The platform senses parameters related to energy consumption of equipment, uses decision making based on a rule-set and communicates the changed or new parameters to equipment/”infrastructure assets”.), and the data set of energy-related data is at least partially generated by a sensor. (Goparaju, para 44-48 - Receive sensory input related to energy consumption of an energy consuming equipment.)
But Goparaju fails to specify the data set of energy-related data is at least partially generated by a set of sensors associated with a set of edge devices.
However Nammouchi teaches the data set of energy-related data is at least partially generated by a set of sensors associated with a set of edge devices. (Nammouchi, pg 2 Sec II-A - The edge devices are responsible to collect real-time data from the sensors at prosumer’s premises including load demand.)
Goparaju and Nammouchi are analogous art because they are from the same field of endeavor. They relate to energy management systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above energy management system, as taught by Goparaju, and incorporating the above limitations, as taught by Nammouchi.
One of ordinary skill in the art would have been motivated to do this modification in order to provide local real time data collection by incorporating the above limitations, as suggested by Nammouchi (pg 2 Sec A).
This rejection also applies to claim 19.
Claim 2
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
Nammouchi further teaches the artificial intelligence system 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. (Nammouchi, pg 1, Sec I – A microgrid that may disconnect/”off-grid” from upstream main grid and includes wind and photovoltaics/”off-grid energy generation system”, energy storage systems, and electric vehicles/”off-grid energy mobilization system”.)
This rejection also applies to claim 20.
Claim 3
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
The combination of Goparaju and Nammouchi further teaches the energy-related operations include at least one energy consumption operation. (Goparaju, para 25 – The platform senses parameters related to energy consumption of equipment.)
Claim 4
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
The combination of Goparaju and Nammouchi further teaches determine at least one modification of the at least one operating parameter to improve predictions of energy-related operations, wherein the at least one modification is based on at least one of, at least one additional historical, current, and/or forecast energy demand parameter associated with the set of infrastructure assets. (Goparaju, para 25, 108, 117 – The platform senses parameters related to energy consumption of equipment/”infrastructure assets” that include historical, current and predicted/forecast energy demand data.)
Claim 5
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
The combination of Goparaju and Nammouchi further teaches orchestrate a delivery of energy to at least one point of consumption based on at least one entity parameter received from at least one entity of the set of infrastructure assets, and the at least one entity parameter includes at least one of, a current energy consumption by the at least one entity, a future energy consumption by the at least one entity. (Goparaju, para 117, 126 - Cognitive platform for energy management receives and sends information from/to cognitive energy device and thus controls the energy consuming equipment/”orchestrate a delivery of energy”, based on sensed parameters that include a current energy consumptions state, energy demand data/”future energy consumption”
Claim 6
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
Nammouchi further teaches orchestrate a delivery of energy to the set of infrastructure assets, and the delivery of the energy includes at least one delivery of stored energy. (Nammouchi, pg 3 Sec III – Optimizing delivery of battery/stored energy to a load/”infrastructure assets”.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above energy management system, as taught by Goparaju and Nammouchi, and incorporating the above limitations, as taught by Nammouchi.
One of ordinary skill in the art would have been motivated to do this modification in order to minimize cost by incorporating the above limitations, as suggested by Nammouchi (pg 3 Sec III).
Claim 7
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
The combination of Goparaju and Nammouchi further teaches the artificial intelligence system is associated with at least one physical machine, the at least one physical machine is associated with the set of infrastructure assets, and the artificial intelligence system is configured to manage at least one process performed by the at least one physical machine. (Goparaju, para 126 – The cognitive platform for energy management is associated with physical equipment and manages processes performed by the equipment by receiving information and sending instructions to equipment and equipment controller as appropriate.)
Claim 8
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
Nammouchi further teaches determine a delivery of energy to the set of infrastructure assets based on a comparison of energy availability at each of two or more energy sources, wherein the comparison includes at least one of, a current quantity of energy stored by at least one of the two or more energy sources, a future quantity of energy stored by at least one of the two or more energy sources, a current resource expenditure associated with acquiring, storing, and/or delivering the energy by at least one of the two or more energy sources. (Nammouchi, pg 3 Sec III – Calculating a global energy balance of the microgrid power that it needs to import from or export to the main grid based on the minimization of cost or maximization of profit/”current resource expenditure”, including a determination of the battery state of energy/”current and future quantity of energy stored by at least one of the two or more energy sources”.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above energy management system, as taught by Goparaju and Nammouchi, and incorporating the above limitations, as taught by Nammouchi.
One of ordinary skill in the art would have been motivated to do this modification in order to minimize cost or maximize profit by incorporating the above limitations, as suggested by Nammouchi (pg 3 Sec III).
Claim 9
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
The combination of Goparaju and Nammouchi further teaches adjust a delivery of energy to the set of infrastructure assets based on at least one of an energy delivery or an energy consumption policy. (Goparaju, para 110 - Energy policy KPI database (EPKD) is a knowledge repository that stores knowledge, information and data related to enterprise policies, planning, and key performance indicators for energy management.)
Claim 11
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
The combination of Goparaju and Nammouchi further teaches generate a simulation of energy-related behavior of the set of infrastructure assets, and generate a predicted state of the set of infrastructure assets, wherein the simulation of energy-related behavior includes a simulation of carbon emissions of the set of infrastructure assets based on at least one of, at least one historical pattern of the set of infrastructure assets, at least one current state of the set of infrastructure assets, or at least one predicted state of the set of infrastructure assets. (Goparaju, para 27-32, 101-102, claim 6 – Simulation of energy management of equipment and estimate/predict energy consumption and demand/”predicted state” using historical, current and future state of equipment data/”state of the assets”.)
Claim 13
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
Nammouchi further teaches the artificial intelligence system is further configured to, monitor at least one of, an overall energy consumption by at least a portion of the set of infrastructure assets, or a role of at least one infrastructure asset of the set of infrastructure assets in an overall energy consumption by at least a portion of the set of infrastructure assets, and based on the monitoring, perform at least one of, managing an energy consumption by the set of infrastructure assets, forecasting an energy consumption by the set of infrastructure assets, or provisioning resources associated with energy consumption by the set of infrastructure assets. (Nammouchi, pg 1 sec I - Real-time computations about energy production, consumption and storage forecasting from distributed energy resources by conducting optimal energy planning in several time-scales by involving distributed energy resources, controllable loads and demand response. )
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above energy management system, as taught by Goparaju and Nammouchi, and incorporating the above limitations, as taught by Nammouchi.
One of ordinary skill in the art would have been motivated to do this modification in order to minimize cost or maximize profit by incorporating the above limitations, as suggested by Nammouchi (pg 3 Sec III).
Claim 16
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
The combination of Goparaju and Nammouchi further teaches the artificial intelligence system is updated based on at least one of a policy of conserving power consumption or a policy of conserving energy consumption associated with the at least one operating parameter. (Goparaju, para 110 – Policies used in training/updating the cognitive platform/”artificial intelligence system” based on energy saving/”saving power or energy” projects that affects energy consumption and demand.)
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goparaju et al, US Patent Pub US 20150301548 A1 (hereinafter Goparaju) in view of Nammouchi et al, “Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management”, Sept 2021, IEEE International Conference, pp 1-4 (hereinafter Nammouchi) as applied to claims 1-9, 11, 13, 16, 19-20 above, and in view of Retsina, US Patent Pub US 20050143953 A1 (hereinafter Retsina)
Claim 12
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
But the combination of Goparaju and Nammouchi fails to specify providing at least one of a visual indicator or an analytic indicator of energy consumption by the set of infrastructure assets, filtering energy data associated with the set of infrastructure assets, highlighting energy data associated with the set of infrastructure assets, adjusting energy data associated with the set of infrastructure assets, or generating at least one of a visual indicator or an analytic indicator of energy consumption by at least one of, at least one machine of the set of infrastructure assets, at least one factory of the set of infrastructure assets, or at least one vehicle of the set of infrastructure assets.
However Retsina teaches providing at least one of a visual indicator or an analytic indicator of energy consumption by the set of infrastructure assets, filtering energy data associated with the set of infrastructure assets, highlighting energy data associated with the set of infrastructure assets, adjusting energy data associated with the set of infrastructure assets, or generating at least one of a visual indicator or an analytic indicator of energy consumption by at least one of, at least one machine of the set of infrastructure assets, at least one factory of the set of infrastructure assets. (Retsina, para 13, 15-16, Fig. 1, Fig. 2 – Display/”visual indicator” of energy consumption by a facility/”factory of the set of infrastructure assets” that includes adjusting KPI targets/”energy data” for the facility and process equipment/”machine of the set of infrastructure assets”, and highlighting corrections to be made to the facility and equipment.)
Goparaju, Nammouchi, and Retsina are analogous art because they are from the same field of endeavor. They relate to energy management systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above energy management system, as taught by Goparaju and Nammouchi, and incorporating the above limitations, as taught by Retsina.
One of ordinary skill in the art would have been motivated to do this modification in order so that users can effectively manage performance on the basis of current information by incorporating the above limitations, as suggested by Retsina (Abstract).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goparaju et al, US Patent Pub US 20150301548 A1 (hereinafter Goparaju) in view of Nammouchi et al, “Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management”, Sept 2021, IEEE International Conference, pp 1-4 (hereinafter Nammouchi) as applied to claims 1-9, 11, 13, 16, 19-20 above, and in view of Orsini, US Patent Pub US 20180299852 A1 (hereinafter Orsini)
Claim 14
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
But the combination of Goparaju and Nammouchi fails to specify record, in a distributed ledger and/or blockchain, at least one energy-related event associated with the set of infrastructure assets, the at least one energy-related event including at least one of, an energy purchase event, an energy sale event, a service charge associated with an energy purchase event, a service charge associated with an 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.
However Orsini teaches to record, in a distributed ledger and/or blockchain, at least one energy-related event associated with the set of infrastructure assets, the at least one energy-related event (Orsini, para 19 - An autonomous, distributed, control system used for a utility grid network that uses an open-source, cryptographically-secure, decentralized application platform of control that is built on blockchain technology that creates a secure ledger that includes a record of the events or transactions that occur on the network.) including at least one of, an energy purchase event, an energy sale event, a service charge associated with an energy purchase event, a service charge associated with an energy sale event, an energy consumption event, an energy generation event, an energy distribution event (Orsini, para 53 - Data related to a wide range of localized and/or global environmental, social and economic impacts deriving from the production, consumption, transmission, distribution, load curtailment, control and purchase of energy, computation, data transfer, and data storage.), 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. (Orsini, para 39, 79-82 – Data relating to power storage, carbon emissions and credits, renewable energy, environmental/pollution impacts and benefits.)
Goparaju, Nammouchi, and Orsini are analogous art because they are from the same field of endeavor. They relate to energy management systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above energy management system, as taught by Goparaju and Nammouchi, and incorporating the above limitations, as taught by Orsini.
One of ordinary skill in the art would have been motivated to do this modification in order provide energy savings by incorporating the above limitations, as suggested by Orsini (Abstract).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goparaju et al, US Patent Pub US 20150301548 A1 (hereinafter Goparaju) in view of Nammouchi et al, “Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management”, Sept 2021, IEEE International Conference, pp 1-4 (hereinafter Nammouchi) as applied to claims 1-9, 11, 13, 16, 19-20 above, and in view of Ellis et al, US Patent Pub US 20210018205 A1 (hereinafter Ellis)
Claim 15
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
But the combination of Goparaju and Nammouchi fails to specify receive an update based on a prediction delta, and the update includes at least one of, a retraining of the artificial intelligence system based on the prediction delta, an adjusting of a prediction correction applied to predictions of the artificial intelligence system based on the prediction delta, a supplementing of the artificial intelligence system with at least one additional trained machine learning model, or a replacing of at least a portion of the artificial intelligence system with at least one substitute trained machine learning model.
However Ellis teaches receive an update based on a prediction delta, and the update includes at least one of, a retraining of the artificial intelligence system based on the prediction delta (Ellis, para 207-209, 262 - The predictive model should be retrained with new training data based on a multi-step ahead prediction error/”prediction delta”.), a replacing of at least a portion of the artificial intelligence system with at least one substitute trained machine learning model. (Ellis, para 207-209 - replace a current predictive model with the updated predictive model based on a multi-step ahead prediction error/”prediction delta”.)
Goparaju, Nammouchi, and Ellis are analogous art because they are from the same field of endeavor. They relate to energy management systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above energy management system, as taught by Goparaju and Nammouchi, and incorporating the above limitations, as taught by Ellis.
One of ordinary skill in the art would have been motivated to do this modification in order provide cost optimization by incorporating the above limitations, as suggested by Ellis (Abstract).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goparaju et al, US Patent Pub US 20150301548 A1 (hereinafter Goparaju) in view of Nammouchi et al, “Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management”, Sept 2021, IEEE International Conference, pp 1-4 (hereinafter Nammouchi) as applied to claims 1-9, 11, 13, 16, 19-20 above, and in view of Zaouali et al, “Smart Home Resource Management based on Multi-Agent System Modeling Combined with SVM Machine Learning for Prediction and Decision-Making”, 2018, ACHI, pp 1-7 (hereinafter Zaouali)
Claim 17
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
The combination of Goparaju and Nammouchi further teaches a set of adaptive, autonomous data handling systems, wherein the set of adaptive, autonomous data handling systems is configured to collect data relating to at least one of energy generation, energy storage, energy delivery, or energy consumption, wherein the data is collected from the set of edge devices. (Goparaju, para 87 – Energy related data received is interpreted and translated for processing by the processor using data interpretation & translation/”adaptive autonomous data handling system”.)
But the combination of Goparaju and Nammouchi fails to specify a set of intelligent agents configured to, by robotic process automation, autonomously adjust, based on the collected data, a set of operational parameters for operational control of at least a portion of the set of infrastructure assets.
However Zaouali teaches a set of intelligent agents configured to, by robotic process automation, autonomously adjust, based on the collected data, a set of operational parameters for operational control of at least a portion of the set of infrastructure assets. (Zaouali, Sec II-V – A multi-agent system using production automation processes/”robotic process automation”, uses collected data and operational parameters to control equipment/”infrastructure assets”.)
Goparaju, Nammouchi, and Zaouali are analogous art because they are from the same field of endeavor. They relate to energy management systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above energy management system, as taught by Goparaju and Nammouchi, and incorporating the above limitations, as taught by Zaouali.
One of ordinary skill in the art would have been motivated to do this modification in order provide energy savings by incorporating the above limitations, as suggested by Zaouali (Abstract).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goparaju et al, US Patent Pub US 20150301548 A1 (hereinafter Goparaju) in view of Nammouchi et al, “Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management”, Sept 2021, IEEE International Conference, pp 1-4 (hereinafter Nammouchi) as applied to claims 1-9, 11, 13, 16, 19-20 above, and in view of Cruise et al, US Patent Pub US 10938634 B1 (hereinafter Cruise)
Claim 18
The combination of Goparaju and Nammouchi teaches all the limitations of the base claims as outlined above.
But the combination of Goparaju and Nammouchi fails to specify an adaptive energy data pipeline configured to receive collected data from the set of edge devices and communicate the collected data using a network, wherein at last one edge device of the set of edge devices is configured to adjust communication with at least one other edge device of the set of edge devices to adapt a reporting, to the at least one other edge device, of data associated with at least one of energy generation, energy storage, energy delivery, or energy consumption.
However Cruise teaches an adaptive data pipeline configured to receive collected data from the set of edge devices and communicate the collected data using a network, wherein at last one edge device of the set of edge devices is configured to adjust communication with at least one other edge device of the set of edge devices to adapt a reporting, to the at least one other edge device, of data. (Cruise, col 4 lines 15-30, col 23 lines 20-33 – Communicating data using at each edge device local instructions that have been updated from data generated by itself and other edge devices to modify the data/”adapt a reporting” sent between edge devices.)
Goparaju, Nammouchi, and Cruise are analogous art because they are from the same field of endeavor. They relate to edge devices.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above edge device, as taught by Goparaju and Nammouchi, and incorporating the above limitations, as taught by Cruise.
One of ordinary skill in the art would have been motivated to do this modification in order to accurately evaluate queries at an edge device by incorporating the above limitations, as suggested by Cruise (col 1 lines 7-12).
Allowable Subject Matter
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Applicant’s claim defines over the prior art of record because the prior art of record, taken either alone or in combination, does not teach determining a delivery of energy to the set of infrastructure assets based on a probability of a deficiency of available energy at the set of infrastructure assets along with a consequence of the deficiency of available energy at the set of infrastructure assets.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ransom et al, US Patent Pub US20040225648A1 relates to claims regarding an enterprise energy management system that collects real-time, near real-time and historical input data from energy data sources, and creates useful information from that data by filtering and isolating relevant data and performing analytics on the data, displays the data to the user.
Tomlinson et al, US Patent Pub US 20100305889 A1 relates to claims regarding identifying energy consumption associated with at least one appliance, and sensor modules.
Dunne et al, US Patent Pub US 20200272899 A1 relates to claims regarding a system for updating a neural network on an edge device, use the received neural network information to update all or a part of the trained neural network, generate updated neural network information based on the updated neural network, and send the updated neural network information to the edge device.
Conclusion
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/DAVID EARL OGG/
Primary Examiner, Art Unit 2119