DETAILED ACTION
Claim 1-20 (filed 12/08/2023) have been considered in this action. Claims 1-20 are newly filed.
Priority
Domestic benefit priority to PCT/US2022/050932 (filed 11/23/2022) is acknowledged as a Continuation-in-Part (CIP). However, the earliest supportive provisional application from which the instant application draws priority is US63/375,225 filed on 09/10/2022.
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1 contains the acronym AI without expanding the meaning of the acronym in the claim to reveal its intended meaning. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 2, 3, 11 and 15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 1, 17, 15 and 16 (respectively) of copending Application No. 18/337,024 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious variants of one another.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented. At the time of writing, Application No. 18/337,024 has been allowed on the basis of claims filed 11/07/2025, but the corresponding fee and patent number have not been processed. Accordingly, the examiner notes that this rejection may become non-provisional when the reference application receives a US patent number and the fee is paid; in that event, this rejection would not be considered a new grounds of rejection, because the same allowed claims are being compared against the claims of the instant application.
Below is an analysis of the claims to show their corresponding nature:
Instant application 18/534,138
Claim 1
Reference Application 18/337,024
Claim 1
An AI-based platform for enabling intelligent orchestration and management of power and energy, comprising
An artificial-intelligence-based (AI-based) platform for enabling intelligent orchestration and management of power and energy, the AI-based platform comprising:
an artificial intelligence system configured to generate a recommendation of a set of operating parameters for satisfaction of an energy demand of a set of entities within a defined domain
provide a recommendation including at least one operating parameter that satisfies at least one of a mobile entity energy demand or a fixed location energy demand in a defined domain, and automatically update a set of consumption points based on the at least one operating parameter, and
wherein the recommendation is based on a data set of at least one of energy generation, energy storage, energy delivery, or energy consumption information
analyze a data set of energy data, wherein the set of energy data includes at least one of current energy generation information, current energy storage information, current energy delivery information, or current energy consumption information,
and the artificial intelligence system has been trained on a set of outcomes associated with at least one of energy generation, energy storage, energy delivery, or energy consumption
an artificial intelligence system that is trained on a set of energy outcomes wherein, the set of energy outcomes includes at least one of an energy generation outcome, an energy storage outcome, an energy delivery outcome, and/or or an energy consumption outcome
Instant application 18/534,138
Claim 1 + 2
Reference Application 18/337,024
Claim 1
An AI-based platform for enabling intelligent orchestration and management of power and energy, comprising (claim 1)
An artificial-intelligence-based (AI-based) platform for enabling intelligent orchestration and management of power and energy, the AI-based platform comprising:
an artificial intelligence system configured to generate a recommendation of a set of operating parameters for satisfaction of an energy demand of a set of entities within a defined domain (claim 1)
wherein the set of entities includes at least one of, at least one mobile entity within the defined domain, or at least one fixed entity within the defined domain (claim 2)
provide a recommendation including at least one operating parameter that satisfies at least one of a mobile entity energy demand or a fixed location energy demand in a defined domain, and automatically update a set of consumption points based on the at least one operating parameter, and
wherein the recommendation is based on a data set of at least one of energy generation, energy storage, energy delivery, or energy consumption information, (claim 1)
analyze a data set of energy data, wherein the set of energy data includes at least one of current energy generation information, current energy storage information, current energy delivery information, or current energy consumption information,
and the artificial intelligence system has been trained on a set of outcomes associated with at least one of energy generation, energy storage, energy delivery, or energy consumption (claim 1)
an artificial intelligence system that is trained on a set of energy outcomes wherein, the set of energy outcomes includes at least one of an energy generation outcome, an energy storage outcome, an energy delivery outcome, and/or or an energy consumption outcome
Instant application 18/534,138
Claim 3
Reference Application 18/337,024
Claim 17
The AI-based platform of claim 1, wherein 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
The AI-based platform of claim 1, wherein, 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
Instant application 18/534,138
Claim 11
Reference Application 18/337,024
Claim 15
The AI-based platform of claim 1, wherein the artificial intelligence system is further configured to orchestrate a delivery of energy to the set of entities within the defined domain, and the delivery of the energy includes at least one of, at least one fixed transmission line, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy
The AI-based platform of claim 1, wherein ,the artificial intelligence system is configured to orchestrate delivery of energy to at least one point of consumption, and the delivery of the energy includes at least one of, at least one fixed transmission line, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy
Instant application 18/534,138
Claim 15
Reference Application 18/337,024
Claim 16
The AI-based platform of claim 1, wherein the artificial intelligence system is further configured to record, in a distributed ledger and/or blockchain, at least one energy-related event associated with the set of entities within the defined domain 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.
The AI-based platform of claim 1, wherein, the artificial intelligence system is configured to record, in a distributed ledger and/or blockchain, at least one energy-related event, and the at least one energy-related event including includes at least one of, an energy purchase event, and/or an energy sale event, a service charge associated with an energy purchase event, and/or 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception in the form of an abstract idea without significantly more. The claims are directed to the statutory category of invention of a machine.
Step 2A Prong One:
Claim(s) 1-18 are apparatus claims directed to steps that are encompassed by Mental processes capable of being performed in the human mind. The acts of generating a recommendation of operating parameters that satisfies a given demand from claim 1 is directed towards an abstract idea in the form of steps that are capable of being performed mentally in the human mind. The claim is recited in a broad manner, such that the scope of the claimed recommendation is capable of being performed in the human mind. For example, a person could think in their mind that they will require a certain amount of energy demanded throughout a day and recommend a certain energy source for satisfying that demand, such that the parameter specifies said source. Claim 1 recites, in part, the use of artificial intelligence in its preamble and uses artificial intelligence to generate the recommendation on the basis of training data. The generating of a recommendation, as drafted, is a process capable of being performed in the human mind, but for the recitation of generic artificial intelligence that is trained on data. That is, other than reciting “a platform” and “artificial-intelligence system”, nothing in the claim precludes the generating step from practically being performed in the human mind.
Step 2A Prong Two:
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception when considered individually and in combination because the additional elements, which are recited at a high level of generality, provide conventional functions that do not add meaningful limits to practicing the abstract idea.
Claim 1 recites, in part, the use of artificial intelligence in its preamble and uses artificial intelligence to generate the recommendation on the basis of training data. The generating of a recommendation, as drafted, is a process capable of being performed in the human mind, but for the recitation of generic artificial intelligence that is trained on data. That is, other than reciting “a platform” and “artificial-intelligence system”, nothing in the claim precludes the generating step from practically being performed in the human mind. For example, but for “a data set of energy [parameters]” and “trained on a set of outcomes associated with energy [parameters]”, the claim encompasses providing a recommendation mentally. The use of this type of data is without a meaningful limit because it generally links the type of data that any form of energy supply/demand system would require without any particular steps that utilize this data is a meaningful and specific way. In other words, the additional elements that encompass the use of artificial intelligence in the claim are elements that amount to little more than generally linking the use of the judicial exception to a particular technological environment or field of use that fail to place meaningful limits on the claim (see MPEP 2106.05(h): “…The courts often cite to Parker v. Flook as providing a classic example of a field of use limitation. See, e.g., Bilski v. Kappos, 561 U.S. 593, 612, 95 USPQ2d 1001, 1010 (2010) ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable") (citing Parker v. Flook, 437 U.S. 584, 198 USPQ 193 (1978)).”).
The abstract idea described in claim 1 is not meaningfully different than those abstract ideas found by the courts, therefor the claim is considered to be directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. The additional elements of using training data with an artificial intelligence system is the generic linking of artificial intelligence to the subject matter, but fails to recite any particular means for arriving at a solution using artificial intelligence. There is no indication that the combination of elements improves the functioning of a computer or improves another technology. Their collective functions merely provide conventional computer implementations and functions.
In regards to Claim 19, the statutory category of invention is drawn to a method. The claimed limitations of claim 19 are directed towards similar subject matter as that of claim 1, and thus claim 19 is rejected under 35 U.S.C. 101 as being directed towards an abstract idea without significantly more using a similar analysis as applied to claim 1.
Dependent claims 2-18 and 20 are drawn to additional elements that fail to establish meaningful limits on the way in which the parameters are generated by artificial intelligence. These limitations are considered to be drawn to the abstract idea without adding significantly more.
Claims 1-18 and 20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. Claims 1-18 and 20 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8, 10-12, 14, 16, 17 and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mangal et al. (US 20220410750, hereinafter Mangal).
In regards to Claim 1, Mangal discloses “An AI-based platform for enabling intelligent orchestration and management of power and energy, comprising: an artificial intelligence system configured to generate a recommendation of a set of operating parameters for satisfaction of an energy demand of a set of entities within a defined domain” ([0008] In an aspect of present invention, a system for management of electric vehicle charging is provided; [0014] The system further comprises a method to optimize charging profile of the electric vehicle. The method comprising: utilizing, by machine learning model, the telematics data of the electric vehicle to predict the start and end time of charging for the electric vehicle; generating a time array of charging time of electric vehicle with a specified time interval; mapping hourly billing charges with the time array; generating a time profile corresponding to the hourly billing charges and the capacity of the charging station. [0031] The system is also in communication with electric utility grids that provides information on demand response programs and electricity pricing information. [0032] The system receives the above information and processes the information through its machine learning algorithms to generate feasible charging and operational information and present them to the fleet operators and drivers of the electric vehicle. The information is accessed by the fleet operator on the fleet dashboard. The fleet dashboard is a management tool that enables the fleet operator to visualize real time vehicle status, such as status of charge (SOC), remaining driving range, speeds, GPS locations, etc. as well as optimized charging plans and schedules, estimated times for completion of charging, vehicle's driving routes, the arrival time of the vehicle, and the potential energy consumptions and predicted driving ranges are predicted using the developed machine learning methods such as deep learning, neural network, decision tree, random forest, multiple regression, support vector machine and clustering/classifications algorithms; wherein a fleet is a defined domain, or the entirety of the components that provide data and are controlled ar ethe defined domain [0107] The system of the present invention manages, monitors, schedules and controls the energy and power flow into the electric vehicles to satisfy the objectives of the fleet operator. In an embodiment of the present invention, the objective of the present invention is to minimize the bill cost associated with charging while satisfying the energy need for fleet operation. This is achieved via a combination of minimization of demand charges and optimization around the Time-Of-Use (TOU) pricing considering the previous and future charging performances in the billing cycle. The system takes into consideration different parameters associated with electric vehicles, energy resources, and grid distribution to create strict constraints, including the predicted energy consumption of the next working period for electric vehicles, the predicted arrival and departure time of electric vehicles, the energy required for electric vehicles, real-time battery state of charge of electric vehicles, power capacity and usage restrictions from the energy resources, bill information and charges levied for electricity at the different time period from the grid, the peak power in the current billing cycle so far, etc.) “wherein the recommendation is based on a data set of at least one of energy generation, energy storage, energy delivery, or energy consumption information” ([0036] Electric vehicle and charging station 106 are connected to network 104. The charging station 106 sends and receives data associated with the charging of electric vehicle, the battery capacity of the electric vehicle, the power capacity of the charging station, the current energy stored in the electric vehicle, the rate of charging of the charging station and the electric vehicle, the price of electricity received from a power grid, identity of the owner and/or operator of electric vehicle and/or any other data relevant to charging or discharging electric vehicle over the network. The charging station 106 also communicates information, including current, voltage, frequency of the electric vehicle's charging power. The charging station 106 communicates to the server 102 on a continuous streaming basis. The system 100 utilizes high speed smart metering in each charging station to provide the charging power data stream to the server. [0044] FIG. 2 is a flow chart diagram showing a method for management of charging of electric vehicles in accordance with an embodiment of the present invention. In the first step 202, the historical and real-time data from the fleet telematics and the charging stations are received. In step 204, the EV's energy consumption prediction method then determines how much energy is needed by each vehicle and by what time) “and the artificial intelligence system has been trained on a set of outcomes associated with at least one of energy generation, energy storage, energy delivery, or energy consumption” ([0102] The dataset for training the machine learning model includes the telematics data and power meter data from the charging station. The telematics data of the vehicle comprises the time of the day, odometer, distance traveled, battery state of charge, charge cycles, GPS data. The power meter data from the charging station comprises three power phase data on total kilowatt-hour, voltage on different phases, current at different phases, power factor for different phases, total watts, frequency, reverse kilowatt-hour on different phases, total net watts, and net watts on different phases. [0104] For generating the prediction model from the dataset of the vehicle, the training set and the test set are in the ratio of 80 to 20 or 70 to 30. The dataset can also be split into training, cross-validation, and test data. The dataset can be split into 60% of training data, 20% for cross-validation, and 20% for testing.).
In regards to Claim 2, Mangal further teaches “The AI-based platform of claim 1, wherein the set of entities includes at least one of, at least one mobile entity within the defined domain, or at least one fixed entity within the defined domain” ([0009] The plurality of data source comprises charging stations, battery energy storage systems, renewable energy source, such as solar photovoltaic, fleet dashboard, traffic data, meteorological data, fleet telematics, power capacity information from electric grid and mobile application. The plurality of energy assets comprises EV charging stations, renewable energy source and battery energy storage systems; wherein a vehicle is a mobile entity, while a charging station or solar are fixed entities).
In regards to Claim 3, Mangal further discloses “The AI-based platform of claim 1, wherein 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” ([0126] In another embodiment of the present invention, the object of the present invention is maximization of utilization of renewable energy, whereby the objective is to maximize the use of local solar energy generated through the solar panels, instead of having that sent back to the electric grid. [0127] The system controls the switch of the energy output of the local solar panel. It can be sent to the electric grid, or to the stationary batteries for future usage, or to the vehicle battery for charging directly. [0128] The system takes solar energy as one of the energy resources to charge the vehicles....When there are vehicles waiting to be charged, the system considers the available energy resources, and uses the solar energy first to charge the vehicle to reduce the energy bill. When there is no vehicle to be charged, the solar energy can be stored in the stationary batteries system and be delivered to the charging station later when the vehicles are ready to be charged).
In regards to Claim 4, Mangal further teaches “The AI-based platform of claim 1, wherein the recommendation is based on at least one of, an energy generation specification associated with the set of entities, an energy transportation specification associated with the set of entities, an energy storage specification associated with the set of entities, an energy transformation specification associated with the set of entities, an energy delivery specification associated with the set of entities, or an energy consumption specification associated with the set of entities” ([0036] Electric vehicle and charging station 106 are connected to network 104. The charging station 106 sends and receives data associated with the charging of electric vehicle, the battery capacity of the electric vehicle, the power capacity of the charging station, the current energy stored in the electric vehicle, the rate of charging of the charging station and the electric vehicle, the price of electricity received from a power grid, identity of the owner and/or operator of electric vehicle and/or any other data relevant to charging or discharging electric vehicle over the network. The charging station 106 also communicates information, including current, voltage, frequency of the electric vehicle's charging power. The charging station 106 communicates to the server 102 on a continuous streaming basis. The system 100 utilizes high speed smart metering in each charging station to provide the charging power data stream to the server).
In regards to Claim 5, Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is further configured to determine at least one modification of the recommendation, and 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 entities within the defined domain, or at least one modification of the at least one additional historical, current, and/or forecast energy demand parameter associated with the set of entities within the defined domain” ([0047] FIG. 4 shows a mobile application interface 400 displaying information to a driver of the vehicle in accordance with an embodiment of present invention. The mobile application 400 is installed on the mobile device of the driver of the electric vehicle. The application provides real time information on the vehicle status, including SOC, remaining miles, driving score which is related to driver behaviors and driving patterns, etc. The app ensures an adequate driving range for a given day's driving needs. The application directs drivers to precise EV charger location to optimize infrastructure usage and minimize electric bill. The application updates electric vehicle information in real time by retrieving information from vehicle's telematics system and displays available chargers by retrieving information from EV charging network; wherein an update is a modification. [0110] The system generates the charging profile for both continuous and discrete controlled energy resources. The system predicts the energy needed for each charging session of each electric vehicle. The system collects current battery SOC from vehicle's telematic data and performs historical time series data to obtained the energy consumed hourly in the future. The system also extracts information on full capacity of the electric vehicle. The details on the charging time, i.e. starting and ending time of vehicle charging is determined. The system computes the battery SOC till the start of charging session and the energy consumed after the charging session. According to the SOC range, the optimal SOC at the end of charging session is calculated. The starting SOC and ending SOC is compared to obtain the energy needed in this charging session. The result of prediction is then calculated for the EV with details such as starting time, ending time, predicted energy needed; wherein a continuous generated charging profile is one which is modified).
In regards to Claim 6, Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is associated with at least one physical machine, the at least one physical machine is associated with the set of entities within the defined domain, and the artificial intelligence system is configured to manage at least one process associated with the at least one physical machine” ([0033] FIG. 1 illustrates system architecture for providing smart charging management of electric vehicles in a fleet in accordance with the embodiment of the present invention; wherein electric vehicles are physical assets).
In regards to Claim 8, Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is further configured to 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 entities within the defined domain” ([0032] The system receives the above information and processes the information through its machine learning algorithms to generate feasible charging and operational information and present them to the fleet operators and drivers of the electric vehicle. The information is accessed by the fleet operator on the fleet dashboard. The fleet dashboard is a management tool that enables the fleet operator to visualize real time vehicle status, such as status of charge (SOC), remaining driving range, speeds, GPS locations, etc. as well as optimized charging plans and schedules, estimated times for completion of charging, vehicle's driving routes, the arrival time of the vehicle, and the potential energy consumptions and predicted driving ranges are predicted using the developed machine learning methods such as deep learning, neural network, decision tree, random forest, multiple regression, support vector machine and clustering/classifications algorithms. The fleet managers and drivers can run the prediction based on different weather, traffic and route conditions and monitor the results through the dashboard. The system of the present invention manages, monitors, schedules and controls the energy and power flow into the electric vehicles to satisfy the objectives of the fleet operator) “and the at least one entity parameter includes at least one of, a current energy status of the at least one entity, a future energy status of the at least one entity, a current energy consumption by the at least one entity, a future energy consumption by the at least one entity, a current activity performed by the at least one entity that is associated with energy consumption, or a future activity performed by the at least one entity that is associated with energy consumption” ([0042] The server 102 is in communication with energy generation system and battery energy storage systems and electric utility grid. The energy renewable generation system 122, such as the solar panels, provides energy production data from on-side generation which includes the amount of power being generated historically and in real time. The battery energy storage system communicates to the server about the state of the battery energy storage system and the information comprises total capacity of the battery in kilowatt-hour (kWh), real SOC of the battery, historical charging and discharging profiles of the battery, etc.).
In regards to Claim 10, Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is further configured to determine a delivery of energy to the set of entities within the defined domain based on a probability of a deficiency of available energy at the set of entities within the defined domain” ([0045] In the next step 206, the server utilizes artificial intelligence enabled optimization to schedule the power charging in combination with the power flows to any of the energy assets to achieve the maximized utilization of renewable sources of energy and to minimize the cost of electricity. After the optimization and power flow sequence is generated by the server, in the next step, the server sends the appropriate control signals to each energy asset. The energy asset comprises EV charging stations, solar panel, or stationary batteries; wherein the maximizing of renewable energy or minimizing of cost is a policy).
In regards to Claim 11, Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is further configured to orchestrate a delivery of energy to the set of entities within the defined domain, and the delivery of the energy includes at least one of, at least one fixed transmission line, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy” ([0035] Electric vehicle connects to charging station 106 via an electrical outlet or other electricity transfer mechanism. The electricity may flow from charging station into electric vehicle to charge electric vehicle; [0035] Electric vehicle connects to charging station 106 via an electrical outlet or other electricity transfer mechanism. The electricity may flow from charging station into electric vehicle to charge electric vehicle; [0108] The system achieves the objective by modulation of the continuous or discrete electric power that is fed into and out of the different distributed energy resources on the sites, including the electric vehicles, electric chargers, stationary batteries, and solar panels, etc. The information such as charging required for the electric vehicle, consumption of power and battery state of charge can be collected from a fleet telematics system. The system then predicts the charging power capacity of the chargers, the starting and ending time of charging for the electric vehicle along with the predicted energy needed for charging the electric vehicle. The system first converts the available charging time of electric vehicles into a time array with a specific time interval… Once the energy needed of electric vehicles and the power capacity of energy resources have been identified, the system determines the time array and the cost associated with the historical power distribution and the current time period in the time array where the charges are minimum. [0110] The system computes the battery SOC till the start of charging session and the energy consumed after the charging session. According to the SOC range, the optimal SOC at the end of charging session is calculated. The starting SOC and ending SOC is compared to obtain the energy needed in this charging session).
In regards to Claim 12, Mangal teaches “The AI-based platform of claim 1, wherein 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 entities within the defined domain, or a role of at least one infrastructure asset of the set of entities within the defined domain in an overall energy consumption by at least a portion of the set of entities within the defined domain,” ([0029] The present invention proposes a method that uses artificial intelligence (AI) based machine learning (ML) algorithms in a server to predict energy usage and optimize the charging schedule. The server is connected to a network that receives historical and live data from multiple sources. The system provides artificial intelligence based smart charging management of electric vehicles in a fleet. The data sources from where the historical and live data are received comprises charging stations, fleet telematics, meteorological services, traffic management, mobile application, fleet dashboard, renewable source of energy, battery energy storage system, electric utility grid, etc. The data received from the charging station comprises three phase energy information on real-time charging power, current and voltage for each phase. It also provides the total energy that has been charged for the specific charger up to now. The telematics data includes every second or every minute information of the vehicle as it is being driven or parked or being charged. The information comprises energy being consumed or recovered or idled or charged; the instantaneous power consumed to drive the vehicle, the instantaneous power fed from the regenerating brakes to the battery in the vehicle, the instantaneous power received from the charger; acceleration/deceleration, the speed of the vehicle, the frequency of braking, odometer, GPS information including latitude, longitude, and altitude; the state of charge of the battery in the vehicle, battery voltage and current, battery temperature; weight of the vehicle, and other variables that are related with the vehicle; wherein energy being consumed or recovered or idled or charged is an overall energy consumption by a vehicle) “and based on the monitoring, perform at least one of, managing an energy consumption by the set of entities within the defined domain, forecasting an energy consumption by the set of entities within the defined domain, or provisioning resources associated with energy consumption by the set of entities within the defined domain” ([0013] The system performs energy consumption prediction of the electric vehicle to forecast the amount of energy the electric vehicle consumes based on the real-time and historical telematics data. [0101] In an embodiment, the present invention provides a system perform the energy consumption prediction of the EVs to forecast how much energy the electric vehicle will consume based on the real-time and historical telematics data. The system utilizes machine learning module that will use input from the vehicle database and output the expected energy consumption of the vehicle. It can provide the continuous energy consumption forecast of each EV in a fleet for up to 24 hours).
In regards to Claim 14, Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is further configured to perform at least one of, providing at least one of a visual indicator or an analytic indicator of energy consumption by the set of entities within the defined domain, filtering energy data associated with the set of entities within the defined domain, highlighting energy data associated with the set of entities within the defined domain, adjusting energy data associated with the set of entities within the defined domain, 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 entities within the defined domain, at least one factory of the set of entities within the defined domain, or at least one vehicle of the set of entities within the defined domain” ([0046] FIG. 3 shows a fleet dashboard 300 to display the fleet information to the fleet manager in accordance with an embodiment of the present invention. The fleet dashboard 300 is integrated with the AL/ML system in the current invention. The cloud-based system enables management and control of charging stations. The dashboard 300 is a management tool provided to the fleet operator and it enables the operator to visualize vehicle's real-time status and the charging status of the chargers. On the vehicle information, the prediction of vehicle SOC, predicted trips/routes and charging operations, etc. are provided based on the results obtained from the AI/ML algorithms. [0047] FIG. 4 shows a mobile application interface 400 displaying information to a driver of the vehicle in accordance with an embodiment of present invention. The mobile application 400 is installed on the mobile device of the driver of the electric vehicle. The application provides real time information on the vehicle status, including SOC, remaining miles, driving score which is related to driver behaviors and driving patterns, etc. The app ensures an adequate driving range for a given day's driving needs. The application directs drivers to precise EV charger location to optimize infrastructure usage and minimize electric bill. The application updates electric vehicle information in real time by retrieving information from vehicle's telematics system and displays available chargers by retrieving information from EV charging network).
In regards to Claim 16, Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is further configured to 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 replacing of at least a portion of the artificial intelligence system with at least one substitute trained machine learning model” ([0091] The machine learning algorithm for the regression and classification involves using machine learning to increase the accuracy, decrease the error and hence improve the overall efficiency. The deep learning algorithm employs deep learning models to perform the classification and regression to further improve the accuracy, reduce the error and catch more details of the relationships between the prediction targets and features. [0105] Each model is evaluated using walk-forward validation and cross validation. A matrix is generated to summarize the validation results of all models. It contains the evaluation categories including mean absolute error (average magnitude of errors, regardless of direction), root mean squared error (square root of average, squared differences), mean absolute percentage error, and R2 score. The model with the best scores in most of the evaluation categories will be chosen for that EV's prediction model; wherein an error of a model is a prediction delta).
In regards to Claim 17, Mangal teaches “The AI-based platform of claim 1, wherein 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 at least one operating parameter” ([0043] The electric utility communicates grid status through Demand Response (DR) program. It offers monetary incentive to help ease stress on the grid and prevent outages. The current invention contains a Demand Response Automation Server (DRAS) that accepts demand response events from the utility and the AI/ML system will increase or reduce vehicle charging power depending on the demand response event received).
In regards to Claim 19, Mangal teaches “A method of enabling intelligent orchestration and management of power and energy via an AI-enabled platform, the method comprising: generating, by an artificial intelligence system, a data set of at least one of energy generation information, energy storage information, energy delivery information, or energy consumption information wherein the data set is associated with a set of entities within a defined domain” ([0008] In an aspect of present invention, a system for management of electric vehicle charging is provided; [0014] The system further comprises a method to optimize charging profile of the electric vehicle. The method comprising: utilizing, by machine learning model, the telematics data of the electric vehicle to predict the start and end time of charging for the electric vehicle; generating a time array of charging time of electric vehicle with a specified time interval; mapping hourly billing charges with the time array; generating a time profile corresponding to the hourly billing charges and the capacity of the charging station. [0031] The system is also in communication with electric utility grids that provides information on demand response programs and electricity pricing information. [0032] The system receives the above information and processes the information through its machine learning algorithms to generate feasible charging and operational information and present them to the fleet operators and drivers of the electric vehicle. The information is accessed by the fleet operator on the fleet dashboard. The fleet dashboard is a management tool that enables the fleet operator to visualize real time vehicle status, such as status of charge (SOC), remaining driving range, speeds, GPS locations, etc. as well as optimized charging plans and schedules, estimated times for completion of charging, vehicle's driving routes, the arrival time of the vehicle, and the potential energy consumptions and predicted driving ranges are predicted using the developed machine learning methods such as deep learning, neural network, decision tree, random forest, multiple regression, support vector machine and clustering/classifications algorithms; wherein a fleet is a defined domain, or the entirety of the components that provide data and are controlled ar ethe defined domain [0107] The system of the present invention manages, monitors, schedules and controls the energy and power flow into the electric vehicles to satisfy the objectives of the fleet operator. In an embodiment of the present invention, the objective of the present invention is to minimize the bill cost associated with charging while satisfying the energy need for fleet operation. This is achieved via a combination of minimization of demand charges and optimization around the Time-Of-Use (TOU) pricing considering the previous and future charging performances in the billing cycle. The system takes into consideration different parameters associated with electric vehicles, energy resources, and grid distribution to create strict constraints, including the predicted energy consumption of the next working period for electric vehicles, the predicted arrival and departure time of electric vehicles, the energy required for electric vehicles, real-time battery state of charge of electric vehicles, power capacity and usage restrictions from the energy resources, bill information and charges levied for electricity at the different time period from the grid, the peak power in the current billing cycle so far, etc.) “and the artificial intelligence system has been trained on a set of outcomes associated with at least one of energy generation, energy storage, energy delivery, or energy consumption” ([0102] The dataset for training the machine learning model includes the telematics data and power meter data from the charging station. The telematics data of the vehicle comprises the time of the day, odometer, distance traveled, battery state of charge, charge cycles, GPS data. The power meter data from the charging station comprises three power phase data on total kilowatt-hour, voltage on different phases, current at different phases, power factor for different phases, total watts, frequency, reverse kilowatt-hour on different phases, total net watts, and net watts on different phases) “and generating, by the artificial intelligence system, a recommendation of a set of operating parameters for satisfaction of an energy demand of the set of entities within the defined domain, wherein the recommendation is based on the data set” ([0044] FIG. 2 is a flow chart diagram showing a method for management of charging of electric vehicles in accordance with an embodiment of the present invention. In the first step 202, the historical and real-time data from the fleet telematics and the charging stations are received. In step 204, the EV's energy consumption prediction method then determines how much energy is needed by each vehicle and by what time)
In regards to Claim 20, Mangal further teaches “The method of claim 19, wherein the AI-enabled platform is further configured to determine at least one modification of the recommendation, and 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 entities within the defined domain, or at least one modification of the at least one additional historical, current, and/or forecast energy demand parameter associated with the set of entities within the defined domain” ([0047] FIG. 4 shows a mobile application interface 400 displaying information to a driver of the vehicle in accordance with an embodiment of present invention. The mobile application 400 is installed on the mobile device of the driver of the electric vehicle. The application provides real time information on the vehicle status, including SOC, remaining miles, driving score which is related to driver behaviors and driving patterns, etc. The app ensures an adequate driving range for a given day's driving needs. The application directs drivers to precise EV charger location to optimize infrastructure usage and minimize electric bill. The application updates electric vehicle information in real time by retrieving information from vehicle's telematics system and displays available chargers by retrieving information from EV charging network; wherein an update is a modification. [0110] The system generates the charging profile for both continuous and discrete controlled energy resources. The system predicts the energy needed for each charging session of each electric vehicle. The system collects current battery SOC from vehicle's telematic data and performs historical time series data to obtained the energy consumed hourly in the future. The system also extracts information on full capacity of the electric vehicle. The details on the charging time, i.e. starting and ending time of vehicle charging is determined. The system computes the battery SOC till the start of charging session and the energy consumed after the charging session. According to the SOC range, the optimal SOC at the end of charging session is calculated. The starting SOC and ending SOC is compared to obtain the energy needed in this charging session. The result of prediction is then calculated for the EV with details such as starting time, ending time, predicted energy needed; wherein a continuous generated charging profile is one which is modified).
Claim Rejections - 35 USC § 103
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 nonobviousness.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Mangal as applied to claim 1 above, and further in view of Sanders et al. (US 20170005515, hereinafter Sanders).
In regards to Claim 7, Mangal teaches the AI-based platform as incorporated by claim 1 above.
Mangal fails to teach “determine a delivery of energy to the set of entities within the defined domain 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, a future resource expenditure associated with acquiring, storing, and/or delivering the energy by at least one of the two or more energy sources, a current demand by other energy consumers for the energy of at least one of the two or more energy sources, or a future demand by other energy consumers for the energy of at least one of the two or more energy sources”.
Sanders teaches “determine a delivery of energy to the set of entities within the defined domain 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, a future resource expenditure associated with acquiring, storing, and/or delivering the energy by at least one of the two or more energy sources, a current demand by other energy consumers for the energy of at least one of the two or more energy sources, or a future demand by other energy consumers for the energy of at least one of the two or more energy sources” (Fig. 6A shows multiple DER-ES (distributive energy resource energy storage) [0023] a method for selling energy back to a utility power grid, comprises steps for providing one or more hybrid inverter/converters; providing one or more data processing gateways; providing one or more charge controllers; providing one or more intelligent battery management systems; providing one or more energy management devices in a compact footprint; defining price points of power obtained from a utility power grid at which a user will discharge energy stored in an energy storage module; defining a percentage of maximum capacity of stored energy in one or more energy storage modules that may be discharged in a single cycle; correlating said price points of power with said percentage of maximum capacity; configuring said price points and said percentage of maximum capacity into one or more sets of rules; calculating the amount of available energy storage capacity based upon the current or expected price of power; and implementing the one or more set of rules; wherein the comparison of stored energy to maximum capacity for determining when to sell is a comparison;[0384] In an embodiment as shown in FIGS. 6A-6C, a solar integrated energy management system (SI-EMS) 06000 executes one or more of the computer implemented methods for monitoring energy described herein including computer-usable readable storage medium having computer-readable program code embodied therein for causing a computer system to perform methods for one or more programs of one or more networked distributed energy resource energy storage apparatus 06001, as shown in FIG. 6A, that each function as a renewable energy site integration system accessed via a virtual energy pool 06002, comprising hardware and software components and one or more data repositories, databases and dashboard indicators 06005, FIGS. 6A and 6C, including one or more processors associated with one or more networked distributed energy resource energy storage (DER-ES) apparatus 06001, each having a common enclosure housing at least one power storage supply device coupled to at least one isolation breaker and integrated with one or more renewable energy generation sources, one or more inverters, one or more charge controllers, one or more energy storage appliances, and one or more gateway controllers 06004).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system that performs recommendations of energy parameters that satisfy a demand for vehicles battery charging, with the use of the plurality of energy storage devices from vehicles that can sell their stored energy back to the supplier when a certain amounts of stored energy in the battery is there so that when the price is favorable and the stored energy meets the threshold, it is sold, because it would gain the obvious benefit of increasing an economic factor (i.e. buy low sell high) when charging/discharging a battery at a charging station. Furthermore, both references can be considered to be in the similar field of use of energy management systems for distributed energy systems, thus obviating their combination. By combining these elements, it can be considered taking the known methods of Sanders in which a plurality of vehicles have their stored vs. maximum charge compared and when the price is favorable, selling the stored charge back to the grid, and incorporated these features into the charge management system of Mangal in a known way that achieves predictable results.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Mangal as applied to claim 1 above, and further in view of Wu et al. (US 20200266631, hereinafter Wu).
In regards to Claim 9, Mangal teaches the AI-based platform as incorporated by claim 1 above.
Mangal fails to teach “determine a delivery of energy to the set of entities within the defined domain based on a probability of a deficiency of available energy at the set of entities within the defined domain, and a consequence of the deficiency of available energy at the set of entities within the defined domain”.
Wu teaches “determine a delivery of energy to the set of entities within the defined domain based on a probability of a deficiency of available energy at the set of entities within the defined domain” ([0020] (1-2-6) A reserve constraint of the power system, which is denoted by a formula of:[formula ]where, {tilde over (w)}.sup.t.sub.j denotes an actual active power of renewable energy power station j at dispatch interval t; w.sup.t.sub.j denotes a scheduled active power of renewable energy power station j at dispatch interval t; R.sup.+ and R.sup.− denote additional reserve demand representing the power system from the dispatch center; ϵ.sub.r.sup.+ denotes a risk of insufficient upward reserve in the power system; ϵ.sub.r.sup.− denotes a risk of insufficient downward reserve in the power system; and Pr(.Math.) denotes a probability of occurrence of insufficient upward reserve and a probability of occurrence of insufficient downward reserve. The probability of occurrence of insufficient upward reserve and the probability of occurrence of insufficient downward reserve may be obtained from the dispatch center) “and a consequence of the deficiency of available energy at the set of entities within the defined domain” ([0036] The result of the optimization is the optimal dispatch decision of the on-off and active power of the conventional thermal power unit and the active power of the renewable energy power station such as wind power/photovoltaic, under the control of operational risk and reduced operating costs. The advantage of the method of the present disclosure, is that the Newton method is used to transform the chance constraints containing the risk level and the random variables into the deterministic mixed integer linear constraints, which effectively improves the efficiency of solving the model. Meanwhile, the model with chance constraints and with adjustable risk level eliminates the conservative nature of the conventional robust unit commitment, to provide a more reasonable dispatch basis for decision makers; wherein the probability of insufficient power is used to determine constraints for when dispatching of energy sources are triggered, with those constraints of the operation being a consequence of deficiency).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the energy management system of Mangal, to utilize the probability risk of deficiency calculations of Wu that determine a probability that the energy reserves will be insufficient to meet the demands, and which optimizes the power delivery to alleviate the risk because it would gain the stated benefit of Wu, namely “[0036] The result of the optimization is the optimal dispatch decision of the on-off and active power of the conventional thermal power unit and the active power of the renewable energy power station such as wind power/photovoltaic, under the control of operational risk and reduced operating costs”. By combining these elements, it can be considered taking the known energy management system of Mangal, and incorporating the features of using upper and lower reserve power probabilities to determine risks and optimize the power distribution in accordance with those risks in a known way that achieves predictable results.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Mangal as applied to claim 1 above, and further in view of Kumar et al. (US 20210276447, hereinafter Kumar).
In regards to Claim 13, Mangal teaches the AI-based platform as incorporated by claim 1 above.
Mangal fails to teach “generate a simulation of energy-related behavior of the set of entities within the defined domain, and generate a predicted state of the set of entities within the defined domain, wherein the simulation of energy-related behavior includes a simulation of carbon emissions of the set of entities within the defined domain based on at least one of, at least one historical pattern of the set of entities within the defined domain, at least one current state of the set of entities within the defined domain, or at least one predicted state of the set of entities within the defined domain”.
Kumar teaches “generate a simulation of energy-related behavior of the set of entities within the defined domain” ([0026] The optimal planning module 18 then takes this data as input and runs a series of defined pre-processing method steps or processes at statistical pre-processing module 18a to aid in solving the optimization problem via stochastic optimization solver 18b. Outputs from the optimization solver 18c specify the resources to be deployed and their usage as a function of time. These resources become inputs to a discrete-event simulator 18c. The simulator 18c uses the resources suggested by the optimizer 18b, together with traffic statistics, to calculate a variety of statistics about waiting times, charging services, and departure SoCs. Traffic statistics are used by a traffic simulator to generate thousands of individual traffic events. The word “events” is used here in the technical sense of probabilistic events. These many simulated events are used to test the adequacy of the optimization solver's recommendations) “and generate a predicted state of the set of entities within the defined domain” ([0034] Exemplary outputs 20e from the discrete event simulator 18c may include EV service data such as EV ID, EV model, arrival state of charge, arrival time, waiting time, charger ID, plug in time, charging start time, charging end time, charging interval, interchange time, fulfillment statistics, plug-out time, actual departure state of charge, instance of impatience, etc. [0035] Further output from the discrete event simulator 18c may include charger usage characteristics 20f, customer demand fulfillment data 20g, queuing data 20h, operation visualization 20i and financial analysis 20j (see FIG. 4C)) “wherein the simulation of energy-related behavior includes a simulation of carbon emissions of the set of entities within the defined domain based on at least one of, at least one historical pattern of the set of entities within the defined domain, at least one current state of the set of entities within the defined domain, or at least one predicted state of the set of entities within the defined domain” ([0037] Further inputs 16 may comprise facility preferences including load management strategies, market participation (vehicle to grid), grid availability, emissions, charger operation hours, queue management strategies, algorithm preferences, and the like. Load management strategies include the operational flexibility available at the facility to shift/curtail electric loads, for example, pre-cooling, shutting off non-critical loads. Market participation covers the ability to use EV batteries, on-site storage and generation sources to supply energy/capacity to energy markets and thereby generate a revenue stream. Grid availability refers to the full/partial/non-availability of the grid, for example, off-grid facilities in remote locations. This could also include power outage scenarios/situations. Emissions covers regulatory requirements and facility owners' preferences with respect to controlling emissions from using non-renewable energy sources. [0065] The other limits could be on the amount of CO2 that can be emitted from the DERs on an annual basis. Different regulatory bodies will have different quantities on these limits. The user 12 may then key in these limits when modeling. This can be framed mathematically as, Emissions=Σ.sub.DERs,t∈TEmission.sub.marginal.sub.DERs*Capacity.sub.DERs Emissions≤Emission.sub.limit Eq. 7 where Emission.sub.limit is the limit entered by the user in the input. [0115] All the data that is liable to be forecasted at some point or another during the controlling process, becomes historical data 94. This data, including the historical data, is stored in the database section of the historical data. The data is used to improve forecasting in both the long term as well as the short term. With more and more historical data, the machine learning algorithms can improve on the accuracy of their prediction.).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the energy management system that manages vehicle charging of Mangal with the use of a simulator that simulates CO2 emissions and makes decisions of optimal charging for vehicles on the basis of the simulated CO2 emissions as taught by Kumar, because it would gain the stated benefit of Kumar, namely optimized control of charging ([0010]). By combining these elements, it can be considered taking the known simulator that simulates emissions for making decisions of vehicle charging, and incorporating these features into the charging system of Mangal in a known way that achieves predictable results.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Mangal as applied to claim 1 above, and further in view of Bathen et al. (US 20220255330, hereinafter Bathen).
In regards to Claim 15, Mangal teaches the AI-based platform as incorporated by claim 1 above.
Mangal fails to teach “record, in a distributed ledger and/or blockchain, at least one energy-related event associated with the set of entities within the defined domain 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”.
Bathen teaches “record, in a distributed ledger and/or blockchain, at least one energy-related event associated with the set of entities within the defined domain the at least one energy-related event” ([0030] the method, system, and/or computer program product can utilize a blockchain that operates arbitrary, programmable logic, tailored to a decentralized storage scheme and referred to as “smart contracts” or “chaincodes.” In some cases, specialized chaincodes may exist for management functions and parameters which are referred to as system chaincode (such as managing energy transfer provenance and exchanges in a blockchain network). In some embodiments, the method, system, and/or computer program product can further utilize smart contracts that are trusted distributed applications which leverage tamper-proof properties of the blockchain database and an underlying agreement between nodes, which is referred to as an endorsement or endorsement policy) “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” ([0065] In some embodiments, upon the application/circuitry 208 identifying that the policy 204 is met (e.g., power threshold), the charge request is sent to the blockchain network 212 (e.g., decentralized exchange). For instance, the charge request could include a user identification, GPS coordinates, the computing device 206 charge (e.g., 5%), and an ask for power (e.g. 0.15 kW/h at 1 W. In some embodiments, simultaneously or nearly simultaneously, the provider devices 214A-C (e.g., charging station, another computing device, a power bank, etc.) via the blockchain network 212 or by IoT integration may also receive the charge request. [0066] Each of the provider devices 214A-C may then respond to the charge request by providing respective charging proposals to the blockchain network 212 that provides them to the computing device 206. The user 202 may select the charging proposal they most like. In other instances, the computing device 206 may automatically select the best charging proposal, e.g., one that matches the exact conditions on the charge request or one that is most withing an acceptance threshold. [0088] For example, Bob has a solar powered battery at his home, and this battery is integrated with circuitry that allows it to be smart enough to know where it can charge his phone/power bank and sign-off on the amount of energy delivered to the phone. With that signature, Bob can attest to the fact that he is sourcing his energy from a green source. This is the minimum requirement for the disclosed ecosystem. Furthering the example, when Alice puts her request for energy, Bob will bid with the claim that he is sourcing his energy from a green source. Alice's wallet may rank bids based on fee (cheapest is better), or service provider ratings, or location/ETA to a meeting point. Once Alice has agreed, Alice's wallet will initiate the transaction with Bob as a payee as well as a small transaction fee being sent to the miner, e.g., whoever is able to add the transaction to a block first).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the energy management system of Mangal, with the use of the blockchain-based database that stores energy events related to carbon emissions or sales of energy as taught by Bathen because it would gain the stated benefits of Bathen, namely that “[0017] there exists a need for secure transactions for renewable generation across the globe. Being able to accurately and securely process transactions for renewables is of paramount importance as the world transitions into a sustainable energy future”. By combining these elements, it can be considered taking the known blockchain-based database used for managing charging requests and implementing those features in the energy management system of Mangal in a known way that achieves predictable results.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Mangal as applied to claim 1 above, and further in view of Kalkunte et al. (US 20230039386, hereinafter Kalkunte).
In regards to Claim 18, Mangal teaches the AI-based platform as incorporated by claim 1 above.
Mangal further teaches “The AI-based platform of claim 1, further comprising an adaptive energy data pipeline configured to, receive collected data from a set of edge devices that are in operational control of at least a portion of the set of entities within the defined domain” ([0008] monitor and determine the plurality of energy assets are performing as per the control signals; modify the control signals if the plurality of energy assets are not performing as per the control signals; [0037] Another source to which server is connected through the network is vehicle telematics 110. The vehicle telematics 110 provides information about the electric vehicle as it is being driven around, or when it is parked, or when it is being charged. The communication between the server 102 and EV telematics 110 is a continuous data stream and the data stream includes information such as, the energy being consumed, the instantaneous power consumed to drive the vehicle, the instantaneous power fed from the regenerating brakes to the battery in the vehicle, acceleration/deceleration, the SOC of the battery 112 in the vehicle, the speed of the vehicle, the frequency of braking and other variables, etc.; wherein the vehicle telematics are a form of edge device) “and communicate the collected data using a network” ([0008] a server receives information from a plurality of data sources connected through a network; the server is configured to: consider fleet's charging energy and scheduling requirements by utilizing an artificial intelligence based machine learning model; perform optimization and generates a power flow sequence; send control signals to each of a plurality of energy assets).
Mangal fails to teach “wherein at least 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 the set of entities within the defined domain”.
Kalkunte teaches “wherein at least 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 the set of entities within the defined domain” ([0016] Certain embodiments of the disclosure may be found in a communication system and a method for controlling cooperation between edge devices arranged in a vehicle for high-performance communication in mobility applications. The communication system and the method of the present disclosure ensure seamless connectivity as well as Quality of Experience (QoE). The communication system and the method of the present disclosure significantly improve performance in terms of data throughput and signal-to-noise ratio (SNR) of one or more UEs present in a vehicle by effectively controlling cooperation between two edge devices arranged in the vehicle. [0039] the central cloud server 102 may be configured to access beamforming coefficients from elements of the one or more signal processing chains to train the machine learning model 214 and use such learnings to configure, and control, and adjust beam patterns to and from each of the plurality of edge devices 104 (i.e., the two edge devices of each vehicle as well as the plurality of RSU devices 114). In a sixth example, as the central cloud server 102 has information associated with elements of one or more cascaded receiver chains and one or more cascaded transmitter chains of each edge device, the central cloud server 102 may configure dynamic partitioning of a plurality of antenna elements of an antenna array into a plurality of spatially separated antenna sub-arrays to generate multiple beams in different directions to establish independent communication channels with the one or more UEs 106 at the same time or in a different time slot. In a seventh example, since the central cloud server 102 has information associated with elements of one or more cascaded receiver chains and one or more cascaded transmitter chains of each edge device, the central cloud server 102 may be further configured to accurately determine a transmit (Tx) beam information, a receive (Rx) beam information, a Physical Cell Identity (PCID), and an absolute radio-frequency channel number (ARFCN), and a signal strength information associated with each of Tx beam and the Rx beam of the plurality of edge devices 104 for the plurality of different WCNs 110. In an eighth example, since the central cloud server 102 has information associated with elements of one or more cascaded receiver chains and one or more cascaded transmitter chains of each edge device, the central cloud server 102 may configure and instruct an edge device (e.g., mounted at each vehicle) for a suitable adjustment of a power back-off to minimize (i.e., substantially reduce) the impact of interference (echo or noise signals) and hence only use as much power as needed to achieve low error communication with one or more base stations in the uplink or the one or more UEs 106 in the downlink communication, wherein the cascade is for edge device communication configuration of antenna that passes data amongst edge devices).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system with edge devices that communicate data through a network as taught by Mangal, with the use of modifying a communication parameter between edge devices so that data reliability improves as taught by Kalkunte. By combining these elements, it can be considered taking the known means of adjusting the beam forming antennas that provide communication between edge devices so that cascaded data from chained edge devices is more reliable, and incorporating these features into the vehicle charging management system of Mangal in a known way that achieves predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jayan et al. (US 20220344934) – teaches an improved energy demand forecasting system using machine learning and artificial intelligence
Sedano et al. (WO 2022147299) – teaches an AI based microgrid management system that orchestrates energy on the basis of energy prices
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