DETAILED ACTION
Claims 1-23 (filed 06/04/2026) have been considered in this action. Claims 1, 3, 5-17, 19 and 20 have been amended. Claims 21-23 are newly filed. Claims 2 and 4 have been presented in the same format as previously presented.
Response to Arguments
Applicant’s arguments, see page 14 paragraph 1, filed 06/04/2026, with respect to objection to claim 1 have been fully considered and are persuasive. The objection of claim 1 has been withdrawn.
Applicant’s arguments, see page 14 paragraph 2, filed 06/04/2026, with respect to provisional double patenting rejection of claims 1-3, 11 and 15 have been fully considered and are persuasive. The provisional double patenting rejection of claims 1-3, 11 and 15 has been withdrawn.
Applicant’s arguments, see page 14 paragraph 3, filed 06/04/2026, with respect to rejection of claims 1-20 under 35 U.S.C. 101 for being directed towards an abstract idea without significantly more have been fully considered and are persuasive. The rejection of claims 1-20 under 35 U.S.C. 101 has been withdrawn.
Applicant’s arguments, see page 15 paragraph 2, filed 06/04/2026, with respect to the rejection(s) of claim(s) 1-6, 8, 10-12, 14, 16-17 and 19-20 under 35 U.S.C. 102 in view of Mangal and claims 7, 9, 13, 15 and 18 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of additional prior art found regarding the amended features. See below for a mapping of these features to the newly provided prior art.
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 1-18, 22 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed towards a machine that under the BRI can be considered software per se.
Claim 1 is directed towards “An artificial intelligence (AI-based) platform… the AI-based platform comprising: an artificial intelligence system configured to…”. Based upon this language, claim 1 is directed towards a form of machine/device. However, under the broadest reasonable interpretation, an “artificial intelligence platform” which comprises “an artificial intelligence system configured to…” encompasses software per se. PHOSITA would understand that the invoking of the term “platform” includes the use of a software-only platform. Without the recitation of a processor, computer, etc. for performing the actions of the artificial intelligence platform, the claims can be considered to be directed towards non-eligible subject matter in the form of software per se. Accordingly, claims 1-18, 22 and 23 are rejected under 35 U.S.C. 101 for being directed towards software per se.
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(s) 1, 2, 4-6, 8, 10-12, 14, 16, 17, 19, 20 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mangal et al. (US 20220410750, hereinafter Mangal) in view of Seki et al. (US 20170161849, hereinafter Seki).
In regards to Claim 1, Mangal teaches “An artificial intelligence (AI-based) platform for enabling intelligent orchestration and management of power and energy, the AI-based platform comprising: an artificial intelligence system configured to” ([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) “determine a second resource demand associated with transporting the item, wherein the second resource demand is associated with the set of resources of the set of entities” (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 are the defined domain [0040] The server is connected to an application (an app) 118 installed on the driver's mobile device. The application 118 communicates information about location of the electric vehicle through GPS and other preferences provided by the driver including constraints on delivery schedule or routing, in the case of EV for pickup and drop; [0088] In a fleet operation, vehicles often have routine trips and routes, for example, transit bus and delivery trucks use the similar routes on their trips. Prediction of the departure time, arriving time and range of the trips are crucial to estimate the energy needed for the electric vehicle to complete these trips. Subsequently, the estimated energy needed can be used for optimizing the electric vehicle charging schedules; wherein a delivery could include delivering/transporting the item) “wherein the recommendation is based on a data set of at least one of energy generation, energy storage, energy delivery, or energy consumption information associated with the first resource demand and the second resource demand” ([0031] One other information that may affect EV charging optimization is energy production data from on-side renewable sources of energy, such as the solar panels, and the battery energy storage system that provides information on capacity of battery, state of charge of the battery, charging and discharging profile of the battery. 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. [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 based on the recommendation, …, and adjust the second resource demand” ([0045] the server utilizes the artificial intelligence-enabled optimization solver to adjust the charging power 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. The server monitors the charging stations every minute to determine if the charging stations are performing as per the control signals sent by the server. In case, if the system detects any happened or potential abnormal phenomenon from the real-time data of the energy assets and the vehicles, the system records the error information, analyzes the possible reasons, takes proper adjustments, and informs the fleet operator with the error notification. The server, therefore, measures the outcome and uses artificial intelligence and machine learning to automatically modify the power flow as needed) “wherein 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” ([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. [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; wherein the actual usage of power data are outcomes).
Mangal fails to teach “determine a first resource demand associated with producing an item, wherein the first resource demand is associated with a set of resources of a set of entities within a defined domain; generate a recommendation of a set of operating parameters for satisfaction of the first resource demand…; wherein the recommendation is based on a data set of at least one of energy generation, energy storage, energy delivery, or energy consumption information associated with the first resource demand…; and based on the recommendation, produce the item at a first location, adjust the first resource demand;”.
Seki teaches “determine a first resource demand associated with producing an item, wherein the first resource demand is associated with a set of resources of a set of entities within a defined domain” ([0022] The energy composition information in the industry demand-response control system of the third aspect of the present invention may include information that represents the electrical energy amount and the non-electrical energy amount set for each quantity of product produced by the production facility. The gain-loss determiner may calculate the non-electrical energy to become surplus, based on information representing the electrical energy amount and the non-electrical energy amount set for each quantity of product included in the energy composition information, and set and/or provisionally set the energy composition of each energy used by the production facility, and the energy supplying facility converts to the electrical energy and supplies the calculated non-electrical energy amount. [0046] The energy conversion facility 210 may have an energy storage facility that stores energy and transmits out energy to the production facility 230 and the power receiving and distribution facility 220 when required. When energy is stored in the energy conversion facility 210, although the energy can be stored in its original form, it also can be stored after converting it to various forms of energy, such as dynamic, mechanical, chemical, thermodynamic, or electrical forms. [0047] The power receiving and distribution facility 220 distributes and supplies electrical energy purchased from the electricity provider 3 to the facilities installed in the production plant 2. FIG. 1 shows the case in which the power receiving and distribution facility 220 supplies electrical energy to the production facility 230. The power receiving and distribution facility 220 supplies to the production facility 230 electrical energy in which electrical energy converted by the energy conversion facility 210 has been added to electrical energy supplied from the electricity provider 3; wherein the energy amount for each product is the demanded energy, and the defined domain is the electricity provider and the energy conversion facility) “generate a recommendation of a set of operating parameters for satisfaction of the first resource demand…” ([0055] If the determination is to accept the demand-response request, that is, if it is possible with the set energy composition to satisfy both the demand-response realization and profitability, the gain-loss determiner 110 outputs to the iDR controller 130 an execute instruction representing execution of the demand-response. The gain-loss determiner 110 outputs to the iDR controller 130 setting values for each of the facilities to achieve running (operation) states in response to the demand-response, that is, setting values at the time of the energy composition when the determination was made to accept the demand-response request, along with the execution instruction. [0065] Based on setting values for the running (operation) state in response to the demand-response for the various facilities input from the gain-loss determiner 110 together with the execute command, the iDR controller 130 outputs control information to control the facilities installed in the production plant 2, that is each of the energy conversion facility 210, the power receiving and distribution facility 220, and the production facility 230 to the energy composition of a running (operation) state in response to the demand-response) “wherein the recommendation is based on a data set of at least one of energy generation, energy storage, energy delivery, or energy consumption information associated with the first resource demand…;” (0053] The gain-loss determiner 110 receives a DR request signal transmitted from the server device of the electricity provider 3. The gain-loss determiner 110, based on a table that is stored in the production energy information storage 120 and summarizes the energy composition used in each of the production facilities (hereinafter, “energy portfolio”), determines whether or not to accept (approve) the demand-response request from the electricity provider 3. The energy portfolio is established, giving consideration to the constraints on quality, cost, delivery, safety and the like, with a reserve, that is, so that minor external disturbances or minor unexpected events do not lead to a shortage. [0057] The production energy information storage 120 is storage that stores the energy portfolio with respect to each of the production facilities in the production plant 2. The production energy information storage 120 is, for example, constituted by a database device. The energy portfolio is data that summarizes, as required for each production facility operation state and brand of produced product, information indicating the types of electrical energy and non-electrical energy consumed, and the breakdown of the fixed part and variable part of each) “and based on the recommendation, produce the item at a first location, adjust the first resource demand” ([0048] The production facility 230 is run (operated) by the non-electrical energy sent from the energy conversion facility 210 and the electrical energy supplied from the power receiving and distribution facility 220 and produces (manufactures) products. That is, in the production plant 2, the production facility 230 corresponds to the production site and is the ultimate consumer that consumes each of the energies. [0065] Based on setting values for the running (operation) state in response to the demand-response for the various facilities input from the gain-loss determiner 110 together with the execute command, the iDR controller 130 outputs control information to control the facilities installed in the production plant 2, that is each of the energy conversion facility 210, the power receiving and distribution facility 220, and the production facility 230 to the energy composition of a running (operation) state in response to the demand-response. [0073] The power receiving and distribution facility 220 supplies to the production facility 230 electrical energy that is the combination of the electrical energy purchased from the electricity provider 3 and the electrical energy generated by the energy conversion facility 210. The example of the production plant 2 producing the product A in the normal state shown in FIG. 3A is the case of the power receiving and distribution facility 220 receiving electrical energy of 50 MWpurchased from the electricity provider 3. This shows the case in which the power receiving and distribution facility 220 supplies the combination of the purchased 50 MWof electrical energy and the 5 MWof electrical energy generated by the energy conversion facility 210, that is, 55 MWof electrical energy to the production facility 230. [0075] Upon receiving a request to suppress 10 MWof electrical energy by a DR request signal transmitted to it from the server device of the electricity provider 3, the gain-loss determiner 110 of the industry demand-response control system 1 sets the energy composition for production of the product A by the production facility 230, based on the energy portfolio stored in the production energy information storage 120. More specifically, the gain-loss determiner 110 assumes as a production amount P an arbitrary value of the number of the reduction in the amount of produced products due to running (operating) in accordance with a demand-response and calculates each of the energies that will become surplus by calculating the energy used in producing the production amount P that is assumed at this stage. The gain-loss determiner 110 sets the energy composition of each of the facilities, based on the energy that will become surplus).
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 artificial intelligence system that makes recommendations for satisfying a second resource demand for transporting an item on the basis of energy data and historical energy data in a demand-response as taught by Mangal, with the use of making recommendations for production processes that can utilize different sources of energy as taught by Seki, because both inventions deal with the control of power on the basis of a demand-response sent from an energy provider, and thus by incorporating the features of Seki into Mangal it can be considered to gain the stated benefit of Seki, namely to keep product at levels of product demand while saving costs on energy ([0009], [0010]). Furthermore, the invention does not contain a linking concept that would pre-suppose the combination of two disparate references as the “recommendation” is a set for satisfying the two demands, which means one recommendation in the set can be for satisfying the first demand, while a second recommendation can be for satisfying the second demand. When taken together, Mangal and Seki teach power management related to transportation and production respectively, and thus their combination could be considered as viable in instances where a power company is sending energy to both electric vehicle transportation management systems that handle demand response, and production facilities that handle demand response, as from the point of view of the power company, both entities needs management. By combining these elements, it can be considered taking the known use of making recommendations for a manufacturer with production management that keeps a production level at an adequate level while curtailing energy usage from the grid to locally produced energy during demand-response as taught by Seki, and using them to improve the energy management system for managing a plurality of electric vehicles whose charging and discharging are controlled to satisfy required schedules including deliveries using recommendations that are determined using artificial intelligence in a known way that achieves predictable results.
In regards to Claim 2, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. 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 4, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. 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, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is 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, 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, 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, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. 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, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is 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, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is configured to adjust a delivery of energy to the set of entities within the defined domain based on at least one of, an energy deliver policy, or an energy consumption policy” ([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, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is 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, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. Mangal further teaches s “The AI-based platform of claim 1, wherein the artificial intelligence system is 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, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. Mangal further teaches “The AI-based platform of claim 1, wherein the artificial intelligence system is 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, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. 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, the combination of Mangal and Seki teaches the artificial intelligence platform as incorporated by claim 1 above. Mangal further 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 artificial-intelligence-enabled (AI- enabled) platform, the method comprising:” ([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) “determining, by the artificial intelligence system, a second resource demand associated with transporting the item, wherein the second resource demand is associated with the set of resources of the set of entities” (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 are the defined domain [0040] The server is connected to an application (an app) 118 installed on the driver's mobile device. The application 118 communicates information about location of the electric vehicle through GPS and other preferences provided by the driver including constraints on delivery schedule or routing, in the case of EV for pickup and drop; [0088] In a fleet operation, vehicles often have routine trips and routes, for example, transit bus and delivery trucks use the similar routes on their trips. Prediction of the departure time, arriving time and range of the trips are crucial to estimate the energy needed for the electric vehicle to complete these trips. Subsequently, the estimated energy needed can be used for optimizing the electric vehicle charging schedules; wherein a delivery could include delivering/transporting the item) “generating, by the 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.) “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” ([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. [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; wherein the actual usage of power data are outcomes) “generating, by the artificial intelligence system, a recommendation of a set of operating parameters for satisfaction of … and the second resource demand wherein the recommendation is based on the data set;” ([0031] One other information that may affect EV charging optimization is energy production data from on-side renewable sources of energy, such as the solar panels, and the battery energy storage system that provides information on capacity of battery, state of charge of the battery, charging and discharging profile of the battery. 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. [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 based on the recommendation, …, and adjusting the second resource demand” ([0045] the server utilizes the artificial intelligence-enabled optimization solver to adjust the charging power 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. The server monitors the charging stations every minute to determine if the charging stations are performing as per the control signals sent by the server. In case, if the system detects any happened or potential abnormal phenomenon from the real-time data of the energy assets and the vehicles, the system records the error information, analyzes the possible reasons, takes proper adjustments, and informs the fleet operator with the error notification. The server, therefore, measures the outcome and uses artificial intelligence and machine learning to automatically modify the power flow as needed).
Mangal fails to teach “determining, by an artificial intelligence system, a first resource demand associated with producing an item, wherein the first resource demand is associated with a set of resources of a set of entities within a defined domain; generating by the artificial intelligence system, a recommendation of a set of operating parameters for satisfaction of the first resource demand… wherein the recommendation is based on the data set; and based on the recommendation, producing the item at a first location, adjusting the first resource demand;”.
Seki teaches “determining, by an artificial intelligence system, a first resource demand associated with producing an item, wherein the first resource demand is associated with a set of resources of a set of entities within a defined domain;” ([0022] The energy composition information in the industry demand-response control system of the third aspect of the present invention may include information that represents the electrical energy amount and the non-electrical energy amount set for each quantity of product produced by the production facility. The gain-loss determiner may calculate the non-electrical energy to become surplus, based on information representing the electrical energy amount and the non-electrical energy amount set for each quantity of product included in the energy composition information, and set and/or provisionally set the energy composition of each energy used by the production facility, and the energy supplying facility converts to the electrical energy and supplies the calculated non-electrical energy amount. [0046] The energy conversion facility 210 may have an energy storage facility that stores energy and transmits out energy to the production facility 230 and the power receiving and distribution facility 220 when required. When energy is stored in the energy conversion facility 210, although the energy can be stored in its original form, it also can be stored after converting it to various forms of energy, such as dynamic, mechanical, chemical, thermodynamic, or electrical forms. [0047] The power receiving and distribution facility 220 distributes and supplies electrical energy purchased from the electricity provider 3 to the facilities installed in the production plant 2. FIG. 1 shows the case in which the power receiving and distribution facility 220 supplies electrical energy to the production facility 230. The power receiving and distribution facility 220 supplies to the production facility 230 electrical energy in which electrical energy converted by the energy conversion facility 210 has been added to electrical energy supplied from the electricity provider 3; wherein the energy amount for each product is the demanded energy, and the defined domain is the electricity provider and the energy conversion facility) “generating by the artificial intelligence system, a recommendation of a set of operating parameters for satisfaction of the first resource demand… wherein the recommendation is based on the data set;” ([0055] If the determination is to accept the demand-response request, that is, if it is possible with the set energy composition to satisfy both the demand-response realization and profitability, the gain-loss determiner 110 outputs to the iDR controller 130 an execute instruction representing execution of the demand-response. The gain-loss determiner 110 outputs to the iDR controller 130 setting values for each of the facilities to achieve running (operation) states in response to the demand-response, that is, setting values at the time of the energy composition when the determination was made to accept the demand-response request, along with the execution instruction. [0065] Based on setting values for the running (operation) state in response to the demand-response for the various facilities input from the gain-loss determiner 110 together with the execute command, the iDR controller 130 outputs control information to control the facilities installed in the production plant 2, that is each of the energy conversion facility 210, the power receiving and distribution facility 220, and the production facility 230 to the energy composition of a running (operation) state in response to the demand-response [0053] The gain-loss determiner 110 receives a DR request signal transmitted from the server device of the electricity provider 3. The gain-loss determiner 110, based on a table that is stored in the production energy information storage 120 and summarizes the energy composition used in each of the production facilities (hereinafter, “energy portfolio”), determines whether or not to accept (approve) the demand-response request from the electricity provider 3. The energy portfolio is established, giving consideration to the constraints on quality, cost, delivery, safety and the like, with a reserve, that is, so that minor external disturbances or minor unexpected events do not lead to a shortage. [0057] The production energy information storage 120 is storage that stores the energy portfolio with respect to each of the production facilities in the production plant 2. The production energy information storage 120 is, for example, constituted by a database device. The energy portfolio is data that summarizes, as required for each production facility operation state and brand of produced product, information indicating the types of electrical energy and non-electrical energy consumed, and the breakdown of the fixed part and variable part of each) “and based on the recommendation, producing the item at a first location, adjusting the first resource demand” ([0048] The production facility 230 is run (operated) by the non-electrical energy sent from the energy conversion facility 210 and the electrical energy supplied from the power receiving and distribution facility 220 and produces (manufactures) products. That is, in the production plant 2, the production facility 230 corresponds to the production site and is the ultimate consumer that consumes each of the energies. [0065] Based on setting values for the running (operation) state in response to the demand-response for the various facilities input from the gain-loss determiner 110 together with the execute command, the iDR controller 130 outputs control information to control the facilities installed in the production plant 2, that is each of the energy conversion facility 210, the power receiving and distribution facility 220, and the production facility 230 to the energy composition of a running (operation) state in response to the demand-response. [0073] The power receiving and distribution facility 220 supplies to the production facility 230 electrical energy that is the combination of the electrical energy purchased from the electricity provider 3 and the electrical energy generated by the energy conversion facility 210. The example of the production plant 2 producing the product A in the normal state shown in FIG. 3A is the case of the power receiving and distribution facility 220 receiving electrical energy of 50 MWpurchased from the electricity provider 3. This shows the case in which the power receiving and distribution facility 220 supplies the combination of the purchased 50 MWof electrical energy and the 5 MWof electrical energy generated by the energy conversion facility 210, that is, 55 MWof electrical energy to the production facility 230. [0075] Upon receiving a request to suppress 10 MWof electrical energy by a DR request signal transmitted to it from the server device of the electricity provider 3, the gain-loss determiner 110 of the industry demand-response control system 1 sets the energy composition for production of the product A by the production facility 230, based on the energy portfolio stored in the production energy information storage 120. More specifically, the gain-loss determiner 110 assumes as a production amount P an arbitrary value of the number of the reduction in the amount of produced products due to running (operating) in accordance with a demand-response and calculates each of the energies that will become surplus by calculating the energy used in producing the production amount P that is assumed at this stage. The gain-loss determiner 110 sets the energy composition of each of the facilities, based on the energy that will become surplus).
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 artificial intelligence system that makes recommendations for satisfying a second resource demand for transporting an item on the basis of energy data and historical energy data in a demand-response as taught by Mangal, with the use of making recommendations for production processes that can utilize different sources of energy as taught by Seki, because both inventions deal with the control of power on the basis of a demand-response sent from an energy provider, and thus by incorporating the features of Seki into Mangal it can be considered to gain the stated benefit of Seki, namely to keep product at levels of product demand while saving costs on energy ([0009], [0010]). Furthermore, the invention does not contain a linking concept that would pre-suppose the combination of two disparate references as the “recommendation” is a set for satisfying the two demands, which means one recommendation in the set can be for satisfying the first demand, while a second recommendation can be for satisfying the second demand. When taken together, Mangal and Seki teach power management related to transportation and production respectively, and thus their combination could be considered as viable in instances where a power company is sending energy to both electric vehicle transportation management systems that handle demand response, and production facilities that handle demand response, as from the point of view of the power company, both entities needs management. By combining these elements, it can be considered taking the known use of making recommendations for a manufacturer with production management that keeps a production level at an adequate level while curtailing energy usage from the grid to locally produced energy during demand-response as taught by Seki, and using them to improve the energy management system for managing a plurality of electric vehicles whose charging and discharging are controlled to satisfy required schedules including deliveries using recommendations that are determined using artificial intelligence in a known way that achieves predictable results.
In regards to Claim 20, the combination of Mangal and Seki teaches the method as incorporated by claim 1 above. Mangal further teaches “The method of claim 19, wherein the AI-enabled platform is 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, 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, 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 23, the combination of Mangal and Seki teaches the method as incorporated by claim 1 above. Mangal further teaches “The AI-based platform of claim 1, wherein the item is a set of crops” ([0040] The application 118 communicates information about location of the electric vehicle through GPS and other preferences provided by the driver including constraints on delivery schedule or routing, in the case of EV for pickup and drop, to be able to serve the duty cycle needs of the feet operation; [0088] In a fleet operation, vehicles often have routine trips and routes, for example, transit bus and delivery trucks use the similar routes on their trips. Prediction of the departure time, arriving time and range of the trips are crucial to estimate the energy needed for the electric vehicle to complete these trips. Subsequently, the estimated energy needed can be used for optimizing the electric vehicle charging schedules [0089] The driving trip prediction uses the last three days' trip records of the vehicle combined with an artificial recurrent neural network (RNN) called Long short-term memory (LSTM) to predict today's potential driving routes; wherein the delivered product can be anything, including crops as this is considered written matter that has no practical affect on the scope of the invention).
Claims 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Mangal and Seki as applied to claim 1 above, and further in view of Sanders et al. (US 20170005515, hereinafter Sanders).
In regards to Claim 3, the combination of Mangal and Seki teaches the AI-based platform as incorporated by claim 1 above.
The combination of Mangal and Seki fails to teach “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”.
Sanders teaches “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” ([0155] In another configuration, an uninterruptible power supply (UPS) application may be provided by one or more SIS appliances and DER-ES apparatus. In the event of a loss of power, the SIS/DER-ES units each automatically isolates itself from the grid, and then delivers its own power to the site without any interruption in service or loss in power quality. The appliances and apparatus can be wired to directly support priority loads, thereby providing energy reliability for critical services. The functions of the SIS/DER-ES units in a UPS application are as follows. A specified amount of energy is held in reserve to be dispatched in case of a loss of grid power. Upon loss of grid power, the SIS and DER-ES units disconnects from the grid in accordance to at least a performance to comply with UL 1741 and IEEE 1547 anti-islanding, and critical loads are immediately powered by a combination of PV or other renewable energy generation sources and battery or other energy storage devices. During a daytime event, both user site generation sources and user site renewable energy storage devices are used to power loads. During a night-time event, just renewable energy storage devices are used to power loads. During an outage, based upon current demand, current available renewable energy from local generation sources, and the amount of energy available in the renewable energy storage devices of the one or more SIS/DER-ES units, a consumer is given an estimate of how much backup power is available at a given time. Once grid power resumes, the SIS/DER-ES units reconnects and resumes scheduled operations).
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 artificial intelligence management of energy consumption and delivery on the basis of scheduling so that certain power flows are met, with the use of a system that is able to disconnect from the grid to operate in an off-grid fashion so that stored energy can be used as taught by Sanders, because it would gain the obvious benefit of being able to operate the power flows when no grid energy is available while still maintaining a capacity reserve. By combining these elements, it can be considered taking the known use of off-grid controls for utilizing stored energy to maintain a level of service as taught by Saunders, and incorporating it into the artificial intelligence energy management system of Mangal in a known way that achieves predictable results.
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 and Seki as applied to claim 1 above, and further in view of Wu et al. (US 20200266631, hereinafter Wu).
In regards to Claim 9, Mangal and Seki teaches the AI-based platform as incorporated by claim 1 above.
Mangal and Seki 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 and Seki as applied to claim 1 above, and further in view of Kumar et al. (US 20210276447, hereinafter Kumar).
In regards to Claim 13, Mangal and Seki 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 and Seki as applied to claim 1 above, and further in view of Bathen et al. (US 20220255330, hereinafter Bathen).
In regards to Claim 15, Mangal and Seki 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 and Seki as applied to claim 1 above, and further in view of Kalkunte et al. (US 20230039386, hereinafter Kalkunte).
In regards to Claim 18, Mangal and Seki 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 and Seki 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.
Claim(s) 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Mangal and Seki as applied to claims 1 and 19 above, and further in view of Bain et al. (US 20250007284, hereinafter Bain).
In regards to Claim 21, the combination of Mangal and Seki teaches the method as incorporated by claim 19 above.
The combination of Mangal and Seki fail to teach “The method of claim 19, wherein, the artificial intelligence system is configured to, based on the recommendation, automatically orchestrate a delivery of energy with a mix of energy types, the mix of energy types includes renewable energy and natural gas, the recommendation includes the mix of energy types, and the mix of energy types is optimized to satisfy the first resource demand based on the first location”.
Bain teaches “The method of claim 19, wherein, the artificial intelligence system is configured to, based on the recommendation, automatically orchestrate a delivery of energy with a mix of energy types, the mix of energy types includes renewable energy and natural gas” ([0060] In embodiments, the type of energy is selected from the list consisting of natural gas, renewable, nuclear, coal, electricity, and oil. [0062] In embodiments, the raw energy source is selected from the list of raw energy sources consisting of solar, wind, hydro, fossil, nuclear, and gravity. [0081] In embodiments, the user interface is configured to provide a visual element that details a recommendation for a consumer action that is configured to result in consumption of energy during availability of the preferred mix of energy sources. In embodiments, the user interface includes a recommendation engine that operates on at least one of historical, real-time, and forward market information regarding at least one of the raw energy-use production sources and the energy pricing information for at least one of the raw energy-use production sources) “the recommendation includes the mix of energy types, and the mix of energy types is optimized to satisfy the first resource demand based on the first location” ([0006] A platform for an automated consumer retail utility marketplace is provided. The platform may include a data repository for handling data from a plurality of data sources that characterize the sources of production of energy for an energy grid during a given period of time and the wholesale energy prices charged by the sources of production; a demand management engine for managing demand by consumers for energy; a machine learning engine for automating at least one component of the platform; and at least one interface by which a consumer is provided visibility in real time to the price of energy and the mix of energy production sources at a given time. [0015] The user interface includes a first visual element representing a mix of raw energy being used to provide energy available through a consumer energy distribution network in a current time interval and a second visual element representing a forecast of the mix of raw energy for a future time interval. The user interface includes a third visual element that is dependent on differences between the forecast mix and the use mix in the current time interval. Consumer interaction with any of the first, second, and third visual element facilitates allocation of at least one raw energy in the mix of raw energy for a future time interval to produce energy for the consumer. [0263] In embodiments, the methods and systems of the present disclosure include a platform for managing Electronic Vehicles (EV). The platform includes a machine learning engine that automatically gathers and analyzes information about driving habits and patterns and based on the analysis makes recommendations for at least one of when to charge and the EV; when to consume energy for the home from the EV battery; and when to deliver energy to the grid from the EV. [0264] In embodiments, the methods and systems of the present disclosure include a platform for data management, insights, and analytics, associated with energy provided over a consumer energy distribution network. The platform includes a repository for storing and retrieving information and a machine learning engine for analyzing datasets and producing recommendations about correlations and insights within a predetermined dataset and between the datasets. [0266] In embodiments, the platform includes a recommendation engine that is configured to automatically identify activities that one of reduce cost and increase consumption of energy from renewable energy sources).
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 recommends an energy distribution for satisfying an energy demand for making a set of products with the demanded energy as taught by Seki, with the use of an energy mix recommendation that mixes different energy sources, including renewable and natural gas resources, so that the recommendation has a mix that satisfies the required demand as taught by Bain, because it would address the problems noted by Bain, namely “[0003] As consumers become increasingly interested in the sources of energy or other utilities (e.g., preferring renewable energy to fossil fuels) and continue to need control of their cost of consuming energy and other utilities, a need exists for methods and systems that provides greater visibility to consumers, that provides increased control for consumers with respect to their consumption of energy and other utilities, and that provides mechanisms for optimizing visibility and control to provide a higher value experience in the acquisition and consumption of energy and other utilities.”. In other words, by taking the features of Bain which allow a mix of energy types between renewables and gas to be recommended and selected by a user, it allows a user to optimize their energy usage in accordance with the type of energy source they desire. By combining these elements, it can be considered taking the known features of Bain, and incorporating them into the methods of Mangal and Seki in a known way that achieves predictable results.
In regards to Claim 22, the combination of Mangal and Seki teaches the system as incorporated by claim 1 above.
The combination of Mangal and Seki fail to teach “The AI-based platform of claim 1, wherein, the artificial intelligence system is configured to, based on the recommendation, automatically orchestrate a delivery of energy with a mix of energy types, the mix of energy types includes renewable energy and natural gas, the recommendation includes the mix of energy types, and the mix of energy types is optimized to satisfy the first resource demand based on the first location”.
Bain teaches “The AI-based platform of claim 1, wherein, the artificial intelligence system is configured to, based on the recommendation, automatically orchestrate a delivery of energy with a mix of energy types, the mix of energy types includes renewable energy and natural gas” ([0060] In embodiments, the type of energy is selected from the list consisting of natural gas, renewable, nuclear, coal, electricity, and oil. [0062] In embodiments, the raw energy source is selected from the list of raw energy sources consisting of solar, wind, hydro, fossil, nuclear, and gravity. [0081] In embodiments, the user interface is configured to provide a visual element that details a recommendation for a consumer action that is configured to result in consumption of energy during availability of the preferred mix of energy sources. In embodiments, the user interface includes a recommendation engine that operates on at least one of historical, real-time, and forward market information regarding at least one of the raw energy-use production sources and the energy pricing information for at least one of the raw energy-use production sources) “the recommendation includes the mix of energy types, and the mix of energy types is optimized to satisfy the first resource demand based on the first location” ([0006] A platform for an automated consumer retail utility marketplace is provided. The platform may include a data repository for handling data from a plurality of data sources that characterize the sources of production of energy for an energy grid during a given period of time and the wholesale energy prices charged by the sources of production; a demand management engine for managing demand by consumers for energy; a machine learning engine for automating at least one component of the platform; and at least one interface by which a consumer is provided visibility in real time to the price of energy and the mix of energy production sources at a given time. [0015] The user interface includes a first visual element representing a mix of raw energy being used to provide energy available through a consumer energy distribution network in a current time interval and a second visual element representing a forecast of the mix of raw energy for a future time interval. The user interface includes a third visual element that is dependent on differences between the forecast mix and the use mix in the current time interval. Consumer interaction with any of the first, second, and third visual element facilitates allocation of at least one raw energy in the mix of raw energy for a future time interval to produce energy for the consumer. [0263] In embodiments, the methods and systems of the present disclosure include a platform for managing Electronic Vehicles (EV). The platform includes a machine learning engine that automatically gathers and analyzes information about driving habits and patterns and based on the analysis makes recommendations for at least one of when to charge and the EV; when to consume energy for the home from the EV battery; and when to deliver energy to the grid from the EV. [0264] In embodiments, the methods and systems of the present disclosure include a platform for data management, insights, and analytics, associated with energy provided over a consumer energy distribution network. The platform includes a repository for storing and retrieving information and a machine learning engine for analyzing datasets and producing recommendations about correlations and insights within a predetermined dataset and between the datasets. [0266] In embodiments, the platform includes a recommendation engine that is configured to automatically identify activities that one of reduce cost and increase consumption of energy from renewable energy sources).
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 recommends an energy distribution for satisfying an energy demand for making a set of products with the demanded energy as taught by Seki, with the use of an energy mix recommendation that mixes different energy sources, including renewable and natural gas resources, so that the recommendation has a mix that satisfies the required demand as taught by Bain, because it would address the problems noted by Bain, namely “[0003] As consumers become increasingly interested in the sources of energy or other utilities (e.g., preferring renewable energy to fossil fuels) and continue to need control of their cost of consuming energy and other utilities, a need exists for methods and systems that provides greater visibility to consumers, that provides increased control for consumers with respect to their consumption of energy and other utilities, and that provides mechanisms for optimizing visibility and control to provide a higher value experience in the acquisition and consumption of energy and other utilities.”. In other words, by taking the features of Bain which allow a mix of energy types between renewables and gas to be recommended and selected by a user, it allows a user to optimize their energy usage in accordance with the type of energy source they desire. By combining these elements, it can be considered taking the known features of Bain, and incorporating them into the methods of Mangal and Seki in a known way that achieves predictable results.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN M SKRZYCKI whose telephone number is (571)272-0933. The examiner can normally be reached M-Th 7:30-3:30.
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/JONATHAN MICHAEL SKRZYCKI/ Examiner, Art Unit 2116
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