DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the applicant’s communication filed on 3/21/2024
Claims 1-10 are pending
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhattarai et al. USPGPUB 2018/0226800 A1 (hereinafter Bhattarai) in view of Bedogni et al. (An Integrated Simulation Framework to Model Electric Vehicle Operations and Services, 2016) (hereinafter Bedogni).
Regarding claim 1, Bhattarai teaches a support system for supporting management of a regional energy system (Par. [0003], “systems, methods, and apparatuses for energy management … using power storage to optimize supply of power to a load by variable power sources.”; Par. [0013], “Embodiments of the energy management system and the techniques for using the same may be integrated into existing utility systems (e.g., nano-grid, microgrid, smart grid, distribution systems), including as software integrated to a control system (e.g., in "big" utility applications) or as separate micro-controllers (e.g., in "small" applications).”), the support system comprising:
an arithmetic device configured to execute predetermined processing (Fig. 2, Par. [0025], “processor(s) will execute one or more of the instructions included in that executable program code”); and
a storage device coupled to the arithmetic device (Fig. 2, Par. [0025], “the memory will store, at one time or another, at least portions of an executable program code”),
the support system is configured with a computer including the arithmetic device and the storage device (Par. [0025], “Various embodiments may include elements described as implemented in a "computer" or a "computer system." Here, the terms "computer" and "computer system" are to be understood to include at least one non transitory computer readable memory and at least one processing unit.”),
the regional energy system including: nanogrid equipment including at least one of a power generation device or an electricity storage device (Par. [0014], “advanced algorithms for peak-shaving and load shaping/following in different levels of electricity utilizations, including nanogrid, microgrid, and electrical distribution systems.”; Par. [0033], “meters 42 and 52 may track the electricity provided by the energy storage device 40 and the variable energy source 50 to the load 60, as well as energy transferred from the variable energy source 50 to the energy storage device 40”); and a vehicle in which a storage battery is installed (Par. [0031], “The load 60 may be any device or facility that requests power, including a building, an HVAC, an appliance, a charging port for an electric vehicle, an installation, and more.” – Because an electric vehicle is charged through the disclosed charging port, Bhattarai suggests an electric vehicle having an onboard rechargeable storage battery.),
wherein the support system is configured to:
receive, by the arithmetic device, power supply-demand information on the nanogrid equipment (Fig. 3, Par. [0041], “system control unit 20 receives forecast data in operation 300, which may include weather and meteorological data, and receives historical data about the variable energy source 50 and load 60 in operation 310”);
calculate nanogrid operation information indicating a power status of the nanogrid equipment during a predetermined period (Par. [0019], “system level control unit to forecast power generation and load demand in an energy distribution system”; Par. [0041], “The processor 22 forecasts power generation at the variable energy source 50 in operation 320, and energy demand at the load 60 in operation 330, according to the control algorithm 24. The forecasts may for one or more defined demand time windows.”; Par. [0065], “measurement data may include the net power, or data based on which the net power may be calculated”); and
generate, by the arithmetic device, data for displaying data organized in terms of power supply and demand for power in the nanogrid equipment (Par. [0043], “load profile and a power generation profile are generated in operation 340 based on the forecasts made in operations 320 and 330 … The load and power generation profiles are made available to the SO engine 26 in operation 350.”; Par. [0027], “Users may interact with the computer systems described herein by way of graphical user interfaces (GUI) on a display”; Par. [0073], “User interface objects may include display regions, user activatable regions, and the like”).
Bhattarai does not explicitly teach transport information relating to transport by the vehicle;
performing simulation through use of the transport information to calculate vehicle operation information indicating a running status and charging and discharging statuses of the vehicle; and
generate data for displaying data in terms of transport by the vehicle based on vehicle operation information.
However, Bedogni teaches transport information relating to transport by the vehicle (Page 5915, “proposed framework integrates models of vehicular mobility, battery charging/discharging, and EM-related city services”; Page 5904, Par. 9, “The battery and mobility information of each EV is periodically updated to the DSIB”); and
performing simulation through use of transport information to calculate vehicle operation information indicating a running status (Page. 5900, Abstract, “we extend the existing cosimulation platform composed of SUMO (a vehicular traffic simulator) and OMNET++ (a network simulator) with realistic models of EVs, electric vehicle supply equipment, and ontology-based communication protocols that enable the deployment of city-wide mobile services (e.g., charging reservation).”); Page. 5905, “Mobility module is responsible for periodically querying the VEINS framework, to obtain the current mobility parameters (location, speed, and acceleration) of the simulated EV directly from SUMO” – running parameters interpreted as running status) and charging and discharging statuses of the vehicle (Page. 5904, OMNET++ Module of an EV, par 1, “an EV can be in one of the three states: Full (F); Discharged (D); and Charging (C)”; Page. 5905, Par. 3, “The Battery module is the main EV component, and it is responsible for modeling the charging/discharging operations of the EV.”); and
generate data for displaying data in terms of transport by the vehicle based on vehicle operation information (Page 5909, Par. 3, “mobile applications that display EV vehicle data (e.g., battery-related information) or context-related information (e.g., closest EVSE).”).
Bhattarai and Bedogni are analogous art because they contain functional similarities. They both relate to energy management involving electric vehicles and impacts on energy infrastructure.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management system, as taught by Bhattarai, and incorporate an EV transport simulation framework and EV transport/charging information, as taught by Bedogni, into Bhattarai’s graphical user interface display regions, so that power supply/demand information and vehicle transport/charging information are displayed together on a single screen.
One of ordinary skill in the art would have been motivated to improve “reliability and the efficiency of the electricity system” as suggested by Bedogni (Page 5901, Models and Tools for Smart-Grid Simulation, Par. 1).
Regarding claim 2, the combination of Bhattarai and Bedogni teaches all the limitations of the base claims as outlined above.
Bhattarai further teaches wherein the arithmetic device is configured to perform, in accordance with input information for switching at least one of variations in power supply and demand for power (Par. [0066], “The processor 32 operating according to the error detection engine 34 identifies variance between the actual measured and forecasted values of one or more of net power/net load, state of charge of the energy storage device 40, and combinations thereof, in operation 620.”) or variations in environment relating to transport, simulation in consideration of the at least one of the variations in power supply and demand for power or the variations in environment relating to transport.
Regarding claim 3, the combination of Bhattarai and Bedogni teaches all the limitations of the base claims as outlined above.
Bedogni further teaches wherein the arithmetic device is configured to perform simulation in accordance with selected information on a priority service on which importance is placed by a user (Pages 5909-5910, Example of Mobile Application Embedding, “the user requests a recharge operation to the CS attaching additional information such as its current position and recharge preferences (i.e., closest EVSE, EVSE with minimum queuing time, and EVSE with lowest energy price). The CS analyzes the user request, computes the best available options over the whole city context based on the current status of the EVSE (these data are available on the C-SIB), and provides the processed alternatives.” – user-prioritized service conditions, such as closest station, lowest queue time, or lowest price, is interpreted as priority service).
Regarding claim 9, Bhattarai teaches a support method of supporting management of a regional energy system by a support system (Par. [0003], “systems, methods, and apparatuses for energy management … using power storage to optimize supply of power to a load by variable power sources.”; Par. [0013], “Embodiments of the energy management system and the techniques for using the same may be integrated into existing utility systems (e.g., nano-grid, microgrid, smart grid, distribution systems), including as software integrated to a control system (e.g., in "big" utility applications) or as separate micro-controllers (e.g., in "small" applications).”),
the support system including a computer including: an arithmetic device configured to execute predetermined processing; and a storage device coupled to the arithmetic device (Par. [0025], “Various embodiments may include elements described as implemented in a "computer" or a "computer system." Here, the terms "computer" and "computer system" are to be understood to include at least one non transitory computer readable memory and at least one processing unit.”), the regional energy system including: nanogrid equipment including at least one of a power generation device or an electricity storage device (Par. [0014], “advanced algorithms for peak-shaving and load shaping/following in different levels of electricity utilizations, including nanogrid, microgrid, and electrical distribution systems.”; Par. [0033], “meters 42 and 52 may track the electricity provided by the energy storage device 40 and the variable energy source 50 to the load 60, as well as energy transferred from the variable energy source 50 to the energy storage device 40”); and a vehicle in which a storage battery is installed (Par. [0031], “The load 60 may be any device or facility that requests power, including a building, an HVAC, an appliance, a charging port for an electric vehicle, an installation, and more.” – Because an electric vehicle is charged through the disclosed charging port, Bhattarai suggests an electric vehicle having an onboard rechargeable storage battery.),
the support method comprising steps of:
receiving, by the arithmetic device, power supply-demand information on the nanogrid equipment (Fig. 3, Par. [0041], “system control unit 20 receives forecast data in operation 300, which may include weather and meteorological data, and receives historical data about the variable energy source 50 and load 60 in operation 310”);
calculating nanogrid operation information indicating a power status of the nanogrid equipment during a predetermined period (Par. [0019], “system level control unit to forecast power generation and load demand in an energy distribution system”; Par. [0041], “ The processor 22 forecasts power generation at the variable energy source 50 in operation 320, and energy demand at the load 60 in operation 330, according to the control algorithm 24. The forecasts may for one or more defined demand time windows.”; Par. [0065], “measurement data may include the net power, or data based on which the net power may be calculated”); and
generating, by the arithmetic device, data for displaying data organized in terms of power supply and demand for power in the nanogrid equipment (Par. [0043], “load profile and a power generation profile are generated in operation 340 based on the forecasts made in operations 320 and 330 … The load and power generation profiles are made available to the SO engine 26 in operation 350.”; Par. [0027], “Users may interact with the computer systems described herein by way of graphical user interfaces (GUI) on a display”; Par. [0073], “User interface objects may include display regions, user activatable regions, and the like”).
Bhattarai does not explicitly teach transport information relating to transport by the vehicle;
performing simulation through use of the transport information to calculate vehicle operation information indicating a running status and charging and discharging statuses of the vehicle; and
generating data for displaying data in terms of transport by the vehicle based on vehicle operation information.
However, Bedogni teaches transport information relating to transport by the vehicle (Page 5915, “proposed framework integrates models of vehicular mobility, battery charging/discharging, and EM-related city services”; Page 5904, Par. 9, “The battery and mobility information of each EV is periodically updated to the DSIB”);
performing simulation through use of the transport information to calculate vehicle operation information indicating a running status (Page. 5900, Abstract, “we extend the existing cosimulation platform composed of SUMO (a vehicular traffic simulator) and OMNET++ (a network simulator) with realistic models of EVs, electric vehicle supply equipment, and ontology-based communication protocols that enable the deployment of city-wide mobile services (e.g., charging reservation).”); Page. 5905, “Mobility module is responsible for periodically querying the VEINS framework, to obtain the current mobility parameters (location, speed, and acceleration) of the simulated EV directly from SUMO” – running parameters interpreted as running status) and charging and discharging statuses of the vehicle (Page. 5904, OMNET++ Module of an EV, par 1, “an EV can be in one of the three states: Full (F); Discharged (D); and Charging (C)”; Page. 5905, Par. 3, “The Battery module is the main EV component, and it is responsible for modeling the charging/discharging operations of the EV.”); and
generating data for displaying data in terms of transport by the vehicle based on vehicle operation information (Page 5909, Par. 3, “mobile applications that display EV vehicle data (e.g., battery-related information) or context-related information (e.g., closest EVSE).”).
Bhattarai and Bedogni are analogous art because they contain functional similarities. They both relate to energy management involving electric vehicles and impacts on energy infrastructure.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management system, as taught by Bhattarai, and incorporate an EV transport simulation framework and EV transport/charging information, as taught by Bedogni, into Bhattarai’s graphical user interface display regions, so that power supply/demand information and vehicle transport/charging information are displayed together on a single screen.
One of ordinary skill in the art would have been motivated to improve “reliability and the efficiency of the electricity system” as suggested by Bedogni (Page 5901, Models and Tools for Smart-Grid Simulation, Par. 1).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhattarai et al. USPGPUB 2018/0226800 A1 (hereinafter Bhattarai) in view of Bedogni et al. (An Integrated Simulation Framework to Model Electric Vehicle Operations and Services, 2016) (hereinafter Bedogni), and further in view of Akiba et al. JP 2015005205 A (hereinafter Akiba).
Regarding claim 4, the combination of Bhattarai and Bedogni teaches all the limitations of the base claims as outlined above.
Bedogni further teaches wherein the arithmetic device is configured to generate data for displaying a position and a stored electricity amount of the vehicle on a map (Page 5908, MAZ: Embedding Real Components Into Simulated Entities, Par. 1, “D-SIB is connected to each simulated EV and stores its current position and battery-related information (e.g., current SOC).”; Page 5909, bullet point 2, “The MAZ sandbox allows for testing geolocalization and route navigation functionalities, due to the fact that 1) simulated EVs can move into realistic road map topologies imported from Open-StreetMap and that 2) the SIB-C module is in charge of converting the position of each simulated entity from the internal coordinates (used by SUMO/OMNET++) to the latitude/longitude coordinate system used by Google Maps. In the SIBs, the localization coordinates are always represented in this latter format; therefore, so they can be directly displayed on the map of the smartphone.”)
Bedogni does not explicitly teach displaying a configuration and a stored energy amount of the nanogrid equipment on the map, or data for the number of transported persons of the vehicle.
However, Bhattarai teaches a configuration and a stored electricity amount of the nanogrid equipment (Par. [0069], “The energy distribution system 7 includes variable power sources 74, loads 72, a smart panel 71, a system control 75, power storage 73, and a device control unit (DCU) 76.”; Par. [0009], “system control unit may be configured to periodically determine a charge/discharge profile for an energy storage device during a demand time window responsive to an energy generation forecast model of a variable energy source and a demand forecast model for a load.”).
Bhattarai further teaches displaying system information using GUI display regions (Par. [0027], “Users may interact with the computer systems described herein by way of graphical user interfaces (GUI) on a display”; Par. [0073], “User interface objects may include display regions, user activatable regions, and the like” - it would have been obvious to display Bhattarai’s nanogrid configuration and stored electricity amount together with Bedogni’s vehicle position and vehicle SOC on Bedogni’s map so that the operator can visually understand the relationship between vehicle transport resources and nanogrid energy resources.)
Bhattarai and Bedogni do not explicitly teach data for the number of transported persons of the vehicle.
However, Akiba teaches data for the number of transported persons of the vehicle (Par. [0012], “The operation data processing device 42 can transmit data such as bus position information, the number of passengers, the number of alighting passengers, power consumption, and a vehicle speed to the server 100.”).
Bhattarai, Bedogni, and Akiba are analogous art because they contain functional similarities. They relate to energy management involving electric vehicles and vehicle operation information.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management and EV simulation system, as taught by Bhattarai and Bedogni, and incorporate passenger count information, as taught by Akiba.
One of ordinary skill in the art would have been motivated to improve management of electric vehicle operation and energy use by considering passenger count together with vehicle power consumption, as suggested by Akiba (Par. [0025]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhattarai et al. USPGPUB 2018/0226800 A1 (hereinafter Bhattarai) in view of Bedogni et al. (An Integrated Simulation Framework to Model Electric Vehicle Operations and Services, 2016) (hereinafter Bedogni), and further in view of Koga JP 2021067958 A (hereinafter Koga).
Regarding claim 5, the combination of Bhattarai and Bedogni teaches all the limitations of the base claims as outlined above.
Bhattarai teaches nanogrid equipment, but Bhattarai and Bedogni do not explicitly teach wherein the arithmetic device is configured to generate, in accordance with a stored electricity amount and a position of the vehicle, data for instructing the vehicle to distribute power to equipment.
However, Koga teaches wherein the arithmetic device is configured to generate, in accordance with a stored electricity amount and a position of the vehicle, data for instructing the vehicle to distribute power to equipment (Par. [0020], “The information processing apparatus 100 may transmit, via the communications network 30, a transportation instruction instructing transportation of energy to the selected supplying energy station”; Par. [0022], “EV240 moves to the target energy station and supplies electric power stored in its own drive battery to the target energy station”; Fig. 5, “traffic information table 510 illustrated in FIG. 5 includes a distance from each energy station 200 to a target energy station, a predicted time for transporting energy from each energy station 200 to the target energy station, which is derived based on the distance and the traffic information, and an amount of power required for the transportation”).
Bhattarai, Bedogni, and Koga are analogous art because they contain functional similarities. They all relate to energy management involving electric vehicles.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management and EV simulation system, as taught by Bhattarai and Bedogni, and incorporate vehicle-based energy transportation instruction, as taught by Koga.
One of ordinary skill in the art would have been motivated to improve “efficiently supply energy to the target energy station” as suggested by Koga (Par [0042]).
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhattarai et al. USPGPUB 2018/0226800 A1 (hereinafter Bhattarai) in view of Bedogni et al. (An Integrated Simulation Framework to Model Electric Vehicle Operations and Services, 2016) (hereinafter Bedogni) and Akiba et al. JP 2015005205 A (hereinafter Akiba), and further in view of Koga JP 2021067958 A (hereinafter Koga).
Regarding claim 6, the combination of Bhattarai and Bedogni teaches all the limitations of the base claims as outlined above.
Bhattarai further teaches wherein the arithmetic device is configured to generate data for displaying a screen including a power data display area for displaying a power generation amount and demand for power of the nano grid equipment (Par. [0043], “A load profile and a power generation profile are generated in operation 340 based on the forecasts made in operations 320 and 330. In one embodiment, the profiles are forecasted values in a vector of time-slots. In another embodiment the profiles are time functions. The load and power generation profiles are made available to the SO engine 26 in operation 350.”), a power generation data display area for displaying power supplied to the nanogrid equipment (Par. [0048], “The net power … equals the algebraic sum of the total demand and PV generation as defined mathematically in Eq. 4”).
Bhattarai and Bedogni do not explicitly teach transport count data.
However, Akiba teaches transport count data (Par. [0012], “The operation data processing device 42 can transmit data such as bus position information, the number of passengers, the number of alighting passengers, power consumption, and a vehicle speed to the server 100.”; Par. [0041], “The number of passengers may be counted, for example, by the number of times of payment of a fare, by using a sensor provided at a bus entrance, by analyzing an image captured by a video camera in real time, or by a method and a counting device combining these.”).
Bhattarai, Bedogni, and Akiba are analogous art because they contain functional similarities. They all relate to managing energy systems involving electric vehicles and impacts on energy infrastructure.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management and EV simulation system containing displayable management data, as taught by Bhattarai and Bedogni, and incorporate transport count information in the transport data display area, as taught by Akiba.
One of ordinary skill in the art would have been motivated to improve management of electric vehicle operation and energy use by considering passenger count together with vehicle power consumption, as suggested by Akiba (Par. [0025]).
Bhattarai, Bedogni, and Akiba do not explicitly teach running distance data for the vehicle.
However, Koga teaches running distance data for the vehicle (Fig. 5, “traffic information table 510 illustrated in FIG. 5 includes a distance from each energy station 200 to a target energy station, a predicted time for transporting energy from each energy station 200 to the target energy station, which is derived based on the distance and the traffic information, and an amount of power required for the transportation. The distances from the energy station 200 to the target energy station may be straight line distances or travel distances by EV240”).
Bhattarai, Bedogni, Akiba, and Koga are analogous art because they contain functional similarities. They all relate to energy management involving electric vehicles, vehicle operation information, and vehicle-based energy transfer.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management system and EV simulation system containing displayable management data, as taught by Bhattarai, Bedogni, and Akiba, and incorporate running distance information in the transport data display area, as taught by Koga.
One of ordinary skill in the art would have been motivated to improve “efficiently supply energy to the target energy station” as suggested by Koga (Par [0042]).
Regarding claim 7, the combination of Bhattarai and Bedogni teaches all the limitations of the base claims as outlined above.
Bhattarai further teaches wherein the arithmetic device is configured to generate data for displaying a screen including a power generation data display area for displaying power supplied to the nanogrid equipment (Par. [0043], “load profile and a power generation profile are generated in operation 340 based on the forecasts made in operations 320 and 330 … The load and power generation profiles are made available to the SO engine 26 in operation 350.”; Par. [0074], “User interface objects may include display regions, user activatable regions, and the like” – Bhattarai teaches generating display data for a screen having display areas, including a display area for power generation/power supply information).
Bedogni further teaches data for both engine vehicles and electric motor vehicles (Page 5904, Par. 6, “the routes of the simulated vehicles (both EVs and non-EVs) are generated on the basis of the demographical data”; Page 5910, Modeling of the EM Scenario, “Fig. 8(b) shows a detail with three vehicles: The green one is a simulated EV, the yellow one is an EV doing charging operations, and the red one is a normal (fossil-fueled) vehicle.”).
Bhattarai and Bedogni do not explicitly teach the transport data display area displaying a transport count and a running distance of the vehicle.
However, Akiba teaches transport count data for a vehicle (Par. [0012], “The operation data processing device 42 can transmit data such as bus position information, the number of passengers, the number of alighting passengers, power consumption, and a vehicle speed to the server 100.”).
Bhattarai, Bedogni, and Akiba are analogous art because they contain functional similarities. They all relate to managing energy systems involving electric vehicles and impacts on energy infrastructure.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management and EV simulation system containing displayable management data, as taught by Bhattarai and Bedogni, and incorporate transport count information in the transport data display area, as taught by Akiba.
One of ordinary skill in the art would have been motivated to improve management of electric vehicle operation and energy use by considering passenger count together with vehicle power consumption, as suggested by Akiba (Par. [0025]).
Bhattarai, Bedogni, and Akiba do not explicitly teach the transport data display area for displaying a running distance of the vehicle.
However, Koga teaches running distance data for the vehicle (Fig. 5, Par. [0053], “traffic information table 510 illustrated in FIG. 5 includes a distance from each energy station 200 to a target energy station, a predicted time for transporting energy from each energy station 200 to the target energy station, which is derived based on the distance and the traffic information, and an amount of power required for the transportation. The distances from the energy station 200 to the target energy station may be straight line distances or travel distances by EV240”).
Bhattarai, Bedogni, Akiba, and Koga are analogous art because they contain functional similarities. They all relate to energy management involving electric vehicles, vehicle operation information, and vehicle-based energy transfer.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management and EV simulation system containing displayable management data, as taught by Bhattarai, Bedogni, and Akiba, and incorporate running distance information in the transport data display area, as taught by Koga.
One of ordinary skill in the art would have been motivated to improve “efficiently supply energy to the target energy station” as suggested by Koga (Par [0042]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhattarai et al. USPGPUB 2018/0226800 A1 (hereinafter Bhattarai) in view of Bedogni et al. (An Integrated Simulation Framework to Model Electric Vehicle Operations and Services, 2016) (hereinafter Bedogni), and further in view of Conley III US 2002/0080027 A1 (hereinafter Conley).
Regarding claim 8, the combination of Bhattarai and Bedogni teaches all the limitations of the base claims as outlined above.
Bhattarai further teaches wherein the arithmetic device is configured to generate data for displaying a screen including a power generation data display area for displaying power supplied to the nanogrid equipment (Par. [0043], “load profile and a power generation profile are generated in operation 340 based on the forecasts made in operations 320 and 330 … The load and power generation profiles are made available to the SO engine 26 in operation 350.”; Par. [0027], “Users may interact with the computer systems described herein by way of graphical user interfaces (GUI) on a display”; Par. [0073], “User interface objects may include display regions, user activatable regions, and the like”).
Bhattarai and Bedogni do not explicitly teach a disaster prevention data display area for displaying states of an important facility in normal time and at a time of a disaster, the important facility including a load that uses power.
However, Conley teaches a display area for displaying states of an important facility in normal time and at a time of a disaster, the important facility including a load that uses power (Par. [0004], “Emergency lights provide temporary lighting in the event of a power failure. During normal operation, power is provided from power mains to operate the lamp and to charge a backup power source (e.g., a battery). When power from the mains is interrupted, the backup power source provides power to the lamp for a limited time”; Par. [0019], “invention also monitors status data from the emergency lighting units to verify nominal light output of the lamp. Light output can be estimated by measuring an appropriate parameter (e.g., battery discharge current).”, Par. [0037], “User interface 13 is in communication with control unit 10 and allows an operator to control all aspects of the emergency lighting system throughout the building”; Par. [0043], “A frequency hop is done every 100 milliseconds which provides sufficient time for either a packet of command data 22 to be transmitted to a lighting unit 12 or a packet of status data 23 to be transmitted to central control unit 10 between frequency hops”).
Bhattarai, Bedogni, and Conley are analogous art because they contain functional similarities. They relate to energy management involving powered loads and maintaining or monitoring powered equipment.
Therefore, at the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management and EV simulation system with GUI display regions, as taught by Bhattarai and Bedogni, and incorporate emergency lighting status data of a building or facility, as taught by Conley.
One of ordinary skill in the art would have been motivated to improve reliability of emergency powered loads by monitoring and displaying emergency light status information, as suggested by Conley (Par. [0011] - [0013]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhattarai et al. USPGPUB 2018/0226800 A1 (hereinafter Bhattarai) in view of Bedogni et al. (An Integrated Simulation Framework to Model Electric Vehicle Operations and Services, 2016) (hereinafter Bedogni), and further in view of Ratti et al. US 2012/0172017 A1 (hereinafter Ratti).
Regarding claim 10, Bhattarai teaches a support system for supporting management of a regional energy system (Par. [0003], “systems, methods, and apparatuses for energy management … using power storage to optimize supply of power to a load by variable power sources.”; Par. [0013], “Embodiments of the energy management system and the techniques for using the same may be integrated into existing utility systems (e.g., nano-grid, microgrid, smart grid, distribution systems), including as software integrated to a control system (e.g., in "big" utility applications) or as separate micro-controllers (e.g., in "small" applications)”), the support system comprising a computer including: an arithmetic device configured to execute predetermined processing; and a storage device coupled to the arithmetic device (Par. [0025], “Various embodiments may include elements described as implemented in a "computer" or a "computer system." Here, the terms "computer" and "computer system" are to be understood to include at least one non transitory computer readable memory and at least one processing unit.”), the regional energy system including: nanogrid equipment including at least one of a power generation device or an electricity storage device (Par. [0014], “advanced algorithms for peak-shaving and load shaping/following in different levels of electricity utilizations, including nanogrid, microgrid, and electrical distribution systems.”; Par. [0033], “meters 42 and 52 may track the electricity provided by the energy storage device 40 and the variable energy source 50 to the load 60, as well as energy transferred from the variable energy source 50 to the energy storage device 40”); and a vehicle in which a storage battery is installed (Par. [0031], “The load 60 may be any device or facility that requests power, including a building, an HVAC, an appliance, a charging port for an electric vehicle, an installation, and more.” – Because an electric vehicle is charged through the disclosed charging port, Bhattarai suggests an electric vehicle having an onboard rechargeable storage battery.),
wherein the support system is configured to:
receive, by the arithmetic device, power supply-demand information on the nanogrid equipment (Fig. 3, Par. [0041], “system control unit 20 receives forecast data in operation 300, which may include weather and meteorological data, and receives historical data about the variable energy source 50 and load 60 in operation 310”);
perform, by the arithmetic device, forecasting through use of the power supply-demand information to calculate nanogrid operation information indicating a power status of the nano grid equipment during a predetermined period (Par. [0019], “system level control unit to forecast power generation and load demand in an energy distribution system”; Par. [0041], “ The processor 22 forecasts power generation at the variable energy source 50 in operation 320, and energy demand at the load 60 in operation 330, according to the control algorithm 24. The forecasts may for one or more defined demand time windows.”; Par. [0065], “measurement data may include the net power, or data based on which the net power may be calculated”); and
generate, by the arithmetic device, based on the nanogrid operation information, data for displaying (Par. [0043], “load profile and a power generation profile are generated in operation 340 based on the forecasts made in operations 320 and 330 … The load and power generation profiles are made available to the SO engine 26 in operation 350.”; Par. [0027], “Users may interact with the computer systems described herein by way of graphical user interfaces (GUI) on a display”; Par. [0073], “User interface objects may include display regions, user activatable regions, and the like”).
Bhattarai does not explicitly teach transport information relating to transport by the vehicle;
performing simulation through use of the transport information to calculate vehicle operation information indicating a running status and charging and discharging statuses of the vehicle; and
data for displaying transport data indicating an environmental value on a screen.
However, Bedogni teaches transport information relating to transport by the vehicle (Page 5915, “proposed framework integrates models of vehicular mobility, battery charging/discharging, and EM-related city services”; Page 5904, Par. 9, “The battery and mobility information of each EV is periodically updated to the DSIB”); and
performing simulation through use of the transport information to calculate vehicle operation information indicating a running status (Page. 5900, Abstract, “we extend the existing cosimulation platform composed of SUMO (a vehicular traffic simulator) and OMNET++ (a network simulator) with realistic models of EVs, electric vehicle supply equipment, and ontology-based communication protocols that enable the deployment of city-wide mobile services (e.g., charging reservation).”); Page. 5905, “Mobility module is responsible for periodically querying the VEINS framework, to obtain the current mobility parameters (location, speed, and acceleration) of the simulated EV directly from SUMO” – running parameters interpreted as running status) and charging and discharging statuses of the vehicle (Page. 5904, OMNET++ Module of an EV, par 1, “an EV can be in one of the three states: Full (F); Discharged (D); and Charging (C)”; Page. 5905, Par. 3, “The Battery module is the main EV component, and it is responsible for modeling the charging/discharging operations of the EV.”).
Bhattarai and Bedogni are analogous art because they contain functional similarities. They both relate to energy management involving electric vehicles and impacts on energy infrastructure.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management system, as taught by Bhattarai, and incorporate an EV transport simulation framework and EV transport/charging information, as taught by Bedogni.
One of ordinary skill in the art would have been motivated to improve “reliability and the efficiency of the electricity system” as suggested by Bedogni (Page 5901, Models and Tools for Smart-Grid Simulation, Par. 1).
Bhattarai and Bedogni do not explicitly teach data for displaying transport data indicating an environmental value on a screen.
However, Ratti teaches data for displaying transport data indicating an environmental value on a screen (Par. [0007], “Carbon dioxide emissions are computed from the mode of transportation and distance travelled”; Par. [0054], “The CO2GO application presents information through a user interface, with the mode of transport shown to ensure the correct functioning. Travel time, distance covered and associated CO2 emissions are depicted in real time, along with a map of the user's route” – CO2 emissions is interpreted as an environmental value).
Bhattarai, Bedogni, and Ratti are analogous art because they contain functional similarities. They all use vehicle or transport related information to generate displayable information for evaluating vehicle operation.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management and EV simulation system as taught by Bhattarai and Bedogni, and incorporate transport environmental value display information, as taught by Ratti.
One of ordinary skill in the art would have been motivated to allow a user to make more informed transportation decisions to reduce CO2 emissions, as suggested by Ratti (Par. [0006])
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chang et al. [US 2019/0081506 A1] teaches an integrated power supply system applied to residential buildings and/or electric vehicles.
Kitahama et al. [US 2021/0209945 A1] teaches a management system including a server that acquires information on a city area via respective clients, analyzes the acquired information, and transmits information determined based on the result of the analysis to the clients
Conclusion
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/PETER XU/ Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
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