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 .
Status of Claims
This action is in reply to the response filed on April 8, 2026.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
The examiner would like to note that this application is being handled by examiner Christine Huynh.
Response to Amendment
The amendment filed April 8, 2026 has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to the claims have overcome the 35 U.S.C. 101 rejection set forth in the Non-Final Office Action mailed January 9, 2026.
Response to Arguments
Applicant’s arguments with respect to independent claim(s) 1, 12, and 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Upon further search and consideration, the amended claims 1, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Baglino et al. (US 20240085203 A1) in view of Wang et al. (US 20250258007 A1). See detailed rejection below.
Dependent claims are rejected for the same reasons as stated above due to dependency. See detailed rejection below.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 5, 7-14, 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baglino et al. (US 20240085203 A1), which was provided in the IDS sent on October 16, 2024, in view of Wang et al. (US 20250258007 A1).
Regarding claims 1-3, 5, 7-14, 16-19:
With respect to claims 1, 12, and 17, Baglino teaches:
receiving by a computing device a destination for a hybrid-electric vehicle to navigate the hybrid-electric vehicle to; (“In a user interface 102, a user inputs an origin 104 and a destination 106… This information is provided to a trip planning component 108 in the system… The trip planning component 108 performs a route-finding operation 110. The route from the specified origin to the specified destination is determined.” [0020]), which shows receiving a destination for navigating the vehicle towards.
accessing by a computing device a current location of the hybrid-electric vehicle; (“In a user interface 102, a user inputs an origin 104 and a destination 106. In some implementations, the origin can be automatically input (e.g., based on current GPS data). This information is provided to a trip planning component 108 in the system.” [0020]), which shows accessing the current location, or origin, of the vehicle.
accessing by the computing device current and historical vehicle data for the hybrid-electric vehicle; (“Based on the determined route, vehicle specifics and information about the road segment(s) to be traveled, the system performs an energy calculation operation 112.” [0020], “Examples of data types include, but are not limited to, a vehicle profile (e.g., the type and model of the vehicle, including any optional equipment), a fleet profile (e.g., data collected from a fleet of vehicles, such as average battery consumption data or driver behavior), a driver error and a model error (e.g., as will be described below)…” [0021]), which shows accessing stored information of the vehicle.
accessing by the computing device current and historical traffic data for the hybrid-electric vehicle; (“Examples of data types include, but are not limited to… road network data (e.g., length, slope and surface type for road segments), weather data (e.g., localized information regarding wind or precipitation) and traffic data. Some of the data source information can be continuously updated, for example to take into account current weather and traffic information, or to adjust the driver error for the current driver.” [0021]), which shows accessing traffic and road data.
accessing by the computing device driver data; (“The predicted driver characteristic includes an estimated driving speed, and wherein the proposed change modifies the estimated driving speed. The predicted driver characteristic reflects a driving record of the driver. The predicted driver characteristic reflects driving records from a fleet of vehicles.” [0005]), which shows accessing driver data.
accessing by the computing device point-of-interest data for the current location in which the hybrid-electric vehicle is located; (FIG. 4C shows different types of charging stops near the planned route, “FIG. 4C shows an example of how the user interface 200 can be updated to show available options for charging. A selection area 402 presents one or more charging stops for the driver to choose between. These options are presented by the system as suggestions to the driver and may have been chosen from among the charging stations in the source 126 (FIG. 1). Any suitable facilities for replenishing the car with energy can be presented.” [0049]), where charging stops are points of interests.
generating automatically by the computing device… one or more driving routes for the hybrid-electric vehicle based upon the destination, current location, current and historical vehicle data, current and historical traffic data, driver data, and point-of-interest data, (“The trip planning component 108 performs a route-finding operation 110. The route from the specified origin to the specified destination is determined. Based on the determined route, vehicle specifics and information about the road segment(s) to be traveled, the system performs an energy calculation operation 112.” [0020], “FIGS. 6A-C show examples of user preferences, suggestions and custom route. In FIG. 6A, the user interface 200 contains a preferences area 600 where the driver can make one or more inputs that will affect how the system routes the trip and/or how the system addresses energy replenishment.” [0055], “FIG. 6B shows an example where the system presents options to the driver to choose between. For example, the trip information area 220 here contains an options area 606 that shows options 608A-B.” [0056]), where a plurality of driving routes are generated auto based upon the destination, current location, vehicle data, traffic data, driver data, and point-of-interest data.
a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations, (“a computer readable storage medium has stored thereon instructions that when executed cause a processor to perform operations” [0006]).
Baglino does not teach, but Wang teaches a hybrid-electric vehicle, training by a computing device a neural network to obtain a trained neural network to determine an efficiency for a hybrid-electric vehicle using data on previously driven routes including data regarding an amount of gasoline utilized, data regarding an amount of electricity utilized by an electric motor, and travel time, and utilizing the trained neural network, (“In at least some implementations, the driving style is determined from vehicle operation by the driver that occurred prior to the trip. In at least some implementations, the energy use model is based at least in part on the energy consumption model of the vehicle, the driving style and the travel path type of each segment.” [0005-0006], “FIG. 1 illustrates a vehicle 10 that includes a prime mover 12 that may include a combustion engine, one or more electric motors or both an engine and motor(s), as in a hybrid vehicle 10.” [0024], “energy level sensors 42 like a fuel gauge or battery charge sensor that provide an indication of propulsion energy level remaining in the vehicle energy supply” [0026], “The data may be analyzed by a machine learning algorithm arranged to review historical data to provide an estimated energy use model for the driver along a given segment of a trip and up to the entire trip.” [0033], “The trip information includes data about the selected travel path. This data may include types of roads (travel path types) and road conditions for each portion of the travel path, as well as traffic, weather, speed limits, and other information relevant to energy and time needed for the trip, such as likely stopping and acceleration events” [0042], “Further, with the nominal or baseline vehicle energy use model or characteristics known (e.g. a miles per gallon rating or miles per energy unit rating) the driving style or driver rating may be determined as a function of a vehicle energy use history when the vehicle 10 is operated by the driver (so vehicle use specific to the driver) and compared to a nominal energy use model (predetermined average energy use or rating) for the vehicle 10 in the same driving conditions. This may enable an adjustment factor or the like to be applied to the nominals energy use model to arrive at a driver-specific energy use model for one or more segments and up to the entire travel path.” [0048], “With historical data for the driver, the energy use model for future trips can be estimated by, for example, comparing the new travel path and segments thereof, with historical data for the same or similar segments. This can provide a much better estimate of energy consumption in different segments of a trip, and from that the system can estimate vehicle range, and, if needed, can recommend charging/refueling stations that enable greater energy efficiency and convenience. Further, when the historical data is captured, real-time driver actions (such as speed, acceleration and steering control) can be fed into a machine learning algorithm to determine a driving style or driver behavior model.” [0055]), where a machine learning model for determining energy efficiency can be trained using data on previously driven routes including data regarding an amount of energy utilized by the hybrid-electric vehicle, in which the energy can be fuel or battery energy used, and travel information, which includes the route type and time.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baglino navigation for vehicle with Wang’s hybrid-electric vehicle and machine learning training because (“With historical data for the driver, the energy use model for future trips can be estimated by, for example, comparing the new travel path and segments thereof, with historical data for the same or similar segments. This can provide a much better estimate of energy consumption in different segments of a trip, and from that the system can estimate vehicle range, and, if needed, can recommend charging/refueling stations that enable greater energy efficiency and convenience. Further, when the historical data is captured, real-time driver actions (such as speed, acceleration and steering control) can be fed into a machine learning algorithm to determine a driving style or driver behavior model.” [0055]), and therefore better estimate information for a generating a hybrid-electric vehicle route.
With respect to claims 2, 14, and 18, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claims 1, 13, and 17. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claims 1, 13, and 17. Baglino further teaches:
accessing by the computing device route preferences of a driver of the hybrid-electric vehicle, wherein generating automatically by the computing device one or more driving routes for the hybrid-electric vehicle further comprises generating two or more driving routes based at least in-part on the one or more route preferences; (“FIGS. 6A-C show examples of user preferences, suggestions and custom route. In FIG. 6A, the user interface 200 contains a preferences area 600 where the driver can make one or more inputs that will affect how the system routes the trip and/or how the system addresses energy replenishment. Particularly, route preferences 602 allow the driver to choose between having the system pick the fastest route or the shortest route, and whether scenic routes should be taken into account. Charging preferences 604 allow the driver to choose between having the system automatically complete the route when adding the charging waypoint(s), or whether pre-drive charging should be considered, or whether a detour to charge should be considered. After the user selects one or more preferences, and/or clears one or more previously selected preferences, the system takes the driver's current preferences into account when relevant.” [0055], “FIG. 6B shows an example where the system presents options to the driver to choose between. For example, the trip information area 220 here contains an options area 606 that shows options 608A-B.” [0056]), where driving routes are generated based on preferences of a driver.
With respect to claims 3, 13, and 19, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claims 2, 12, and 18. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claims 2, 12, and 18. Baglino further teaches:
requesting the hybrid-electric vehicle display the two or more driving routes to the driver of the hybrid-electric vehicle for selection by the driver; (“FIG. 6B shows an example where the system presents options to the driver to choose between. For example, the trip information area 220 here contains an options area 606 that shows options 608A-B.” [0056]), where the plurality of generated routes are displayed for selection for the driver.
With respect to claims 5 and 16, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claims 2 and 14. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claims 2 and 14. Baglino further teaches:
wherein the route preferences include selectively one or more of the following: time optimization, cost optimization, and distance optimization; (“Particularly, route preferences 602 allow the driver to choose between having the system pick the fastest route or the shortest route, and whether scenic routes should be taken into account. Charging preferences 604 allow the driver to choose between having the system automatically complete the route when adding the charging waypoint(s), or whether pre-drive charging should be considered, or whether a detour to charge should be considered. After the user selects one or more preferences, and/or clears one or more previously selected preferences, the system takes the driver's current preferences into account when relevant.” [0055], “FIG. 6B shows an example where the system presents options to the driver to choose between. For example, the trip information area 220 here contains an options area 606 that shows options 608A-B. For example, the option 608A contains a short route (350 miles) that will require only one charging stop. This option may be attractive to a driver who prioritizes speed in order to make the trip faster. The option 608B, in contrast, includes a scenic route and therefore corresponds to a longer route (500 miles), which will require two charging stops.” [0056]), where the generated routes are based on preferences such as the fastest or shortest route, which are time or distance optimizations.
With respect to claim 7, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claim 1. Baglino further teaches:
wherein current and historical vehicle data includes selectively one or more of the following: vehicle speed, battery consumption rate, fuel tank capacity, battery capacity, and battery charging time; (“The energy calculation for the trip will then be performed and, as indicated, the current example assumes that the vehicle already has sufficient energy (e.g., enough battery charge) for the entire trip.” [0030], “The initial calculation of expected remaining energy (i.e., as reflected in the end value 810 and the energy indicator 812) can take into account a number of types of information…” [0071]), which shows a list of different vehicle data related to the battery.
With respect to claim 8, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claim 1. Baglino further teaches:
wherein current and historical traffic data includes selectively one or more of the following: road conditions, traffic statutes, regulation areas, and weather information; (“The operation 110 can use one or more navigational tools, and the energy calculation operation 112 can use one or more road load equations. Both operations can take into account one or more types of data from a data source 113. Examples of data types include, but are not limited to… road network data (e.g., length, slope and surface type for road segments), weather data (e.g., localized information regarding wind or precipitation) and traffic data. Some of the data source information can be continuously updated, for example to take into account current weather and traffic information, or to adjust the driver error for the current driver.” [0021]), which shows that the traffic data includes road and traffic data and weather information.
With respect to claim 9, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claim 1. Baglino further teaches:
wherein driver data includes selectively one or more of the following: driver behavior data, special driver requirements, and noise level; (“The operation 110 can use one or more navigational tools, and the energy calculation operation 112 can use one or more road load equations. Both operations can take into account one or more types of data from a data source 113. Examples of data types include, but are not limited to… a fleet profile (e.g., data collected from a fleet of vehicles, such as average battery consumption data or driver behavior), a driver error and a model error (e.g., as will be described below)… Some of the data source information can be continuously updated, for example to take into account current weather and traffic information, or to adjust the driver error for the current driver.” [0021]), which shows the driver data includes driver behavior information.
With respect to claim 10, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claim 1. Baglino further teaches:
wherein point-of-interest data includes selectively one of the following: gasoline station location data, charging station location data, charging station type information, fuel price, charging prices, charging station power information, and charging station interface information; (FIG. 4C shows different types of charging stops near the planned route, “FIG. 4C shows an example of how the user interface 200 can be updated to show available options for charging. A selection area 402 presents one or more charging stops for the driver to choose between. These options are presented by the system as suggestions to the driver and may have been chosen from among the charging stations in the source 126 (FIG. 1). Any suitable facilities for replenishing the car with energy can be presented.” [0049]), which shows the point-of-interest data includes charging station data.
With respect to claim 11, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claim 1. Baglino further teaches:
collecting real-time information, and using the real-time information to determine whether to update the one or more driving routes and, if a determination is made that the one or more driving routes need to be updated, updating the one or more driving routes; (“Output from the energy calculation can be used in updating a user interface 114 in the system. In some implementations, a map interface 116 can be updated, for example to show the predicted amount of energy remaining at the destination. In some implementations, an analytical interface 118 can be updated, for example to show an energy-versus-distance chart.” [0022], “The initial calculation of expected remaining energy (i.e., as reflected in the end value 810 and the energy indicator 812) can take into account a number of types of information. In some implementations, the following can be used: Road segment definitions (e.g., latitude/longitude specifications) Required segment data (e.g., type of road, speed limits). Traffic information (e.g., real-time information on traffic speeds)” [0071-0073], “Real-time information relating to charging stations can be taken into account. In some implementations, information such as the options 404A-C and/or the box 406 (FIG. 4C) can be presented. For example, regarding each charging station the driver can be informed about relevant traffic congestion, a fill level provided by the station, and/or an availability of the station and its chargers.” [0118]), where real-time information is collected. The driving routes can be updated using the collected information.
Claim(s) 4, 6, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baglino et al. (US 20240085203 A1), which was provided in the IDS sent on October 16, 2024, in view of Wang et al. (US 20250258007 A1) and Sawada et al. (US 20170240174 A1).
Regarding claims 4, 6, 15, and 20:
With respect to claims 4, 15, and 20, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claims 2, 13, and 19. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claims 2, 13, and 19. Baglino does not teach, but Sawada teaches:
wherein when the driver of the hybrid-electric vehicle selects a preferred route of the generated two or more driving routes, the computing device requests an engine scheduler for the hybrid-electric vehicle associated with the preferred route…; (“The “travel plan mode” is a mode in which a travel plan (planned travel mode) is set for low fuel travel based on travel environment information on a planned travel route that is set by the navigation system 4, to be described later, and the drive source is controlled according to the travel plan. That is, in the “travel plan mode,” the planned travel route is first divided into a plurality of sections and the driver's requested drive force is assumed for each section based on the travel environment information. Then, the traveling mode in each section is set according to the assumed requested drive force, to configure the travel plan (planned travel mode). Then, the drive source, such as the engine Eng and the motor/generator MG, is controlled to be in low fuel travel when in a traveling mode that is set by the travel plan.” [0032], “(1) A hybrid vehicle control device, mounted in a hybrid vehicle S including a drive source having an engine Eng and a motor (motor/generator MG), and a navigation system 4 that acquires travel environment information of a planned travel route, comprising a drive source controller (vehicle control unit 1) that carries out control of the drive source (engine Eng, motor/generator MG) in accordance with a driving mode of the hybrid vehicle S, wherein the hybrid vehicle S comprises, as driving modes, a “travel plan mode” that controls the drive source (engine Eng, motor/generator MG) in accordance with a travel plan that is set for low fuel travel based on the travel environment information acquired from the navigation system 4, and an “eco-mode” that controls the drive source (engine Eng, motor/generator MG) prioritizing fuel efficiency over power performance, the drive source controller (vehicle control unit 1) being configured such that, upon selection of the “travel plan mode” in the absence of the selection of the “eco-mode”, the selection of the “travel plan mode” is linked with a setting operation of the “eco-mode”.” [0064]), where the planned travel route is divided into a plurality of sections and the driver's requested drive force is assumed for each section based on the travel environment information. Therefore, the traveling mode in each section is set according to the information. This shows a set schedule for the engine during specific sections of the determined route.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baglino-Wang’s navigation for vehicles with Sawada’s hybrid-electric vehicle because (“by carrying out a control of the drive source in accordance with the travel plan, a low fuel travel becomes possible to thereby improve the fuel efficiency.” [0061], “It is thereby possible to control the drive source in accordance with the travel plan, and low fuel travel becomes possible to thereby improve fuel efficiency.” [0064]).
However, Baglino does not teach, …the engine scheduler associated with a machine learning model to obtain best efficiency results during travel of the two or more driving routes, but Wang teaches (“In at least some implementations, the driving style is determined from vehicle operation by the driver that occurred prior to the trip. In at least some implementations, the energy use model is based at least in part on the energy consumption model of the vehicle, the driving style and the travel path type of each segment.” [0005-0006], “energy level sensors 42 like a fuel gauge or battery charge sensor that provide an indication of propulsion energy level remaining in the vehicle energy supply” [0026], “Further, with the nominal or baseline vehicle energy use model or characteristics known (e.g. a miles per gallon rating or miles per energy unit rating) the driving style or driver rating may be determined as a function of a vehicle energy use history when the vehicle 10 is operated by the driver (so vehicle use specific to the driver) and compared to a nominal energy use model (predetermined average energy use or rating) for the vehicle 10 in the same driving conditions. This may enable an adjustment factor or the like to be applied to the nominals energy use model to arrive at a driver-specific energy use model for one or more segments and up to the entire travel path.” [0048], “With historical data for the driver, the energy use model for future trips can be estimated by, for example, comparing the new travel path and segments thereof, with historical data for the same or similar segments. This can provide a much better estimate of energy consumption in different segments of a trip, and from that the system can estimate vehicle range, and, if needed, can recommend charging/refueling stations that enable greater energy efficiency and convenience. Further, when the historical data is captured, real-time driver actions (such as speed, acceleration and steering control) can be fed into a machine learning algorithm to determine a driving style or driver behavior model.” [0055]), where a machine learning model can be trained using data regarding an amount of energy utilized by the vehicle, in which the energy can be fuel or battery energy used, in order to determine energy efficiency of a route.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baglino’s navigation for a vehicle with Wang’s machine learning training because (“With historical data for the driver, the energy use model for future trips can be estimated by, for example, comparing the new travel path and segments thereof, with historical data for the same or similar segments. This can provide a much better estimate of energy consumption in different segments of a trip, and from that the system can estimate vehicle range, and, if needed, can recommend charging/refueling stations that enable greater energy efficiency and convenience. Further, when the historical data is captured, real-time driver actions (such as speed, acceleration and steering control) can be fed into a machine learning algorithm to determine a driving style or driver behavior model.” [0055]), and therefore better estimate information for a generating a hybrid-electric vehicle route.
With respect to claim 6, Baglino in combination with Wang, as shown in the rejection above, discloses the limitations of claim 3. The combination of Baglino and Wang teaches providing navigation for hybrid-electric vehicle of claim 3. Baglino does not teach, but Sawada teaches:
wherein the displayed two or more driving routes also include display of an engine usage strategy for the hybrid-electric vehicle for each of the two or more driving routes; (““Induce to set the ‘eco-mode’” here means, for example, displaying a setting screen of the “eco-mode” on a display of the navigation system 4, which is not shown, or illuminating a setting button for the “eco-mode”. In addition, the driver may be urged to set the “eco-mode” by voice as well. In addition, in the first embodiment and the second embodiment, examples were shown in which the setting of the travel plan is carried out by setting a planned travel mode for each section after dividing the planned travel route into a plurality of sections, but the invention is not limited thereto. For example, a consumption management of the battery SOC may be planned, or a drive force distribution of the engine Eng and the motor/generator MG may be set in sections in which the planned travel mode is the “HEV mode”.” [0086-0087]), where the travel plan sections are displayed.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baglino-Wang’s navigation for hybrid-electric vehicle with Sawada’s hybrid-electric vehicle because (“by carrying out a control of the drive source in accordance with the travel plan, a low fuel travel becomes possible to thereby improve the fuel efficiency.” [0061], “It is thereby possible to control the drive source in accordance with the travel plan, and low fuel travel becomes possible to thereby improve fuel efficiency.” [0064]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Shojaei et al. (US 20260029239 A1) is pertinent because (“In a second type, historical datasets from the previous trips of the electrified vehicle are used to train a machine learning algorithm. These datasets include factors contributing to energy consumption (accelerator pedal position, brake pedal position, selected gear, air conditioning system state, road topology, etc.), as well as the achieved range from previous trips. By training the machine learning algorithms on historical trip datasets, this type of method aims to be able to associate the operation state of the electrified vehicle in its current trip to the most similar historical data and predict the remaining range accordingly.” [0017)]), which pertains to training a neural network on historic data.
Panneer Selvam et al. (US 20250198775 A1) is pertinent because (“In some embodiments, using machine learning or deep learning to train models for energy use prediction as well as using the trained models for energy use prediction to inform, manage, or control an end-user such as a person or a mobile machine involves the application of neural network architectures to analyze and predict the energy use based on historical data (e.g., see scheme 707 shown in FIG. 7, which includes an artificial neural network or ANN). In some cases, by leveraging machine or deep learning, a model can capture complex patterns and dependencies within the various types of information described herein (e.g., see task information 104, actor information 105, speed-time information 108b, and predicted energy-use information 118b), allowing for more efficient and effective energy use prediction, which in some instances can be done in real time when the end-user is performing a task or in route.” [0037], “Energy prediction can be used by vehicle operators or logistics planners to estimate the amount of fuel or battery energy that will be required to complete a route or mission. It can also be used in hybrid vehicles, where two energy storage sources are available, to control which energy source will be used during the route to optimize a desired parameter. Such optimizations could include fuel use minimization or battery use minimization. Fuel efficiency could also be optimized.” [0060]), which pertains to training a neural network on historic data.
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 Christine N Huynh whose telephone number is (571)272-9980. The examiner can normally be reached Monday - Friday 8 am - 4 pm.
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/CHRISTINE NGUYEN HUYNH/Examiner, Art Unit 3662
/Madison R. Inserra/Primary Examiner, Art Unit 3662
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