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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/16/2024.The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgement is made of applicants claim for foreign priority under 35 U.S.C. 119(a)-(d) and (f). The certified copy has been filed in parent application KR10-2024-0008802 filed on 01/19/2024.
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, 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
On January 7, 2019, the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 1 is directed toward non-statutory subject matter, as shown below:
STEP 1: Do the claims fall within one of the statutory categories?
Yes claims 1, 12 are directed towards a device and a method, respectively.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
Yes, the claims are directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The process in claims 1, 12 is a mental process that can be practicably performed in the human mind, or with the aid of pen and paper and as such is directed toward and abstract idea. The claim consists of predicting a change in speed based on a route and map information which is similar to a human determining based on having to pass by a route that has an incline that the vehicle will slow down. Dividing a route is similar to a human dividing the route between straight, incline roads which will cause a speed change. Obtaining state of charge information is similar to a human determining that the SOC will change between a straight road and inclined roads. Obtaining a ratio information is similar to a human determining that 10 percent of the route would time would be Notably incline and thus that is the time a vehicle would be in a hybrid mode. Predicting an acceleration is similar to a human predicting that the car would have less acceleration in an incline section of the route based on a sensor indicating incline or a sensor indicating that there are obstacles and thus the acceleration has to change. Obtaining a fuel consumption information is similar to a human determining speeds that would help reduce consumption based on the ev and hybrid mode ratios and the acceleration information. Notably, the claim does not positively recite any limitations regarding actual determination of the attitude of the vehicle.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
An additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1, 12 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. The additional limitations include The processor, memory, battery, control device, vehicle which are all cited with high level of generality and are considered at the apply it level technology. conrol the vehicle along the route, using the fuel consumption is recited with high level of generality and is considered apply it level of an abstract idea of applying a mode to the vehicle which could also be a mere data gathering of a signal to switch a mode without controlling any actuators of the vehicle. The sensors are considered generic linking.
Thus, it is clear that the abstract idea is merely implemented on a computer at the “apply it level”, which is indicative of the abstract solution having not been integrated into a practical application.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claims 1,12 do not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field. The processor, memory, battery, control device, vehicle which are all cited with high level of generality and are considered at the apply it level technology. conrol the vehicle along the route, using the fuel consumption is recited with high level of generality and is considered apply it level of an abstract idea of applying a mode to the vehicle which could also be a mere data gathering of a signal to switch a mode without controlling any actuators of the vehicle. The sensors are considered generic linking.
CONCLUSION
Thus, since claims 1,12: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that the claims are directed towards non-statutory subject matter.
Claims 2, 13: models are recited with high level of generality and are applying calculations using computers.
Claim 3, 14: predicting the acceleration is similar to a human determining vehicle will slow down based on an obstacle close by or by calculations using paper and pen.
Claim 4, 15: identifying an average speed is similar to a human guessing an average speed based on speed limit, obtaining SOC is done by simple calculations or determining an increase or decrease in the SOC based on information.
Claim 5, 16: the predictions are similar to a human calculating or determining the information based on sensing that the air resistance is high or low and based on the car being an SUV or sedan or truck, predicting a speed based on an acceleration is similar to a human determining a speed reduction due to the vehicle slowing down, obtaining a fuel consumption is similar to a human determining speeds based on the information to be efficient.
Claim 6, 17: predicting a first and second energy amounts are similar to a human determining the energy consumption via known equations and thus the fuel consumption can be determined via an equation combining the two.
Claim 7, 18: calculating SOC is similar to a human calculating the SOC using known equations and then obtaining the fuel consumption by the value calculated.
Claim 8, 19: identifying a transit time is similar to a human determining using known equations how much time a vehicle will sped in a certain mode based on the determined speed and the determined sections to be in HEV.
Claim 9, 20: controlling the vehicle is cited with a high level of generality and is considered apply it level of an abstract idea of determining the mode based on information and could also be mere data gathering of switching mode as a signal without any further control of an engine or other actuators.
Claim 10, 21: obtaining a ratio is just a mathematical calculation of a ratio that indicates some information.
Claim 11, 22: just recites data used.
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.
Claims 1, 3, 4, 8, 10, 11, 12, 14, 15, 19, 21, 22 are rejected under 35 U.S.C. 103 as being unpatentable by Huh (US20230009058) in view of Morisaki (US20160137185) and Hoffberg (US20060167784).
Regarding claim 1, Huh vehicle control device, comprising:
a processor ([0083] disclosing a processor;
a sensor ([0055] disclosing a sensor);
a battery ([0015] disclosing a battery) ; and
a memory ([0083] disclosing a memory),
wherein the processor is configured to: predict a change in speed of a vehicle based on a route of the vehicle, while driving the vehicle ([0035] disclosing a plurality of sections based on speed predicted. [0065]-[0069] disclosing segmenting the route based on speed changes wherein speed is predicted from route information from a map);
divide the route of the vehicle into a plurality of partial routes, using the change in speed of the vehicle [0068]-[0069] disclosing segmenting the route based on speed changes);
obtain state of charge (SOC) information of the battery in each partial route of the plurality of partial routes, wherein the SOC information changes based on the change in speed of the vehicle ([0073]-[0075] disclosing obtaining the SOC changes based on the route which is determined in each section based on the speed predicted and then a power needed based on the speed and then the SOC predicted);
obtaining ratio information between a transit time when the vehicle passes through each partial route and a driving time of the vehicle based on hybrid electric vehicle mode in each partial route, using SOC information (Fig. 12 and [0080] disclosing the ratio information indicating portions of the route time spent with electric vs engine on/hybrid).
Huh does not teach map information received from an external server.
Morisaki teaches map information ([0053] disclosing determining an average speed of the vehicle based on inclination of a road in each segment based on map information, see [0037] for map information).
Obtain fuel consumption information for minimizing fuel consumed while the vehicle is traveling along the route, in the partial route in which the vehicle is located, using the ratio information ([0065]-[0067] disclosing fuel consumption information as being improved by utilizing a ratio of hybrid until the destination at the end portion of the route, i.e., using ratio information to obtain fuel consumption information indicating an improved fuel consumption).
Control the vehicle along the route based on electric mode or the HEV mode using the fuel consumption information ([0065]-[0067] disclosing controlling the vehicle switching based on the fuel consumption information).
It would have been obvious to incorporate Morisaki method of speed prediction along a route using a map in order to predict the accurate speed based on map information such as road inclination and speed limits [0053] and determine an optimized fuel consumption of the vehicle to improve efficiency based on a hybrid and electric utilization as taught by Morisaki [0066]-[0067]. While Morisaki does not explicitly state from an external server, obtaining map information from remote servers is well known in the art, substituting the method of obtaining a map for an external server is obvious in order to save memory storage on the vehicle and reduce a load when information is not used.
Huh as modified by Morisaki does not teach predict acceleration of the vehicle, in a partial route in which the vehicle is located among the plurality of partial routes, using the sensor; obtain fuel consumption information for minimizing fuel consumed while the vehicle is traveling along the route, in the partial route in which the vehicle is located, using acceleration information indicating the predicted acceleration of the vehicle.
Hoffberg teaches using acceleration information indicating the predicted acceleration ([2023] disclosing the prediction of the acceleration to predict fuel efficient acceleration);
predict acceleration of the vehicle, in a partial route in which the vehicle is located among the plurality of partial routes, using the sensor ([2023] disclosing predicting acceleration using sensor information based on objects in front or behind the vehicle);
obtain fuel consumption information using the acceleration information indicating the predicted acceleration of the vehicle ([2023] disclosing the fuel efficiency based on the predicted acceleration of the vehicle).
it would be obvious to combine the teaching of Hoffberg to the partial routes of Huh as modified by Morisaki yielding predictable results and improving the safety of the vehicle by accounting to objects and also predicting a more accurate fuel efficiency based on the environment of the vehicle determining the optimum acceleration that is safe. Huh as modified by Morisaki teaches an efficient fuel consumption and controlling the vehicle mode based on the efficient fuel consumption, thus incorporating the acceleration of Hoffberg would ensure accounting for safe accelerations when determining fuel efficient maneuvers by accounting for objects in the surrounding.
Regarding claim 3, Huh as modified by Morisaki and Hoffberg teaches the vehicle control device of claim 1, wherein the processor is configured to: predict the acceleration of the vehicle, based on at least
one of a relative location of another vehicle located around the vehicle and a speed of the other vehicle, using the sensor (Hoffberg disclosing the predicted acceleration based on the relative speed using sensors).
it would be obvious to combine the teaching of Hoffberg to the partial routes of Huh as modified by Morisaki yielding predictable results and improving the safety of the vehicle by accounting to objects and also predicting a more accurate fuel efficiency based on the environment of the vehicle determining the optimum acceleration that is safe. Huh as modified by Morisaki teaches an efficient fuel consumption and controlling the vehicle mode based on the efficient fuel consumption, thus incorporating the acceleration of Hoffberg would ensure accounting for safe accelerations when determining fuel efficient maneuvers by accounting for objects in the surrounding.
Regarding claim 4, Huh as modified by Morisaki and Hoffberg teaches the vehicle control device of claim 1, wherein the processor is configured to:
identify an average speed of the vehicle in each partial route, wherein the average speed follows the change in speed of the vehicle (Huh in abstract disclosing determining average speed in each section); and
obtain the SOC information of the battery in each partial route, the SOC information indicating an SOC of the battery, and the SOC information corresponding to the average speed of the vehicle (Huh abstract, [0019]-[0028] disclosing determining the SOC based on the average speed for each section).
Regarding claim 8, Huh as modified by Morisaki and Hoffberg teaches the vehicle control device of claim 1, wherein the processor is configured to: identify the transit time when the vehicle passes through each partial route based on an average speed of the vehicle, wherein the average speed follows the change in speed of the vehicle (Huh [0065]-[0077] disclosing the determining of the timing of switch based on the average speed which is determined based on the changed speed).
Regarding claim 10, Huh as modified by Morisaki and Hoffberg teaches the vehicle control device of claim 1, further comprising: an engine, wherein the processor is configured to: obtain the ratio information, using engine information indicating whether to drive the engine for controlling the vehicle based on the HEV mode (Huh [0073]-[0077] and figure 12 disclosing the ratio is information indicating when to start an engine).
Regarding claim 11, Huh as modified by vehicle control device of claim 1, wherein the map information includes at least one of grade information of a road corresponding to the route, a speed limit of the road, traffic volume on the road, or any combination thereof (Morisaki [0053] disclosing the inclination information and speed information).
It would have been obvious to combine the inclination information and speed information of Huh in order to allow for an accurate prediction of a change in speed of Huh yielding predictable results, see [0053] of Morisaki.
Claims 12, 14-15, 19, 21, 22 is rejected for similar reasons as claim 1, 3-4, 8, 10, 11 respectively, see above rejection.
Claims 2, 13 are rejected under 35 U.S.C. 103 as being unpatentable by Huh (US20230009058) in view of Morisaki (US20160137185) and Hoffberg (US20060167784) and Nikovski (US20160362096) and Nelson (US20240375637).
Regarding claim 2, Huh as modified by Morisaki and Hoffberg teaches the vehicle control device of claim 1, wherein the processor is configured to: obtain the ratio information from the map information, (Huh [0077]-[0080] disclosing optimization of the switching between the modes i.e., ratio, to improve fuel efficiency based on map information).
Nikovski teaches ratio information in a layer using dynamic programming ([0034]-[0046] disclosing the dynamic programming to minimize fuel and energy by optimizing switching times of electric and combustion engines using machine learning).
It would have been obvious to one of ordinary skill in the art to combine/substitute the teaching of Nikovski of applying known mathematical optimization techniques to establish a minimum amount of cost of consumption of fuel yielding predictable.
Huh as modified by Morisaki and Hoffberg and Nikovski does not teach using the ratio information obtained in the first layer, in a second layer including at least one of an acceleration prediction model, a vehicle required power model, or a vehicle control model, and associated with local path planning.
Nelson teaches obtain the fuel consumption information, using the ratio information obtained in the first layer, in a second layer including at least one of an acceleration prediction model, a vehicle required power model, or a vehicle control model, and associated with local path planning ([0037]-[0041] disclosing the optimization of fuel consumption by optimizing the power distribution using neural network and including power ratio models);
It would have been obvious to incorporate a machine learning model to the method of fuel information as taught by Huh as modified by Morisaki and Hoffberg which will learn the optimal timing for switching based on input and output of the machine over time as taught by Nelson thus improving the decision making and by automation of tasks. The use of different layer structures is an obvious design choice of neural network based on the input and output data. It would be obvious to combine the technique to a local path segment of Huh yielding predictable results for optimization of switching times in each section.
Claim 13 is rejected for similar reasons as claim 2, see above rejection.
Claims 5, 16 are rejected under 35 U.S.C. 103 as being unpatentable by Huh (US20230009058) in view of Morisaki (US20160137185) and Hoffberg (US20060167784) and Gesang (US20250269856) and Ortmann (US20220144241).
Regarding claim 5, Huh as modified by Morisaki and Hoffberg does not teach the vehicle control device of claim 1, wherein the processor is configured to: predict power, using at least one of a rolling resistance coefficient (RRC) of a wheel of the vehicle, an aerodynamic coefficient, an equivalent test weight (ETW), or any combination thereof; predict a speed of the vehicle, the speed to be obtained based on the acceleration; and obtain the fuel consumption information, using at least one of power information indicating the predicted power, speed information indicating the speed of the vehicle, the ratio information, or any combination thereof.
Gesang teaches wherein the processor is configured to: predict power, using at least one of a rolling resistance coefficient (RRC) of a wheel of the vehicle, an aerodynamic coefficient, an equivalent test weight (ETW), or any combination thereof ([0149] disclosing predicting power based on air resistance coefficient);
obtain the fuel consumption information, using at least one of power information indicating the predicted power, speed information indicating the speed of the vehicle, the ratio information, or any combination thereof ([0149] disclosing using the power information to obtain fuel consumption information for fuel energy saving).
It would have been obvious to incorporate the teaching of Gesang in order to determine an energy efficient control strategy based on the power as taught by Gesang [0149].
Ortmann teaches predict a speed of the vehicle, the speed to be obtained based on the acceleration ([0032] disclosing determining the speeds based on the acceleration predictions).
It would have been obvious to substitute the method of determining the speed of Huh with the method of Ortmann yielding predictable results and enabling the determination of speed based on accelerations based on route information in order to solve the same problem of optimizing the fuel of the vehicle.
Claim 16 is rejected for similar reasons as claim 5, see above rejection.
Claims 6, 7, 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable by Huh (US20230009058) in view of Morisaki (US20160137185) and Hoffberg (US20060167784) and Yang (US20230339451).
Regarding claim 6, Huh as modified by Morisaki and Hoffberg and Gesang and Ortmann does not teach the vehicle control device of claim 5, wherein the processor is configured to: predict a first energy amount to be consumed when controlling the vehicle along at least a portion of the route based on the HEV mode, using the power information and the speed information; predict a second energy amount to be consumed when controlling the vehicle along at least a portion of the route based on the EV mode; and obtain the fuel consumption information, using the first energy amount and the second energy amount.
Yang teaches predict a first energy amount to be consumed when controlling the vehicle along at least a portion of the route based on the HEV mode, using the power information and the speed information ([0039]-[0040] disclosing determining based on a speed of the vehicle and power, an electric energy consumed in a hybrid mode);
predict a second energy amount to be consumed when controlling the vehicle along at least a portion of the route based on the EV mode ([0039]-[0040] disclosing determining an electric energy consumed in an electric mode);
and obtain the fuel consumption information, using the first energy amount and the second energy amount ([0039]-[0040] disclosing determining the energies for both cases by converting the electric energy into fuel).
Huh aims at selecting a mode for fuel efficiency at a portion of the route, It would be obvious to combine the method of Yang to be applied at the portions of the route for determining electrical and hybrid energy consumptions, yielding predictable results and selecting the mode that uses less fuel consumption as taught by Yang (abstract, [0039]-[0040].
Regarding claim 7, Huh as modified by Morisaki and Hoffberg and Gesang and Yang teaches the vehicle control device of claim 6, wherein the processor is configured to:
calculate another piece of SOC information indicating an SOC of the battery using the power information and the speed information, the other piece of SOC information changes when controlling the vehicle based on the EV mode (Huh [0073]-[0077] disclosing determining plurality of SOC based on route information in each section and determining the ratio changing for best fuel efficiency); and
obtain the fuel consumption information, using the calculated other SOC information and the ratio information (Huh [0073]-[0077] disclosing determining plurality of SOC based on route information in each section and determining the ratio changing for best fuel efficiency);
Claim 17, 18 is rejected for similar reasons as claim 6, 7, respectively, see above rejection.
Claims 9, 20 are rejected under 35 U.S.C. 103 as being unpatentable by Huh (US20230009058) in view of Morisaki (US20160137185) and Hoffberg (US20060167784) and Makimura (US20150336572).
Regarding claim 9, Huh as modified by Morisaki and Hoffberg teaches the vehicle control device of claim 1, wherein the processor is configured to:
control the vehicle based on the EV mode or the HEV mode, along the partial route in which the vehicle is located, using sub-ratio information corresponding to the partial route in which the vehicle is located in the ratio information ([0065]-[0077] disclosing the operation of the vehicle in an electric of HEV mode based on the ratio information using the ratio information, the switch is based on the segment thus indicative of the sub ratio where the vehicle is located at the time).
While Huh does not teach based on acceleration information.
Makimura teaches switching based on acceleration information ([0034] disclosing the switching between electric mode based on an acceleration requiring more engine power).
It would be obvious to combine the teaching of Makimura with the teaching of Huh yielding predictable results in order to efficiently switch from an electric mode when a power is required based on the acceleration information.
Conclusion
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure. The prior art cited in PTO-892 and not mentioned above disclose related devices and methods.
US20170036663 discloses a method for controlling a hybrid vehicle is provided. The method includes setting a driving path of the vehicle based on an input destination and current position and predicting a future speed of the vehicle using information regarding the driving path, environmental information, and driving pattern information of a driver. An optimum power distribution map is derived including an optimum SOC trajectory and a power distribution ratio of the engine and the motor using the predicted future speed. Additionally, engine power and motor power is distributed using the optimum SOC trajectory and a power distribution ratio of the engine and the motor.
US20190001957 discloses a hybrid vehicle, and a control method of a driving mode therefor includes steps of determining a traveling path, dividing the traveling path into a plurality of sections according to a driving condition, allocating a class corresponding to a driving condition of a corresponding section among a plurality of predetermined classes, to each of the plurality of sections, calculating energy consumption of each of the plurality of sections, sequentially summing the energy consumption of the plurality of sections in an order determined with reference to energy consumption rates for modes corresponding to the respective classes until a predetermined first condition is satisfied, and determining a first class corresponding to a section as a last target of summing when the first condition is satisfied, as a second condition and as a reference for switching from a first driving mode to a second driving mode.
US20210101582 disclosing a hybrid vehicle calculating a ratio of employing EV and HEV modes may be predicted by predicting driving load based on a forward path in order to predict a gear shift control. Further disclosing method of controlling gear shift of a hybrid vehicle, the method comprising: predicting, via a hybrid controller, required power of a forward driving path; determining a representative driving mode based on mode switch power as a reference of switch between a first driving mode using only an electric motor and a second driving mode using at least an engine and the predicted required power; and applying, via a transmission controller, any one of a first gear shift map corresponding to the first driving mode or a second gear shift map corresponding to the second driving mode based on the representative driving mode determined by the hybrid controller.
US20160207521 disclosing The vehicle 100 according to the present embodiment has, for example, an EV mode, in which the vehicle 100 runs with the motor as a drive source, and an HV mode, in which the vehicle 100 runs with a motor and an engine as drive sources. The hybrid controller 108 according to the present embodiment exerts control in which switching between the EV mode and the HV mode is executed according to, for example, the result of selection made by the driver of the vehicle 100. Additionally, the hybrid controller 108 according to the present embodiment has, for example, an automatic switching function for the EV mode and the HV mode, and exerts control for switching between the EV mode and the HV mode based on information indicating the travel route of the vehicle 100 and/or movement cost taken for the travel route, which is input from the on-vehicle controller 120. A road load is the amount of load amount per unit distance in respective sections, and is an average load amount required for travelling a section. An accumulated value of the road load required in completely traveling the section is defined as consumption energy.
US20160221567 disclosing The hybrid controller 108 identifies the section where the vehicle 100 is currently traveling by suitably acquiring currently traveling location information from the on-vehicle controller 120, and causes the vehicle 100 to travel in the travel mode planned for the identified section. That is, the hybrid controller 108 switches the travel mode of the vehicle 100 to the EV mode or the HV mode assigned to the section each time the travel section of the vehicle 100 changes. Due to this, the vehicle 100 travels in the travel mode planned for the section where the vehicle 100 is currently traveling. Further, the hybrid controller 108 includes a mode setter 108a that performs setting of a travel mode plan assigned to sections of the acquired travel route. The mode setter 108a configures a part of the travel support device. The functions of the mode setter 108a are exhibited by execution of programs in the hybrid controller 108. The mode setter 108a has a function to set a travel mode of each section in accordance with the remaining charge of the battery 110. The “setting” performed by the mode setter 108a includes “changing” of the travel mode, “updating” of the travel mode, and “resetting” of the travel mode.
US20220242390 disclosing optimization of switching times between electric motor and engine to optimize fuel saving.
US20230036756 disclosing a a mode for carrying out the disclosure will be described with reference to an embodiment. FIG. 1 is a block diagram illustrating an example of a configuration of a travel support control device for a hybrid vehicle 20 according to an embodiment of the disclosure with a hybrid electronic control unit (hereinafter referred to as a hybrid ECU) 50 as a central block. The electronic control unit 50 corresponds to the travel support control device. The hybrid vehicle 20 according to the embodiment includes an engine EG and a motor MG as power sources as illustrated in the drawing. The hybrid vehicle 20 according to the embodiment travels while switching a travel mode between a charge depleting mode (a CD mode) in which electric traveling has priority such that a state of charge SOC of a battery 40 decreases and a charge sustaining mode (a CS mode) in which electric traveling and hybrid traveling are used together such that the state of charge SOC of the battery 40 is maintained at a target value. Electric traveling is a mode in which the hybrid vehicle travels using only power from the motor MG in a state in which operating of the engine EG has been stopped, and hybrid traveling is a mode in which the engine EG operates and the hybrid vehicle travels using power from the engine EG and power from the motor MG.
US20230227019 disclosing motor controller may determine a mode of the hybrid electric vehicle according to a mode switch signal, and then choose different driving strategies. Generally, the hybrid electric vehicle includes two working modes (electric mode and hybrid mode, namely EV mode and HEV mode) and two driving modes (Economy mode and Sport mode, namely ECO mode and Sport mode). Therefore, the hybrid electric vehicle may have four kinds of operating modes, such as EV-ECO mode, EV-Sport mode, HEV-ECO mode and HEV-Sport mode. In the EV mode, the vehicle is in a pure electric energy consumption mode and the motor outputs power separately; in the HEV mode, the vehicle is in a hybrid energy consumption mode, and a ratio of power output by the motor to power output by the engine is determined according to a preset strategy. In the ECO mode, the power output from the motor and the engine is limited since the economy is a primary control target; in the Sport mode, the power output from the motor and the engine is not limited since the power performance is the primary control target, especially, in the hybrid sport mode (HEV-Sport mode), the engine remains running.
US20190389451 disclosing solving a full trajectory optimization problem in real-time for a hybrid electric vehicle (HEV) such that future driving conditions and energy usage may be fully considered in determining optimal engine energy usage and battery energy usage in real-time during a trip. An electronic control unit of the HEV may be configured to: receive route information for a route to be driven by the HEV; and after receiving the route information, iterating the operations of: measuring a current state of charge (SOC) of the battery; using at least the measured SOC and an initial co-state value stored in a memory, performing a process to iteratively update the co-state value to obtain an updated co-state value; using at least the updated co-state value, computing an updated control value; and applying the updated control value to control a usage of the battery and the internal combustion engine.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMAD O EL SAYAH whose telephone number is (571)270-7734. The examiner can normally be reached on M-F 7:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramon Mercado can be reached on (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMAD O EL SAYAH/Examiner, Art Unit 3658B