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 response to the amendments filed on 12/18/2025, in which claims 1 and 12 are amended, claim 3 is cancelled. Claims 1, 2, and 4-20 are rejected.
Response to Arguments
Applicant’s arguments, see REMARKS, filed 12/18/2025, with respect to the rejection of claims 1, 2, 4-8, 12, 13, 16, 18, and 19, under 35 USC §102, have been fully considered and are persuasive. Therefore the previous rejections have been withdrawn. However, a new rejection is presented below in view of Jeong.
Applicant’s arguments, with respect to the rejection of claims 9-11, 14, 15, 17, and 20, under 35 USC §103, have been fully considered and are persuasive. Therefore the previous rejections have been withdrawn. However, a new rejection is presented below in view of Jeong.
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.
Claim(s) 1, 2, 4-8, 12, 13, 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jeong et al. (US 2022/0080980 A1, “Jeong”) in view of Pan et al. (US 2021/0046918 A1, “Pan”).
Regarding claims 1 and 12, Jeong discloses a device for predicting speed of vehicle and method thereof and teaches:
A system for controlling a hybrid powertrain in a vehicle, comprising: (FIG. 9 is a block diagram illustrating a computing system for performing a method of predicting a speed of a vehicle, according to various exemplary embodiments of the present invention – See at least ¶ [0076])
one or more processors; (In the meantime, the controller 40 may perform overall control such that each of the components is configured for normally performing functions of the components. The controller 40 may be implemented in a form of hardware, may be implemented in a form of software, or may be implemented in a form of the combination of hardware and software. Favorably, the controller 40 may be implemented as a microprocessor, but is not limited thereto – See at least ¶ [0062])
a memory; and (The storage 10 may include at least one type of a storage medium among a flash memory type of a memory, a hard disk type of a memory, a micro type of a memory, and a card type (e.g., a Secure Digital (SD) card or an eXtream Digital (XD) Card) of a memory, a Random Access Memory (RAM) type of a memory, a Static RAM (SRAM) type of a memory, a Read-Only Memory (ROM) type of a memory, a Programmable ROM (PROM) type of a memory, an Electrically Erasable PROM (EEPROM) type of a memory, an Magnetic RAM (MRAM) type of a memory, a magnetic disk type of a memory, and an optical disc type of a memory – See at least ¶ [0041])
one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the one or more programs including instructions to: (In another exemplary embodiment of the present invention, the storage 10 may store various logics, algorithms, and programs that are required in a processing of entering the time-series data for the driving profile before the prediction time point and the driving profile at the prediction time point into the encoder, learning a second vehicle speed model for predicting a speed of a vehicle based on a low-dimensional representation (Z), which is an output of the encoder, and a vehicle speed at the prediction time point which is additionally entered, and predicting the speed of the vehicle based on the learned second vehicle speed model – See at least ¶ [0040])
determine a first driving profile over a first time period, where the first time period is an immediately preceding time period (Herein, the driving profile before the prediction time point is a value measured during a predetermined time period based on a current time point, and refers to information for forming a driving pattern of the vehicle. The driving profile may include a gas pedal position (GPP) value, revolutions per minute (RPM), a gear stage, a vehicle speed, a gradient of a road, a curvature of the road, a steering angle, a brake pedal position (BPP) value (brake on/off or brake pressure), a separation distance from a preceding vehicle, a relative speed with the preceding vehicle, traffic light information in front, or the like. At the instant time, the driving profile is time-series data measured during a predetermined time period – See at least ¶ [0043]) that includes a range of time that is up to one hundred twenty seconds in duration and extends to or within five seconds of a time at which the first driving profile is determined; (As illustrated in FIG. 2, the learning device 30 included in a speed prediction device of a vehicle according to various exemplary embodiments of the present invention may learn a first vehicle speed model based on VAE. '100' denotes a probabilistic encoder. ‘110’ is learning data for a driving profile, and denotes time-series data ( X ) during a reference time (Tpast~Tpresent) before a prediction time point (Tpresent) – See at least ¶ [0050]; Examiner notes that the time periods are shown in Fig. 6a and 6b and are from 0 seconds to at least 12 seconds, i.e., includes a range that is up to one hundred and twenty seconds in duration. Examiner further notes that the first time period is a past time up to a present time, i.e., a extends to a time at which the first driving profile is determined. ¶ [0050])
determine a second driving profile for a second time period, where the second time period includes at least some future time; (‘240’ denotes a predicted future speed (Y') of a vehicle. At the instant time, Y' may be a speed of the vehicle during a period (T present~Tfuture) after a reference time from a current time point, and may be expressed in the format of time-series data – See at least ¶ [0051])
Jeong does not explicitly teach determine a powertrain control instruction based at least in part on the second driving profile; and control the powertrain as a function of the powertrain control instruction. However, Pan discloses hybrid vehicle transmission control using driver statistics and teaches:
determine a powertrain control instruction based at least in part on the second driving profile; and (Based on the predicted short-horizon driver demand, Layer 2 uses the policy generated by Layer 1 to determine the target engine state and operating settings for the predicted driver demand and also determines whether the engine and the motor(s) can be operated to reach the target settings/states for the predicted driver demand within a defined time step – See at least ¶ [0031])
control the powertrain as a function of the powertrain control instruction. (Accordingly, in Layer 3, the controller 101 determines control constraints based, for example, on the actual current driver demand, current operating settings, and system/component limitations. In some implementations, these control constraints determined by Layer 3 indicate the maximum and/or minimum values of operating settings that can be applied without violating performance metrics based, for example, on drivability and NVH (noise, vibration, and harshness) – See at least ¶ [0032]; Examiner notes that layer three determines the control constraints based on the maximum and/or minimum values of operating settings that can be applied without violating metrics, this would include the output from layers 1 and 2, i.e., is a function of the layer 2 instructions.)
Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong to provide for the hybrid vehicle transmission control using driver statistics, as taught in Pan, to optimize a performance metric of a hybrid electric vehicle (e.g., fuel efficiency) while also operating within defined tolerances for additional performance parameters including, for example, drivability and NVH (i.e., noise, vibration, & harshness). (At Pan ¶ [0002])
Regarding claims 2 and 16, Jeong does not explicitly teach, but Pan further teaches:
wherein the second time period includes a time period including up to 30 seconds from a time at which the second driving profile is determined. (the short-horizon period of time is defined as a period of a few seconds (e.g., 25 seconds) – See at least ¶ [0031])
Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong to provide for the hybrid vehicle transmission control using driver statistics, as taught in Pan, to optimize a performance metric of a hybrid electric vehicle (e.g., fuel efficiency) while also operating within defined tolerances for additional performance parameters including, for example, drivability and NVH (i.e., noise, vibration, & harshness). (At Pan ¶ [0002])
Regarding claim 4, Jeong does not explicitly teach, but Pan further teaches:
wherein the second driving profile is determined at least in part as a function of the location of the vehicle. (Such control mechanism may be based on determining a probability of driver demand based on stored historical data regarding, for example, desired vehicle acceleration and velocity based on user control input for particular driving routes and conditions. For example, a driver may frequently operate the vehicle along a same route under similar conditions (e.g., driving from home to work at approximately the same time each day). By accumulating driver demand information from this routes, the system will be able to predict a driver demand profile for future operation of the vehicle by the same driver along the same route in a way that is steady and not sensitive to instantaneous demand changes – See at least ¶ [0025]; Examiner notes that a route based driving profile includes the location of the vehicle.)
Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong to provide for the hybrid vehicle transmission control using driver statistics, as taught in Pan, to optimize a performance metric of a hybrid electric vehicle (e.g., fuel efficiency) while also operating within defined tolerances for additional performance parameters including, for example, drivability and NVH (i.e., noise, vibration, & harshness). (At Pan ¶ [0002])
Regarding claim 5, Jeong does not explicitly teach, but Pan further teaches:
wherein the second driving profile is determined at least in part as a function of a road on which the vehicle is traveling. (Such control mechanism may be based on determining a probability of driver demand based on stored historical data regarding, for example, desired vehicle acceleration and velocity based on user control input for particular driving routes and conditions. For example, a driver may frequently operate the vehicle along a same route under similar conditions (e.g., driving from home to work at approximately the same time each day). By accumulating driver demand information from this routes, the system will be able to predict a driver demand profile for future operation of the vehicle by the same driver along the same route in a way that is steady and not sensitive to instantaneous demand changes – See at least ¶ [0025])
Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong to provide for the hybrid vehicle transmission control using driver statistics, as taught in Pan, to optimize a performance metric of a hybrid electric vehicle (e.g., fuel efficiency) while also operating within defined tolerances for additional performance parameters including, for example, drivability and NVH (i.e., noise, vibration, & harshness). (At Pan ¶ [0002])
Regarding claims 6 and 18, Jeong does not explicitly teach, but Pan further teaches:
wherein the second driving profile is determined at least in part as a function of a current torque demand on the powertrain. (For example, if the controller 101 is currently operating the vehicle in an “engine off ” state and the predicted short-horizon driver demand indicates that the driver will likely increase the vehicle torque or speed demand such that the engine will need to operate at a relatively high torque level, the short-horizon policy generated by the controller 101 at the second layer may be configured to indicate that the combustion engine should be transitioned to the “engine on” state at a lower actual driver demand such that the engine torque can be gradually increased to the required operating setting if the actual driver demand continues over the short-horizon period of time as predicted by the second layer of the control mechanism – See at least ¶ [0038])
Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong to provide for the hybrid vehicle transmission control using driver statistics, as taught in Pan, to optimize a performance metric of a hybrid electric vehicle (e.g., fuel efficiency) while also operating within defined tolerances for additional performance parameters including, for example, drivability and NVH (i.e., noise, vibration, & harshness). (At Pan ¶ [0002])
Regarding claim 7, Jeong does not explicitly teach, but Pan further teaches:
wherein the second driving profile is determined at least in part as a function of a driving behavior historical data. (Layer 2 is configured to predict the short-horizon driver demand based on historical driver statistics as well as immediate environment information including, for example, data from vehicle cameras and sensors regarding the presence of other nearby vehicles. For example, if the sensor data indicates that the hybrid vehicle is approaching another vehicle from behind, the short horizon driver demand prediction may indicate that the driver is likely to decelerate the vehicle – See at least ¶ [0030])
Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong to provide for the hybrid vehicle transmission control using driver statistics, as taught in Pan, to optimize a performance metric of a hybrid electric vehicle (e.g., fuel efficiency) while also operating within defined tolerances for additional performance parameters including, for example, drivability and NVH (i.e., noise, vibration, & harshness). (At Pan ¶ [0002])
Regarding claims 8 and 19, Jeong does not explicitly teach, but Pan further teaches:
wherein the second driving profile is determined at least in part as a function of a driving behavior historical data. (Layer 2 is configured to predict the short-horizon driver demand based on historical driver statistics as well as immediate environment information including, for example, data from vehicle cameras and sensors regarding the presence of other nearby vehicles. For example, if the sensor data indicates that the hybrid vehicle is approaching another vehicle from behind, the short horizon driver demand prediction may indicate that the driver is likely to decelerate the vehicle – See at least ¶ [0030])
Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong to provide for the hybrid vehicle transmission control using driver statistics, as taught in Pan, to optimize a performance metric of a hybrid electric vehicle (e.g., fuel efficiency) while also operating within defined tolerances for additional performance parameters including, for example, drivability and NVH (i.e., noise, vibration, & harshness). (At Pan ¶ [0002])
Regarding claim 13, Jeong does not explicitly teach, but Pan further teaches:
wherein the one or more processors are part of a vehicle control system. (As illustrated in the example of FIG.1, the controller 101 includes an electronic processor 103 a non-transitory computer-readable memory– See at least ¶ [0020])
Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong to provide for the hybrid vehicle transmission control using driver statistics, as taught in Pan, to optimize a performance metric of a hybrid electric vehicle (e.g., fuel efficiency) while also operating within defined tolerances for additional performance parameters including, for example, drivability and NVH (i.e., noise, vibration, & harshness). (At Pan ¶ [0002])
Regarding claim 17,
wherein the first time period includes a range of time that is up to one hundred twenty seconds in duration and extends to or within five seconds of a time at which the first driving profile is determined. (As illustrated in FIG. 2, the learning device 30 included in a speed prediction device of a vehicle according to various exemplary embodiments of the present invention may learn a first vehicle speed model based on VAE. '100' denotes a probabilistic encoder. ‘110’ is learning data for a driving profile, and denotes time-series data ( X ) during a reference time (Tpast~Tpresent) before a prediction time point (Tpresent) – See at least ¶ [0050]; Examiner notes that the time periods are shown in Fig. 6a and 6b and are from 0 seconds to at least 12 seconds, i.e., includes a range that is up to one hundred and twenty seconds in duration. Examiner further notes that the first time period is a past time up to a present time, i.e., a extends to a time at which the first driving profile is determined. ¶ [0050])
Claim(s) 9, 11, 14, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Pan, as applied to claims 1 and 12, in view of Liao et al (US 2024/0025418 A1, “Liao”).
Regarding claims 9 and 20, the combination of Jeong and Pan does not explicitly teach the driving behavior historical data includes a driver aggression rating. However, Liao discloses profile modeling and teaches:
the driving behavior historical data includes a driver aggression rating. (A data type of the second set of data may be driving style information associated with an individual, and may include aggressive, anxious, keen, or sedate. According to another aspect, the driving style may be labeled from mild to aggressive, driving performance may be labeled from good to bad, and dynamic demand analyzed (e.g., sport, moderate, economical, etc.).– See at least ¶ [0033])
In summary, Pan discloses using driver historical data, e.g., driving demands, routes, and preferences, to determine driving profiles over long and short horizon times. Pan does not explicitly disclose the historical data including driver aggression. However, Liao discloses profile modeling and teaches identifying the historical and current mood state of an driver to develop a driving profile, because mood states affects the way people respond to stimuli.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong and Pan to provide for the profile modeling, as taught in Liao, to determine or estimate risky driving styles. (At Liao ¶ [0045])
Regarding claim 11, the combination of Jeong and Pan does not explicitly teach, but Liao further teaches:
the second driving profile is determined at least in part based upon a determined driver state. (A data type of the second set of data may be driving style information associated with an individual, and may include aggressive, anxious, keen, or sedate. According to another aspect, the driving style may be labeled from mild to aggressive, driving performance may be labeled from good to bad, and dynamic demand analyzed (e.g., sport, moderate, economical, etc.).– See at least ¶ [0033])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong and Pan to provide for the profile modeling, as taught in Liao, to determine or estimate risky driving styles. (At Liao ¶ [0045])
Regarding claim 14, Jeong discloses using vehicle states to determine a second driving profile and Pan teaches using sensor data to determine a second driving profile. The combination of Jeong and Pan does not explicitly teach that the sensors are accelerometers. However, Liao further teaches:
which includes one or more accelerometers that are responsive to accelerations and are communicated with the one or more processors to provide acceleration data to the one or more processors, and wherein the second driving profile is based at least in part on the acceleration data. (Sensors from the vehicle 150 or the mobile device 180 may be used to estimate or determine the driving style information associated with the individual (e.g., the first input or the second input for the prediction model 310). For example, the mobile device 180 may have an accelerometer which may measure how quickly the individual accelerates while driving – See at least ¶ [0044])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong and Pan to provide for the profile modeling, as taught in Liao, to determine or estimate risky driving styles. (At Liao ¶ [0045])
Regarding claim 15, the combination of Jeong and Pan does not explicitly teach, however, Liao further teaches:
which includes one or more sensors by which a power output or torque can be determined for an internal combustion engine and one or more electric motors of the powertrain, and the second driving profile is based at least in part on the power output or torque. (Sensors from the vehicle 150 or the mobile device 180 may be used to estimate or determine the driving style information associated with the individual (e.g., the first input or the second input for the prediction model 310). For example, the mobile device 180 may have an accelerometer which may measure how quickly the individual accelerates while driving. Similarly, the vehicle 150 may be equipped with one or more vehicle systems 162 which may measure or detect driving maneuvers and associated driving style information. Other examples of information obtained as the first input or the second input may include vehicle operation states (e.g., speed, acceleration, angular speed, etc.) – See at least ¶ [0044])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong and Pan to provide for the profile modeling, as taught in Liao, to determine or estimate risky driving styles. (At Liao ¶ [0045])
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Pan, as applied to claim 1, in view of Mahajan et al (US 2020/0216079 A1, “Mahajan”).
Regarding claim 10, the combination of Jeong and Pan does not explicitly disclose wherein the second driving profile is determined at least in part based upon a determined need for continued deceleration of the vehicle. However, Mahajan discloses systems and methods for driver profile based warning and vehicle control and teaches:
wherein the second driving profile is determined at least in part based upon a determined need for continued deceleration of the vehicle. (For example, an auto mated braking system can be adjusted to automatically assist operator braking and increase a distance between vehicle 102 and other vehicles on a roadway, an autonomous vehicle driving system may take over control of all operation of the vehicle, etc. in response to driver profile variable being outside of safe values. Therefore, embodiments as discussed herein enable different users of vehicle 102 to operate vehicle 102 in their own natural way. Furthermore, when it is detected that one or more variables associated with a current operator’s driver operation profile is outside acceptable value(s) (e.g. , satisfying threshold value(s) associated with the variable(s) of a driver operation profile and/or having variable values associated with known unsafe driving), the vehicle 102 can partially and/or fully control various operations, i.e., deceleration, of the vehicle 102 until it is determined that the user has resumed their normal control of the vehicle 102 – See at least ¶ [0012])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the device for predicting speed of vehicle and method thereof of Jeong and Pan to provide for the systems and methods for driver profile based warning and vehicle control, as taught in Mahajan, to more accurately respond to the natural driving pattern of different drivers based on a plurality of different factors. (At Mahajan ¶ [0053])
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHASE L COOLEY whose telephone number is (303)297-4355. The examiner can normally be reached Monday-Thursday 7-5MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached at 571-270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/C.L.C./Examiner, Art Unit 3662
Prosecution Insights