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 communication is in response to Application 18/385,884 filed on 06/03/2026. Claims 1, 9 and 17 are amended. Claims 23-24 are new claims. Claims 1-6, 9-14 and 17-24 are currently pending.
Response to Arguments
Applicant’s arguments, filed 06/03/2026, with respect to the rejection(s) under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Zhu et al., US 20230304810A1, in view of John-Michael Mcnew, US 20200215914A1 and in view of Wang et al., US 20230204369A1
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, 6, 9, 14, 17, 21-22 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al., US 20230304810A1, in view of John-Michael Mcnew, US 20200215914A1 and in view of Wang et al., US 20230204369A1, hereinafter referred to as Zhu, Mcnew and Wang, respectively.
Regarding claim 1, Zhu discloses a method comprising:
receiving sensor data from a hardware sensor of a vehicle on a route with a speed limit (Zhu discloses receiving and storing sensor data from vehicle sensors, including cameras, object detection sensors, accelerometers and vehicle velocity sensor and collecting vehicle speed and acceleration information while a driver travels along a route having authorized traffic speed limits – See at least ¶45, 47, 55 and 62);
generating a sequence of predicted speed values for the vehicle at future locations on the route based on execution of a direct learning model on the sensor data (Zhu discloses using a machine learning dynamic driving model to calculate predicted driver speeds over a route based on vehicle speed information and driver specific historical driving information, including predicting driver speed over the entirety of the route – See at least ¶36-37, 65, 67-68 and 79).
Zhu fails to disclose generating, by execution of an indirect learning model on the sensor data and the speed limit, a sequence of predicted speed limit offset values comprising driver-specific deviations from the speed limit at the future locations on the route and adjusting the sequence of predicted speed values based on the sequence of predicted speed limit offset values.
However, Mcnew teaches:
generating, by execution of an indirect learning model on the sensor data and the speed limit, a sequence of predicted speed limit offset values comprising driver-specific deviations from the speed limit at the future locations on the route (Mcnew teaches s speed map having a plurality of entries associating respective offset values with respective speed limits, wherein the offset values may be machine learned by observing speeds driven in different speed limit zones, and wherein separate learned speed maps may be maintained for individual drivers – See at least ¶49-50, 58-60 and FIGS. 4 and 7) and
adjusting the sequence of predicted speed values based on the sequence of predicted speed limit offset values (Mcnew determines an offset value corresponding to an applicable speed limit and determines a desired vehicle speed by adding the offset value to the applicable speed limit and determines a desired vehicle speed by adding the offset value to the applicable speed limit, and repeatedly obtains respective speed limits and corresponding offset values for controlling vehicle speed – See at least ¶51-52).
It would have been obvious to one of ordinsry skill in the art before the effective filing date of the claimed invention to modify the invention of Zhu and include the feature of generating, by execution of an indirect learning model on the sensor data and the speed limit, a sequence of predicted speed limit offset values comprising driver-specific deviations from the speed limit at the future locations on the route and adjusting the sequence of predicted speed values based on the sequence of predicted speed limit offset values, as taught by Mcnew, to accurately reflect the particular drivers learned tendency to operate at applicable speed limits.
The combination of Zhu and Mcnew fail to disclose determining a first energy consumption based on the adjusted sequence of predicted speed values and a second energy consumption based on the sequence of predicted speed limit offset values, determining a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the first and second energy consumptions generating a travel route for the vehicle based on the range estimation; and controlling the vehicle to move autonomously along the travel route.
However, Wang teaches:
determining a first energy consumption based on the adjusted sequence of predicted speed values and a second energy consumption based on the sequence of predicted speed limit offset values (Wang teaches learning a particular drivers speed behavior, including whether the driver operates over, at, or under an authorized traffic speed limit, and determining dynamic energy associated with such individual driver speed behavior – See at least ¶57-60. Energy adjustments associated with over speeding and under speeding, including energy adjustments corresponding to driving 5mph or 10mpg above the applicable speed limit – See at least ¶85-87),
determining a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the first and second energy consumptions (Wang discloses determining total trip energy based on a baseline energy component and a dynamic energy component, determines remaining propulsion energy including battery state of charge, and determines distances that can be traveled using the current state of charge based on the determined energy consumption – See at least ¶87-89);
generating a travel route for the vehicle based on the range estimation (Wang determines one or more paths between a starting location and a destination, evaluates the predicted energy required for the paths in view of the available vehicle energy, and selects a path for travel based on the predicted energy usage and remaining propulsion energy – See at least ¶64, 88-89); and
controlling the vehicle to move autonomously along the travel route (In one embodiment, the path is selected after performing operations and based on predicted energy usage determined for each possible path. The path that requires the least amount of energy usage may be selected. This selection may be performed by one of the stated modules. The control module may then, if the vehicle is a partially or fully autonomous vehicle, assist, direct, and/or cause the vehicle to driver from Point A to Point B – See at least ¶64).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu and Mcnew and include the feature of determining a first energy consumption based on the adjusted sequence of predicted speed values and a second energy consumption based on the sequence of predicted speed limit offset values, determining a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the first and second energy consumptions generating a travel route for the vehicle based on the range estimation; and controlling the vehicle to move autonomously along the travel route, as taught by Wang, to estimate an amount of energy for an electric vehicle to travel from a first location to a second location.
Regarding claim 6, the combination of Zhu and Mcnew fail to disclose wherein the method further comprises training the indirect learning model based on historical driving data of a user associated with the vehicle to generate a user-specific indirect learning model, and the determining comprises determining the range estimation of the vehicle based on execution the user-specific indirect learning model.
However, Wang teaches wherein the method further comprises training the indirect learning model based on historical driving data of a user associated with the vehicle to generate a user-specific indirect learning model, and the determining comprises determining the range estimation of the vehicle based on execution the user-specific indirect learning model (Wang teaches stored drivers speed/acceleration data and performs statistical learning for later energy estimation, including data learned from the drivers previous driving history – See at least ¶57-60 and 81-89).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu and Mcnew and include the feature of wherein the method further comprises training the indirect learning model based on historical driving data of a user associated with the vehicle to generate a user-specific indirect learning model, and the determining comprises determining the range estimation of the vehicle based on execution the user-specific indirect learning model, as taught by Wang, to estimate an amount of energy for an electric vehicle to travel from a first location to a second location.
Regarding claim 9, Zhu discloses an apparatus comprising:
a storage configured to store a machine learning model (The dynamic driving module is configured to execute a machine learning algorithm to calculate the predicted driver speed – See at least ¶4); and
a processor configured to (Processor – See at least ¶85)
receive sensor data from a hardware sensor of a vehicle while the vehicle is travelling on a route with a speed limit (Zhu discloses receiving and storing sensor data from vehicle sensors, including cameras, object detection sensors, accelerometers and vehicle velocity sensor and collecting vehicle speed and acceleration information while a driver travels along a route having authorized traffic speed limits – See at least ¶45, 47, 55 and 62),
generate a sequence of predicted speed values for the vehicle at future locations on the route based on execution of a direct learning model on the sensor data (Zhu discloses using a machine learning dynamic driving model to calculate predicted driver speeds over a route based on vehicle speed information and driver specific historical driving information, including predicting driver speed over the entirety of the route – See at least ¶36-37, 65, 67-68 and 79).
Zhu fails to disclose generate, by execution of an indirect learning model on the sensor data and the speed limit, a sequence of predicted speed limit offset values comprising driver- specific deviations from the speed limit at the future locations on the route, and adjust the sequence of predicted speed values based on the sequence of predicted speed limit offset values.
However, Mcnew teaches:
generate, by execution of an indirect learning model on the sensor data and the speed limit, a sequence of predicted speed limit offset values comprising driver- specific deviations from the speed limit at the future locations on the route (Mcnew teaches s speed map having a plurality of entries associating respective offset values with respective speed limits, wherein the offset values may be machine learned by observing speeds driven in different speed limit zones, and wherein separate learned speed maps may be maintained for individual drivers – See at least ¶49-50, 58-60 and FIGS. 4 and 7) and
adjust the sequence of predicted speed values based on the sequence of predicted speed limit offset values (Mcnew determines an offset value corresponding to an applicable speed limit and determines a desired vehicle speed by adding the offset value to the applicable speed limit and determines a desired vehicle speed by adding the offset value to the applicable speed limit, and repeatedly obtains respective speed limits and corresponding offset values for controlling vehicle speed – See at least ¶51-52).
It would have been obvious to one of ordinsry skill in the art before the effective filing date of the claimed invention to modify the invention of Zhu and include the feature of generating, by execution of an indirect learning model on the sensor data and the speed limit, a sequence of predicted speed limit offset values comprising driver-specific deviations from the speed limit at the future locations on the route and adjusting the sequence of predicted speed values based on the sequence of predicted speed limit offset values, as taught by Mcnew, to accurately reflect the particular drivers learned tendency to operate at applicable speed limits.
The combination of Zhu and Mcnew fail to disclose determine a first energy consumption based on the adjusted sequence of predicted speed values and a second energy consumption based on the sequence of predicted speed limit offset values, determine a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the first and second energy consumptions, generate a travel route for the vehicle based on the range estimation, and control the vehicle to move autonomously along the travel route.
However, Wang teaches:
determine a first energy consumption based on the adjusted sequence of predicted speed values and a second energy consumption based on the sequence of predicted speed limit offset values (Wang teaches learning a particular drivers speed behavior, including whether the driver operates over, at, or under an authorized traffic speed limit, and determining dynamic energy associated with such individual driver speed behavior – See at least ¶57-60. Energy adjustments associated with over speeding and under speeding, including energy adjustments corresponding to driving 5mph or 10mpg above the applicable speed limit – See at least ¶85-87),
determine a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the first and second energy consumptions (Wang discloses determining total trip energy based on a baseline energy component and a dynamic energy component, determines remaining propulsion energy including battery state of charge, and determines distances that can be traveled using the current state of charge based on the determined energy consumption – See at least ¶87-89);
generate a travel route for the vehicle based on the range estimation (Wang determines one or more paths between a starting location and a destination, evaluates the predicted energy required for the paths in view of the available vehicle energy, and selects a path for travel based on the predicted energy usage and remaining propulsion energy – See at least ¶64, 88-89); and
control the vehicle to move autonomously along the travel route (In one embodiment, the path is selected after performing operations and based on predicted energy usage determined for each possible path. The path that requires the least amount of energy usage may be selected. This selection may be performed by one of the stated modules. The control module may then, if the vehicle is a partially or fully autonomous vehicle, assist, direct, and/or cause the vehicle to driver from Point A to Point B – See at least ¶64).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu and Mcnew and include the feature of determining a first energy consumption based on the adjusted sequence of predicted speed values and a second energy consumption based on the sequence of predicted speed limit offset values, determining a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the first and second energy consumptions generating a travel route for the vehicle based on the range estimation; and controlling the vehicle to move autonomously along the travel route, as taught by Wang, to estimate an amount of energy for an electric vehicle to travel from a first location to a second location.
Regarding claim 14, the combination of Zhu and Mcnew fail to disclose wherein the processor is further configured to train the indirect learning model based on historical driving data of a user associated with the vehicle to generate a user-specific indirect learning model, and determine the range estimation of the vehicle based on execution the user-specific indirect learning model.
However, Wang teaches wherein the processor is further configured to train the indirect learning model based on historical driving data of a user associated with the vehicle to generate a user-specific indirect learning model, and determine the range estimation of the vehicle based on execution the user-specific indirect learning model (Wang teaches stored drivers speed/acceleration data and performs statistical learning for later energy estimation, including data learned from the drivers previous driving history – See at least ¶57-60 and 81-89).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu and Mcnew and include the feature of wherein the processor is further configured to train the indirect learning model based on historical driving data of a user associated with the vehicle to generate a user-specific indirect learning model, and determine the range estimation of the vehicle based on execution the user-specific indirect learning model, as taught by Wang, to estimate an amount of energy for an electric vehicle to travel from a first location to a second location.
Regarding claim 17, Zhu discloses a computer-readable storage medium comprising instructions, that when read by a processor, cause a computer to perform:
receiving sensor data from a hardware sensor of a vehicle on a route with a speed limit (Zhu discloses receiving and storing sensor data from vehicle sensors, including cameras, object detection sensors, accelerometers and vehicle velocity sensor and collecting vehicle speed and acceleration information while a driver travels along a route having authorized traffic speed limits – See at least ¶45, 47, 55 and 62);
generating a sequence of predicted speed values for the vehicle at future locations on the route based on execution of a direct learning model on the sensor data (Zhu discloses using a machine learning dynamic driving model to calculate predicted driver speeds over a route based on vehicle speed information and driver specific historical driving information, including predicting driver speed over the entirety of the route – See at least ¶36-37, 65, 67-68 and 79).
Zhu fails to disclose generating, by execution of an indirect learning model on the sensor data and the speed limit, a sequence of predicted speed limit offset values comprising driver-specific deviations from the speed limit at the future locations on the route and adjusting the sequence of predicted speed values based on the sequence of predicted speed limit offset values.
However, Mcnew teaches:
generating, by execution of an indirect learning model on the sensor data and the speed limit, a sequence of predicted speed limit offset values comprising driver-specific deviations from the speed limit at the future locations on the route (Mcnew teaches s speed map having a plurality of entries associating respective offset values with respective speed limits, wherein the offset values may be machine learned by observing speeds driven in different speed limit zones, and wherein separate learned speed maps may be maintained for individual drivers – See at least ¶49-50, 58-60 and FIGS. 4 and 7) and
adjusting the sequence of predicted speed values based on the sequence of predicted speed limit offset values (Mcnew determines an offset value corresponding to an applicable speed limit and determines a desired vehicle speed by adding the offset value to the applicable speed limit and determines a desired vehicle speed by adding the offset value to the applicable speed limit, and repeatedly obtains respective speed limits and corresponding offset values for controlling vehicle speed – See at least ¶51-52).
It would have been obvious to one of ordinsry skill in the art before the effective filing date of the claimed invention to modify the invention of Zhu and include the feature of generating, by execution of an indirect learning model on the sensor data and the speed limit, a sequence of predicted speed limit offset values comprising driver-specific deviations from the speed limit at the future locations on the route and adjusting the sequence of predicted speed values based on the sequence of predicted speed limit offset values, as taught by Mcnew, to accurately reflect the particular drivers learned tendency to operate at applicable speed limits.
The combination of Zhu and Mcnew fail to disclose determining a first energy consumption based on the adjusted sequence of predicted speed values and a second energy consumption based on the sequence of predicted speed limit offset values, determining a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the first and second energy consumptions generating a travel route for the vehicle based on the range estimation; and controlling the vehicle to move autonomously along the travel route.
However, Wang teaches:
determining a first energy consumption based on the adjusted sequence of predicted speed values and a second energy consumption based on the sequence of predicted speed limit offset values (Wang teaches learning a particular drivers speed behavior, including whether the driver operates over, at, or under an authorized traffic speed limit, and determining dynamic energy associated with such individual driver speed behavior – See at least ¶57-60. Energy adjustments associated with over speeding and under speeding, including energy adjustments corresponding to driving 5mph or 10mpg above the applicable speed limit – See at least ¶85-87),
determining a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the first and second energy consumptions (Wang discloses determining total trip energy based on a baseline energy component and a dynamic energy component, determines remaining propulsion energy including battery state of charge, and determines distances that can be traveled using the current state of charge based on the determined energy consumption – See at least ¶87-89);
generating a travel route for the vehicle based on the range estimation (Wang determines one or more paths between a starting location and a destination, evaluates the predicted energy required for the paths in view of the available vehicle energy, and selects a path for travel based on the predicted energy usage and remaining propulsion energy – See at least ¶64, 88-89); and
controlling the vehicle to move autonomously along the travel route (In one embodiment, the path is selected after performing operations and based on predicted energy usage determined for each possible path. The path that requires the least amount of energy usage may be selected. This selection may be performed by one of the stated modules. The control module may then, if the vehicle is a partially or fully autonomous vehicle, assist, direct, and/or cause the vehicle to driver from Point A to Point B – See at least ¶64).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu and Mcnew and include the feature of determining a first energy consumption based on the adjusted sequence of predicted speed values and a second energy consumption based on the sequence of predicted speed limit offset values, determining a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the first and second energy consumptions generating a travel route for the vehicle based on the range estimation; and controlling the vehicle to move autonomously along the travel route, as taught by Wang, to estimate an amount of energy for an electric vehicle to travel from a first location to a second location.
Regarding claim 21, Zhu discloses wherein the generating the sequence of predicted speed values comprises outputting, by the direct learning model, a speed profile comprising a plurality of speed values at a plurality of future points in time, and the determining comprises determining the range estimation based on the speed profile (The dynamic driving module receives inputs including, but not limited to, a perceived vehicle speed (e.g., from a perceived speed module), a grade information input (e.g., from a grade module), a turn information input (e.g., from a turn module), and, as a closed-loop feedback input, a previously predicted driver speed output of the dynamic driving module. For example, the dynamic driving module calculates and outputs the predicted driver speed in accordance with Equation 2 as described above, which corresponds to an adaptive, personalized driver model that is adapted over time. In other words, the dynamic driving model is implemented as a personalized driver module that adapts to driver behavior over time, such as a machine learning network or algorithm (e.g., an NNT-trained machine learning algorithm). The output of the dynamic driving module may further include other values indicative of energy used by the vehicle – See at least ¶74).
Regarding claim 22, wherein the generating the sequence of predicted speed values comprises outputting, by the indirect learning model, a speed profile comprising a plurality of speed limit offset values with respect to the speed limit at a plurality of future points in time, and the determining comprises determining the range estimation based on the speed profile (The dynamic driving module receives inputs including, but not limited to, a perceived vehicle speed (e.g., from a perceived speed module), a grade information input (e.g., from a grade module), a turn information input (e.g., from a turn module), and, as a closed-loop feedback input, a previously predicted driver speed output of the dynamic driving module. For example, the dynamic driving module calculates and outputs the predicted driver speed in accordance with Equation 2 as described above, which corresponds to an adaptive, personalized driver model that is adapted over time. In other words, the dynamic driving model is implemented as a personalized driver module that adapts to driver behavior over time, such as a machine learning network or algorithm (e.g., an NNT-trained machine learning algorithm). The output of the dynamic driving module may further include other values indicative of energy used by the vehicle – See at least ¶74).
Regarding claim 24, the combination of Zhu and Mcnew fail to disclose wherein the determining the range estimation comprises combining the first energy consumption and the second energy consumption based on respective confidence values associated with the direct learning model and the indirect learning model.
However, Wang teaches wherein the determining the range estimation comprises combining the first energy consumption and the second energy consumption based on respective confidence values associated with the direct learning model and the indirect learning model (Wang discloses determining total trip energy based on a baseline energy component and a dynamic energy component, determines remaining propulsion energy including battery state of charge, and determines distances that can be traveled using the current state of charge based on the determined energy consumption – See at least ¶87-89).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu and Mcnew and include the feature of wherein the determining the range estimation comprises combining the first energy consumption and the second energy consumption based on respective confidence values associated with the direct learning model and the indirect learning model, as taught by Wang, to estimate an amount of energy for an electric vehicle to travel from a first location to a second location.
Claim(s) 2, 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al., US 20230304810A1, in view of John-Michael Mcnew, US 20200215914A1, in view of Wang et al., US 20230204369A1, as applied to claims 1, 9 and 17 above, in view of Nam Hyuk Kim, US 20230298461A1 and in view of Slaton et al., US 20140266660A1, hereinafter referred to as Zhu, Mcnew, Wang, Kim and Slaton, respectively.
Regarding claim 2, the combination of Zhu, Mcnew and Wang fail to disclose wherein the indirect learning model comprises a deep learning neural network that comprises an input layer configured to receive the sequence of predicted speed limit offset values, an output layer configured to output an average speed limit offset, and one or more hidden layers configured to determine the average speed limit offset from the predicted speed limit offset values.
However, Kim teaches:
wherein the indirect learning model comprises a deep learning neural network that comprises an input layer configured to receive the sequence of predicted speed limit offset values, an output layer configured to output an average speed limit offset (Referring to FIG. 1 , according to an exemplary embodiment of the present disclosure, the apparatus for predicting the congestion time point may include a plurality of artificial neural networks (ANNs). The apparatus for predicting the congestion time point may control at least one ANN using a processor. For example, the apparatus for predicting the congestion time point may input data to an ANN and may provide a congestion time point prediction function based on a driving situation (e.g., a traffic speed and/or traffic volume) of a vehicle by output data output through various layers included in the ANN – See at least ¶50).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu, Mcnew and Wang and include the feature of wherein the indirect learning model comprises a deep learning neural network that comprises an input layer configured to receive the sequence of predicted speed limit offset values, an output layer configured to output an average speed limit offset, as taught by Kim, to develop a technology for predicting a congestion time point by further considering various parameters rather than only the traffic speed to more accurately predict a possibility of congestion or a congestion time point.
The combination of Zhu, Mcnew, Wang and Kim fail to disclose one or more hidden layers configured to determine the average speed limit offset from the predicted speed limit offset values.
However, Slaton teaches one or more hidden layers configured to determine the average speed limit offset from the predicted speed limit offset values (At block 330, an overall speed offset and a speed limit based on the speed offset of the limiting input is determined. In other words, the overall speed offset is the value of the speed offset of the limiting input. It should be understood by those of ordinary skill in the art that other methods of determining a speed offset are possible, such as a speed offset based on the value of the average speed offset of all of the inputs – See at least ¶62).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu, Mcnew, Wang and Kim and include the feature of one or more hidden layers configured to determine the average speed limit offset from the predicted speed limit offset values, as taught by Slaton, to obtain a representative aggregate value from the plurality of predicted speed limit offset values for subsequent vehicle speed and range calculations.
*Claims 10 and 18 are rejected for the same reasons as claim 2, with corresponding apparatus and computer-readable storage medium language.
Claim(s) 3, 5, 11, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al., US 20230304810A1, in view of John-Michael Mcnew, US 20200215914A1, in view of Wang et al., US 20230204369A1, as applied to claims 1, 9 and 17 above and further in view of Park et al., US 20190016329A1, hereinafter referred to as Zhu, Mcnew, Wang and Park, respectively.
Regarding claim 3, the combination of Zhu, Mcnew and Wang fail to disclose wherein the method further comprises generating a sequence of predicted acceleration values for the vehicle at the future locations on the route based on execution of the indirect learning model on the sensor data and the speed limit of the route, and further determining the range estimation of the vehicle based on the sequence of predicted acceleration values for the vehicle at the future locations on the route.
However, Park teaches wherein the method further comprises generating a sequence of predicted acceleration values for the vehicle at the future locations on the route based on execution of the indirect learning model on the sensor data and the speed limit of the route, and further determining the range estimation of the vehicle based on the sequence of predicted acceleration values for the vehicle at the future locations on the route (The controller may also include a delta speed logic block operable for calculating a delta speed value indicative of predicted acceleration of the vehicle along the predetermined travel route, and for predicting the energy consumption of the vehicle using the delta speed value – See at least ¶13. For instance, the memory (M) of the controller of FIG. 1 may be programmed with driving characteristic profiles for multiple operators, e.g., drivers A and B. Driver A may have a history of aggressive driving, such as a demonstrated energy-depleting tendency to rapidly accelerate, corner, and brake, while driver B may have a history of gradual acceleration and braking conducive to promoting energy efficiency. The HVAC energy use (HVAC) is the actual usage of heating or air conditioning systems in the vehicle through the trip, either of which presents a load on the ESS that affects the operating efficiency of the powertrain – See at least ¶41).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu, Mcnew and Wang and include the feature of wherein the method further comprises generating a sequence of predicted acceleration values for the vehicle at the future locations on the route based on execution of the indirect learning model on the sensor data and the speed limit of the route, and further determining the range estimation of the vehicle based on the sequence of predicted acceleration values for the vehicle at the future locations on the route, as taught by Park, because predicted acceleration represents an additional propulsion energy requirement along upcoming portions of the route and therefore improves the trip energy and range prediction.
*Claims 11 and 19 are rejected for the same reasons as claim 3.
Regarding claim 5, the combination of Zhu, Mcnew and Wang fail to disclose wherein the method further comprises receiving one or more of a current setting of a heating ventilation and air conditioning (HVAC) setting within the vehicle and a tire pressure sensor value, and the determining further comprises determining the range estimation of the vehicle based on the one or more of the current setting of the HVAC and the tire pressure sensor value.
However, Park teaches wherein the method further comprises receiving one or more of a current setting of a heating ventilation and air conditioning (HVAC) setting within the vehicle and a tire pressure sensor value, and the determining further comprises determining the range estimation of the vehicle based on the one or more of the current setting of the HVAC and the tire pressure sensor value (A logic block 136 may receive the ambient temperature (TA) along the route, current HVAC settings and data (HVAC), and the battery state of charge (SOC), and output an estimated HVAC energy usage (HVACEST). Logic block 138, referred to herein as the “delta speed” block, uses the compensated speed (NCOMP) from logic block 132 to determine an amount of energy associated with changes or “deltas” in vehicle speed, e.g., from acceleration due to upcoming on-ramps or other segments in which the vehicle 10 is expected to accelerate – See at least ¶37. Propulsion energy/power consumption (arrow E1) is then calculated using a logic block 133, with inputs to the logic block 133 being the transmission spin losses (LSP), delta speed (NΔ), position (POS), and calibrated vehicle parameters (arrow VP) such as mass, aerodynamics, tire pressure/rolling resistance – See at least ¶38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu, Mcnew and Wang and include the feature of wherein the method further comprises receiving one or more of a current setting of a heating ventilation and air conditioning (HVAC) setting within the vehicle and a tire pressure sensor value, and the determining further comprises determining the range estimation of the vehicle based on the one or more of the current setting of the HVAC and the tire pressure sensor value, as taught by Park, because predicted acceleration represents an additional propulsion energy requirement along upcoming portions of the route and therefore improves the trip energy and range prediction.
*Claim 13 is rejected for the same reason as claim 5.
Claim(s) 4, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al., US 20230304810A1, in view of John-Michael Mcnew, US 20200215914A1, in view of Wang et al., US 20230204369A1, as applied to claims 1, 9 and 17 above and further in view of Tang et al., US 20240308387A1, hereinafter referred to as Zhu, Mcnew, Wang and Tang, respectively.
Regarding claim 4, the combination of Zhu, Mcnew and Wang fail to disclose wherein the determining the range estimation further comprises determining an estimated amount of energy needed to finish a trip along the route based on execution of the direct learning model on the current amount of charge of the rechargeable battery and the sequence of predicted speed values at the future locations.
However, Tang teaches wherein the determining the range estimation further comprises determining an estimated amount of energy needed to finish a trip along the route based on execution of the direct learning model on the current amount of charge of the rechargeable battery and the sequence of predicted speed values at the future locations (At 208, as input to the ML model (e.g., which may include an ML regression model), for each segment, the energy specifications along with additional information like segment length, expected travel time on the segment, battery state of charge (SoC) at the beginning of the segment, environmental conditions on the segment, and/or any other suitable features (e.g., based on the specific vehicle application, and available information – See at least ¶39. In some embodiments, the ML model may be trained to predict the segment battery energy consumption and the expected SoC at the end of the segment given the segment input features. The ML model may be used in a recursive manner to estimate the battery energy consumption for each segment on the route. The systems and methods described herein may be configured to sum the estimated battery energy consumption for all the segments together to determine the total battery energy consumption for the remaining route. As the vehicle travels along the route, the systems and methods described herein may be configured to update (e.g., at any suitable interval or period) the battery energy consumption for the remaining route using updated traffic information, ambient conditions, and based on the latest vehicle-operating conditions – See at least ¶40).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu, Mcnew and Wang and include the feature of wherein the determining the range estimation further comprises determining an estimated amount of energy needed to finish a trip along the route based on execution of the direct learning model on the current amount of charge of the rechargeable battery and the sequence of predicted speed values at the future locations, as taught by Tang, to accurately predicting the remaining distance that the vehicle can travel with the remaining battery charge.
*Claims 12 and 20 are rejected for the same reason as claim 4.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al., US 20230304810A1, in view of John-Michael Mcnew, US 20200215914A1, in view of Wang et al., US 20230204369A1, as applied to claim 1 above and further in view of Shirvani et al., US 20190235515A1, hereinafter referred to as Zhu, Mcnew, Wang and Shirvani, respectively.
Regarding claim 23, the combination of Zhu, Mcnew and Wang fail to disclose wherein the generating the sequence of predicted speed values and the generating the sequence of predicted speed limit offset values comprises simultaneously executing the direct learning model and the indirect learning model on the sensor data.
However, Shirvani teaches wherein the generating the sequence of predicted speed values and the generating the sequence of predicted speed limit offset values comprises simultaneously executing the direct learning model and the indirect learning model on the sensor data (SafetyNet works in parallel with PlanningNet or other main neural network (and may use the same object detection mechanisms) but analyzes inputs from a different perspective or approach (e.g., how not to drive). The fault coverage of such a SafetyNet network is training dependent and is the main area of complexity – See at least ¶70. It is this LegalNet and/or PlanningNet that should know, based on the country, what legal or illegal actions are, for example, to allow speeding up. For example, LegalNet could make speed determinations based on detection of speed limit signs, both static and dynamic speed limits Such dynamic posted speed limits may be based on conditions such as traffic, daytime or nighttime, rain or shine, etc. Moreover, speed limits can be determined by referencing data in maps – See at least ¶169).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zhu, Mcnew and Wang and include the feature of wherein the generating the sequence of predicted speed values and the generating the sequence of predicted speed limit offset values comprises simultaneously executing the direct learning model and the indirect learning model on the sensor data, as taught by Shirvani, because predicted acceleration represents an additional propulsion energy requirement along upcoming portions of the route and therefore improves the trip energy and range prediction.
Conclusion
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/MAHMOUD M KAZIMI/Examiner, Art Unit 3665
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