Meet yNotice 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 .
DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 - 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 - 3, 9 - 11, & 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thien (US 2024/0338623 A1) in view of Narula (US 2024/0195206 A1) and Galbraith (US 2024/0343149 A1).
Regarding Claim 1:
Thien discloses: A charge-discharge method applied to an electronic device, the electronic device communicates with a charging pile, the charge-discharge method comprising: (Thien discloses in at least Paragraphs 0006 & 0008 a method for determining a charging strategy for an energy storage device of an electric vehicle based on a determined probable departure time, said departure time being determined on the basis of energy consumption patterns of home appliances as disclosed in at least Paragraph 0009 of Thien [i.e. a charge-discharge method]. At least Paragraph 0039 of Thien discloses wherein the charging device for the vehicle energy storage device is in communication with the energy management system [i.e. the electronic device communicates with a charging pile])
collecting operation behavior information of a user with respect to household appliances; (Thien discloses in at least Paragraphs 0009, 0050, & 0053 wherein the temporal profile of power usage of household appliances may be provided as a consumption variable curve, indicating the operation of particular domestic appliances, such as a coffee machine turning on/off and the like [i.e. operation behavior information of a user with respect to household appliances is collected])
inputting the operation behavior information into a preset travel time prediction model to obtain a first driving travel time of the user; (Thien discloses in at least Paragraphs 0048, 0054, & 0055 wherein the energy management system can comprise a departure time estimation device, which includes a departure time model trained on the basis of historical usage data [i.e. a preset travel time prediction model]. At least Paragraphs 0023, 0024, & 0057 of Thien discloses wherein the consumption variable curves [i.e. the operational behavior information] are provided to the departure time model in order to determine an estimated or probable departure time for the vehicle [i.e. inputting the operation behavior information into a preset travel time prediction model to obtain a first driving travel time of the user])
determining a first charge-discharge strategy of a vehicle of the user based on the first driving travel time; and (Thien discloses in at least Paragraphs 0042 & 0048 wherein the determined estimate of departure time [i.e. first driving travel time] is transmitted to the charging strategy unit, which determines a charging strategy for the vehicle energy storage device, said strategy including a temporal curve of the charging current for the charging process [i.e. determining a first charge-discharge strategy of a vehicle of the user based on the first driving travel time])
Thien however appears to be silent regarding:
establishing a constrained optimization model, wherein the constrained optimization model takes a charge-discharge strategy as a decision variable, and a maximum battery life under the charge-discharge strategy is an objective function;
wherein the first charge-discharge strategy is obtained by solving the constrained optimization model;
transmitting the first charge-discharge strategy to the charging pile, wherein the charging pile charges the vehicle or controls the vehicle to discharge based on the first charge-discharge strategy.
However Narula teaches wherein following the determination of a charging strategy for a vehicle, the determined strategy may be transmitted to and executed by the charger.
transmitting the first charge-discharge strategy to the charging pile, wherein the charging pile charges the vehicle or controls the vehicle to discharge based on the first charge-discharge strategy. (However Narula teaches in at least Paragraphs 0012 & 0074 wherein a charging profile calculated via a network remote from the battery/charger is transmitted wirelessly to the battery charger, the battery charger subsequently being controlled to charge the battery based on the received charging profile [i.e. transmitting the first charge-discharge strategy to the charging pile, wherein the charging pile charges the vehicle or controls the vehicle to discharge based on the first charge-discharge strategy])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the transmission and execution of charging strategy determined as taught by Narula.
The motivation to do so is that, as acknowledged by Narula in at least Paragraphs 0012 & 0074, and as would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention, a determined charging profile may be actually and practically implemented at a charging device to execute charging of a vehicle.
However Galbraith teaches wherein a constrained optimization model may be used to determine an optimal amount of power to supply to electric vehicle chargers in order to ensure the electric vehicle receives an amount of energy required to meet a target state of charge before a scheduled departure time.
establishing a constrained optimization model, wherein the constrained optimization model takes a charge-discharge strategy as a decision variable, and a maximum battery life under the charge-discharge strategy is an objective function; (However Galbraith teaches in at least Paragraphs 0206 – 0209 wherein the EV management system for a building may utilize a mathematical optimization model including a constrained objective function relating decision variables and one or more objectives of a system over a time horizon [i.e. establishing a constrained optimization model]. At least Paragraphs 0122 & 0123 of Galbraith teach wherein the decision variables may include the amount of power that should flow through each electric vehicle supply equipment [i.e. charger] for each period of time [i.e. the constrained optimization model takes a charge-discharge strategy as a decision variable]. At least Paragraphs 0188 – 0194 of Galbraith further teach wherein the objective function may be configured to maximize a reward function, which may include objectives such as ensuring the EV receives an amount of energy required to meet a target state of charge before a scheduled departure time [i.e. a maximum battery life under the charge-discharge strategy is an objective function])
wherein the first charge-discharge strategy is obtained by solving the constrained optimization model; (However Galbraith teaches in at least Paragraphs 0122, 0123, & 0196 wherein the optimization problem may be solved to determine an optimal numerical value for each decision variable, which may include the amount of power that should flow through each electric vehicle supply equipment [i.e. charger] for each period of time [i.e. the first charge-discharge strategy is obtained by solving the constrained optimization model])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the use of a constrained optimization model to determine a first charge-discharge strategy as taught by Galbraith.
The motivation to do so is that, as acknowledged by Galbraith in at least Paragraph 0122, 0123, & 0199, an optimal set of decision variables, corresponding to the amount of power that should flow through each electric vehicle supply equipment [i.e. charger] for each period of time may be identified, improving the optimal supply of power to the electrical vehicle during charging operations.
Regarding Claim 2:
The charge-discharge method of claim 1, wherein the operation behavior information comprises operation data of the user for operating the household appliances, the preset travel time prediction model is configured to: obtain a type of a household appliance operated by the user of each of the operation data, predict a second driving travel time corresponding to each type of the household appliances based on the operation data of the same type of the household appliances, and determine the first driving travel time based on the second driving travel time corresponding to each type of the household appliances.
Thien discloses in at least Paragraphs 0050, 0052, & 0053 wherein the usage behavior of a particular domestic appliance may be indicative of an impending departure, and be used as the consumption variable curve which is used to predict the upcoming departure. This may include acquiring the patterns of a coffee maker or hot water heater [i.e. obtain a type of a household appliance operated by the user of each of the operation data] and analyzing the consumption variable curves for each through a cluster analysis to determine expected departure time(s) through cluster analysis of each consumption variable curve as disclosed in at least Paragraphs 0055 – 0057 of Thien [i.e. predict a second driving travel time corresponding to each type of the household appliances based on the operation data of the same type of the household appliances, and determine the first driving travel time based on the second driving travel time corresponding to each type of the household appliances].
Regarding Claim 3:
The charge-discharge method of claim 2, wherein predicting the second driving travel time corresponding to each type of the household appliances based on the operation data of the same type of the household appliances further comprises: predicting a driving travel time based on each of the operation data to obtain a third driving travel time corresponding to each of the operation data; classifying the third driving travel time to obtain driving travel time sets based on types of the household appliances of each of the operation data, wherein each of the driving travel time sets is corresponding to each type of the household appliances; and combining the third driving travel time belonged to the same driving travel time set to obtain the second driving travel time.
Thien discloses in at least Paragraphs 0055 & 0056 wherein consumption variable curves for each of the appliances, such as a coffee maker or water consumption for a water heater, may be clustered [i.e. combining the third driving travel time belonged to the same driving travel time set to obtain the second driving travel time] to determine patterns in specific appliances, which may be used to determine an expected departure time for the specific appliances [i.e. performing a driving travel time prediction based on each of the operation data to obtain a third driving travel time corresponding to each of the operation data and classifying the third driving travel time to obtain driving travel time sets based on types of the household appliances of each of the operation data].
Regarding Claim 9:
Thien discloses: An electronic device, comprising: (Thien discloses in at least Paragraphs 0006 & 0008 a method for determining a charging strategy for an energy storage device of an electric vehicle based on a determined probable departure time, said departure time being determined on the basis of energy consumption patterns of home appliances as disclosed in at least Paragraph 0009 of Thien [i.e. an electronic device])
collect operation behavior information of a user with respect to household appliances, (Thien discloses in at least Paragraphs 0009, 0050, & 0053 wherein the temporal profile of power usage of household appliances may be provided as a consumption variable curve, indicating the operation of particular domestic appliances, such as a coffee machine turning on/off and the like [i.e. operation behavior information of a user with respect to household appliances is collected])
input the operation behavior information into a preset travel time prediction model to obtain a first driving travel time of the user, (Thien discloses in at least Paragraphs 0048, 0054, & 0055 wherein the energy management system can comprise a departure time estimation device, which includes a departure time model trained on the basis of historical usage data [i.e. a preset travel time prediction model]. At least Paragraphs 0023, 0024, & 0057 of Thien discloses wherein the consumption variable curves [i.e. the operational behavior information] are provided to the departure time model in order to determine an estimated or probable departure time for the vehicle [i.e. inputting the operation behavior information into a preset travel time prediction model to obtain a first driving travel time of the user])
determine a first charge-discharge strategy of a vehicle of the user based on the first driving travel time, and (Thien discloses in at least Paragraphs 0042 & 0048 wherein the determined estimate of departure time [i.e. first driving travel time] is transmitted to the charging strategy unit, which determines a charging strategy for the vehicle energy storage device, said strategy including a temporal curve of the charging current for the charging process [i.e. determining a first charge-discharge strategy of a vehicle of the user based on the first driving travel time])
Thien however appears to be silent regarding:
at least one processor; and a data storage storing one or more programs which when executed by the at least one processor, cause the at least one processor to:
establishing a constrained optimization model, wherein the constrained optimization model takes a charge-discharge strategy as a decision variable, and a maximum battery life under the charge-discharge strategy is an objective function;
wherein the first charge-discharge strategy is obtained by solving the constrained optimization model;
transmit the first charge-discharge strategy to the charging pile, wherein the charging pile charges the vehicle or controls the vehicle to discharge based on the first charge-discharge strategy.
However Narula teaches wherein following the determination of a charging strategy for a vehicle by computing elements, the determined strategy may be transmitted to and executed by the charger.
at least one processor; and a data storage storing one or more programs which when executed by the at least one processor, cause the at least one processor to: (However Narula teaches in at least Paragraphs 0012 & 0029 wherein the system for determining a battery charging profile may be implemented using one or more non-transient computer readable storage media storing instructions, for example software code, which may be implemented using one or more processors [i.e. at least one processor; and a data storage storing one or more programs])
transmit the first charge-discharge strategy to the charging pile, wherein the charging pile charges the vehicle or controls the vehicle to discharge based on the first charge-discharge strategy. (However Narula teaches in at least Paragraphs 0012 & 0074 wherein a charging profile calculated via a network remote from the battery/charger is transmitted wirelessly to the battery charger, the battery charger subsequently being controlled to charge the battery based on the received charging profile [i.e. transmitting the first charge-discharge strategy to the charging pile, wherein the charging pile charges the vehicle or controls the vehicle to discharge based on the first charge-discharge strategy])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the transmission and execution of charging strategy determined through computing elements as taught by Narula.
The motivation to do so is that, as acknowledged by Narula in at least Paragraphs 0012 & 0074, and as would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention, a determined charging profile may be actually and practically implemented at a charging device to execute charging of a vehicle.
However Galbraith teaches wherein a constrained optimization model may be used to determine an optimal amount of power to supply to electric vehicle chargers in order to ensure the electric vehicle receives an amount of energy required to meet a target state of charge before a scheduled departure time.
establishing a constrained optimization model, wherein the constrained optimization model takes a charge-discharge strategy as a decision variable, and a maximum battery life under the charge-discharge strategy is an objective function; (However Galbraith teaches in at least Paragraphs 0206 – 0209 wherein the EV management system for a building may utilize a mathematical optimization model including a constrained objective function relating decision variables and one or more objectives of a system over a time horizon [i.e. establishing a constrained optimization model]. At least Paragraphs 0122 & 0123 of Galbraith teach wherein the decision variables may include the amount of power that should flow through each electric vehicle supply equipment [i.e. charger] for each period of time [i.e. the constrained optimization model takes a charge-discharge strategy as a decision variable]. At least Paragraphs 0188 – 0194 of Galbraith further teach wherein the objective function may be configured to maximize a reward function, which may include objectives such as ensuring the EV receives an amount of energy required to meet a target state of charge before a scheduled departure time [i.e. a maximum battery life under the charge-discharge strategy is an objective function])
wherein the first charge-discharge strategy is obtained by solving the constrained optimization model; (However Galbraith teaches in at least Paragraphs 0122, 0123, & 0196 wherein the optimization problem may be solved to determine an optimal numerical value for each decision variable, which may include the amount of power that should flow through each electric vehicle supply equipment [i.e. charger] for each period of time [i.e. the first charge-discharge strategy is obtained by solving the constrained optimization model])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the use of a constrained optimization model to determine a first charge-discharge strategy as taught by Galbraith.
The motivation to do so is that, as acknowledged by Galbraith in at least Paragraph 0122, 0123, & 0199, an optimal set of decision variables, corresponding to the amount of power that should flow through each electric vehicle supply equipment [i.e. charger] for each period of time may be identified, improving the optimal supply of power to the electrical vehicle during charging operations.
Regarding Claim 10:
Claim 10 recites substantially similar limitations as those found in Claim 2, above, and is rejected under similar rationale.
Regarding Claim 11:
Claim 11 recites substantially similar limitations as those found in Claim 3, above, and is rejected under similar rationale.
Regarding Claim 17:
…a charge-discharge method, the charge-discharge method comprising: (Thien discloses in at least Paragraphs 0006 & 0008 a method for determining a charging strategy for an energy storage device of an electric vehicle based on a determined probable departure time, said departure time being determined on the basis of energy consumption patterns of home appliances as disclosed in at least Paragraph 0009 of Thien [i.e. a charge-discharge method])
collecting operation behavior information of a user with respect to household appliances; (Thien discloses in at least Paragraphs 0009, 0050, & 0053 wherein the temporal profile of power usage of household appliances may be provided as a consumption variable curve, indicating the operation of particular domestic appliances, such as a coffee machine turning on/off and the like [i.e. operation behavior information of a user with respect to household appliances is collected])
inputting the operation behavior information into a preset travel time prediction model to obtain a first driving travel time of the user; (Thien discloses in at least Paragraphs 0048, 0054, & 0055 wherein the energy management system can comprise a departure time estimation device, which includes a departure time model trained on the basis of historical usage data [i.e. a preset travel time prediction model]. At least Paragraphs 0023, 0024, & 0057 of Thien discloses wherein the consumption variable curves [i.e. the operational behavior information] are provided to the departure time model in order to determine an estimated or probable departure time for the vehicle [i.e. inputting the operation behavior information into a preset travel time prediction model to obtain a first driving travel time of the user])
determining a first charge-discharge strategy of a vehicle of the user based on the first driving travel time; and (Thien discloses in at least Paragraphs 0042 & 0048 wherein the determined estimate of departure time [i.e. first driving travel time] is transmitted to the charging strategy unit, which determines a charging strategy for the vehicle energy storage device, said strategy including a temporal curve of the charging current for the charging process [i.e. determining a first charge-discharge strategy of a vehicle of the user based on the first driving travel time])
Thien however appears to be silent regarding:
A non-transitory storage medium having stored thereon instructions that, when executed by a processor of an electronic device, causes the electronic device to perform
establishing a constrained optimization model, wherein the constrained optimization model takes a charge-discharge strategy as a decision variable, and a maximum battery life under the charge-discharge strategy is an objective function;
wherein the first charge-discharge strategy is obtained by solving the constrained optimization model;
transmitting the first charge-discharge strategy to the charging pile, wherein the charging pile charges the vehicle or controls the vehicle to discharge based on the first charge-discharge strategy.
However Narula teaches wherein following the determination of a charging strategy for a vehicle by computing elements, the determined strategy may be transmitted to and executed by the charger.
A non-transitory storage medium having stored thereon instructions that, when executed by a processor of an electronic device, causes the electronic device to perform (However Narula teaches in at least Paragraphs 0012 & 0029 wherein the system for determining a battery charging profile may be implemented using one or more non-transient computer readable storage media storing instructions, for example software code, which may be implemented using one or more processors [i.e. at least one processor; and a data storage storing one or more programs])
transmitting the first charge-discharge strategy to the charging pile, wherein the charging pile charges the vehicle or controls the vehicle to discharge based on the first charge-discharge strategy. (However Narula teaches in at least Paragraphs 0012 & 0074 wherein a charging profile calculated via a network remote from the battery/charger is transmitted wirelessly to the battery charger, the battery charger subsequently being controlled to charge the battery based on the received charging profile [i.e. transmitting the first charge-discharge strategy to the charging pile, wherein the charging pile charges the vehicle or controls the vehicle to discharge based on the first charge-discharge strategy])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the transmission and execution of charging strategy determined through computing elements as taught by Narula.
The motivation to do so is that, as acknowledged by Narula in at least Paragraphs 0012 & 0074, and as would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention, a determined charging profile may be actually and practically implemented at a charging device to execute charging of a vehicle.
However Galbraith teaches wherein a constrained optimization model may be used to determine an optimal amount of power to supply to electric vehicle chargers in order to ensure the electric vehicle receives an amount of energy required to meet a target state of charge before a scheduled departure time.
establishing a constrained optimization model, wherein the constrained optimization model takes a charge-discharge strategy as a decision variable, and a maximum battery life under the charge-discharge strategy is an objective function; (However Galbraith teaches in at least Paragraphs 0206 – 0209 wherein the EV management system for a building may utilize a mathematical optimization model including a constrained objective function relating decision variables and one or more objectives of a system over a time horizon [i.e. establishing a constrained optimization model]. At least Paragraphs 0122 & 0123 of Galbraith teach wherein the decision variables may include the amount of power that should flow through each electric vehicle supply equipment [i.e. charger] for each period of time [i.e. the constrained optimization model takes a charge-discharge strategy as a decision variable]. At least Paragraphs 0188 – 0194 of Galbraith further teach wherein the objective function may be configured to maximize a reward function, which may include objectives such as ensuring the EV receives an amount of energy required to meet a target state of charge before a scheduled departure time [i.e. a maximum battery life under the charge-discharge strategy is an objective function])
wherein the first charge-discharge strategy is obtained by solving the constrained optimization model; (However Galbraith teaches in at least Paragraphs 0122, 0123, & 0196 wherein the optimization problem may be solved to determine an optimal numerical value for each decision variable, which may include the amount of power that should flow through each electric vehicle supply equipment [i.e. charger] for each period of time [i.e. the first charge-discharge strategy is obtained by solving the constrained optimization model])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the use of a constrained optimization model to determine a first charge-discharge strategy as taught by Galbraith.
The motivation to do so is that, as acknowledged by Galbraith in at least Paragraph 0122, 0123, & 0199, an optimal set of decision variables, corresponding to the amount of power that should flow through each electric vehicle supply equipment [i.e. charger] for each period of time may be identified, improving the optimal supply of power to the electrical vehicle during charging operations.
Claim(s) 4 & 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thien (US 2024/0338623 A1) in view of Narula (US 2024/0195206 A1) and Galbraith (US 2024/0343149 A1) as applied to claims 2 & 10 above, and further in view of Ucar (US 2023/0196917 A1).
Regarding Claim 4:
The charge-discharge method of claim 2, wherein determining the first driving travel time based on the second driving travel time corresponding to each type of the household appliances further comprises: obtaining a weight corresponding to each type of the household appliances; and obtaining the first driving travel time based on the second driving travel time corresponding to each type of the household appliances and the weight corresponding to each type of the household appliances.
Thien does not appear to specifically disclose determining a departure time of the vehicle based on the weighting of appliance power usage.
However Ucar teaches in at least Paragraphs 0042 & 0048 wherein a departure indicator database may provide weights associated with different features that may be predictive of a departure of a vehicle [i.e. obtaining a weight corresponding to each type of the household appliances], such that the weights may be applied to the features and summed to determine a departure time of the vehicle. The features may include, as disclosed in at least Paragraphs 0040, 0048, & 0051 both intrinsic and extrinsic features, such as navigation information of the vehicle, and human behaviors detected [i.e. obtaining the first driving travel time based on the second driving travel time corresponding to each type of the household appliances and the weight corresponding to each type of the household appliances]. Examiner notes that while Ucar does not appear to specifically apply the weighting or prediction to appliances, Ucar’s teachings in view of Thien would appear to render obvious such a limitation in the view of the Examiner, as Thien discloses in at least Paragraphs 0019, 0052, & 0053 wherein different features, namely water consumption patterns and coffee maker power usage, may be used to predict the departure time. Thus, as Ucar teaches determining a departure time based on weighting different predictive features, one of ordinary skill in the art before the effective filing date of the present claimed invention would have been motivated to weight the utility features of Thien to determine a departure time.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the weighting of features to determine a departure time for the vehicle as taught by Ucar.
The motivation to do so is that, as acknowledged by Ucar in at least Paragraph 0048, aggregated features may be used to determine the estimated departure time of the vehicle, improving said estimation through the combination of predictive features.
Regarding Claim 12:
Claim 12 recites substantially similar limitations as those found in Claim 4, above, and is rejected under similar rationale.
Claim(s) 5 & 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thien (US 2024/0338623 A1) in view of Narula (US 2024/0195206 A1) and Galbraith (US 2024/0343149 A1) as applied to claims 1 & 9 above, and further in view of Wang (US 2024/0163298 A1).
Regarding Claim 5:
The charge-discharge method of claim 1, wherein the operation behavior information comprises operation data of the user for operating the household appliances, after collecting operation behavior information of a user with respect to household appliances, the method further comprises: detecting whether the operation data is in a preset database, and marking a training state of the operation data as known information if the operation data is detected in the preset database, wherein the preset database comprises a sample for training the travel time prediction model; marking the training state of the operation data as unknown information if the operation data is not detected in the preset database; storing the operation data and the training state of the operation data in the preset database; and retraining the travel time prediction model based on the operation data and the training state stored in the preset database.
Thien does not appear to specifically disclose wherein the model is trained in the manner set forth above.
However Wang teaches in at least Paragraphs 0030 & 0031 wherein an unsupervised machine learning model may process unlabeled data to identify if new data types are present in the unlabeled data based on a comparison of the data to pre-existing data characteristics assigned to labels, marking the data as such a label if a match is found [i.e. detecting whether the operation data is in a preset database, and marking a training state of the operation data as known information if the operation data is detected in the preset database, wherein the preset database comprises a sample for training the travel time prediction model], with a new label being generated and assigned to the unlabeled data if no match is found, the label and data being stored in the data store [i.e. database] alongside the corresponding data as taught in at least Paragraphs 0031 & 0059 of Wang [i.e. marking the training state of the operation data as unknown information if the operation data is not detected in the preset database and storing the operation data and the training state of the operation data in the preset database]. At least Paragraphs 0080 & 0175 – 0177 of Wang further teaches wherein the machine learning model may be retrained based on the new data and associated labels [i.e. retraining the travel time prediction model based on the operation data and the training state stored in the preset database].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the retraining of the prediction model based on marking new data acquired as new or previously known as taught by Wang.
The motivation to do so is that, as acknowledged by Wang in at least Paragraphs 0030 & 0031, the data utilized for training may be better classified for data types, improving the determination of the effect of new data types on the prediction model.
Regarding Claim 13:
Claim 13 recites substantially similar limitations as those found in Claim 5, above, and is rejected under similar rationale.
Claim(s) 6, 7, 14, & 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thien (US 2024/0338623 A1) in view of Narula (US 2024/0195206 A1) and Galbraith (US 2024/0343149 A1) as applied to claims 1 & 9 above, and further in view of Simonis (US 2023/0163618 A1).
Regarding Claim 6:
The charge-discharge method of claim 1, wherein determining the first charge-discharge strategy of the vehicle based on the first driving travel time further comprises: obtaining historical charge-discharge data of the vehicle; inputting the historical charge-discharge data into a preset charge-discharge strategy formulation model to obtain a second charge-discharge strategy of the vehicle; and adjusting the second charge-discharge strategy to obtain the first charge-discharge strategy based on the first driving travel time.
Thien discloses in at least Paragraphs 0042, 0047, & 0059 wherein a charging strategy may be adapted on the basis of an estimated departure time in order to ensure that the vehicle is sufficiently charged in a timely manner [i.e. adjusting the second charge-discharge strategy to obtain the first charge-discharge strategy based on the first driving travel time]. Thien however appears to be silent regarding wherein a charging strategy is obtained by obtaining historical charge-discharge data of the vehicle, inputting the historical charge-discharge data into a preset charge-discharge strategy formulation model to obtain a second charge-discharge strategy of the vehicle.
However Simonis teaches in at least Paragraphs 0008 & 0025 wherein historical usage patterns for a battery of a battery-operated device, such as a vehicle, may be detected [i.e. obtaining historical charge-discharge data of the vehicle]. At least Paragraphs 0026, 0029, & 0051 of Simonis further teach wherein the historical usage patterns may be used to generate predicted usage patterns, which may assign relevant charging strategies, as well as simulate the effects of said charging strategies and present the results for user selection [i.e. inputting the historical charge-discharge data into a preset charge-discharge strategy formulation model to obtain a second charge-discharge strategy of the vehicle].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating he simulation of various charging strategies based on historical usage data as taught by Simonis.
The motivation to do so is that, as acknowledged by Simonis in at least Paragraphs 0026 & 0029, relevant charging strategies may be assessed for the vehicle, improving a selection of suitable charging strategy for the vehicle based on the past usage behavior of the vehicle.
Regarding Claim 7:
The charge-discharge method of claim 6, wherein the charge-discharge strategy formulation model is configured to: predict multiple third charge-discharge strategies based on the historical charge-discharge data, obtain a life improvement probability of each of the multiple third charge-discharge strategies for a battery of the vehicle, screen objective charge-discharge strategies which meet a preset battery life improvement condition from the multiple third charge strategies based on the life improvement probability of each of the multiple third charge strategies, and generate the second charge-discharge strategy based on the objective charge-discharge strategies.
Thien does not appear to specifically disclose wherein multiple strategies for charging are predicted and evaluated for battery life improvement probability.
However Simonis teaches in at least Paragraphs 0061 & 0062 wherein at least two battery charging strategies are simulated to determine the effect of the charging on battery aging state based on the historical operating variable profile [i.e. predict multiple third charge-discharge strategies based on the historical charge-discharge data and obtain a life improvement probability of each of the multiple third charge-discharge strategies for a battery of the vehicle]. At least Paragraphs 0072 – 0076 of Simonis further teach wherein the determined aging indication for various charging strategies may be compared to an aging trajectory, with a charging strategy being prompted to the user based on if the new charging strategy provides a lower load to the vehicle battery [i.e. screen objective charge-discharge strategies which meet a preset battery life improvement condition from the multiple third charge strategies based on the life improvement probability of each of the multiple third charge strategies, and generate the second charge-discharge strategy based on the objective charge-discharge strategies].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the determination of charging strategy based on minimizing aging indications of batteries as taught by Simonis.
The motivation to do so is that, as acknowledged by Simonis in at least Paragraphs 0025 & 0029, the service life of the battery may be improved by selecting charging strategies with a lower load on the vehicle battery, improving the service lifetime of the vehicle.
Regarding Claim 14:
Claim 14 recites substantially similar limitations as those found in Claim 6, above, and is rejected under similar rationale.
Regarding Claim 15:
Claim 15 recites substantially similar limitations as those found in Claim 7, above, and is rejected under similar rationale.
Claim(s) 8 & 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thien (US 2024/0338623 A1) in view of Narula (US 2024/0195206 A1), Galbraith (US 2024/0343149 A1), and Simonis (US 2023/0163618 A1) as applied to claims 8 & 14 above, and further in view of Calabro (US 2025/0128632 A1).
Regarding Claim 8:
The charge-discharge method of claim 6, wherein adjusting the second charge-discharge strategy to obtain the first charge-discharge strategy based on the first driving travel time further comprises: obtaining a real-time electricity price; and adjusting the second charge-discharge strategy to obtain the first charge-discharge strategy based on the first driving travel time and the real-time electricity price.
Thien does not appear to specifically disclose adjusting the second charge-discharge strategy to obtain the first charge-discharge strategy based on the first driving travel time and the real-time electricity price.
However Calabro teaches in at least Paragraphs 0128, 0129, 0326, & 0327 wherein upon the arrival of an electric vehicle, a charging schedule is set based on the departure time of the electric vehicle, as well as the required amount of charging at each charger and the current spot price of electric power in order to minimize the cost of charging [i.e. obtaining a real-time electricity price; and adjusting the second charge-discharge strategy to obtain the first charge-discharge strategy based on the first driving travel time and the real-time electricity price].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Thien by incorporating the determination of charging strategy based on both departure time and electricity price as taught by Calabro.
The motivation to do so is that, as acknowledged by Calabro in at least Paragraphs 0128 & 0325, the cost of power for EV charging may be reduced while ensuring the vehicle is charged to the required amount by the set departure time of the vehicle, improving the cost of charging of the EV without sacrificing charging convenience.
Regarding Claim 16:
Claim 16 recites substantially similar limitations as those found in Claim 8, above, and is rejected under similar rationale.
Conclusion
The following prior art made of record but not relied upon is considered pertinent to the Applicant’s disclosure:
Xu (CN 111717072 A): Xu recites a charging optimization method for electric vehicle batteries, including the acquisition of the historical operation data of the vehicle battery, such as the amount of charging/discharging taking place over specific intervals, and characteristics of the battery such as temperature profile. Based on historical and predicted values, an optimization of charging power may take place.
Min (US 11,270,586 B2): Min recites a system for providing information regarding a parking space, including the prediction of the departure time of the parked vehicles based on the vehicle and occupant information.
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.
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/CHRISTOPHER R CARDIMINO/Examiner, Art Unit 3661
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661
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