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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/03/2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to independent claims 1, 12 and 15 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
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-7, 9-11 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Vrampas et al. (US 20230112801 A1; hereinafter Vrampas) in view of LV et al. (CN 117812566 A; hereinafter LV).
Regarding claim 1, Vrampas teaches a method comprising: obtaining, by a processor, sensor data from at least one sensor of a vehicle (see at least, [0041] the vehicle charging system 44, the electrified vehicle 10 may include a telecommunications module 46, a global positioning system (GPS) 48; [0049] The control module 52 may receive and process various inputs); predicting, by a machine learning model, trained on the sensor data, for monitoring a charging port closure of the vehicle; a likelihood that an authorized user of the vehicle will utilize a charging port of the vehicle (see at least, [0050] The control module 52 may be programmed to predict when the charging event is likely to occur by assessing learned charging habits of the user of the electrified vehicle 10…may be inferred or
learned values that are based on historical charging-related data associated with the electrified vehicle 10); causing, by the processor and based on the likelihood determined by the machine learning model, a first actuation to open the charging port closure (see at least, [0058] The command signal 92
may instruct the charge port assembly 32 to open a charge port door 40 (e.g., via an automatic
door opener of the charge port assembly 32)…The electrified vehicle 10 may thus be further prepared for accomplishing the impending charging event).
Vrampas does not explicitly teach after causing the first actuation to open the charging port closure, determining by the processor, that an amount of time has elapsed; and causing, by the processor and based on the machine learning model, a second actuation to close the charging port closure based on the amount of time. However, LV teaches this limitation.
LV teaches after causing the first actuation to open the charging port closure, determining by the processor, that an amount of time has elapsed; and causing, by the processor and based on the machine learning model (see at least, [0079] the computing platform 130 may include processors 131 to 13n …can also be a hardware circuit designed for artificial intelligence…such as neural network processing …deep learning processing unit (DPU)), a second actuation to close the charging port closure based on the amount of time (see at least, [0154] the charging cover can be automatically closed when the positioning effective time satisfies the preset condition that the charging cover is opened…the charging cover opening of the vehicle can be controlled to be opened when the time is greater than or equal to the preset time (for example, 1 minute). For another example, it can be set within a preset time period (for example, 3 minutes)…at which time the charging port cover can be automatically
closed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include after causing the first actuation to open the charging port closure, determining by the processor, that an amount of time has elapsed; and causing, by the processor and based on the machine learning model as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Regarding claim 2, the combination of Vrampas and LV teaches the method of claim 1. Vrampas further teaches wherein the sensor data corresponds to a battery charge state of a battery of the vehicle (see at least, [0054] the learned charging habits of the user may be based on battery charge level data 86
that may be received as yet another input to the control module 52…may include information concerning the specific state of charge (SOC) of the traction battery pack 12 each time the user plugs-in to charge the electrified vehicle 10).
Regarding claim 4, the combination of Vrampas and LV teaches the method of claim 1. LV further teaches wherein the sensor data corresponds to a proximity of the vehicle to a charging connector that is configured to connect to the charging port covered by the charging port closure ([0017] the charging gun can send the positioning information to the charging pile, and then the charging pile sends the positioning information to the vehicle to inform the position of the charging gun).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include the sensor data corresponds to a proximity of the vehicle to a charging connector that is configured to connect to the charging port covered by the charging port closure as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Regarding claim 5, the combination of Vrampas and LV teaches the method of claim 1. Vrampas wherein the sensor data corresponds to a proximity to one of one or more locations at which the vehicle is most frequently charged (see at least, [0011] The system may further obtain vehicle user historical driving pattern that may include driving routes, daily activities and schedules, destination locations
…including day and time to charge the vehicle, preferred charging locations, etc.).
Regarding claim 6, the combination of Vrampas and LV teaches the method of claim 1. LV further teaches wherein the sensor data corresponds to a proximity of the authorized user to the charging port closure (see at least, Fig 5, as shown in (c) in FIG. 5, The charging cover 506 is in a closed state. The user can move the charging gun 503 in the direction 504, and when the position of the charging gun 503 is moved to the position 2, the distance 505 is less than a preset distance (e.g., 5 meters), at which time, as shown in (d) in FIG. 5, the charging cap 506 can be automatically flicked.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include the sensor data corresponds to a proximity of the authorized user to the charging port closure as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Regarding claim 7, the combination of combination of Vrampas and LV teaches the method of claim 1. LV further teaches wherein the sensor data corresponds to a detection that the authorized user is holding a charging connector that is configured to connect to the charging port covered by the charging port closure ([0002] charging process of the vehicle is that the vehicle stops at the front of the charging device, the user holds the charging gun to insert into the charging port of the vehicle to charge).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include the sensor data corresponds to a detection that the authorized user is holding a charging connector that is configured to connect to the charging port covered by the charging port closure as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Regarding claim 9, the combination of Vrampas and LV teaches the method of claim 1. LV further teaches the machine learning model is further trained based on charging port closure actuation data corresponding to multiple of additional users and multiple additional vehicles (see at least, [0154] the same charging gun may trigger the charging cover openings of the two vehicles to open at the same time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include the machine learning model is further trained based on charging port closure actuation data corresponding to multiple of additional users and multiple additional vehicles as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Regarding claim 10, the combination of Vrampas and LV teaches the method of claim 9. LV further teaches the actuation is caused when the likelihood exceeds a threshold value (see at least, claim 6, controlling the charging port cover to open, comprising: when the distance is less than or equal to the time length of the first threshold value is greater than or equal to the first time length, controlling the opening of the charging cover).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include t the actuation is caused when the likelihood exceeds a threshold value as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Regarding claim 11, the combination of Vrampas and LV teaches the method of claim 10. Vrampas further teaches collecting user-specific charging port closure actuation data corresponding to the authorized user and the vehicle ([0050] The control module 52 may be programmed to predict when the charging event is likely to occur by assessing learned charging habits of the user of the electrified vehicle 10…may be inferred or learned values that are based on historical charging-related data associated with the electrified vehicle 10); and refining the machine learning model based at least in part on the user-specific charging port closure actuation data ([0051] the control module 52 may employ a learning tool such as a probabilistic model or neural network for inferring when future charging events are likely to occur based on the learned charging habit).
Regarding claim 21, the combination of Vrampas and LV teaches the method of claim 1. LV further teaches wherein the amount of time is an amount of time in which a charging connector has not been connected to the charging port (see at least, [0179] the vehicle 501 does not detect insertion of the charging gun 604 into the charging flap 506 within a predetermined time period (e .g., 3 minutes), and the vehicle may automatically close the charging flap 506).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include the amount of time is an amount of time in which a charging connector has not been connected to the charging port as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Claims 3, 14, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Vrampas et al. (US 20230112801 A1; hereinafter Vrampas) in view of LV et al. (CN 117812566 A; hereinafter LV) in further view of Tseng et al. (US 20240289700 A1; hereinafter Tseng).
Regarding claim 3, the combination of Vrampas and LV teaches the method of claim 1. The combination does not explicitly teach wherein the sensor data corresponds to an amount of time since a battery of the vehicle was last charged. However, Tseng teaches this limitation.
Tseng teaches wherein the sensor data corresponds to an amount of time since a battery of the vehicle was last charged (see at least, [0024] The historical vehicle battery charging information may include last battery charge time/day, SOC level at which the vehicle 105 was last charged, time for which the battery was last charged (e.g., 30 minutes or 15 minutes)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Vrampas and LV to include the sensor data corresponds to an amount of time since a battery of the vehicle was last charged as taught by Tseng in order to determine the user intent based on user's historical battery charging pattern (Tseng, [0008]).
Regarding claim 14, the combination of Vrampas and Tseng teaches the semiconductor device of claim 12. The combination does not explicitly teach wherein the circuitry is further configured to obtain user data corresponding to a location of the authorized user with respect to the charging port closure, wherein determining the likelihood that the authorized user will utilize the charging port is further based at least in part on the user data. However, LV teaches this limitation.
LV teaches wherein the circuitry is further configured to obtain user data corresponding to a location of the authorized user with respect to the charging port closure (see at least, [0017] the charging gun can send the positioning information to the charging pile, and then the charging pile sends the positioning information to the vehicle to inform the position of the charging gun), wherein determining the likelihood that the authorized user will utilize the charging port is further based at least in part on the user data (see at least, [0176] the transmitting/receiving unit 810 is further configured to receive a positioning message sent by the charging device, wherein the positioning message is used to indicate the position of the charging gun, and the charging device includes the charging gun; The processing unit 830 is further used for controlling the opening of the charging cover of the vehicle according to the positioning message).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Vrampas and Tseng to include obtain user data corresponding to a location of the authorized user with respect to the charging port closure, wherein determining the likelihood that the authorized user will utilize the charging port is further based at least in part on the user data as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Regarding claim 18, the combination of Vrampas and Tseng teaches the non-transitory machine-readable medium of claim 15. The combination does not explicitly teach wherein the location of the vehicle indicates a proximity of the vehicle to a charging connector that is configured to connect to the charging port. However, LV teaches this limitation.
LV teaches wherein the location of the vehicle indicates a proximity of the vehicle to a charging connector that is configured to connect to the charging port (see at least, [0017] the charging gun can send the positioning information to the charging pile, and then the charging pile sends the positioning information to the vehicle to inform the position of the charging gun).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Vrampas and Tseng to include the location of the vehicle indicates a proximity of the vehicle to a charging connector that is configured to connect to the charging port as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Regarding claim 20, the combination of Vrampas and Tseng teaches the non-transitory machine-readable medium of claim 15. The combination does not explicitly teach wherein the sensor data corresponds to a proximity of the authorized user to the charging port closure. However, LV teaches this limitation.
LV teaches wherein the location of the vehicle indicates a proximity of the vehicle to a charging connector that is configured to connect to the charging port (see at least, Fig 5, as shown in (c) in FIG. 5, The charging cover 506 is in a closed state. The user can move the charging gun 503 in the direction 504, and when the position of the charging gun 503 is moved to the position 2, the distance 505 is less than a preset distance (e.g., 5 meters), at which time, as shown in (d) in FIG. 5, the charging cap 506 can be automatically flicked).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Vrampas and Tseng to include the location of the vehicle indicates a proximity of the vehicle to a charging connector that is configured to connect to the charging port as taught by LV so that the charging port cover can be closed in time after the charging port cover is triggered by mistake, which ensures the charging safety of the vehicle (LV, [0155]).
Claims 12-13 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Vrampas et al. (US 20230112801 A1; hereinafter Vrampas) in view of Tseng et al. (US 20240289700 A1; hereinafter Tseng).
Regarding claim 12, Vrampas semiconductor device comprising: circuitry configured to: obtain battery data corresponding to a battery of a vehicle (see at least, [0054] the learned charging habits of the user may be based on battery charge level data 86 that may be received as yet another input to the control module 52), the battery data comprising a battery charge state of the battery (see at least, [0054] …may include information concerning the specific state of charge (SOC) of the traction battery pack
12 each time the user plugs-in to charge the electrified vehicle 10); determine, based in part on the battery data, whether a machine learning model is trained; and in response to a determination that the machine learning model is trained ([0054] the learned charging habits of the user may be based on battery charge level data 86 that may be received as yet another input to the control module 52…For example, the user may only charge when the SOC of the traction battery pack 12 is below a certain level, and thus this type of charge level information may be learned and recorded for helping predict future charging events): determine, by the machine learning model, a likelihood that an authorized user of the vehicle will utilize a charging port of the vehicle based at least in part on the battery data (see at least, [0050] The control module 52 may be programmed to predict when the charging event is likely to occur by assessing learned charging habits of the user of the electrified vehicle 10…may be inferred or learned values that are based on historical charging-related data associated with the electrified vehicle 10); and cause, based in part on the machine learning model, an actuation to open a charging port closure based on the likelihood (see at least, [0058] The command signal 92 may instruct the charge port assembly 32 to open a charge port door 40 (e.g., via an automatic door opener of the charge port assembly 32)…The electrified vehicle 10 may thus be further prepared for accomplishing the impending charging event).
Vrampas does not explicitly teach the battery data comprising the battery and an amount of time since the battery was last charged. However, Tseng teaches this limitation.
Tseng teaches the battery data comprising the battery and an amount of time since the battery was last charged (see at least, [0024] The historical vehicle battery charging information may include last
battery charge time/day, SOC level at which the vehicle 105 was last charged, time for which the battery was last charged (e.g., 30 minutes or 15 minutes)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Vrampas and Tseng to include the battery data comprising the battery and an amount of time since the battery was last charged as taught by Tseng in order to determine the user intent based on user's historical battery charging pattern (Tseng, [0008]).
Regarding claim 13, the combination of Vrampas and Tseng teaches the semiconductor device of claim 12. Tseng further teaches wherein the circuitry is further configured to obtain location data corresponding to a location of the vehicle with respect to a charging station ([0027] the charging management system 125 may search for and identify charger location), wherein determining the likelihood that the authorized user will utilize the charging port is further based at least in part on the location data (see at least, [0066] when the system processor 222 determines that the maximum vehicle user intent is less than the threshold value and when the vehicle user may be approaching a preferred charging location (that may be frequently used by the vehicle user)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include wherein the circuitry is further configured to obtain location data corresponding to a location of the vehicle with respect to a charging station, wherein determining the likelihood that the authorized user will utilize the charging port is further based at least in part on the location data as taught by Tseng as taught by Tseng in order to determine the user intent based on user's historical battery charging pattern (Tseng, [0008]).
Regarding claim 15, Vrampas includes a non-transitory machine-readable medium storing instructions that, when executed by one or more processors (see at least, [0047] a processor 78 and non-transitory memory 80 for executing various control strategies and modes associated with the vehicle charging system), cause the one or more processors to perform operations comprising: obtaining sensor data from sensors of a vehicle (see at least, [0041] the vehicle charging system 44, the electrified vehicle
10 may include a telecommunications module 46, a global positioning system (GPS) 48; [0049] The control module 52 may receive and process various inputs), the sensor data comprising: a battery charge state of a battery of the vehicle (see at least, [0054] the learned charging habits of the user may be based on battery charge level data 86 that may be received as yet another input to the control module) and corresponds to a condition of a battery of the vehicle (see at least, [0054] input to
the control module 52…may include information concerning the specific state of charge (SOC) of the traction battery pack 12 each time the user plugs-in to charge the electrified vehicle 10) and a proximity to a location at which the vehicle is most frequently charged (see at least, [0011] The system may further obtain vehicle user historical driving pattern that may include driving routes, daily activities and schedules, destination locations…including day and time to charge the vehicle, preferred charging locations); predicting, by a machine learning model, trained on the sensor data, for monitoring a charging port closure of the vehicle, a likelihood that an authorized user of the vehicle will utilize a charging port of the vehicle (see at least, [0050] The control module 52 may be programmed to
predict when the charging event is likely to occur by assessing learned charging habits of the user of the electrified vehicle 10…may be inferred or learned values that are based on historical charging-related data associated with the electrified vehicle 10) and causing, based in part on the machine learning model (see at least, [0061] predict whether a charging event is likely to soon occur based on the learned charging habits), an actuation to open charging port closure based on the likelihood (see at least, [0058] The command signal 92 may instruct the charge port assembly 32 to open a charge port door 40…e.g., via an automatic door opener of the charge port assembly 32…The electrified
vehicle 10 may thus be further prepared for accomplishing the impending charging event).
Vrampas does not explicitly teach the sensor data comprising: a time since a last charge of the battery. However, Tseng teaches this limitation.
Tseng teaches the sensor data comprising: a time since a last charge of the battery (see at least [0024] The historical vehicle battery charging information may include last battery charge time/day,
SOC level at which the vehicle 105 was last charged, time for which the battery was last charged (e.g., 30 minutes or 15 minutes)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include the sensor data comprising: a time since a last charge of the battery as taught by Tseng as taught by Tseng in order to determine the user intent based on user's historical battery charging pattern (Tseng, [0008]).
Regarding claim 16, the combination of Vrampas and Tseng teaches the non-transitory machine-readable medium of claim 15. Vrampas further teaches wherein the condition of the battery is charge state of the battery (see at least, [0054] the learned charging habits of the user may be based on battery charge level data 86 that may be received as yet another input to the control module 52…may include information concerning the specific state of charge (SOC) of the traction battery pack 12 each time the user plugs-in to charge the electrified vehicle 10).
Regarding claim 17, the combination of Vrampas and Tseng teaches the non-transitory machine-readable medium of claim 15. Tseng further teaches wherein the condition of the battery is amount of time since the battery was last charged (see at least, [0024] The historical vehicle battery charging
information may include last battery charge time/day, SOC level at which the vehicle 105 was last charged, time for which the battery was last charged (e.g., 30 minutes or 15 minutes)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vrampas to include the condition of the battery is amount of time since the battery was last charged as taught by Tseng in order to determine the user intent based on user's historical battery charging pattern (Tseng, [0008]
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Vrampas et al. (US 20230112801 A1; hereinafter Vrampas) in view of LV et al. (CN 117812566 A; hereinafter LV) and in further view of Broecker et al. (US 20150329002 A1; hereafter Broecker).
Regarding claim 22, the combination of Vrampas and LV teaches the method of claim 1. Vrampas further teaches comprising: after causing the actuation to open the charging port closure and by the processor ([0019] commanding a charge port door of a charge port assembly of the vehicle charging system to move to an open position in response to the predicting).
The combination does not explicitly teach detecting the authorized user moving away from the charging port; and in response to detecting the authorized user moving away from the charging port, causing the other actuation to close the charging port closure. However, Broecker teaches this limitation.
Broecker teaches detecting the authorized user moving away from the charging port; and in response to detecting the authorized user moving away from the charging port (see at least, [0017] the vehicle key outside a specific distance to the charging port. In other words, the charge plug is locked when the user's movement profile indicates movement away from the charging port), causing the other actuation to close the charging port closure (see at least, [0016] the user has again moved away from the vehicle without removing…the charging port is automatically locked again).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of Vrampas and LV to include detecting the authorized user moving away from the charging port; and in response to detecting the authorized user moving away from the charging port, causing the other actuation to close the charging port closure as taught by Broecker so that the state of charge can indicate that no charging process is currently taking place, when the vehicle is currently not being charged in a timer operating mode, but the charging process is to start at a later time (Broecker, [0031]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Coleman et al. (US 20160380440 A1) discloses refining the base machine learning model based at least in part on the user-specific charging port closure actuation data ([0063] machine learning algorithms, improves the quality of predictions for when and where the user will need to recharge).
Wiebenga et al. (US 20220355697 A1) discloses the sensor data corresponds to an amount of time since a battery of the vehicle was last charged ([0012] the controller may also use the received battery data to determine a number of charges (low SOC charges) in which the battery was recharged with a starting SOC value that is below the predefined low SOC threshold).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOYA PETTIEGREW whose telephone number is (313)446-6636. The examiner can normally be reached 8:30pm - 5:00pm M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at 571-270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TOYA PETTIEGREW/Primary Examiner, Art Unit 3662