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 . This action is made final.
Claims 1-4 and 7-22 filed on 07/22/2026 have been reviewed and considered by this office action.
Claims 1,14,18, and 20 are amended.
Claims 5 and 6 are canceled.
Claims 21 and 22 are newly added.
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.
Claims 1, 10, 12, 14, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ferone et al. (US 20250256611 A1), herein Ferone), further in view of Kim et al. (US 20250091460 A1, herein Km), and in further view of Flasher et al. (US 20190208386 A1), herein Flasher).
Regarding Claim 1, Ferone teaches A first vehicle comprising:
a sensor unit configured to capture inputs comprising one or more images associated with a charging station when the first vehicle is located at the charging station ([0081] Vehicles are equipped with diverse sensors, cameras, radars, and LIDARs, which collect a vast array of data, such as images, speed readings, GPS data, and acceleration metrics; [0084] these sensors 312 send data to a database 320 that stores data about the vehicle and occupants of the vehicle. In some embodiments, these sensors 312 send data to one or more decision subsystems 316 in vehicle node 310 to assist in decision-making.
Ferone further teaches [0126] The sensor set 412E may include any sensors in the vehicle 410E generating sensor data. For example, the sensor set 412E may include short-range sensors and long-range sensors. In some embodiments, the sensor set 412E of the vehicle 410E may include one or more of the following vehicle sensors: a camera,);
and a processor communicatively coupled with the sensor unit (see FIG. 4D; [0120] the vehicle 410E includes a processor 420E, a memory 422E, a communication unit 424E, and an electronic display 426E. see also FIG. 1A)
wherein the processor is configured to:
obtain the inputs from the sensor unit (see FIG. 2B; [0068] “processor 204 can further communicate with one or more elements 230 including sensor 212);
determine that the charging station is in a suboptimal condition ([0051] the processor 111 or the server 123 determines that the first vehicle 102 is unable to receive a charge at the charging station 107.; the citation illustrates an instance of suboptimal condition) by executing one or more image processing algorithms on the one or more images (Ferone teaches the vehicle using a camera as sensor inputs [0126] the sensor set 412E of the vehicle 410E may include one or more of the following vehicle sensors: a camera,; [0081] In one embodiment, Generative AI (GenAI) may be used by the instant solution in the transformation of data. Vehicles are equipped with diverse sensors, cameras, radars, and LIDARs, which collect a vast array of data, such as images, speed readings
Ferone further teaches the processing of images using algorithms (e.g synthesis of sensor data) [0083] In the instant solution, data generation is then performed on the data. Tools like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are trained on existing datasets to generate new, plausible data samples. For example, GANs might be tasked with crafting images showcasing vehicles in uncharted conditions or from unique perspectives. As another example, the synthesis of sensor data may be performed to model and create synthetic readings for such scenarios, enabling thorough system testing without actual physical encounters.; [0088] An AI/ML development system 340 creates one or more AI/ML models 332. In some embodiments, the AI/ML development system 340 utilizes data in the database 320 to develop and train one or more AI models 332.; [0083] the synthesis of sensor data may be performed to model and create synthetic readings for such scenarios, enabling thorough system testing without actual physical encounters. A critical step in the use of GenAI, given the safety-critical nature of vehicles, is validation. This validation might include the output data being compared with real-world datasets or using specialized tools like a GAN discriminator to gauge the realism of the crafted samples.);
classify the suboptimal condition into a predefined type, of a plurality of predefined types ([0087] An AI/ML production system 330 may be used by a decision subsystem 316 in a vehicle node 310 to assist in its decision-making process. The AI/ML production system 330 includes one or more AI/ML models 332 that are executed to retrieve the needed data, such as, but not limited to, a prediction, a categorization, a UI prompt, etc.), by executing the one or more image processing algorithms on the one or more images ([0099] Upon receiving the API 334 request, the AI/ML server process 336 may need to transform the data payload or portions of the data payload to be valid feature values into an AI/ML model 332. Data transformation may include but is not limited to combining data values, normalizing data values, and enriching the incoming data with data from other data sources. Once any required data transformation occurs, the AI/ML server process 336 executes the appropriate AI/ML model 332 using the transformed input data);
Ferone does not explicitly teach and perform a remedial action based on the predefined type, wherein, when the predefined type is a type indicating that a charger at the charging station is occupied by a second vehicle that is not getting charged at the charger or is charged to a predefined state of charge (SOC) level
performing the remedial action comprises transmitting a vehicle parking adjustment request, the vehicle parking adjustment request causing the second vehicle to autonomously move and park at a different location such that the charger becomes available to the first vehicle.
Kim teaches perform a remedial action based on the predefined type, wherein, when the predefined type is a type indicating that a charger at the charging station is occupied by a second vehicle that is not getting charged at the charger or is charged to a predefined state of charge (SOC) level ([0027] According to an embodiment, the preset condition may include at least one of a case when charging of the vehicle reaches a target charge and is complete, a case when a certain time has elapsed after charging is completed, a case when the vehicle in a standby state after charging is complete is present, and a condition set by a user.)
performing the remedial action comprises transmitting a vehicle parking adjustment request, the vehicle parking adjustment request causing the second vehicle to autonomously move and park at a different location such that the charger becomes available to the first vehicle ([0047] the processor may receive information about a standby location in the charging station and a driving path to the standby location from the server, and when a movement request to the vehicle is received from a pre-authenticated user terminal).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Kim so as to include an instance of transmitting a movement request when the charging station is occupied. Doing so would allow optimization of chargers within the charging station (Kim [0053] vehicles being charged and chargers in a charging station may be managed, and charging-related services as well as charging-related controls may be easily provided.)
Ferone in view of Kim does not explicitly teach the processor in the vehicle submitting a vehicle movement request. However, Flasher teaches the advantages of V2V communication over a centralized server.
Flasher teaches the advantages of having a decentralized system over a centralized system ([0004] Conventionally, the collection and analysis of such “Big Data” has been centralized. Various centralized solutions have been proposed to utilize data from vehicles. However, the centralized approaches have proven to be inflexible and cumbersome, often resulting in bottlenecks depending on the amount of data and analysis involved. Therefore, there is a need for a more flexible system for collecting and analyzing real-time data from vehicles, and also for allowing multiple vehicles to share critical data through a more streamlined communication network.
[0016] A dynamic and decentralized technique for implementing a system of Internet of Things (IoT) on vehicles is provided. The technique facilitates communications within a vehicle, between vehicles, and between vehicles and data centers.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone in view of Kim and in to incorporate the teachings of Flasher so as to adapt the decentralized system. As set forth in MPEP § 2143, by combining the known technique of a decentralized system (vehicle to vehicle/ vehicle to user) as taught by Flasher with the processor of Ferone in view of Matsumoto in view of Kim, one of ordinary skill would expect to achieve the predictable result of processor performing all the actions in Claim 1.
Regarding claim 10, Ferone in view of Kim and further in view of Flasher teaches the first vehicle of claim 1,
Ferone further teaches wherein the predefined type is a third predefined type when the processor determines that at least one charger at the charging station is not operational ([0051] In some embodiments, the processor 111 or the server 123 determines that the first vehicle 102 is unable to receive a charge at the charging station 107.), and
wherein the processor performs the remedial action by transmitting an error notification to a server or a computing system associated with the charging station ([0053] When the processor 111 determines that the first vehicle 102 is unable to receive the charge, the processor 111 may send a notification over a network 104 to the server 123 indicating that the first vehicle 102 is unable to receive the charge.) when the predefined type is the third predefined type.
Regarding claim 12, Ferone in view of Kim and further in view of Flasher teaches the first vehicle of claim 1,
Ferone further teaches wherein the sensor unit comprises at least one of a vehicle camera, a Radio Detection and Ranging (radar) sensor, or a Light Detection and Ranging (lidar) sensor (FIG. 4D, 412E; [0126] The sensor set 412E may include any sensors in the vehicle 410E generating sensor data. For example, the sensor set 412E may include short-range sensors and long-range sensors. In some embodiments, the sensor set 412E of the vehicle 410E may include one or more of the following vehicle sensors: a camera, a Light Detection and Ranging (Lidar) sensor, a radar sensor.).
Regarding claim 14, Ferone teaches teaches a method comprising:
obtaining, by a processor, inputs comprising one or more images from a sensor unit associated with a first vehicle (see FIG. 4D; [0120] the vehicle 410E includes a processor 420E, a memory 422E, a communication unit 424E, and an electronic display 426E. see also FIG. 1A
Ferone further teaches [0126] The sensor set 412E may include any sensors in the vehicle 410E generating sensor data. For example, the sensor set 412E may include short-range sensors and long-range sensors. In some embodiments, the sensor set 412E of the vehicle 410E may include one or more of the following vehicle sensors: a camera,),
wherein the sensor unit is configured to capture the inputs associated with a charging station when the first vehicle is located at the charging station ([0081] Vehicles are equipped with diverse sensors, cameras, radars, and LIDARs, which collect a vast array of data, such as images, speed readings, GPS data, and acceleration metrics; [0084] these sensors 312 send data to a database 320 that stores data about the vehicle and occupants of the vehicle. In some embodiments, these sensors 312 send data to one or more decision subsystems 316 in vehicle node 310 to assist in decision-making.);
determining, by the processor executing one or more image processing algorithms on the one or more images
(Ferone teaches the vehicle using a camera as sensor inputs [0126] the sensor set 412E of the vehicle 410E may include one or more of the following vehicle sensors: a camera,; [0081] In one embodiment, Generative AI (GenAI) may be used by the instant solution in the transformation of data. Vehicles are equipped with diverse sensors, cameras, radars, and LIDARs, which collect a vast array of data, such as images, speed readings
Ferone further teaches the processing of images using algorithms (e.g synthesis of sensor data) [0083] In the instant solution, data generation is then performed on the data. Tools like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are trained on existing datasets to generate new, plausible data samples. For example, GANs might be tasked with crafting images showcasing vehicles in uncharted conditions or from unique perspectives. As another example, the synthesis of sensor data may be performed to model and create synthetic readings for such scenarios, enabling thorough system testing without actual physical encounters.; [0088] An AI/ML development system 340 creates one or more AI/ML models 332. In some embodiments, the AI/ML development system 340 utilizes data in the database 320 to develop and train one or more AI models 332.; [0083] the synthesis of sensor data may be performed to model and create synthetic readings for such scenarios, enabling thorough system testing without actual physical encounters. A critical step in the use of GenAI, given the safety-critical nature of vehicles, is validation. This validation might include the output data being compared with real-world datasets or using specialized tools like a GAN discriminator to gauge the realism of the crafted samples.);
, that the charging station is in a suboptimal condition ([0051] the processor 111 or the server 123 determines that the first vehicle 102 is unable to receive a charge at the charging station 107.; the citation illustrates an instance of suboptimal condition);
classifying, by the processor executing the one or more image processing algorithms on the one or more images ([0099] Upon receiving the API 334 request, the AI/ML server process 336 may need to transform the data payload or portions of the data payload to be valid feature values into an AI/ML model 332. Data transformation may include but is not limited to combining data values, normalizing data values, and enriching the incoming data with data from other data sources. Once any required data transformation occurs, the AI/ML server process 336 executes the appropriate AI/ML model 332 using the transformed input data)), the suboptimal condition into a predefined type, of a plurality of predefined types ([0087] An AI/ML production system 330 may be used by a decision subsystem 316 in a vehicle node 310 to assist in its decision-making process. The AI/ML production system 330 includes one or more AI/ML models 332 that are executed to retrieve the needed data, such as, but not limited to, a prediction, a categorization, a UI prompt, etc.); and
Ferone does not explicitly teach performing, by the processor, a remedial action based on the predefined type, wherein, when the predefined type is a type indicating that a charger at the charging station is occupied by a second vehicle that is not getting charged at the charger or is charged to a predefined state of charge (SOC) level, performing the remedial action comprises
transmitting a vehicle parking adjustment request, the vehicle parking adjustment request causing the second vehicle to autonomously move and park at a different location such that the charger becomes available to the first vehicle.
Kim teaches performing, by the processor, a remedial action based on the predefined type, wherein, when the predefined type is a type indicating that a charger at the charging station is occupied by a second vehicle that is not getting charged at the charger or is charged to a predefined state of charge (SOC) level ([0027] According to an embodiment, the preset condition may include at least one of a case when charging of the vehicle reaches a target charge and is complete, a case when a certain time has elapsed after charging is completed, a case when the vehicle in a standby state after charging is complete is present, and a condition set by a user.), performing the remedial action comprises
transmitting a vehicle parking adjustment request, the vehicle parking adjustment request causing the second vehicle to autonomously move and park at a different location such that the charger becomes available to the first vehicle ([0047] the processor may receive information about a standby location in the charging station and a driving path to the standby location from the server, and when a movement request to the vehicle is received from a pre-authenticated user terminal).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Kim so as to include an instance of transmitting a movement request when the charging station is occupied. Doing so would allow optimization of chargers within the charging station (Kim [0053] vehicles being charged and chargers in a charging station may be managed, and charging-related services as well as charging-related controls may be easily provided.)
Ferone in view of Kim does not explicitly teach the processor in the vehicle submitting a vehicle movement request. However, Flasher teaches the advantages of V2V communication over a centralized server.
Flasher teaches the advantages of having a decentralized system over a centralized system ([0004] Conventionally, the collection and analysis of such “Big Data” has been centralized. Various centralized solutions have been proposed to utilize data from vehicles. However, the centralized approaches have proven to be inflexible and cumbersome, often resulting in bottlenecks depending on the amount of data and analysis involved. Therefore, there is a need for a more flexible system for collecting and analyzing real-time data from vehicles, and also for allowing multiple vehicles to share critical data through a more streamlined communication network.
[0016] A dynamic and decentralized technique for implementing a system of Internet of Things (IoT) on vehicles is provided. The technique facilitates communications within a vehicle, between vehicles, and between vehicles and data centers.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone in view of Kim and in to incorporate the teachings of Flasher so as to adapt the decentralized system. As set forth in MPEP § 2143, by combining the known technique of a decentralized system (vehicle to vehicle/ vehicle to user) as taught by Flasher with the processor of Ferone in view of Matsumoto in view of Kim, one of ordinary skill would expect to achieve the predictable result of processor performing all the actions in Claim 14.
Regarding claim 19, Ferone in view of Kim further in view of Flasher teaches the method of claim 14,
Ferone teaches wherein the predefined type is a third predefined type when at least one charger at the charging station is not operational ([0053] When the processor 111 determines that the first vehicle 102 is unable to receive the charge, the processor 111 may send a notification over a network 104 to the server 123 indicating that the first vehicle 102 is unable to receive the charge.), and
wherein performing the remedial action comprises
transmitting an error notification to a server or a computing system associated with the charging station when the predefined type is the third predefined type ([0053] When the processor 111 determines that the first vehicle 102 is unable to receive the charge, the processor 111 may send a notification over a network 104 to the server 123 indicating that the first vehicle 102 is unable to receive the charge.).
Regarding Claim 20, Ferone teaches a non-transitory computer-readable storage medium having instructions stored thereupon which, when executed by a processor, cause the processor to:
obtain inputs comprising one or more images from a sensor unit (see FIG. 2B; [0068] “processor 204 can further communicate with one or more elements 230 including sensor 212
Ferone further teaches [0126] The sensor set 412E may include any sensors in the vehicle 410E generating sensor data. For example, the sensor set 412E may include short-range sensors and long-range sensors. In some embodiments, the sensor set 412E of the vehicle 410E may include one or more of the following vehicle sensors: a camera,) associated with a vehicle, wherein the sensor unit is configured to capture the inputs associated with a charging station when the vehicle is located at the charging station ([0081] Vehicles are equipped with diverse sensors, cameras, radars, and LIDARs, which collect a vast array of data, such as images, speed readings, GPS data, and acceleration metrics; [0084] these sensors 312 send data to a database 320 that stores data about the vehicle and occupants of the vehicle. In some embodiments, these sensors 312 send data to one or more decision subsystems 316 in vehicle node 310 to assist in decision-making.);
determine that the charging station is in a suboptimal condition ([0051] the processor 111 or the server 123 determines that the first vehicle 102 is unable to receive a charge at the charging station 107.; the citation illustrates an instance of suboptimal condition) by executing one or more image processing algorithms on the one or more images
(Ferone teaches the vehicle using a camera as sensor inputs [0126] the sensor set 412E of the vehicle 410E may include one or more of the following vehicle sensors: a camera,; [0081] In one embodiment, Generative AI (GenAI) may be used by the instant solution in the transformation of data. Vehicles are equipped with diverse sensors, cameras, radars, and LIDARs, which collect a vast array of data, such as images, speed readings
Ferone further teaches the processing of images using algorithms (e.g synthesis of sensor data) [0083] In the instant solution, data generation is then performed on the data. Tools like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are trained on existing datasets to generate new, plausible data samples. For example, GANs might be tasked with crafting images showcasing vehicles in uncharted conditions or from unique perspectives. As another example, the synthesis of sensor data may be performed to model and create synthetic readings for such scenarios, enabling thorough system testing without actual physical encounters.; [0088] An AI/ML development system 340 creates one or more AI/ML models 332. In some embodiments, the AI/ML development system 340 utilizes data in the database 320 to develop and train one or more AI models 332.; [0083] the synthesis of sensor data may be performed to model and create synthetic readings for such scenarios, enabling thorough system testing without actual physical encounters. A critical step in the use of GenAI, given the safety-critical nature of vehicles, is validation. This validation might include the output data being compared with real-world datasets or using specialized tools like a GAN discriminator to gauge the realism of the crafted samples.);
);
classify the suboptimal condition into a predefined type ([0087] An AI/ML production system 330 may be used by a decision subsystem 316 in a vehicle node 310 to assist in its decision-making process. The AI/ML production system 330 includes one or more AI/ML models 332 that are executed to retrieve the needed data, such as, but not limited to, a prediction, a categorization, a UI prompt, etc.), of a plurality of predefined types, by executing the one or more image processing algorithms on the one or more images ([0099] Upon receiving the API 334 request, the AI/ML server process 336 may need to transform the data payload or portions of the data payload to be valid feature values into an AI/ML model 332. Data transformation may include but is not limited to combining data values, normalizing data values, and enriching the incoming data with data from other data sources. Once any required data transformation occurs, the AI/ML server process 336 executes the appropriate AI/ML model 332 using the transformed input data); and
Ferone does not explicitly teach perform a remedial action based on the predefined type, wherein, when the predefined type is a type indicating that a charger at the charging station is occupied by a second vehicle that is not getting charged at the charger or is charged to a predefined state of charge (SOC) level,
performing the remedial action comprises transmitting a vehicle parking adjustment request, the vehicle parking adjustment request causing the second vehicle to autonomously move and park at a different location such that the charger becomes available to the vehicle.
Kim teaches perform a remedial action based on the predefined type, wherein, when the predefined type is a type indicating that a charger at the charging station is occupied by a second vehicle that is not getting charged at the charger or is charged to a predefined state of charge (SOC) level ([0027] According to an embodiment, the preset condition may include at least one of a case when charging of the vehicle reaches a target charge and is complete, a case when a certain time has elapsed after charging is completed, a case when the vehicle in a standby state after charging is complete is present, and a condition set by a user.) ,
performing the remedial action comprises transmitting a vehicle parking adjustment request, the vehicle parking adjustment request causing the second vehicle to autonomously move and park at a different location such that the charger becomes available to the vehicle ([0047] the processor may receive information about a standby location in the charging station and a driving path to the standby location from the server, and when a movement request to the vehicle is received from a pre-authenticated user terminal).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Kim so as to include an instance of transmitting a movement request when the charging station is occupied. Doing so would allow optimization of chargers within the charging station (Kim [0053] vehicles being charged and chargers in a charging station may be managed, and charging-related services as well as charging-related controls may be easily provided.)
Ferone in view of Kim does not explicitly teach the processor in the vehicle submitting a vehicle movement request. However, Flasher teaches the advantages of V2V communication over a centralized server.
Flasher teaches the advantages of having a decentralized system over a centralized system ([0004] Conventionally, the collection and analysis of such “Big Data” has been centralized. Various centralized solutions have been proposed to utilize data from vehicles. However, the centralized approaches have proven to be inflexible and cumbersome, often resulting in bottlenecks depending on the amount of data and analysis involved. Therefore, there is a need for a more flexible system for collecting and analyzing real-time data from vehicles, and also for allowing multiple vehicles to share critical data through a more streamlined communication network.
[0016] A dynamic and decentralized technique for implementing a system of Internet of Things (IoT) on vehicles is provided. The technique facilitates communications within a vehicle, between vehicles, and between vehicles and data centers.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone in view of Kim and in to incorporate the teachings of Flasher so as to adapt the decentralized system. As set forth in MPEP § 2143, by combining the known technique of a decentralized system (vehicle to vehicle/ vehicle to user) as taught by Flasher with the processor of Ferone in view of Matsumoto in view of Kim, one of ordinary skill would expect to achieve the predictable result of processor performing all the actions in Claim 20.
Claims 2, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ferone in view of Kim in view of Flasher, and further in view of Cun et al. (US 20150321570 A1, herein Cun).
Regarding claim 2, Ferone in view of Kim and in further view of Flasher teaches the first vehicle of claim 1,
Ferone further teaches wherein the processor is further configured to:
output a classification confirmation request responsive to classifying the suboptimal condition ([0057] When the processor 111 determines that the problem exists at the first vehicle 102, such as the problem with the charging port 115, the processor 111 may send a notification to a device associated with an occupant of the first vehicle 102, such as a mobile device 118.; The citation illustrates the suboptimal condition as an instance when a problem exists at the charging port, and the classification confirmation request is illustrated as a notification),
wherein the classification confirmation request
Ferone doesn’t explicitly teach request comprises information associated with the predefined type; and perform the remedial action responsive to obtaining the confirmation; obtain a confirmation from a user responsive to outputting the classification confirmation request and obtain a confirmation from a user.
Kim teaches teach request comprises information associated with the predefined type ([0027] According to an embodiment, the preset condition may include at least one of a case when charging of the vehicle reaches a target charge and is complete, a case when a certain time has elapsed after charging is completed, a case when the vehicle in a standby state after charging is complete is present, and a condition set by a user.); and perform the remedial action responsive to obtaining the confirmation ([0047] the processor may receive information about a standby location in the charging station and a driving path to the standby location from the server, and when a movement request to the vehicle is received from a pre-authenticated user terminal).
Ferone in view of Kim doesn’t explicitly teach obtain a confirmation from a user responsive to outputting the classification confirmation request and obtain a confirmation from a user.
Cun teaches obtain a confirmation from a user responsive to outputting the classification confirmation request and obtain a conformation from a user ([0041] The controller 106 processes the charging information from the charging station 200. The controller 106 may prompt the operator, such as at the user interface 130, to select charging characteristics at 306, which may be based on the charging information from the charging station 200 and/or based on other charging information from the EV 100.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Cun so as to include the user interface to have user interaction. Doing so would allow improvement for functions related to vehicles (Cun [0050] the operator does not necessarily have to return to the EV to make the selections).
Regarding claim 15, Ferone in view of Kim and in further view of Flasher teaches the method of claim 14 further comprising:
outputting a classification confirmation request responsive to classifying the suboptimal condition ([0057] When the processor 111 determines that the problem exists at the first vehicle 102, such as the problem with the charging port 115, the processor 111 may send a notification to a device associated with an occupant of the first vehicle 102, such as a mobile device 118.; The citation illustrates the suboptimal condition as an instance when a problem exists at the charging port, and the classification confirmation request is illustrated as a notification),
wherein the classification confirmation request comprises ([0057] When the processor 111 determines that the problem exists at the first vehicle 102, such as the problem with the charging port 115, the processor 111 may send a notification to a device associated with an occupant of the first vehicle 102, such as a mobile device 118.);
Ferone doesn’t explicitly teach information associated with the predefined type and performing the remedial action responsive to obtaining the confirmation; and obtain a confirmation from a user responsive to outputting the classification confirmation request and obtaining a confirmation from a user responsive to outputting the classification confirmation request.
Kim teaches information associated with the predefined type ([0027] According to an embodiment, the preset condition may include at least one of a case when charging of the vehicle reaches a target charge and is complete, a case when a certain time has elapsed after charging is completed, a case when the vehicle in a standby state after charging is complete is present, and a condition set by a user.); and performing the remedial action responsive to obtaining the confirmation ([0047] the processor may receive information about a standby location in the charging station and a driving path to the standby location from the server, and when a movement request to the vehicle is received from a pre-authenticated user terminal).
Ferone in view of Kim doesn’t explicitly teach obtain a confirmation from a user responsive to outputting the classification confirmation request and obtain a confirmation from a user.
Cun teaches obtain a confirmation from a user responsive to outputting the classification confirmation request and obtain a conformation from a user ([0041] The controller 106 processes the charging information from the charging station 200. The controller 106 may prompt the operator, such as at the user interface 130, to select charging characteristics at 306, which may be based on the charging information from the charging station 200 and/or based on other charging information from the EV 100.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Cun so as to include the user interface to have user interaction. Doing so would allow improvement for functions related to vehicles (Cun [0050] the operator does not necessarily have to return to the EV to make the selections).
Claims 3, 11, 16, 21, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Ferone in view of Kim, in view of Flasher in view of Cun, and further in view of Iwamura et al. (US 20150298565 A1, herein Iwamura).
Regarding Claim 3, Ferone in view of Kim and in further view of Flasher teaches the first vehicle of Claim 1
Ferone further teaches wherein the processor is further configured to:
and perform the remedial action responsive to obtaining the confirmation,
wherein the remedial action comprises:
transmitting the charging station review to a server ([0053] When the processor 111 determines that the first vehicle 102 is unable to receive the charge, the processor 111 may send a notification over a network 104 to the server 123 indicating that the first vehicle 102 is unable to receive the charge).
Ferone explicitly does not teach generating and outputting a charging station review based on the inputs, and obtain a user confirmation.
Iwamura teaches generate a charging station review based on the inputs (see FIG.4; charging station application system collects the inputs [0115] A functional configuration of the charging station use application system 150 will be described with reference to FIG. 4. The charging station use application system 150 determines available charging stations by controlling availability of charging stations in accordance with a used time, power conditions, states of the facility (functionally normal or abnormal), for example.
The review can further include user preferences such as [0234] The charging station data selection unit 154 calculates an evaluation value S for setting the priority on the basis of a preference parameter of the EV user.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Iwamura so as to generate a charging station review based on the real time charging statation situation. As set forth in MPEP § 2143, by combining the known review of an instance as taught by Iwamura with the processor of Ferone, one of ordinary skill would expect to achieve the predictable result of the processor generating the charging station review based on a plurality of inputs.
Ferone in view of Iwamura does not teach outputting a review confirmation to the user containing the charging station review.
Cun teaches output a review confirmation request responsive and obtain a confirmation from a user responsive to outputting the review confirmation request ([0041] The controller 106 processes the charging information from the charging station 200. The controller 106 may prompt the operator, such as at the user interface 130, to select charging characteristics at 306, which may be based on the charging information from the charging station 200 and/or based on other charging information from the EV 100.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Cun so as to include the user interface to have user interaction. Doing so would allow improvement for functions related to vehicles (Cun [0050] the operator does not necessarily have to return to the EV to make the selections).
Regarding claim 11, Ferone in view of Kim and further in view of Flasher teaches the first vehicle of claim 10,
Ferone teaches wherein the processor is further configured to:
output an confirmation request ([0057] When the processor 111 determines that the problem exists at the first vehicle 102, such as the problem with the charging port 115, the processor 111 may send a notification to a device associated with an occupant of the first vehicle 102, such as a mobile device 118.),
and transmit the error notification responsive to obtaining the confirmation (a notification is an “error notification” [0056] The processor 111 of the first vehicle 102 may transmit a notification over the network 104 to the processor 131 of the second vehicle 106, wherein the notification includes the identified problem. The processor 131 of the second vehicle 106 may receive the notification over the network 104 and attempt to confirm the problem identified by the first vehicle 102.),
Ferone teaches a charger at the charging station not working but doesn’t teach an identifier information and an issue type associated with the at least one charger; obtain a confirmation from a user responsive to outputting the issue type confirmation request.
Iwamura teaches an identifier information and an issue type associated with the at least one charger ([0145] The charging station identification code is information for uniquely identifying each charging station in the managed region.
[0146] The use identification code is information indicating the use state of the charging station. The use state includes the operation state, the non-operation state, an unavailable state, and the like.; The citation illustrates the use state as an issue type.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Iwamura so as to generate a charging station review based on the real time charging statation situation. As set forth in MPEP § 2143, by combining the known review of an instance as taught by Iwamura with the processor of Ferone, one of ordinary skill would expect to achieve the predictable result of the processor generating the charging station review based on a plurality of inputs.
Ferone in view of Iwamura does not explicitly teach obtain a confirmation from a user responsive to outputting the issue type confirmation request.
Cun teaches obtain a confirmation from a user responsive to outputting the issue type confirmation request ([0041] The controller 106 processes the charging information from the charging station 200. The controller 106 may prompt the operator, such as at the user interface 130, to select charging characteristics at 306, which may be based on the charging information from the charging station 200 and/or based on other charging information from the EV 100. For example, the user interface 130 may display one or more prompts on the display 132 that allow the operator to select charging characteristics to control the charging operation.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Cun so as to include the user interface to have user interaction. Doing so would allow improvement for functions related to vehicles (Cun [0050] the operator does not necessarily have to return to the EV to make the selections).
Regarding Claim 16, Ferone in view of Kim and further in view of Flasher teaches The method of claim 14 further comprising:
Ferone further teaches performing the remedial action responsive to obtaining the confirmation, wherein the remedial action comprises:
transmitting the charging station review to a server ([0053] When the processor 111 determines that the first vehicle 102 is unable to receive the charge, the processor 111 may send a notification over a network 104 to the server 123 indicating that the first vehicle 102 is unable to receive the charge).
Ferone explicitly does not teach generating and outputting a charging station review based on the inputs, and obtain a user confirmation.
Iwamura teaches generate a charging station review based on the inputs (see FIG.4; charging station application system collects the inputs [0115] A functional configuration of the charging station use application system 150 will be described with reference to FIG. 4. The charging station use application system 150 determines available charging stations by controlling availability of charging stations in accordance with a used time, power conditions, states of the facility (functionally normal or abnormal), for example.
The review can further include user preferences such as [0234] The charging station data selection unit 154 calculates an evaluation value S for setting the priority on the basis of a preference parameter of the EV user.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Iwamura so as to generate a charging station review based on the real time charging statation situation. As set forth in MPEP § 2143, by combining the known review of an instance as taught by Iwamura with the processor of Ferone, one of ordinary skill would expect to achieve the predictable result of the processor generating the charging station review based on a plurality of inputs.
Ferone in view of Iwamura does not teach outputting a review confirmation to the user containing the charging station review and obtaining a confirmation from the said user.
Cun teaches output a review confirmation request responsive to generating the charging station review, wherein the review confirmation request comprises the charging station review and obtain a confirmation from a user responsive to outputting the review confirmation request ([0041] The controller 106 processes the charging information from the charging station 200. The controller 106 may prompt the operator, such as at the user interface 130, to select charging characteristics at 306, which may be based on the charging information from the charging station 200 and/or based on other charging information from the EV 100. For example, the user interface 130 may display one or more prompts on the display 132 that allow the operator to select charging characteristics to control the charging operation.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Cun so as to include the user interface to have user interaction. Doing so would allow improvement for functions related to vehicles (Cun [0050] the operator does not necessarily have to return to the EV to make the selections).
Regarding claim 21, Ferone in view of Kim and further in view of Flasher teaches the first vehicle of claim 1,
Ferone further teaches wherein [0055] the processor 111 of first vehicle 102 or the server 123 of the charging station 107 may send a request over the network 104 to a device integrated with the second vehicle 106, such as an infotainment system 137, to a device associated with the second vehicle 106, or a mobile device associated with an occupant of the second vehicle 106.),
Ferone does not explicitly teach
Cun teaches [0041] For example, the user interface 130 may display one or more prompts on the display 132 that allow the operator to select charging characteristics to control the charging operation.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Cun so as to include the user interface to have user interaction. Doing so would allow improvement for functions related to vehicles (Cun [0050] the operator does not necessarily have to return to the EV to make the selections).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Cun so as to include the user interface to have user interaction. Doing so would allow improvement for functions related to vehicles (Cun [0050] the operator does not necessarily have to return to the EV to make the selections).
Ferone in view of Cun does not explicitly teach the vehicle parking adjustment request and wherein the second vehicle autonomously moves and parks at the different location.
Kim further teaches the vehicle parking adjustment request and wherein the second vehicle autonomously moves and parks at the different location (Kim [0047]).
Regarding claim 22, Ferone in view of Kim further in view of Flasher teaches the method of claim 14.
Ferone further teaches wherein [0055] the processor 111 of first vehicle 102 or the seri havever 123 of the charging station 107 may send a request over the network 104 to a device integrated with the second vehicle 106, such as an infotainment system 137, to a device associated with the second vehicle 106, or a mobile device associated with an occupant of the second vehicle 106.), and
Ferone doesn’t explicitly teach
Cun teaches [0041] For example, the user interface 130 may display one or more prompts on the display 132 that allow the operator to select charging characteristics to control the charging operation.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Cun so as to include the user interface to have user interaction. Doing so would allow improvement for functions related to vehicles (Cun [0050] the operator does not necessarily have to return to the EV to make the selections).
Ferone in view of Cun does not explicitly teach the vehicle parking adjustment request and wherein the second vehicle autonomously moves and parks at the different location.
Kim further teaches the vehicle parking adjustment request and wherein the second vehicle autonomously moves and parks at the different location (Kim [0047]).
Claims 4, 7, 8, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ferone in view of Kim, in view of Flasher, and further in view of Matsumoto et al. (US 20240037589 A1, herein Matsumoto).
Regarding claim 4, Ferone in view of Kim and further in view of Flasher teaches the first vehicle of claim 1 but does not teach the further claim limitations in Claim 4,
Matsumoto further teaches wherein the predefined type is a first predefined type when the [server] (see FIG. 1, 2; “congestion” corresponds to a scenario where “all chargers are occupied” [0043] Specifically, as illustrated in FIG. 2, the server 100 (communication unit 103) acquires position information from each of the electrified vehicles 10 in response to the electric power control request (requisition) through communication. Also, the processor 101 calculates the number of electrified vehicles 10 within a radius R (e.g., 100 m) from the EVSE unit 20A, for example, based on the position information that is acquired. The processor 101 (determination unit 101a) then determines that congestion is occurring at the EVSE unit 20A when the calculated number of vehicles is no less than a predetermined threshold value (e.g., 10 vehicles).)
Matsumoto does not explicitly teach the processor determining the charging station is preoccupied.
Flasher teaches the advantages of having a decentralized system over a centralized system (see Flasher, [0004], [0016])
Regarding claim 7, Ferone in view of Kim in further view of Flasher and further in view of Matsumoto teaches the first vehicle of claim 4,
Ferone further teaches wherein the processor performs the remedial action by outputting a recommendation for a different charging station in a route of the first vehicle (FIG. 2D [0073] wherein, in response to determining that the first vehicle is unable to receive the charge, sending to the first vehicle a location of an alternate charging station that can provide a charge to the first vehicle 247D)
Matsumoto further teaches when the predefined type is the first predefined type (Matsumoto [0043]).
Regarding claim 8, Ferone in view of Kim in further view of Flasher further in view of Matsumoto teaches the first vehicle of claim 7,
Matsumoto further teaches wherein the processor is further configured to output information associated with one or more incentives for an owner associated with the first vehicle when the predefined type is the first predefined type ([0006] a setting unit that sets an incentive for each of the electrified vehicles to perform the electric power control at the second charging station when the determination unit determines that congestion is occurring at the first charging station).
Regarding claim 17, Ferone in view of Kim and further in view of Flasher teaches the method of claim 14,
Ferone further teaches
wherein performing the remedial action comprises
outputting a recommendation for a different charging station (FIG. 2D [0073] wherein, in response to determining that the first vehicle is unable to receive the charge, sending to the first vehicle a location of an alternate charging station that can provide a charge to the first vehicle 247D).
Ferone teaches recommending a different charging station but doesn’t explicitly state it is “in route of “
Matsumoto teaches in route of (in route of is interpreted as “closest to the first charging station” [0011] When the determination unit determines that congestion is occurring at the first charging station, the setting unit sets the incentive to perform electric power control at the second charging station that is closest to the first charging station out of the second charging stations. According to this configuration, the time necessary to go from the first charging station to the second charging station is shortened.)
For the purposes of examination in route of is interpreted as a nearby (closest) charging station. It is mentioned in the specification that [0033] “the server 202 may be associated with a firm that manages/tracks availability status of each charger of a plurality of charging stations (including the charging station 104)”. The specification further states [0031] “the server 202 may be configured to recommend”. The firm managing a plurality of charging stations as well as the server (which recommends the charging station based on the inputs) indicates the second charging station recommendation will be at a close proximity to the first charging station.
Matsumoto teaches when the predefined type is the first predefined type when all chargers at the charging station are occupied (Matsumoto [0007] “when congestion is occurring at the first charging station” is interpreted as “when all the chargers at the charging station are occupied”)
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ferone in view of Kim, in view of Flasher, and further in view of Kinsey et al. (US 20220144110 A1, herein Kinsey).
Regarding claim 9, Ferone in view of Kim in further view of Flasher teaches the first vehicle of Claim 1:
Ferone further teaches performs the remedial action by transmitting owner of the third vehicle [0055] the processor 111 of first vehicle 102 or the server 123 of the charging station 107 may send a request over the network 104 to a device integrated with the second vehicle 106, such as an infotainment system 137, to a device associated with the second vehicle 106, or a mobile device associated with an occupant of the second vehicle 106.),
Ferone does not explicitly teach a small vehicle movement request and wherein the predefined type is a second predefined type when the processor determines that a tire associated
Kinsey teaches wherein the predefined type is a second predefined type when the processor determines that a tire associated ([0088] For example, an external object (e.g., a shopping cart, a tire, a package, etc.) may be on the charging cable 102).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Kinsey so as to include defining a predefined type to be an instance of a vehicle is parked on the charging chord of a charger. Doing so would allow (see Kinsey [0008] reduce the need for repairing charging cables by ensuring that they are properly returned to a charging station.)
However, the combination of Ferone and Kinsey do not teach a small vehicle movement request.
Kim teaches a small vehicle movement request (see FIG. 17; the vehicle at the charging station is “the third vehicle” [0047] According to an embodiment, when the user is not present in the vehicle when charging of the vehicle is stopped, the processor may receive information about a standby location in the charging station and a driving path to the standby location from the server, and when a movement request to the vehicle is received from a pre-authenticated user terminal, the processor may transmit a control right to control the vehicle to the server, and the vehicle may be moved along the driving path to the standby location under control by the server.)
Regarding claim 18, Ferone in view of Kim and further in view of Flasher teaches The method of claim 14:
Ferone further teaches performs the remedial action by transmitting [0055] the processor 111 of first vehicle 102 or the server 123 of the charging station 107 may send a request over the network 104 to a device integrated with the second vehicle 106, such as an infotainment system 137, to a device associated with the second vehicle 106, or a mobile device associated with an occupant of the second vehicle 106.),
Ferone does not explicitly teach a small vehicle movement request and wherein the predefined type is a second predefined type when the processor determines that a tire associated
Kinsey teaches wherein the predefined type is a second predefined type when a tire associated with a third vehicle is parked on a charging cord of a charger at the charging station ([0088] For example, an external object (e.g., a shopping cart, a tire, a package, etc.) may be on the charging cable 102),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the processor of Ferone to incorporate the teachings of Kinsey so as to include defining a predefined type to be an instance of a vehicle is parked on the charging chord of a charger. Doing so would allow (see Kinsey [0008] reduce the need for repairing charging cables by ensuring that they are properly returned to a charging station.)
However, the combination of Ferone and Kinsey do not teach a small vehicle movement request.
Kim teaches a small vehicle movement request to the second vehicle (Kim [0047]).
Response to Arguments
Applicant’s amendments, see page 1 of applicant’s arguments, filed 07/22/2026 (herein, Applicant), with respect to the rejection(s) of claim(s) 1-4 and 7-22 under 35 USC § 101 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
Applicant’s arguments under 35 USC § 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
However, a new ground of rejection is made in view of Ferone in view of Kim and in further in view of Flasher. Regarding Claims 1, 14, and 20, Applicant argues (see page 10, para. 3) that Ferone does not teach the vehicle equipped with a Camera with image processing capabilities via an algorithm. Dependent claims 2-4, 7-13, and 21 depend on Claim 1; dependent claim 15-19, and 22 depend on Claim 14. Claim 20 is an independent claim with no dependents. The examiner respectfully disagrees.
Ferone teaches a vehicle sensor equipped with a camera (see para. [0081], [0126]) comprised within the sensor set and the sensor collecting a vast array of data (including images; see para. [0081]). The sensors are coupled with the processor (see para. [0120]; also see FIG. 4D) in order to send data to one or more subsystems. Ferone further teaches an AI/ML production system able to categorize (e.g. classify) the data received from the sensors (e.g. images) (see para. [0087], [0088], [0099]).
Kim further teaches the claim limitations regarding a “predefined type” (when a charger at the charging station is not getting charged or to the SOC level) and a “remedial action” (transmitting a vehicle parking adjustment) (see Kim para. [0027], [0047]). Kim does not explicitly teach the processor doing all the actions; however, Flasher teaches the advantages of a decentralized system in V2V (vehicle to vehicle) communications over a centralized system (e.g. a centralized server) (see para. [0004], [0016]).
Regarding Applicant’s arguments under 35 USC § 103:
Further responding to the Applicant’s argument that the dependent claims are not obvious over the cited combinations (see page 12, para. 4) due to the “none of the references teaches or suggests those limitations” (see page 12 para. 3). The examiner respectfully disagrees.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the combination of references teaching the limitations of the defined “remedial actions” in the claims.
For at least these reasons, the rejection is still deemed proper and has been maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jason Choi whose telephone number is (571) 270 -0512. The examiner can normally be reached Mon-Thurs 8:00-6:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached on (571)272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.J.C./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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