DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is in response to Applicant’s filing dated 08/20/2025. Claims 1, 9 and 20 have been amended. Claims 6-7, 15-16, 18-19, 21-22 and 25 are canceled. Claims 1-5, 8-14, 17, 20, 23-24 and 26-29 are currently pending and examined below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/20/2025 has been entered.
Response to Amendment/Arguments
Applicant’s arguments, filed 08/20/2025, with respect to the rejection(s) of claim(s) 1-5, 8-14, 17, 20, 23-24 and 26-29 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Penilla et al., US 20160104486 A1, in view of Reddy et al., US 20150370272 A1, in view of Fischer, US 20190049981 A1 and in view of Attard et al., US 20150149017 A1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4, 5, 9-14, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Penilla et al., US 20160104486 A1, in view of Reddy et al., US 20150370272 A1, in view of Fischer, US 20190049981 A1, and in view of Attard et al., US 20150149017 A1, hereinafter referred to as Penilla, Reddy, Fischer and Attard, respectfully.
Regarding claim 1, Penilla discloses a system comprising;
at least one processor (Processor – See at least ¶24); and
memory storing instructions configured to instruct the at least one processor to (Memory – See at least ¶128);
collect second sensor data regarding at least one activity of the user inside the fixed structure (Receiving, from the cloud processing server, voice profiles for the user profile. Each voice profile is associated with a tone identifier. The voice profiles for the user are learned from a plurality of voice inputs made to the vehicle by the user in one or more prior sessions of use of the vehicle — See at least ¶11);
analyze the second sensor data to determine a characteristic of the user (The vehicle includes an on-board computer for processing instructions for the vehicle and processing wireless communication to exchange data with the cloud processing server. A user's tone of voice is analyzed to determine matches in predefined tones. The tones are matched to voice profiles that determine or correlate to a selected vehicle response – See at least ¶9);
generate, based on the determined characteristic a configuration used to control a vehicle of a user (in various configurations, a user's touch characteristic is analyzed to determine matches in predefined touch characteristics. The touch characteristic, are matched to touch profiles that determine or correlate to a selected vehicle response. The vehicle response to touch input, i.e. task information, can include, for example, controlling a system of the vehicle – See at least ¶44); and
send, by a communication interface, the generated configuration to the vehicle (Configuration is generated using tools and programs made available on a website. The tools and programs may be executed by computers, such as computers of a data center to provide cloud based processing. The data centers can be distributed geographically and the communication to specific vehicles can be dynamically assigned to various geographic data centers, as the vehicles move around geographically – See at least ¶39).
Penilla fails to explicitly disclose train an artificial neural network (ANN) using first sensor data associated with activities of a user while inside a fixed structure and analyze the audio data using the ANN to determine a characteristic of the user.
However, Reddy teaches:
train an artificial neural network (ANN) using first sensor data associated with activities of a user while inside a fixed structure (One or more user-interface components in the smart home device may receive input from the user and/or present information to the user when the user interacts in person with the smart home device – See at least ¶33. The activity sensing systems can be placed into a “training mode” for the particular home in which they are installed, wherein they “listen” and “learn” the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard – See at least ¶80);
analyze the audio data using the ANN to determine a characteristic of the user (The activity sensing systems can be placed into a “training mode” for the particular home in which they are installed, wherein they “listen” and “learn” the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard – See at least ¶80); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Penilla and include the feature of train an artificial neural network (ANN) using sensor data associated with activities of a user of the vehicle, as taught by Reddy, utilizing the information captured and/or collected to provide a robust performance even when faced with imperfect or partial information with vehicles.
The combination of Penilla and Reddy fails to explicitly disclose wherein the generated configuration is used to configure firmware for a controller of the vehicle.
However, Fischer teaches wherein the generated configuration is used to configure firmware for a controller of the vehicle (Server can modify autonomous vehicle systems include altering one or more control algorithms such as updating firmware – See at least ¶30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Penilla and Reddy and include the feature of wherein the generated configuration is used to configure firmware for a controller of the vehicle, as taught by Fischer, to utilize the information captured and/or collected to enhance wireless interfacing and networking with vehicles.
The combination of Penilla, Reddy and Fischer fail to disclose wherein the second sensor data is collected by at least one sensor inside the fixed structure.
However, Attard teaches wherein the second sensor data is collected by at least one sensor inside the fixed structure (A user device may be any one of a variety of computing devices including a processor and a memory, as well as communication capabilities. For example, the user device may be a smart phone that includes capabilities for wireless communications. Further, the user device may use such communication capabilities to communicate via the network including with a vehicle computer. A user device could communicate with a vehicle computer the other mechanisms, such as a network in the vehicle, a known protocols such as Bluetooth, etc. Accordingly, a user device may be used to carry out certain operations herein ascribed to a data collector, e.g., voice recognition functions, cameras, global positioning system (GPS) functions, etc., in a user device could be used to provide data to the computer. Further, a user device could be used to provide a human machine interface (HMI) to the computer – See at least ¶26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Penilla, Reddy and Fischer and include the feature of wherein the second sensor data is collected by at least one sensor inside the fixed structure, as taught by Attard, to enhance wireless interfacing and networking with vehicles.
Regarding claim 2, Penilla, as modified discloses wherein the controller controls an infotainment system (The vehicle system can be set by the user to function with varying levels of autonomy. A user can set the vehicle to a high level of autonomy – See at least ¶42. The vehicle includes control functions that includes navigation functions, entertainment functions, safety functions, operational functions, and communication functions – See at least ¶115. Processing can be performed by a specialized circuit, integrated circuit, or via software and a processor, or firmware – See at least ¶291).
Regarding claim 4, Penilla, as modified discloses wherein the generated configuration is loaded into memory of the controller (The vehicle includes an on-board computer for processing instructions for the vehicle and processing wireless communication to exchange data with the cloud processing server. Receiving, at the vehicle, data for a user account to use the vehicle. The cloud processing server uses the user account to identify a user profile of a user. Then, receiving, from the cloud processing server, voice profiles, i.e. microphone, for the user profile – See at least abstract).
Regarding claim 5, Penilla, as modified discloses wherein the generated configuration is provided to a server for sending to the vehicle as an update (The training and/or calibration data can then be transferred to the vehicle as a setting update, e.g., via the internet using a wireless connection – See at least ¶140).
Regarding claim 9, Penilla, as modified discloses a method comprising:
receiving, by at least one processor second data collected by the sensor regarding physical activity of the user, when inside the fixed structure (Receiving, from the cloud processing server, voice profiles for the user profile. Each voice profile is associated with a tone identifier. The voice profiles for the user are learned from a plurality of voice inputs made to the vehicle by the user in one or more prior sessions of use of the vehicle. The vehicle response is then generated for the voice input. The vehicle response is selected based on the tone identifier of the identified voice profile — See at least ¶11. The tone identifiers identify a mood of the user — See at least ¶12. Each voice profile is associated with a tone identifier. The voice profiles for the user are learned from a plurality of voice inputs made to the vehicle by the user in one or more prior sessions of use of the vehicle – See at least ¶118);
analyzing, by the at least one processor, the second data to determine a characteristic of the user (The vehicle includes an on-board computer for processing instructions for the vehicle and processing wireless communication to exchange data with the cloud processing server. A user's tone of voice is analyzed to determine matches in predefined tones. The tones are matched to voice profiles that determine or correlate to a selected vehicle response – See at least ¶9); and
configuring, based on the determined characteristic of a vehicle by sending an update to a communication interface of the vehicle (in various configurations, a user's touch characteristic is analyzed to determine matches in predefined touch characteristics. The touch characteristic, are matched to touch profiles that determine or correlate to a selected vehicle response. The vehicle response to touch input, i.e. task information, can include, for example, controlling a system of the vehicle – See at least ¶44. Using this information, which can be shared and/or co-processed in the cloud, the vehicle can be configured to apply responsive action – See at least ¶151).
Penilla fails to explicitly disclose train an artificial neural network (ANN) using first sensor data associated with activities of a user while inside a fixed structure and analyze the audio data using the ANN to determine a characteristic of the user and.
However, Reddy teaches:
training an artificial neural network (ANN) using first sensor data collected from at least one sensor located inside a fixed structure and sued to monitor physical activity of a user (One or more user-interface components in the smart home device may receive input from the user and/or present information to the user when the user interacts in person with the smart home device – See at least ¶33. The activity sensing systems can be placed into a “training mode” for the particular home in which they are installed, wherein they “listen” and “learn” the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard – See at least ¶80);
analyze the audio data using the ANN to determine a characteristic of the user (The activity sensing systems can be placed into a “training mode” for the particular home in which they are installed, wherein they “listen” and “learn” the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard – See at least ¶80); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Penilla and include the feature of train an artificial neural network (ANN) using sensor data associated with activities of a user of the vehicle, as taught by Reddy, utilizing the information captured and/or collected to provide a robust performance even when faced with imperfect or partial information with vehicles.
The combination of Penilla and Reddy fails to explicitly disclose wherein the generated configuration is used to configure firmware for a controller of the vehicle.
However, Fischer teaches wherein the generated configuration is used to configure firmware for a controller of the vehicle (Server can modify autonomous vehicle systems include altering one or more control algorithms such as updating firmware – See at least ¶30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Penilla and Reddy and include the feature of wherein the generated configuration is used to configure firmware for a controller of the vehicle, as taught by Fischer, to utilize the information captured and/or collected to enhance wireless interfacing and networking with vehicles.
The combination of Penilla, Reddy and Fischer fail to explicitly disclose wherein the second data comprises image data and wherein the analyzing of the second data comprises performing facial recognition on the image data to identify facial features, and wherein the characteristic of the user determined is an emotional state of the user.
However, Attard teaches:
wherein the second data comprises image data (For example, sensor data collectors 110 could include mechanisms such as cameras or other image capture devices – See at least ¶11) and
wherein the analyzing of the second data comprises performing facial recognition on the image data to identify facial features, and wherein the characteristic of the user determined is an emotional state of the user (A data collector may further include biometric sensors and/or other devices that may be used for identifying an operator of a vehicle – See at least ¶12. Additionally or alternatively, parameters could be based at least in part on operator characteristics, e.g., identified by use of biometric data collectors, an operator profile stored in the computer and/or retrieved from the server – See at least ¶20).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Penilla and Reddy and include the feature of wherein the second data comprises image data and wherein the analyzing of the second data comprises performing facial recognition on the image data to identify facial features, and wherein the characteristic of the user determined is an emotional state of the user, as taught by Attard, to enhance wireless interfacing and networking with vehicles.
Regarding claim 10, Penilla, as modified discloses wherein configuring the firmware comprises loading user data of the user into a memory of the vehicle (The vehicle includes an on-board computer for processing instructions for the vehicle and processing wireless communication to exchange data with the cloud processing server. Receiving, at the vehicle, data for a user account to use the vehicle. The cloud processing server uses the user account to identify a user profile of a user. Then, receiving, from the cloud processing server, voice profiles, i.e. microphone, for the user profile – See at least abstract).
Regarding claim 11, Penilla, as modified discloses wherein the generated configuration is provided to a server for sending to the vehicle as an update (The training and/or calibration data can then be transferred to the vehicle as a setting update, e.g., via the internet using a wireless connection – See at least ¶140).
Regarding claim 12, Penilla, as modified discloses configuring, based on the determined characteristic, a volume associated with playing media for the user on an infotainment system of the vehicle (For example, the up and down arrows on window opening controls are typically fixed. Other fixed controls may include the buttons for turning up or turning down the air conditioning, buttons for opening a door or locking a door, volume controls for audio – See at least ¶302).
Regarding claim 13, Penilla, as modified discloses configuring, based on the determined characteristic, a sequence of media when played by an infotainment system (The tone of voice is thus viewed as a tone identifier. The tone identifier, in one configuration, can be identified from the audio signature produced from the voice input. The audio signature can then be used to identify a type of vehicle response that is most appropriate for the tone in which the voice input is made – See at least ¶18. Examiner notes the audio signature is the media).
Regarding claim 14, Penilla, as modified discloses wherein the characteristic of the user is a mood of the user (Tone identifiers identify a mood of the user – See at least ¶12).
Regarding claim 28, Penilla, as modified discloses wherein the second sensor data comprises image data, and analyzing the second sensor data comprises performing facial recognition on the image data to identify facial features for determining an emotional state of the user (The vehicle includes an on-board computer for processing instructions for the vehicle and processing wireless communication to exchange data with the cloud processing server. The method then receives, at the vehicle, data for a user account to use the vehicle – See at least abstract. Training can be transferred as a starting calibration for the vehicle – See at least ¶140).
Claims 3 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Penilla et al., US 20160104486 A1, in view of Reddy et al., US 20150370272 A1, in view of Fischer, US 20190049981 A1, in view of Attard et al., US 20150149017 A1, as applied to claim 1 above and in view of Kwok-Suzuki et al., US 20160070898 A1, hereinafter referred to as Penilla, Reddy, Fischer, Attard and Kwok-Suzuki, respectively.
Regarding claim 3, the combination of Penilla, Reddy, Fischer and Attard fails to explicitly disclose wherein at least one of the first or second sensor data is collected by at least one microphone mounted on the wall of a fixed structure.
However, Kwok-Suzuki teaches wherein at least one of the first or second sensor data is collected by at least one microphone mounted on the wall of a fixed structure (Data collectors may collect data from users interfaces associated with various types of appliances and computing devices. The data may be collected from user’s interface implemented in a large home appliance – See at least ¶43. Examiner notes the large home appliance is the fixed structure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Penilla, Reddy, Fischer and Attard and include the feature of wherein at least one of the first or second sensor data is collected by at least one microphone mounted on the wall of a fixed structure, as taught by Kwok-Suzuki, to uniquely identify a user or driver of a vehicle and to distinguish the user from any other user.
Regarding claim 29, the combination of Penilla, Reddy, Fischer and Attard fails to explicitly disclose wherein the first and second sensor data are collected from an appliance of the user, wherein the appliance is located in the fixed structure.
However, Kwok-Suzuki teaches wherein the first and second sensor data are collected from an appliance of the user, wherein the appliance is located in the fixed structure (Data collectors may collect data from user’s interfaces associated with various types of appliances and computing devices. The data may be collected from user’s interfaces implemented in large home appliances – See at least ¶43).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Penilla, Reddy, Fischer and Attard and include the feature of wherein the first and second sensor data are collected from an appliance of the user, wherein the appliance is located in the fixed structure, as taught by Kwok-Suzuki, to uniquely identify a user or driver of a vehicle and to distinguish the user from any other user.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Penilla et al., US 20160104486 A1, in view of Reddy et al., US 20150370272 A1, in view of Fischer, US 20190049981 A1, in view of Attard et al., US 20150149017 A1, as applied to claim 1 above and further in view of Haas, US 20170237944 A1, hereinafter referred to as Penilla, Reddy, Fischer, Attard and Haas, respectively.
Regarding claim 8, the combination of Penilla, Reddy, Fischer and Attard fails to explicitly disclose at least one of the first or second sensor data is collected from a sensor of a charging device located inside the fixed structure and used to charge the vehicle.
However, Haas teaches at least one of the first or second sensor data is collected from a sensor of a charging device located inside the fixed structure and used to charge the vehicle (Electric vehicle charging station includes built-in cameras to allow videos to be taken by the camera to be transmitted to a server or a mobile phone and stored on the electric vehicle charging station and processed locally as a camera for performing various camera function such as authentication – See at least ¶9. A audio recorder coupled to the processor may be sampled nearly simultaneously with the encoded and scaled video stream by the processor and combined to generate an audio data – See at least ¶35).
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Penilla, Reddy, Fischer and Attard and include the feature of at least one of the first or second sensor data is collected from a sensor of a charging device located inside the fixed structure and used to charge the vehicle, as taught by Haas, to uniquely identify a user or driver of a vehicle and to distinguish the user from any other user.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Penilla et al., US 20160104486 A1, in view of Reddy et al., US 20150370272 A1, in view of Attard et al., US 20150149017 A1, as applied to claim 9 above and further in view of Haas, US 20170237944 A1, hereinafter referred to as Penilla, Reddy, Attard and Haas, respectively.
Regarding claim 17, the combination of Penilla, Reddy and Attard fails to explicitly disclose wherein the first and second data is collected from a charging device located inside the fixed structure and used to charge the vehicle.
However, Haas teaches wherein the first and second data is collected from a charging device located inside the fixed structure and used to charge the vehicle (Electric vehicle charging station includes built-in cameras to allow videos to be taken by the camera to be transmitted to a server or a mobile phone and stored on the electric vehicle charging station and processed locally as a camera for performing various camera function such as authentication – See at least ¶9. A audio recorder coupled to the processor may be sampled nearly simultaneously with the encoded and scaled video stream by the processor and combined to generate an audio data – See at least ¶35).
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Penilla, Reddy and Attard and include the feature of wherein the first and second data is collected from a charging device located inside the fixed structure and used to charge the vehicle, as taught by Haas, to uniquely identify a user or driver of a vehicle and to distinguish the user from any other user.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Penilla et al., US 20160104486 A1, in view of Reddy et al., US 20150370272 A1, and in view of Attard et al., US 20150149017 A1, hereinafter referred to as Penilla, Reddy, and Attard, respectfully.
Regarding claim 20, Penilla, as modified discloses a non-transitory computer storage medium storing instruction which, when executed on a computing device, cause the computing device to perform a method comprising:
receiving first data collected by the sensor regarding physical activity of the user, (The vehicle includes an on-board computer for processing instructions for the vehicle and processing wireless communication to exchange data with the cloud processing server. Receiving, at the vehicle, data for a user account to use the vehicle. The cloud processing server uses the user account to identify a user profile of a user. Then, receiving, from the cloud processing server, voice profiles, user profile – See at least abstract. Vocal parameters and prosody features such as pitch variables and speech rate may be analyzed through pattern recognition, e.g., using one or more microphones of a vehicle – See at least ¶136);
analyzing the first data to determine a characteristic of the user (The vehicle includes an on-board computer for processing instructions for the vehicle and processing wireless communication to exchange data with the cloud processing server. A user's tone of voice is analyzed to determine matches in predefined tones. The tones are matched to voice profiles that determine or correlate to a selected vehicle response – See at least ¶9); and
configuring, based on the determined characteristic, acceleration associated with a driving style of a vehicle by configuration data to a communication interface of the vehicle (The processing system enables the vehicle response to be tailored to respond to the user's voice input in a way that respects or understands the user's possible mood or possible state of mind. If the user's tone implies that the user is rushed, the system will process that tone in the voice and will provide a vehicle response in a more expedited manner. If the tone implies that the user is relaxed, the system may provide supplemental information in addition to responding to the voice input – See at least ¶10. The vehicle includes control functions that includes navigation functions, entertainment functions, safety functions, operational functions, and communication functions – See at least ¶115).
Penilla fails to explicitly disclose training an artificial neural network (ANN) using data collected from at least one sensor located inside a fixed structure to monitor physical activity of a user and analyze the audio data using the ANN to determine a characteristic of the user and.
However, Reddy teaches:
training an artificial neural network (ANN) using data collected from at least one sensor located inside a fixed structure to monitor physical activity of a user (One or more user-interface components in the smart home device may receive input from the user and/or present information to the user when the user interacts in person with the smart home device – See at least ¶33. The activity sensing systems can be placed into a “training mode” for the particular home in which they are installed, wherein they “listen” and “learn” the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard – See at least ¶80);
analyze the audio data using the ANN to determine a characteristic of the user (The activity sensing systems can be placed into a “training mode” for the particular home in which they are installed, wherein they “listen” and “learn” the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard – See at least ¶80); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Penilla and include the feature of train an artificial neural network (ANN) using sensor data associated with activities of a user of the vehicle, as taught by Reddy, utilizing the information captured and/or collected to provide a robust performance even when faced with imperfect or partial information with vehicles.
The combination of Penilla and Reddy fail to disclose wherein the second sensor data is collected by at least one sensor inside the fixed structure.
However, Attard teaches wherein the second sensor data is collected by at least one sensor inside the fixed structure (A user device may be any one of a variety of computing devices including a processor and a memory, as well as communication capabilities. For example, the user device may be a smart phone that includes capabilities for wireless communications. Further, the user device may use such communication capabilities to communicate via the network including with a vehicle computer. A user device could communicate with a vehicle computer the other mechanisms, such as a network in the vehicle, a known protocols such as Bluetooth, etc. Accordingly, a user device may be used to carry out certain operations herein ascribed to a data collector, e.g., voice recognition functions, cameras, global positioning system (GPS) functions, etc., in a user device could be used to provide data to the computer. Further, a user device could be used to provide a human machine interface (HMI) to the computer – See at least ¶26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Penilla and Reddy and include the feature of wherein the second sensor data is collected by at least one sensor inside the fixed structure, as taught by Attard, to enhance wireless interfacing and networking with vehicles.
Claims 23-24, 26 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Penilla et al., US 20160104486 A1, in view of Reddy et al., US 20150370272 A1, and in view of Haas, US 20170237944 A1, hereinafter referred to as Penilla, Reddy, and Haas, respectively.
Regarding claim 23, Penilla discloses a system comprising;
at least one processor (Processor – See at least ¶24); and
memory storing instructions configured to instruct the at least one processor to (Memory – See at least ¶128);
generate, using second data collected from the sensor (in various configurations, a user's touch characteristic is analyzed to determine matches in predefined touch characteristics. The touch characteristic, are matched to touch profiles that determine or correlate to a selected vehicle response. The vehicle response to touch input, i.e. task information, can include, for example, controlling a system of the vehicle - See at least ¶44); and
send, by the communication interface, the generated configuration to the vehicle (Configuration is generated using tools and programs made available on a website. The tools and programs may be executed by computers, such as computers of a data center to provide cloud based processing. The data centers can be distributed geographically and the communication to specific vehicles can be dynamically assigned to various geographic data centers, as the vehicles move around geographically - See at least ¶39).
Penilla fails to explicitly disclose train, by a server, an artificial neural network using first data collected from a sensor located inside a fixed structure and generate, using the new calendar or task information, as input to the ANN.
However, Reddy teaches:
train, by a server, an artificial neural network using first data collected from a sensor located inside a fixed structure (One or more user-interface components in the smart home device may receive input from the user and/or present information to the user when the user interacts in person with the smart home device - See at least ||33. The activity sensing systems can be placed into a "training mode" for the particular home in which they are installed, wherein they "listen" and "learn" the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard - See at least ¶80);
as input to the ANN, a configuration for the vehicle (The activity sensing systems can be placed into a "training mode" for the particular home in which they are installed, wherein they "listen" and "learn" the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard - See at least ¶80).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Penilla and include the feature of train, by the server, an artificial neural network (ANN) using the prior calendar or task information, as taught by Reddy, utilizing the information captured and/or collected to provide a robust performance even when faced with imperfect or partial information with vehicles.
The combination of Penilla and Reddy fail to explicitly disclose train, by a server, an artificial neural network using first data collected from a sensor located inside a fixed structure in which an electric vehicle is being charged .
However, Haas teaches train, by a server, an artificial neural network using first data collected from a sensor located inside a fixed structure in which an electric vehicle is being charged (Electric vehicle charging station includes built-in cameras to allow videos to be taken by the camera to be transmitted to a server or a mobile phone and stored on the electric vehicle charging station and processed locally as a camera for performing various camera function such as authentication – See at least ¶9. A audio recorder coupled to the processor may be sampled nearly simultaneously with the encoded and scaled video stream by the processor and combined to generate an audio data – See at least ¶35.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Penilla and include the feature of train, by a server, an artificial neural network using first data collected from a sensor located inside a fixed structure in which an electric vehicle is being charged, as taught by Haas, to uniquely identify a user or driver of a vehicle and to distinguish the user from any other user.
Regarding claim 24, Penilla, as modified a discloses wherein: the vehicle is a first vehicle; the instructions are further configured to instruct the processor to collect third data from sensors of at least one second vehicle (The vehicle includes an on- board computer for processing instructions for the vehicle and processing wireless communication to exchange data with the cloud processing server. The method then receives, at the vehicle, data for a user account to use the vehicle - See at least abstract. Training can be transferred as a starting calibration for the vehicle - See at least ¶140).
Penilla fails to explicitly disclose training the ANN further uses the third data.
However, Reddy teaches training the ANN further uses the third data (The activity sensing systems can be placed into a "training mode" for the particular home in which they are installed, wherein they "listen" and "learn" the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard - See at least ¶80).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Penilla and include the feature of training the ANN further uses the third data, as taught by Reddy, utilizing the information captured and/or collected to provide a robust performance even when faced with imperfect or partial information with vehicles.
Regarding claim 26, Penilla, as modified discloses wherein: the instructions are further configured to instruct the processor to collect electronic communications associated with a user of the vehicle (The vehicle includes an on-board computer for processing instructions for the vehicle and processing wireless communication to exchange data with the cloud processing server. The method then receives, at the vehicle, data for a user account to use the vehicle - See at least abstract. Training can be transferred as a starting calibration for the vehicle - See at least ¶140).
Penilla fails to explicitly disclose training the ANN further uses the electronic communications.
However, Reddy teaches training the ANN further uses the electronic communications (The activity sensing systems can be placed into a "training mode" for the particular home in which they are installed, wherein they "listen" and "learn" the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard - See at least ¶80).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Penilla and include the feature of training the ANN further uses the electronic communications, as taught by Reddy, utilizing the information captured and/or collected to provide a robust performance even when faced with imperfect or partial information with vehicles.
Regarding clam 27, Penilla, as modified discloses wherein the generated configuration configures at least one action performed by the vehicle when traveling to a destination associated with the electronic communications (in various configurations, a user's touch characteristic is analyzed to determine matches in predefined touch characteristics. The touch characteristic, are matched to touch profiles that determine or correlate to a selected vehicle response. The vehicle response to touch input, i.e. task information, can include, for example, controlling a system of the vehicle - See at least ¶44).
Conclusion
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/MAHMOUD M KAZIMI/Examiner, Art Unit 3665