CTFR 18/827,425 CTFR 95971 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis 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. 12-151 AIA 26-51 12-51 Status of Claims This Office Action is in response to the Applicant’s Response dated 4/6/2026. Applicant has filed a provisional application and thus the domestic benefit of 9/13/2023 is the effective filing date. Claims 1-20 are presently pending and are presented for examination. Information Disclosure Statement 06-52 The information disclosure statement (IDS) was submitted on 4/6/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Response to Amendment Applicant’s amendments, see pages 11-12 of 15, filed 4/6/2026, with respect to specification objections, claim objections, and 112(b) rejections have been fully considered and are persuasive. The specification objections, claim objections, and 112(b) rejections have been withdrawn. Response to Arguments Applicant's arguments, see page 13 of 15, filed 4/6/2026, have been fully considered but they are not persuasive. The Applicant has argued that neither primary reference Jiang nor secondary reference Zhang disclose or teach the amended limitations of claim 1, specifically “real-time driving assistance to a driver of the vehicle” and “towing of an object” however the Examiner respectfully disagrees. The Examiner notes that primary reference Jiang discloses a vehicle which provides real-time control commands (see Jiang at least [0031] and [0072]) , whereas the teachings of secondary reference Zhang detail a non-autonomous vehicle capable of towing a trailer (see Zhang at least [0020], [0096], and [0098]) . A detailed rejection follows below. Claim Objections 07-29-01 AIA Claim s 7 and 18 are objected to because of the following informalities: Claim 7 as currently presented states "...wherein the generative Al model is trained to generate the real-time driving assistance based upon the at least one of the GPS data, the road data, or the route data in addition to the sensor data; and generating, via the generative Al model, the real-time driving assistance based upon the at least one of the GPS data, the road data, or the route data in addition to the sensor data..." which the Examiner recommends updating to instead state "...wherein the generative Al model is trained to generate the real-time driving assistance based upon the at least one of the GPS data, the road data, or the route data in addition to the sensor data ; and generating, via the generative Al model, the real-time driving assistance based upon the at least one of the GPS data, the road data, or the route data in addition to the sensor data ..." so as to remove the redundant limitation. Claim 18 as currently presented states "...wherein the generative Al model is trained to generate the real-time driving assistance based upon the virtual travel environment in addition to the vehicle sensor data and the external sensor data; and generate, via the generative Al model, the real-time driving assistance based upon the virtual travel environment in addition to the vehicle sensor data and the external sensor data..." which the Examiner recommends updating to instead state "...wherein the generative Al model is trained to generate the real-time driving assistance based upon the virtual travel environment in addition to the vehicle sensor data and the external sensor data ; and generate, via the generative Al model, the real-time driving assistance based upon the virtual travel environment in addition to the vehicle sensor data and the external sensor data ..." so as to remove the redundant limitation . Appropriate correction is required. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1, 3, 5, 7-9, 11-12, 14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang et al. (US-2021/0291862; hereinafter Jiang ; already of record) in view of Zhang et al. (US-2024/0185523; hereinafter Zhang ; already of record) . Regarding claim 1 , Jiang discloses a computer-implemented method for providing real-time driving assistance associated with … a vehicle (see Jiang at least Abs and [0079]) , the computer-implemented method comprising: inputting sensor data into a … model trained to generate the real-time driving assistance (see Jiang at least [0030]-[0031] "Server 103 may be a data analytics system to perform data analytics services for a variety of clients. In one embodiment, data analytics system 103 includes data collector 121 and machine learning engine 122. Data collector 121 collects driving statistics 123 from a variety of vehicles, either autonomous vehicles or regular vehicles driven by human drivers. Driving statistics 123 include information indicating the driving commands (e.g., throttle, brake, steering commands) issued and responses of the vehicles (e.g., speeds, accelerations, decelerations, directions) captured by sensors of the vehicles at different points in time. Driving statistics 123 may further include information describing the driving environments at different points in time, such as, for example, routes (including starting and destination locations), MPOIs, road conditions, weather conditions, etc. Based on driving statistics 123, machine learning engine 122 generates or trains a set of rules, algorithms, and/or predictive models 124 for a variety of purposes . In one embodiment, algorithms 124 may include algorithms used by a model predictive controller of the present disclosure. Algorithms 124 can then be uploaded on ADVs to be utilized during autonomous driving in real-time." ) ; generating, via the … model, the real-time driving assistance based at least in part on the sensor data (see Jiang at least [0031] " Based on driving statistics 123, machine learning engine 122 generates or trains a set of rules, algorithms, and/or predictive models 124 for a variety of purposes . In one embodiment, algorithms 124 may include algorithms used by a model predictive controller of the present disclosure. Algorithms 124 can then be uploaded on ADVs to be utilized during autonomous driving in real-time." ) , wherein: the real-time driving assistance comprises one or more of directions, instructions, or indicators associated with the … vehicle (see Jiang at least [0031] " Based on driving statistics 123, machine learning engine 122 generates or trains a set of rules, algorithms, and/or predictive models 124 for a variety of purposes . In one embodiment, algorithms 124 may include algorithms used by a model predictive controller of the present disclosure. Algorithms 124 can then be uploaded on ADVs to be utilized during autonomous driving in real-time ." and [0072] "As discussed, learning based MPC module can adjust dynamically (e.g., while the ADV is driving) to account for real-time environmental conditions of the ADV. These environmental conditions can be gathered from servers 104 and 103, as well as localization module 301, map and route information 311, sensor system 115, and other modules. After adjusting to the environment, and accounting for physical attributes of the ADV, the MPC module can generate and optimized control command (e.g., throttle, steering, and/or brake) to be communicated to the control system 111 ." ) ; and providing, via at least one of the … model, an associated user interface, or a user device, the real-time driving assistance to … the vehicle (see Jiang at least [0031] "…Algorithms 124 can then be uploaded on ADVs to be utilized during autonomous driving in real-time ." and [0072] "As discussed, learning based MPC module can adjust dynamically (e.g., while the ADV is driving) to account for real-time environmental conditions of the ADV. These environmental conditions can be gathered from servers 104 and 103, as well as localization module 301, map and route information 311, sensor system 115, and other modules. After adjusting to the environment, and accounting for physical attributes of the ADV, the MPC module can generate and optimized control command (e.g., throttle, steering, and/or brake) to be communicated to the control system 111 ." ) . However, Jiang does not explicitly disclose the following: …towing of an object by a vehicle… …inputting sensor data into a generative artificial intelligence (AI) model… …generating, via the generative AI model… …towing of the object by the vehicle… …the generative AI model … [provide information to] a driver of the vehicle… Zhang , in the same field of endeavor, teaches the following: …towing of an object by a vehicle (see Zhang at least [0096] "The systems and methods described herein may be used by, without limitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers , flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types… ) … …inputting sensor data into a generative artificial intelligence (AI) model (see Zhang at least [0096] "…Further, the systems and methods described herein may be used for a variety of purposes, by way of example and without limitation, for machine control, machine locomotion, machine driving, synthetic data generation, generative AI , model training , perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, simulation and digital twinning, autonomous or semi-autonomous machine applications, deep learning, environment simulation, object or actor simulation and/or digital twinning, data center processing, conversational AI, light transport simulation (e.g., ray-tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing and/or any other suitable applications." ) … …generating, via the generative AI model (see Zhang at least [0096] "…Further, the systems and methods described herein may be used for a variety of purposes, by way of example and without limitation, for machine control, machine locomotion, machine driving, synthetic data generation, generative AI , model training , perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, simulation and digital twinning, autonomous or semi-autonomous machine applications, deep learning, environment simulation, object or actor simulation and/or digital twinning, data center processing, conversational AI, light transport simulation (e.g., ray-tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing and/or any other suitable applications." ) … …towing of the object by the vehicle (see Zhang at least [0096] "The systems and methods described herein may be used by, without limitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers , flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types… ) … …the generative AI model … [provide information to] a driver of the vehicle (see Zhang at least [0020] "...Although the present disclosure may be described with respect to an example autonomous vehicle or semi-autonomous vehicle or machine 500 (alternatively referred to herein as “vehicle 500” or “ego-vehicle 500,” an example of which is described with respect to FIGS. 5A-5D), this is not intended to be limiting. For example, the systems and methods described herein may be used by, without limitation, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in one or more adaptive driver assistance systems (ADAS)), autonomous vehicles or machines, piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types..." [0096] "The systems and methods described herein may be used by, without limitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers , flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types. Further, the systems and methods described herein may be used for a variety of purposes, by way of example and without limitation, for machine control, machine locomotion, machine driving, synthetic data generation, generative AI , model training , perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, simulation and digital twinning, autonomous or semi-autonomous machine applications, deep learning, environment simulation, object or actor simulation and/or digital twinning, data center processing, conversational AI, light transport simulation (e.g., ray-tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing and/or any other suitable applications." and [0104] "...For example, the HMI display 534 may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit 34B in two miles, etc.)." ) … 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 vehicle controls as disclosed by Jiang with a generative AI model such as taught by Zhang with a reasonable expectation of success for the sake of refined controls (see Zhang at least [0002]-[0005]) . Regarding claim 3 , Jiang in view of Zhang teach the computer-implemented method of claim 1, wherein the real-time driving assistance: comprises at least one of visuals, graphics, holograms, or text (see Zhang at least [0104] "...For example, the HMI display 534 may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit 34B in two miles, etc.)." ) , and is displayed upon a display screen of the associated user interface or the user device, or is projected onto a surface or a window (see Zhang at least [0104] "...For example, the HMI display 534 may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit 34B in two miles, etc.)." ) . 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 driving assistance as disclosed by Jiang with a display such as taught by Zhang with a reasonable expectation of success so as to provide a driver with information during operation (see Zhang at least [0187]-[0190]) . Regarding claim 5 , Jiang in view of Zhang teach the computer-implemented method of claim 1, wherein: the sensor data comprises at least one of audio data, image data, GPS data, vehicle telematics data, or lane marker data (see Jiang at least [0023]-[0024] "Referring now to FIG. 2, in one embodiment, sensor system 115 includes, but it is not limited to, one or more cameras 211, global positioning system (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit 214, and a light detection and range (LIDAR) unit 215... Sensor system 115 may further include other sensors, such as, a sonar sensor, an infrared sensor , a steering sensor, a throttle sensor, a braking sensor, and an audio sensor (e.g., microphone)..." and [0035] "Based on the sensor data provided by sensor system 115 and localization information obtained by localization module 301, a perception of the surrounding environment is determined by perception module 302. The perception information may represent what an ordinary driver would perceive surrounding a vehicle in which the driver is driving. The perception can include the lane configuration , traffic light signals, a relative position of another vehicle, a pedestrian, a building, crosswalk, or other traffic related signs (e.g., stop signs, yield signs), etc., for example, in a form of an object. The lane configuration includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc." ) , and the sensor data is generated and/or collected from one or more sources, including at least one of: (i) the vehicle (see Jiang at least [0020]) towing the object; (ii) the object; (iii) other vehicles on a road via vehicle-to-vehicle (V2V) wireless communication (see Jiang at least [0019] and [0030]) ; (iv) smart infrastructure data; (v) aerial devices; or (vi) mobile devices of the driver or passengers within the vehicle or other nearby vehicles. Regarding claim 7 , Jiang in view of Zhang teach the computer-implemented method of claim 1, the computer-implemented method further comprising: inputting at least one of GPS data, road data, or route data into the generative AI model (see Zhang at least [0096]) , wherein the generative AI model is trained to generate the real-time driving assistance based upon the at least one of the GPS data, the road data, or the route data in addition to the sensor data (see Jiang at least [0030]-[0031] "... Driving statistics 123 include information indicating the driving commands (e.g., throttle, brake, steering commands) issued and responses of the vehicles (e.g., speeds, accelerations, decelerations, directions) captured by sensors of the vehicles at different points in time. Driving statistics 123 may further include information describing the driving environments at different points in time, such as, for example, routes (including starting and destination locations), MPOIs, road conditions, weather conditions, etc. Based on driving statistics 123, machine learning engine 122 generates or trains a set of rules, algorithms, and/or predictive models 124 for a variety of purposes . In one embodiment, algorithms 124 may include algorithms used by a model predictive controller of the present disclosure. Algorithms 124 can then be uploaded on ADVs to be utilized during autonomous driving in real-time." ) ; and generating, via the generative AI model (see Zhang at least [0096]) , the real-time driving assistance based upon the at least one of the GPS data, the road data, or the route data in addition to the sensor data (see Jiang at least [0030]-[0031] "... Driving statistics 123 include information indicating the driving commands (e.g., throttle, brake, steering commands) issued and responses of the vehicles (e.g., speeds, accelerations, decelerations, directions) captured by sensors of the vehicles at different points in time. Driving statistics 123 may further include information describing the driving environments at different points in time, such as, for example, routes (including starting and destination locations), MPOIs, road conditions, weather conditions, etc. Based on driving statistics 123, machine learning engine 122 generates or trains a set of rules, algorithms, and/or predictive models 124 for a variety of purposes . In one embodiment, algorithms 124 may include algorithms used by a model predictive controller of the present disclosure. Algorithms 124 can then be uploaded on ADVs to be utilized during autonomous driving in real-time." ) . 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 vehicle controls as disclosed by Jiang with a generative AI model such as taught by Zhang with a reasonable expectation of success for reasons similar to those provided above in claim 1. Regarding claim 8 , Jiang in view of Zhang teach the computer-implemented method of claim 1, the computer-implemented method further comprising: building or generating, via one or more processors, a virtual travel environment surrounding the vehicle based upon the sensor data (see Jiang at least [0055] "Further, although not required, simulation of the dynamic model can be performed in a virtual environment that can include objects and structures that are currently sensed around the ADV, to account for the ADV's current environment when generating control commands. The virtual environment can include a two-dimensional or three-dimensional representation of a current environment around the ADV . Although simplified, this environment can include geometry that defines boundaries of objects (e.g., pedestrians, vehicles, structures), as well as road boundaries. This virtual environment can be generated based on sensed data from sensor system 115, and/or information from map and route information 311, localization module 301, and other modules from perception and planning system 110." ) ; inputting the virtual travel environment into the generative AI model (see Zhang at least [0096]) , wherein the generative AI model is trained to generate the real-time driving assistance based upon the virtual travel environment in addition to the sensor data (see Jiang at least [0055] "Further, although not required, simulation of the dynamic model can be performed in a virtual environment that can include objects and structures that are currently sensed around the ADV, to account for the ADV's current environment when generating control commands . The virtual environment can include a two-dimensional or three-dimensional representation of a current environment around the ADV. Although simplified, this environment can include geometry that defines boundaries of objects (e.g., pedestrians, vehicles, structures), as well as road boundaries. This virtual environment can be generated based on sensed data from sensor system 115, and/or information from map and route information 311, localization module 301, and other modules from perception and planning system 110." ) ; and generating and presenting, via the generative AI model, the real-time driving assistance based upon the virtual travel environment in addition to the sensor data ((see Jiang at least [0031] “Based on driving statistics 123, machine learning engine 122 generates or trains a set of rules, algorithms, and/or predictive models 124 for a variety of purposes . In one embodiment, algorithms 124 may include algorithms used by a model predictive controller of the present disclosure. Algorithms 124 can then be uploaded on ADVs to be utilized during autonomous driving in real-time ." ) and (see Zhang at least [0096] and [0104] "...For example, the HMI display 534 may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit 34B in two miles, etc.)." )) . 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 vehicle controls as disclosed by Jiang with a generative AI model such as taught by Zhang with a reasonable expectation of success for reasons similar to those provided above in claim 1. Regarding claim 9 , Jiang in view of Zhang teach the computer-implemented method of claim 8, wherein the virtual travel environment indicates a direction, a heading, a GPS location, a lane of travel, and a speed of travel of one or more other vehicles in a vicinity of and/or surrounding the vehicle (see Jiang at least [0055] "Further, although not required, simulation of the dynamic model can be performed in a virtual environment that can include objects and structures that are currently sensed around the ADV, to account for the ADV's current environment when generating control commands. The virtual environment can include a two-dimensional or three-dimensional representation of a current environment around the ADV. Although simplified, this environment can include geometry that defines boundaries of objects (e.g., pedestrians , vehicles , structures), as well as road boundaries. This virtual environment can be generated based on sensed data from sensor system 115, and/or information from map and route information 311, localization module 301, and other modules from perception and planning system 110." ) towing the object (see Zhang at least [0096]) . 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 vehicle controls as disclosed by Jiang with a towed object such as taught by Zhang with a reasonable expectation of success for reasons similar to those provided above in claim 1. Regarding claim 11 , Jiang in view of Zhang teach the computer-implemented method of claim 1, wherein the real-time driving assistance provides steering directions indicating when to turn and how much to turn a steering wheel of the vehicle to facilitate keeping the vehicle in a correct lane while turning (see Jiang at least [0042] "In one embodiment, the planning phase is performed in a number of planning cycles, also referred to as driving cycles, such as, for example, in every time interval of 100 milliseconds (ms). For each of the planning cycles or driving cycles, one or more control commands will be issued based on the planning and control data. That is, for every 100 ms, planning module 305 plans a next route segment or path segment, for example, including a target position and the time required for the ADV to reach the target position. Alternatively, planning module 305 may further specify the specific speed, direction, and/or steering angle , etc. In one embodiment, planning module 305 plans a route segment or path segment for the next predetermined period of time such as 5 seconds. For each planning cycle, planning module 305 plans a target position for the current cycle (e.g., next 5 seconds) based on a target position planned in a previous cycle. Control module 306 then generates one or more control commands (e.g., throttle, brake, steering control commands ) based on the planning and control data of the current cycle." and [0064] "The MPC module 602 can include a vehicle model 670 and a cost function 674. The cost function can include cost terms 678, and associated weights 676. The MPC module can generate a sequence of future commands 672 (e.g., throttle, brake, and steering ) that will predictively effect movement of the vehicle model such that the vehicle model tracks the reference, while minimizing the cost function." ) . Regarding claim 12 , Jiang in view of Zhang teach the analogous material of that in claim 1 as recited in the instant claim and is rejected for similar reasons. Additionally, Jiang and Zhang teach the following: …a computer system configured to provide real-time driving assistance (see Jiang at least Abs and [0016]) associated with towing of an object by a vehicle (see Zhang at least [0096]) , the computer system comprising… …one or more local or remote processors (see Jiang at least [0027]) , servers (see Jiang at least [0026]) , transceivers, sensors, cameras, memory units (see Jiang at least [0027]) , mobile devices, wearables, smart watches, smart contact lenses, smart glasses, augmented reality (AR) glasses, virtual reality (VR) headsets, mixed reality (MR) or extended reality glasses or headsets, voice bots or chatbots, or ChatGPT or ChatGPT-based bots, which may be in wired or wireless communication with one another, wherein… …one or more processors and/or associated transceivers are configured to… …(i) receive, collect, or generate vehicle sensor data from one or more of the sensors mounted on the vehicle (see Jiang at least [0024] " Sensor system 115 may further include other sensors, such as, a sonar sensor, an infrared sensor, a steering sensor, a throttle sensor, a braking sensor, and an audio sensor (e.g., microphone). An audio sensor may be configured to capture sound from the environment surrounding the autonomous vehicle. A steering sensor may be configured to sense the steering angle of a steering wheel , wheels of the vehicle, or a combination thereof. A throttle sensor and a braking sensor sense the throttle position and braking position of the vehicle , respectively. In some situations, a throttle sensor and a braking sensor may be integrated as an integrated throttle/braking sensor." ) and/or the object being towed by the vehicle… …(ii) receive external sensor data from one or more external sources via wireless communication over one or more radio frequency links (see Jiang at least [0019] "FIG. 1 is a block diagram illustrating an autonomous vehicle network configuration according to one embodiment of the disclosure. Referring to FIG. 1, network configuration 100 includes autonomous vehicle 101 that may be communicatively coupled to one or more servers 103-104 over a network 102 . Although there is one autonomous vehicle shown, multiple autonomous vehicles can be coupled to each other and/or coupled to servers 103-104 over network 102 . Network 102 may be any type of networks such as a local area network (LAN), a wide area network (WAN) such as the Internet, a cellular network, a satellite network, or a combination thereof, wired or wireless. Server(s) 103-104 may be any kind of servers or a cluster of servers, such as Web or cloud servers, application servers, backend servers, or a combination thereof. Servers 103-104 may be data analytics servers, content servers, traffic information servers, map and point of interest (MPOI) servers, or location servers, etc." and [0072] "As discussed, learning based MPC module can adjust dynamically (e.g., while the ADV is driving) to account for real-time environmental conditions of the ADV. These environmental conditions can be gathered from servers 104 and 103 , as well as localization module 301, map and route information 311, sensor system 115, and other modules. After adjusting to the environment, and accounting for physical attributes of the ADV, the MPC module can generate and optimized control command (e.g., throttle, steering, and/or brake) to be communicated to the control system 111." ) … It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the system as disclosed by Jiang with a trailer such as further taught by Zhang with a reasonable expectation of success for reasons similar to those provided above in claim 1. Regarding claim 14 , Jiang in view of Zhang teach the analogous material of that in claim 3 as recited in the instant claim and is rejected for similar reasons. Regarding claim 16 , Jiang in view of Zhang teach the analogous material of that in claim 5 as recited in the instant claim and is rejected for similar reasons. Regarding claim 17 , Jiang in view of Zhang teach the analogous material of that in claim 7 as recited in the instant claim and is rejected for similar reasons. Additionally, Jiang discloses …generate the real-time driving assistance…[based upon both] the vehicle sensor data and the external sensor data (see Jiang at least [0030]-[0031]) … Regarding claim 18 , Jiang in view of Zhang teach the analogous material of that in claim 8 as recited in the instant claim and is rejected for similar reasons. Additionally, Jiang discloses …generate the real-time driving assistance…[based upon both] the vehicle sensor data and the external sensor data (see Jiang at least [0030]-[0031]) … Regarding claim 20 , Jiang in view of Zhang teach the analogous material of that in claim 11 as recited in the instant claim and is rejected for similar reasons . 07-21-aia AIA Claim s 2, 4, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang in view of Zhang, and further in view of Carbune et al. (US-2023/0143177; hereinafter Carbune ; already of record) . Regarding claim 2 , Jiang in view of Zhang teach the computer-implemented method of claim 1. While Jiang discloses a user interface with a microphone and speaker, neither Jiang nor Zhang explicitly disclose or teach the following: …the at least one of the generative Al model, the associated user interface, or the user device comprises a chatbot or a voice bot… …the real-time driving assistance comprises audible or verbal output. Carbune , in the same field of endeavor, teaches the following: …the at least one of the generative Al model, the associated user interface, or the user device comprises a chatbot or a voice bot (see Carbune at least [0001] "Humans may engage in human-to-computer dialogs with interactive software applications referred to herein as “automated assistants” (also referred to as “ chatbots ,” “interactive personal assistants,” “intelligent personal assistants,” “personal voice assistants,” “conversational agents,” etc.). For example, humans (which when they interact with automated assistants may be referred to as “users”) may provide spoken natural language input (i.e., spoken utterances) to an automated assistant , which may in some cases be converted into text and then processed, and/or by providing textual (e.g., typed) natural language input. An automated assistant generally responds to the spoken utterances by providing responsive user interface output (e.g., audible and/or visual user interface output), controlling smart device(s), and/or performing other action(s)." and [0026]-[0027] "The client device 110 may be, for example, one or more of: a desktop computer, a laptop computer, a tablet, a mobile phone, a computing device of a vehicle (e.g., an in-vehicle communications system, an in-vehicle entertainment system, an in-vehicle navigation system), a standalone interactive speaker (optionally having a display), a smart appliance such as a smart television, and/or a wearable apparatus of the user that includes a computing device (e.g., a watch of the user having a computing device, glasses of the user having a computing device, a virtual or augmented reality computing device). Additional and/or alternative client devices may be provided. The client device 110 can execute an automated assistant client 114 . An instance of the automated assistant client 114 can be an application that is separate from an operating system of the client device 110 (e.g., installed “on top” of the operating system) - or can alternatively be implemented directly by the operating system of the client device 110. The automated assistant client 114 can interact with the warm word system 180 implemented locally at the client device 110 or via one or more of the networks 199 as depicted in FIG. 1. The automated assistant client 114 (and optionally by way of its interactions with other remote system (e.g., server(s))) may form what appears to be, from a user’s perspective, a logical instance of an automated assistant 115 with which the user may engage in a human-to-computer dialog. An instance of the automated assistant 115 is depicted in FIG. 1, and is encompassed by a dashed line that includes the automated assistant client 114 of the client device 110 and the warm word system 180. It thus should be understood that a user that engages with the automated assistant client 114 executing on the client device 110 may, in effect, engage with his or her own logical instance of the automated assistant 115 (or a logical instance of the automated assistant 115 that is shared amongst a household or other group of users). For the sake of brevity and simplicity, the automated assistant 115 as used herein will refer to the automated assistant client 114 executing on the client device 110 and/or one or more servers that may implement the warm word system 180." ) … …the real-time driving assistance comprises audible or verbal output (see Carbune at least [0001] and [0026]-[0027]) . 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 user interface as disclosed by Jiang with a chatbot such as taught by Carbune with a reasonable expectation of success so as to provide responsive audible assistance to a user’s input (see Carbune at least [0001]) . Regarding claim 4 , Jiang in view of Zhang teach the computer-implemented method of claim 1. While Jiang discloses real-time driving assistance information and Zhang teaches the use of augmented reality, neither Jiang nor Zhang explicitly disclose or teach the following: … the associated user interface or the user device comprises Augmented Reality (AR) glasses… …the [information] comprises at least one of visuals, graphics, icons, or text… …the [information] is displayed via the AR glasses as an overlay over actual images of an environment. Carbune , in the same field of endeavor, teaches the following: … the associated user interface or the user device comprises Augmented Reality (AR) glasses (see Carbune at least [0026]-[0027] "The client device 110 may be, for example, one or more of: a desktop computer, a laptop computer, a tablet, a mobile phone, a computing device of a vehicle (e.g., an in-vehicle communications system, an in-vehicle entertainment system, an in-vehicle navigation system), a standalone interactive speaker (optionally having a display), a smart appliance such as a smart television, and/or a wearable apparatus of the user that includes a computing device (e.g., a watch of the user having a computing device, glasses of the user having a computing device , a virtual or augmented reality computing device ). Additional and/or alternative client devices may be provided. The client device 110 can execute an automated assistant client 114 . An instance of the automated assistant client 114 can be an application that is separate from an operating system of the client device 110 (e.g., installed “on top” of the operating system) - or can alternatively be implemented directly by the operating system of the client device 110. The automated assistant client 114 can interact with the warm word system 180 implemented locally at the client device 110 or via one or more of the networks 199 as depicted in FIG. 1. The automated assistant client 114 (and optionally by way of its interactions with other remote system (e.g., server(s))) may form what appears to be, from a user’s perspective, a logical instance of an automated assistant 115 with which the user may engage in a human-to-computer dialog. An instance of the automated assistant 115 is depicted in FIG. 1, and is encompassed by a dashed line that includes the automated assistant client 114 of the client device 110 and the warm word system 180. It thus should be understood that a user that engages with the automated assistant client 114 executing on the client device 110 may, in effect, engage with his or her own logical instance of the automated assistant 115 (or a logical instance of the automated assistant 115 that is shared amongst a household or other group of users). For the sake of brevity and simplicity, the automated assistant 115 as used herein will refer to the automated assistant client 114 executing on the client device 110 and/or one or more servers that may implement the warm word system 180." ) … …the [information] comprises at least one of visuals, graphics, icons, or text (see Carbune at least [0026] "The client device 110 may be, for example, one or more of: a desktop computer, a laptop computer, a tablet, a mobile phone, a computing device of a vehicle ( e.g., an in-vehicle communications system, an in-vehicle entertainment system, an in-vehicle navigation system), a standalone interactive speaker (optionally having a display), a smart appliance such as a smart television, and/or a wearable apparatus of the user that includes a computing device (e.g., a watch of the user having a computing device, glasses of the user having a computing device , a virtual or augmented reality computing device )..." and [0073] "...The warm word activation event can include, for example, a phone call being received at a client device, a text message being received at a client device , an email being received at a client device, an alarm or timer sounding at a client device, media being played at a client device or an additional client device in an environment of the client device, a notification being received at a client device , a location of a client device, a software application being accessible at a client device, and/or other events associated with a client device in which the user can provide a spoken utterance to cause the client device, or an additional client device in communication with the client device, to be controlled..." ) … …the [information] is displayed via the AR glasses as an overlay over actual images of an environment (see Carbune at least [0026]-[0027] "The client device 110 may be, for example, one or more of: a desktop computer, a laptop computer, a tablet, a mobile phone, a computing device of a vehicle (e.g., an in-vehicle communications system, an in-vehicle entertainment system, an in-vehicle navigation system), a standalone interactive speaker (optionally having a display), a smart appliance such as a smart television, and/or a wearable apparatus of the user that includes a computing device (e.g., a watch of the user having a computing device, glasses of the user having a computing device , a virtual or augmented reality computing device ). Additional and/or alternative client devices may be provided. The client device 110 can execute an automated assistant client 114 . An instance of the automated assistant client 114 can be an application that is separate from an operating system of the client device 110 (e.g., installed “on top” of the operating system) - or can alternatively be implemented directly by the operating system of the client device 110. The automated assistant client 114 can interact with the warm word system 180 implemented locally at the client device 110 or via one or more of the networks 199 as depicted in FIG. 1. The automated assistant client 114 (and optionally by way of its interactions with other remote system (e.g., server(s))) may form what appears to be, from a user’s perspective, a logical instance of an automated assistant 115 with which the user may engage in a human-to-computer dialog. An instance of the automated assistant 115 is depicted in FIG. 1, and is encompassed by a dashed line that includes the automated assistant client 114 of the client device 110 and the warm word system 180. It thus should be understood that a user that engages with the automated assistant client 114 executing on the client device 110 may, in effect, engage with his or her own logical instance of the automated assistant 115 (or a logical instance of the automated assistant 115 that is shared amongst a household or other group of users). For the sake of brevity and simplicity, the automated assistant 115 as used herein will refer to the automated assistant client 114 executing on the client device 110 and/or one or more servers that may implement the warm word system 180." ) . 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 user interface as disclosed by Jiang with a AR glasses such as taught by Carbune with a reasonable expectation of success so as to provide interactive assistance to a user’s input (see Carbune at least [0001]) . Regarding claim 13 , Jiang in view of Zhang and Carbune teach the analogous material of that in claim 2 as recited in the instant claim and is rejected for similar reasons. Regarding claim 15 , Jiang in view of Zhang and Carbune teach the analogous material of that in claim 4 as recited in the instant claim and is rejected for similar reasons . 07-21-aia AIA Claim s 6, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang in view of Zhang, and further in view of Biberstein et al. (US-2022/0237952; hereinafter Biberstein ) . Regarding claim 6 , Jiang in view of Zhang teach the computer-implemented method of claim 5, wherein: … the sensor data is collected while the particular driver is driving the vehicle and while the vehicle is towing the object to provide the generative AI model (see Zhang at least [0096] "The systems and methods described herein may be used by, without limitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers , flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types. Further, the systems and methods described herein may be used for a variety of purposes, by way of example and without limitation, for machine control, machine locomotion, machine driving, synthetic data generation, generative AI , model training , perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, simulation and digital twinning, autonomous or semi-autonomous machine applications, deep learning, environment simulation, object or actor simulation and/or digital twinning, data center processing, conversational AI, light transport simulation (e.g., ray-tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing and/or any other suitable applications." and [0186] "AEB systems detect an impending forward collision with another vehicle or other object, and may automatically apply the brakes if the driver does not take corrective action within a specified time or distance parameter. AEB systems may use front-facing camera(s) and/or RADAR sensor(s) 560, coupled to a dedicated processor, DSP, FPGA, and/or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision and, if the driver does not take corrective action, the AEB system may automatically apply the brakes in an effort to prevent, or at least mitigate, the impact of the predicted collision. AEB systems, may include techniques such as dynamic brake support and/or crash imminent braking." ) with data that reflects how the vehicle handles while (i) towing the object (see Zhang at least [0096]) , and (ii) being driven by the … driver (see Jiang at least [0029] "...Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environment data detected or sensed by sensor system 115 (e.g., obstacles, objects, nearby vehicles), perception and planning system 110 can plan an optimal route and drive vehicle 101, for example, via control system 111, according to the planned route to reach the specified destination safely and efficiently." ) . 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 vehicle controls as disclosed by Jiang with a generative AI model such as taught by Zhang with a reasonable expectation of success for reasons similar to those provided above in claim 1. However, while Zhang details a driver of a vehicle towing a trailer, neither Jiang nor Zhang explicitly disclose or teach the following: …the driver is a particular driver distinct from other drivers… …data that reflects how the vehicle handles while … being driven by the particular driver. Biberstein , in the same field of endeavor, teaches the following: …the driver is a particular driver distinct from other drivers (see Biberstein at least [0024] "The trailer management system 114 allows for experimental/empirical determination of trailer air resistance in view of various driving style ( how one or more drivers operate the vehicle ) and the creation of a database of specific permutations of trailers/drivers/vehicles . The trailer management system 114 can also evaluate prevailing winds (based on up-to-date, predicted, or real-time weather) can also be factored into air resistance estimations which impact range-estimates, also referred to as distance-to-empty calculations." ) … …data that reflects how the vehicle handles while … being driven by the particular driver (see Biberstein at least [0024] "The trailer management system 114 allows for experimental/empirical determination of trailer air resistance in view of various driving style ( how one or more drivers operate the vehicle ) and the creation of a database of specific permutations of trailers/drivers/vehicles . The trailer management system 114 can also evaluate prevailing winds (based on up-to-date, predicted, or real-time weather) can also be factored into air resistance estimations which impact range-estimates, also referred to as distance-to-empty calculations." and [0026] "...As noted above, the trailer management system 114 can also factor driver-specific behaviors into the air resistance determinations, such as acceleration profile, how often a driver exceeds the speed limit, and so forth." ) . 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 driver recognition as taught by Jiang in view of Zhang with a particular driver and associated profile such as taught by Biberstein with a reasonable expectation of success so as to monitor and predict specific occurrences a vehicle may experience specific to a certain driver (see Biberstein at least [0026]) . Regarding claim 10 , Jiang in view of Zhang teach the computer-implemented method of claim 1, wherein: … … the generative AI model (see Zhang at least [0096] and [0186]) is trained to determine [environmental conditions] ((see Jiang at least [0029] "...Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environment data detected or sensed by sensor system 115 (e.g., obstacles, objects, nearby vehicles), perception and planning system 110 can plan an optimal route and drive vehicle 101, for example, via control system 111, according to the planned route to reach the specified destination safely and efficiently." ) and (see Zhang at least [0096])) … 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 vehicle controls as disclosed by Jiang with a generative AI model such as taught by Zhang with a reasonable expectation of success for reasons similar to those provided above in claim 1. However, while Jiang describes sensor readings which indicate environment conditions, neither Jiang nor Zhang explicitly disclose or teach the following: …the vehicle is a particular vehicle distinct from other vehicles… …the object is a particular object distinct from other towable objects… …determine at least one of how the particular vehicle travels or handles when towing the particular object while traveling on a road… Biberstein , in the same field of endeavor, teaches the following: …the vehicle is a particular vehicle distinct from other vehicles (see Biberstein at least [0024] "The trailer management system 114 allows for experimental/empirical determination of trailer air resistance in view of various driving style ( how one or more drivers operate the vehicle ) and the creation of a database of specific permutations of trailers/drivers/ vehicles . The trailer management system 114 can also evaluate prevailing winds (based on up-to-date, predicted, or real-time weather) can also be factored into air resistance estimations which impact range-estimates, also referred to as distance-to-empty calculations." ) … …the object is a particular object distinct from other towable objects (see Biberstein at least [0024] "The trailer management system 114 allows for experimental/empirical determination of trailer air resistance in view of various driving style ( how one or more drivers operate the vehicle ) and the creation of a database of specific permutations of trailers /drivers/vehicles. The trailer management system 114 can also evaluate prevailing winds (based on up-to-date, predicted, or real-time weather) can also be factored into air resistance estimations which impact range-estimates, also referred to as distance-to-empty calculations." ) … …determine at least one of how the particular vehicle travels or handles when towing the particular object while traveling on a road (see Biberstein at least [0020]-[0021] "Referring now to FIGS. 1 and 2 collectively, once the surface mapping of the trailer has been completed, the trailer management system 114 can utilize the surface mapping to determine or estimate a drag coefficient for the trailer 104 . The trailer management system 114 can utilize the ML to estimate the drag coefficient. For example, training data of trailer profiles 202 and their associated drag coefficients can be used to train a model to predict drag coefficient(s) based on trailer surface qualities. An example set of training data for various vehicle surface mappings (training data of trailer profiles 202) and drag coefficients can be compared to a surface mapping 204 of the vehicle 102 to provide a real-time prediction model 206 ... The trailer management system 114 can convert the drag coefficient into drag force estimation(s) as the vehicle 102 tows the trailer 104 . Again, veh icle operating data such as vehicle speed and/or wind speed can be used by the trailer management system 114 to convert the drag coefficient into drag force estimation(s)..." ) … It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify generative AI model as taught by Jiang in view of Zhang with information pertaining to a particular vehicle and object such as taught by Biberstein with a reasonable expectation of success so as to monitor and predict specific occurrences a vehicle may experience specific to a certain conditions (see Biberstein at least [0020]-[0021]) . Regarding claim 19 , Jiang in view of Zhang and Biberstein teach the analogous material of that in claim 10 as recited in the instant claim and is rejected for similar reasons . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Seitz et al. (US-2022/0144083) teaches vehicle information within an environment as well as various forms of control assistance . 07-40 AIA 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 extension fee 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 date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN REIDY whose telephone number is (571) 272-7660. The examiner can normally be reached on M-F 7:00 AM- 3:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached on (571) 272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /S.P.R./Examiner, Art Unit 3663 /ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663 Application/Control Number: 18/827,425 Page 2 Art Unit: 3663 Application/Control Number: 18/827,425 Page 3 Art Unit: 3663 Application/Control Number: 18/827,425 Page 4 Art Unit: 3663 Application/Control Number: 18/827,425 Page 5 Art Unit: 3663 Application/Control Number: 18/827,425 Page 6 Art Unit: 3663 Application/Control Number: 18/827,425 Page 7 Art Unit: 3663 Application/Control Number: 18/827,425 Page 8 Art Unit: 3663 Application/Control Number: 18/827,425 Page 9 Art Unit: 3663 Application/Control Number: 18/827,425 Page 10 Art Unit: 3663 Application/Control Number: 18/827,425 Page 11 Art Unit: 3663 Application/Control Number: 18/827,425 Page 12 Art Unit: 3663 Application/Control Number: 18/827,425 Page 13 Art Unit: 3663 Application/Control Number: 18/827,425 Page 14 Art Unit: 3663 Application/Control Number: 18/827,425 Page 15 Art Unit: 3663 Application/Control Number: 18/827,425 Page 16 Art Unit: 3663 Application/Control Number: 18/827,425 Page 17 Art Unit: 3663 Application/Control Number: 18/827,425 Page 18 Art Unit: 3663 Application/Control Number: 18/827,425 Page 19 Art Unit: 3663 Application/Control Number: 18/827,425 Page 20 Art Unit: 3663 Application/Control Number: 18/827,425 Page 21 Art Unit: 3663 Application/Control Number: 18/827,425 Page 22 Art Unit: 3663 Application/Control Number: 18/827,425 Page 23 Art Unit: 3663 Application/Control Number: 18/827,425 Page 24 Art Unit: 3663 Application/Control Number: 18/827,425 Page 25 Art Unit: 3663 Application/Control Number: 18/827,425 Page 26 Art Unit: 3663 Application/Control Number: 18/827,425 Page 27 Art Unit: 3663
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