Detailed Action
Notice of 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 .
Priority
Applicant is reminded that in order for a patent issuing on the instant application to obtain priority under 35 U.S.C. 119(a)-(d) or (f), 365(a) or (b), or 386(a) or (b), based on priority papers filed in a parent or related Application No. TW114102201 (to which the present application claims the benefit under 35 U.S.C. 120, 121, 365(c), or 386(c) or is a reissue application of a patent issued on the related application), a claim for such foreign priority must be timely made in this application. To satisfy the requirement of 37 CFR 1.55 for a certified copy of the foreign application, applicant may simply identify the parent nonprovisional application or patent for which reissue is sought containing the certified copy. In order for priority to be perfected, an English translation of the parent patent grant from Taiwan must be submitted.
Specification
The disclosure is objected to because of the following informalities:
“detailed structure and operations of the IMN 130” should be changed to “detailed structure and operations of the IMU 130” (PG. 18; Changing IMN 130 to IMU 130 as is consistent with the rest of the specification).
“the acceleration data T2 provided by the IMN 130” should be changed to “the acceleration data T2 provided by the IMU 130” (PG. 18; Changing IMN 130 to IMU 130 as is consistent with the rest of the specification).
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8 & 10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Singh et al. (U.S. Patent Application Publication No. 2025/0222887 A1, hereinafter referred to as Singh). For clarity, all claim limitations are bolded and the direct cites to the prior art are illustrated below. The entirety of the cited prior art reference is relevant, however, the directly cited portions contain the best teachings of the claim limitations.
Regarding claim 1, Singh teaches A vehicle safety assistance system adapted to providing a driving assistance move for assisting driving operations performed on a vehicle, wherein the vehicle safety assistance system is characterized in comprising: (Par. 0132; See "The term “vehicle setting” or “vehicle settings” as used herein refers to the customizable configurations and preferences within a vehicle that cater to the comfort, safety, and convenience of its occupant/s. These settings encompass a range of features, including seat adjustments (such as position, lumbar support, and memory presets), climate control preferences, mirror positions, steering wheel settings, cruise settings, and personalized configurations for entertainment and infotainment systems. Vehicle settings may allow occupants to tailor their in-vehicle experience, ensuring a personalized and ergonomic environment. Vehicle settings may be stored to the occupant's profile. It is also referred to as user preferences or occupant preferences.") a storage apparatus, which is adapted to storing a plurality of safety parameter sets determined previously; (Par. 0064; See "The computer-readable medium may be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter affecting a machine-readable propagated signal, or a combination of one or more of them.") a recognition apparatus, which is adapted to recognizing surroundings of the vehicle for generating a recognition result accordingly; (Par. 0113; See "The term “computer vision module” or “computer vision system” allows the vehicle to “see” and interpret the world around it. This system uses a combination of cameras, sensors, and other technologies such as Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR), Sound Navigation and Ranging (SONAR), Global Positioning System (GPS), and Machine learning algorithms, etc. to collect visual data about the vehicle's surroundings and to analyze that data in real-time. The computer vision system is designed to perform a range of tasks, including object detection, lane detection, and pedestrian recognition. It uses deep learning algorithms and other machine learning techniques to analyze visual data and make decisions about how to control the vehicle. For example, the computer vision system may use object detection algorithms to identify other vehicles, pedestrians, and obstacles in the vehicle's path. It can then use this information to calculate the vehicle's speed and direction, adjust its trajectory to avoid collisions, and apply the brakes or accelerate as needed. It allows the vehicle to navigate safely and efficiently in a variety of driving conditions.") and a processor, which is electrically coupled to the storage apparatus and the recognition apparatus, (Par. 0082; See "Further, a computer system including one or more processors and computer-readable media such as computer memory may practice the methods. In particular, one or more processors execute computer-executable instructions, stored in the computer memory, to perform various functions such as the acts recited in the embodiments.") wherein, the processor obtains the recognition result, generates a parameter index in accordance with the recognition result, (Pars. 0169-0170; See "FIG. 3 shows the block diagram of the system 300 for accessing user profile and configuring functions of a vehicle smart system based on a user profile according to an embodiment. The system comprises a processor 302, memory 304, communication module 306, vehicle details module 308, owner's profile/default profile module 310, occupant recognition module 312, occupant profile access module 314, conflict resolution module 316, vehicle settings module 318, limitation settings module 320, settings and limitations monitoring module 322, and display module 324." & "Processor 302 may be a high-performance, multi-core CPU or system-on-chip (SoC) solution to process vast amounts of data from various sensors that may be used. Processor 302 processes data from sensors, such as cameras, LIDAR, radar, and other inputs to make real-time decisions, recommendations, and to execute control actions for the vehicle. Processor 302 may comprise Graphics Processing Units (GPUs)." The profiles may be created based on data received from the recognizing surroundings. For an example see Par. 0243; "In an embodiment, the training data sample may also include contextual data/sensor data 564 relating to the surrounding environment. This may include, for example, location of the vehicle, current weather conditions, temperature, time of day, traffic conditions in the region, number of lanes, other obstacles, uphill segments of the road, etc. The system may also garner contextual information from a device associated with the user. For example, through an application installed on the device, such as an online mapping service, like Google® maps, and location services, the system may know the vehicle details. Real-time sensor data may be collected which may include, for example, video, image, audio, infrared, temperature, 3D modeling, and any other suitable types of data that capture the current state around the vehicle. The current contextual information 564 includes real-time sensor data from the vehicle and the user device.") selects one of the safety parameter sets which corresponds to the parameter index as a vehicle setting parameter set, (Par. 0189; See "Occupant profile access module 314 accesses the stored occupant profile, creates the occupant profile if it doesn't exist and stores it in the database. The occupant profile can include standard settings that may be set for the various vehicles, or custom settings that can be selected for the user based on learned settings over time by use of various vehicles. In one embodiment, the user profiles are continuously updated and stored to a database, which is accessible by cloud services. Databases may include data storage, such as cloud storage, data center databases, distributed databases, local storage on vehicles, network storage, and the like. FIG. 4D shows the vehicle system 430 accessing the occupant profile from an occupant data system 440 according to an embodiment. The vehicle system 430 comprises sensors 432, communication module 434, vehicle seats 436, and vehicle controls 438. The vehicle system 430 comprises a combination of hardware and software. The occupant data system 440 may comprise a data store 442 comprising an occupant account 444, wherein the occupant account 444 comprises an occupant profile 446. The occupant data system 440 further comprises a customization application 448 with a user interface 450.") and sets a driver assistance procedure in accordance with the vehicle setting parameter set so that the driver assistance procedure is operated and the driving assistance move is provided accordingly. (Par. 0143; See "In an aspect, a user profile can be provided for a driver or the passenger. In case of the driver, the system can adjust seats, mirrors, and temperature on the driver's side. Based on limitations associated with transmitter/driver, the system may limit where the driver is allowed to travel (geofencing or route limitations), and how fast the driver can accelerate, or not allow the driver to go over any posted speed limit without an emergency. If no limitations are provided for the driver/borrower, the system can create limitations based on the passengers being added. For example, the driver is not provided limitations at the beginning of the borrowing period, but when adding passengers of a certain age to become occupants of the vehicle, such as elderly or minors becoming passengers, the system determines when elderly or minors are passengers and automatically places limitations on acceleration or maximum speed. In an aspect, the limitation may be modified based on the number of passengers added after the initial connection with the smart vehicle system. For example, the driver was allowed to go to location X, but now with additional passengers added, the driver is not allowed to go to location X. Also, if a minor enters the vehicle, the vehicle can no longer be driven over a certain speed limit, or the sport mode option is not available.")
Regarding claim 2, Singh teaches The vehicle safety assistance system according to claim 1, wherein the recognition apparatus comprises a locating device which generates a location information of the vehicle in accordance with a wireless signal received by the locating device and sets the location information as the recognition result; (Par. 0153; See "Sensors 206 are arranged in and/or around the vehicle to monitor properties of the vehicle and/or an environment in which the vehicle is located. One or more of the sensors 206 may be mounted to measure properties around an exterior of the vehicle. Additionally, or alternatively, one or more of sensors 206 may be mounted inside a cabin of the vehicle or in a body of the vehicle (e.g., an engine compartment, wheel wells, etc.) to measure properties of the vehicle and/or interior sensing of the vehicle. For example, the sensors 206 include accelerometers, odometers, tachometers, pitch and yaw sensors, wheel speed sensors, microphones, tire pressure sensors, biometric sensors, ultrasonic sensors, infrared sensors, Light Detection and Ranging (LIDAR/lidar), Radio Detection and Ranging System (radar), Global Positioning System (GPS), millimeter wave (mmWave) sensors, cameras and/or sensors of any other suitable type. Sensors may comprise object detection sensors 206-1 such as LIDAR, radar, cameras, ultrasonic sensors, GPS sensors, etc., to detect distances between the vehicle and an object or target in its vicinity.") wherein, the processor is provided with a map divided into a plurality of areas, determines a current position of the vehicle in accordance with the location information, determines which of the areas the current position is within, and sets the area within which the current position is as the parameter index. (Par. 0216; See "Hence, the proposed solution involves giving user profiles a priority level, with safety-related attributes preferably taking precedence over comfort-related ones. This ensures that, when conflicting profiles are detected, the system prioritizes safety aspects, such as limiting the maximum speed of the vehicle, maintaining a safe distance between vehicles, limiting the route or geographic area for the vehicle, etc., while allowing flexibility in other non-safety-related settings." & Par. 0240; See "Limitation settings module 320 comprises vehicle controls operable for applying limitation settings once the system accesses the limitations from the occupant profile corresponding to the identified user. In an embodiment, the limitation settings that need to be set are based on the preset profile of the occupant. Limitation settings may include geographic area, geographic boundaries, speed restrictions, trailing distance, lane change frequency, maximum acceleration, and any other safety related restrictions." Maps may be divided using geographic boundaries and the vehicle location is known within these boundaries because some user profiles limit where the vehicle can go within these areas. See Par. 0143; "In an aspect, a user profile can be provided for a driver or the passenger. In case of the driver, the system can adjust seats, mirrors, and temperature on the driver's side. Based on limitations associated with transmitter/driver, the system may limit where the driver is allowed to travel (geofencing or route limitations), ")
Regarding claim 3, Singh teaches The vehicle safety assistance system according to claim 2, further comprises: an inertial measurement unit, which is electrically coupled to the processor and generates a three-dimensional angular velocity and an acceleration data of the vehicle; (Par. 0097; See "The term “autonomous vehicle” also referred to as self-driving vehicle, driverless vehicle, robotic vehicle as used herein refers to a vehicle incorporating vehicular automation, that is, a vehicle that can sense its environment and move safely with little or no human input. Self-driving vehicles combine a variety of sensors to perceive their surroundings, such as thermographic cameras, Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR), Sound Navigation and Ranging (SONAR), Global Positioning System (GPS), odometry and inertial measurement unit. Control systems are designed for the purpose of interpreting sensor information to identify appropriate navigation paths, as well as obstacles and relevant signage." & Par. 0153; See "Sensors 206 are arranged in and/or around the vehicle to monitor properties of the vehicle and/or an environment in which the vehicle is located. One or more of the sensors 206 may be mounted to measure properties around an exterior of the vehicle. Additionally, or alternatively, one or more of sensors 206 may be mounted inside a cabin of the vehicle or in a body of the vehicle (e.g., an engine compartment, wheel wells, etc.) to measure properties of the vehicle and/or interior sensing of the vehicle. For example, the sensors 206 include accelerometers, odometers, tachometers, pitch and yaw sensors, wheel speed sensors, microphones, tire pressure sensors, biometric sensors, ultrasonic sensors, infrared sensors, Light Detection and Ranging (LIDAR/lidar), Radio Detection and Ranging System (radar), Global Positioning System (GPS), millimeter wave (mmWave) sensors, cameras and/or sensors of any other suitable type. Sensors may comprise object detection sensors 206-1 such as LIDAR, radar, cameras, ultrasonic sensors, GPS sensors, etc., to detect distances between the vehicle and an object or target in its vicinity.") and a velocity measurement unit, which is electrically coupled to the processor and generates a linear velocity data of the vehicle, wherein, the processor obtains the three-dimensional angular velocity and the acceleration data from the inertial measurement unit, obtains the linear velocity data from the velocity measurement unit, (Par. 0097; See "The term “autonomous vehicle” also referred to as self-driving vehicle, driverless vehicle, robotic vehicle as used herein refers to a vehicle incorporating vehicular automation, that is, a vehicle that can sense its environment and move safely with little or no human input. Self-driving vehicles combine a variety of sensors to perceive their surroundings, such as thermographic cameras, Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR), Sound Navigation and Ranging (SONAR), Global Positioning System (GPS), odometry and inertial measurement unit. Control systems are designed for the purpose of interpreting sensor information to identify appropriate navigation paths, as well as obstacles and relevant signage." Standard for inertial measurement units to determine the angular velocity and acceleration data.) and determines which of the areas the current position is within in accordance with the location information, the three-dimensional angular velocity, the acceleration data and the linear velocity data. (Par. 0153; See "Sensors 206 are arranged in and/or around the vehicle to monitor properties of the vehicle and/or an environment in which the vehicle is located. One or more of the sensors 206 may be mounted to measure properties around an exterior of the vehicle. Additionally, or alternatively, one or more of sensors 206 may be mounted inside a cabin of the vehicle or in a body of the vehicle (e.g., an engine compartment, wheel wells, etc.) to measure properties of the vehicle and/or interior sensing of the vehicle. For example, the sensors 206 include accelerometers, odometers, tachometers, pitch and yaw sensors, wheel speed sensors, microphones, tire pressure sensors, biometric sensors, ultrasonic sensors, infrared sensors, Light Detection and Ranging (LIDAR/lidar), Radio Detection and Ranging System (radar), Global Positioning System (GPS), millimeter wave (mmWave) sensors, cameras and/or sensors of any other suitable type. Sensors may comprise object detection sensors 206-1 such as LIDAR, radar, cameras, ultrasonic sensors, GPS sensors, etc., to detect distances between the vehicle and an object or target in its vicinity." The location (GPS) data of the vehicle is combined with the inertial measurement unit data which includes angular velocity data, acceleration data, and linear velocity data.)
Regarding claim 4, Singh teaches The vehicle safety assistance system according to claim 3, wherein a frequency at which the inertial measurement unit generates the three-dimensional angular velocity and the acceleration data is higher than a frequency at which the locating device generates the location information. (Par. 0166; See "In various embodiments, data is communicated and transferred at a suitable time interval, including, for example, 200 millisecond (ms) intervals, 100 ms intervals, 50 ms intervals, 20 ms intervals, 10 ms intervals, or even more frequent and/or in real-time or near real-time, in order to allow a vehicle to respond to, or otherwise react to, data. Bidirectional communication may be used to facilitate data exchange." 10ms to 100ms is 100Hz -10hz, which is equal to or greater than the standard GPS signal of 10hz which is used, See Par. 0153; "Global Positioning System (GPS)")
Regarding claim 5, Singh teaches The vehicle safety assistance system according to claim 4, wherein the processor estimates the current position by fusing the three-dimensional angular velocity, the acceleration data and the linear velocity data during a time period between consecutively generating the location information by the locating device. (Par. 0153; See "Sensors 206 are arranged in and/or around the vehicle to monitor properties of the vehicle and/or an environment in which the vehicle is located. One or more of the sensors 206 may be mounted to measure properties around an exterior of the vehicle. Additionally, or alternatively, one or more of sensors 206 may be mounted inside a cabin of the vehicle or in a body of the vehicle (e.g., an engine compartment, wheel wells, etc.) to measure properties of the vehicle and/or interior sensing of the vehicle. For example, the sensors 206 include accelerometers, odometers, tachometers, pitch and yaw sensors, wheel speed sensors, microphones, tire pressure sensors, biometric sensors, ultrasonic sensors, infrared sensors, Light Detection and Ranging (LIDAR/lidar), Radio Detection and Ranging System (radar), Global Positioning System (GPS), millimeter wave (mmWave) sensors, cameras and/or sensors of any other suitable type. Sensors may comprise object detection sensors 206-1 such as LIDAR, radar, cameras, ultrasonic sensors, GPS sensors, etc., to detect distances between the vehicle and an object or target in its vicinity." The location (GPS) data of the vehicle is combined with the inertial measurement unit data which includes angular velocity data, acceleration data, and linear velocity data. Data is sent at faster intervals than standard GPS, See Par. 0166; "Bidirectional communication has various advantages as described herein. In various embodiments, data is communicated and transferred at a suitable time interval, including, for example, 200 millisecond (ms) intervals, 100 ms intervals, 50 ms intervals, 20 ms intervals, 10 ms intervals, or even more frequent and/or in real-time or near real-time, in order to allow a vehicle to respond to, or otherwise react to, data. Bidirectional communication may be used to facilitate data exchange.")
Regarding claim 6, Singh teaches The vehicle safety assistance system according to claim 1, wherein the recognition result comprises a status of surroundings outside the vehicle generated through image identification. (Par. 0231; See "FIG. 5B shows a flowchart for adjusting vehicle settings and limitations using a machine learning model according to an embodiment. The vehicle system is operable for continuous monitoring and adaptively adjusting the vehicle settings and limitations when more than one occupant is present. The system may receive real-time data from the sensors associated with the vehicle related to the occupancy as shown at 532. Any type of sensor may be used to gather data pertaining to the vehicle. A sensor output may be, for example, images, videos, audios, LiDAR measures, infrared measures, temperature measures, GPS data, or any other information measured or detected by sensors. In an embodiment, a sensor output may be the result of one or more sensors capturing environmental information associated with the surroundings of the vehicle, which may include traffic at the location, road surface condition, etc. The system may receive any data associated with the sensor output from sensors, including raw sensory output and/or any derivative data. In an embodiment, the system may process the received data and identify any actionable parameter of interest using a machine learning model, trained using a set of training data. It may receive other data 536, such as weather conditions, road conditions, traffic conditions, humidity, temperature, driver behavior, tire tread, tire conditions, tire pressure, etc., from other sensors of the vehicle.")
Regarding claim 7, Singh teaches A vehicle safety assistance method adapted to providing a driving assistance move for assisting driving operations performed on a vehicle, wherein the vehicle safety assistance method is characterized in comprising: (Par. 0143; See "In an aspect, a user profile can be provided for a driver or the passenger. In case of the driver, the system can adjust seats, mirrors, and temperature on the driver's side. Based on limitations associated with transmitter/driver, the system may limit where the driver is allowed to travel (geofencing or route limitations), and how fast the driver can accelerate, or not allow the driver to go over any posted speed limit without an emergency. If no limitations are provided for the driver/borrower, the system can create limitations based on the passengers being added. For example, the driver is not provided limitations at the beginning of the borrowing period, but when adding passengers of a certain age to become occupants of the vehicle, such as elderly or minors becoming passengers, the system determines when elderly or minors are passengers and automatically places limitations on acceleration or maximum speed. In an aspect, the limitation may be modified based on the number of passengers added after the initial connection with the smart vehicle system. For example, the driver was allowed to go to location X, but now with additional passengers added, the driver is not allowed to go to location X. Also, if a minor enters the vehicle, the vehicle can no longer be driven over a certain speed limit, or the sport mode option is not available.") obtaining a recognition result from a recognition apparatus, wherein the recognition result is generated through recognizing surroundings of the vehicle by the recognition apparatus; (Par. 0113; See "The term “computer vision module” or “computer vision system” allows the vehicle to “see” and interpret the world around it. This system uses a combination of cameras, sensors, and other technologies such as Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR), Sound Navigation and Ranging (SONAR), Global Positioning System (GPS), and Machine learning algorithms, etc. to collect visual data about the vehicle's surroundings and to analyze that data in real-time. The computer vision system is designed to perform a range of tasks, including object detection, lane detection, and pedestrian recognition. It uses deep learning algorithms and other machine learning techniques to analyze visual data and make decisions about how to control the vehicle. For example, the computer vision system may use object detection algorithms to identify other vehicles, pedestrians, and obstacles in the vehicle's path. It can then use this information to calculate the vehicle's speed and direction, adjust its trajectory to avoid collisions, and apply the brakes or accelerate as needed. It allows the vehicle to navigate safely and efficiently in a variety of driving conditions.") determining a parameter index corresponding to the recognition result in accordance with a predetermined rule and the recognition result; (Par. 0189; See "Occupant profile access module 314 accesses the stored occupant profile, creates the occupant profile if it doesn't exist and stores it in the database. The occupant profile can include standard settings that may be set for the various vehicles, or custom settings that can be selected for the user based on learned settings over time by use of various vehicles. In one embodiment, the user profiles are continuously updated and stored to a database, which is accessible by cloud services. Databases may include data storage, such as cloud storage, data center databases, distributed databases, local storage on vehicles, network storage, and the like. FIG. 4D shows the vehicle system 430 accessing the occupant profile from an occupant data system 440 according to an embodiment. The vehicle system 430 comprises sensors 432, communication module 434, vehicle seats 436, and vehicle controls 438. The vehicle system 430 comprises a combination of hardware and software. The occupant data system 440 may comprise a data store 442 comprising an occupant account 444, wherein the occupant account 444 comprises an occupant profile 446. The occupant data system 440 further comprises a customization application 448 with a user interface 450.") selecting one of a plurality of safety parameter sets, which corresponds to the parameter index, as a vehicle setting parameter set; (Par. 0189; See "Occupant profile access module 314 accesses the stored occupant profile, creates the occupant profile if it doesn't exist and stores it in the database. The occupant profile can include standard settings that may be set for the various vehicles, or custom settings that can be selected for the user based on learned settings over time by use of various vehicles. In one embodiment, the user profiles are continuously updated and stored to a database, which is accessible by cloud services. Databases may include data storage, such as cloud storage, data center databases, distributed databases, local storage on vehicles, network storage, and the like. FIG. 4D shows the vehicle system 430 accessing the occupant profile from an occupant data system 440 according to an embodiment. The vehicle system 430 comprises sensors 432, communication module 434, vehicle seats 436, and vehicle controls 438. The vehicle system 430 comprises a combination of hardware and software. The occupant data system 440 may comprise a data store 442 comprising an occupant account 444, wherein the occupant account 444 comprises an occupant profile 446. The occupant data system 440 further comprises a customization application 448 with a user interface 450.") setting a driver assistance procedure in accordance with the vehicle setting parameter set; and operating the driver assistance procedure to generate and provide the driving assistance move. (Par. 0143; See "In an aspect, a user profile can be provided for a driver or the passenger. In case of the driver, the system can adjust seats, mirrors, and temperature on the driver's side. Based on limitations associated with transmitter/driver, the system may limit where the driver is allowed to travel (geofencing or route limitations), and how fast the driver can accelerate, or not allow the driver to go over any posted speed limit without an emergency. If no limitations are provided for the driver/borrower, the system can create limitations based on the passengers being added. For example, the driver is not provided limitations at the beginning of the borrowing period, but when adding passengers of a certain age to become occupants of the vehicle, such as elderly or minors becoming passengers, the system determines when elderly or minors are passengers and automatically places limitations on acceleration or maximum speed. In an aspect, the limitation may be modified based on the number of passengers added after the initial connection with the smart vehicle system. For example, the driver was allowed to go to location X, but now with additional passengers added, the driver is not allowed to go to location X. Also, if a minor enters the vehicle, the vehicle can no longer be driven over a certain speed limit, or the sport mode option is not available.")
Regarding claim 8, Singh teaches The vehicle safety assistance method according to claim 7, wherein the recognition result comprises a location information, and determining the parameter index corresponding to the recognition result in accordance with the predetermined rule and the recognition result comprises: (Par. 0143; See "In an aspect, a user profile can be provided for a driver or the passenger. In case of the driver, the system can adjust seats, mirrors, and temperature on the driver's side. Based on limitations associated with transmitter/driver, the system may limit where the driver is allowed to travel (geofencing or route limitations), and how fast the driver can accelerate, or not allow the driver to go over any posted speed limit without an emergency. If no limitations are provided for the driver/borrower, the system can create limitations based on the passengers being added. For example, the driver is not provided limitations at the beginning of the borrowing period, but when adding passengers of a certain age to become occupants of the vehicle, such as elderly or minors becoming passengers, the system determines when elderly or minors are passengers and automatically places limitations on acceleration or maximum speed. In an aspect, the limitation may be modified based on the number of passengers added after the initial connection with the smart vehicle system. For example, the driver was allowed to go to location X, but now with additional passengers added, the driver is not allowed to go to location X. Also, if a minor enters the vehicle, the vehicle can no longer be driven over a certain speed limit, or the sport mode option is not available.") determining a current position of the vehicle in accordance with the location information; (Par. 0150; See "FIG. 1 is an illustration of a vehicle with various sensors, actuators, and systems according to an embodiment. The system comprises various sensors, such as ultrasonic sensor, LIDAR sensors, radar sensors, etc. , actuators such as brake actuators, steering actuators, etc. ., and various subsystems such as propulsion system, steering system, brake sensor system, communication system, etc. FIG. 1 is depicted as an example system; neither is it limited by the systems depicted nor is it an exhaustive list of the sensors, actuators, and systems/subsystems, and/or features of the autonomous vehicle. Further, the vehicle shown should not be construed as limiting in terms of the arrangement of any of the sensors, actuators, and systems/subsystems depicted. These sensors, actuators, and systems/subsystems can be arranged as suited for a purpose to be performed by the autonomous vehicle. Autonomous vehicles, also known as self-driving vehicles or driverless vehicles, are vehicles that can navigate and operate without human intervention. Sensors, for example, including cameras, LIDARs, radars, and ultrasonic sensors enable autonomous vehicles to detect and recognize objects, obstacles, and pedestrians on the road. Autonomous vehicles use advanced control systems to make real-time decisions based on sensor data and pre-programmed rules or intelligence-based decision systems. These systems control, for example, acceleration, braking, steering, and communication of the vehicle. Navigation systems such as GPS, maps, and other location-based technologies help autonomous vehicles navigate and plan the optimal route to a destination. Communication systems of autonomous vehicles help them communicate with other vehicles and infrastructure, such as traffic lights and road signs, to exchange information and optimize traffic flow. Autonomous vehicles have several safety features, including collision avoidance systems, emergency braking, and backup systems in case of system failures. Autonomous vehicles are assisted by artificial intelligence and machine learning algorithms to analyze data, recognize patterns, and improve performance over time.") performing a comparison operation to compare the current position with boundaries of a plurality of areas; determining which of the areas the current position is within in accordance with a comparison result generated by the comparison operation; (Par. 0143; See "In an aspect, a user profile can be provided for a driver or the passenger. In case of the driver, the system can adjust seats, mirrors, and temperature on the driver's side. Based on limitations associated with transmitter/driver, the system may limit where the driver is allowed to travel (geofencing or route limitations), and how fast the driver can accelerate, or not allow the driver to go over any posted speed limit without an emergency. If no limitations are provided for the driver/borrower, the system can create limitations based on the passengers being added. For example, the driver is not provided limitations at the beginning of the borrowing period, but when adding passengers of a certain age to become occupants of the vehicle, such as elderly or minors becoming passengers, the system determines when elderly or minors are passengers and automatically places limitations on acceleration or maximum speed. In an aspect, the limitation may be modified based on the number of passengers added after the initial connection with the smart vehicle system. For example, the driver was allowed to go to location X, but now with additional passengers added, the driver is not allowed to go to location X. Also, if a minor enters the vehicle, the vehicle can no longer be driven over a certain speed limit, or the sport mode option is not available." The vehicle uses GPS locating and other sensors to determine location and must compare to a boundary in order for the vehicle to maintain the rules and parameters set by the user and/or for the user. ) and set the area within which the current position is as the parameter index. (Par. 0216; See "In an embodiment, even if the vehicle has a preset profile or no preset profile, the system should automatically apply the passenger's profile upon entry. This becomes crucial for scenarios where a parent wants to ensure certain safety parameters, such as speed limits, trailing distance, lane change frequency, etc., are enforced when their child is driving or accompanying someone else's vehicle. In this context, the system needs to recognize and prioritize user profiles, particularly those related to safety attributes. Hence, the proposed solution involves giving user profiles a priority level, with safety-related attributes preferably taking precedence over comfort-related ones. This ensures that, when conflicting profiles are detected, the system prioritizes safety aspects, such as limiting the maximum speed of the vehicle, maintaining a safe distance between vehicles, limiting the route or geographic area for the vehicle, etc., while allowing flexibility in other non-safety-related settings.")
Regarding claim 10, Singh teaches The vehicle safety assistance method according to claim 7, wherein the recognition result comprises a status of surroundings, which is outside the vehicle, generated through image identification. (Par. 0153; See "Sensors 206 are arranged in and/or around the vehicle to monitor properties of the vehicle and/or an environment in which the vehicle is located. One or more of the sensors 206 may be mounted to measure properties around an exterior of the vehicle. Additionally, or alternatively, one or more of sensors 206 may be mounted inside a cabin of the vehicle or in a body of the vehicle ( e.g., an engine compartment, wheel wells, etc.) to measure properties of the vehicle and/or interior sensing of the vehicle. For example, the sensors 206 include accelerometers, odometers, tachometers, pitch and yaw sensors, wheel speed sensors, microphones, tire pressure sensors, biometric sensors, ultrasonic sensors, infrared sensors, Light Detection and Ranging (LIDAR/lidar), Radio Detection and Ranging System (radar), Global Positioning System (GPS), millimeter wave (mm Wave) sensors, cameras and/or sensors of any other suitable type. Sensors may comprise object detection sensors 206-1 such as LIDAR, radar, cameras, ultrasonic sensors, GPS sensors, etc., to detect distances between the vehicle and an object or target in its vicinity.")
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (U.S. Patent Application Publication No. 2025/0222887 A1) in view of Hou et al. (U.S. Patent Application Publication No. 2022/0138896 A1, hereinafter referred to as Hou)
Regarding claim 9, Singh teaches The vehicle safety assistance method according to claim 8, wherein determining the parameter index corresponding to the recognition result in accordance with the predetermined rule and the recognition result comprises: (Par. 0143; See "In an aspect, a user profile can be provided for a driver or the passenger. In case of the driver, the system can adjust seats, mirrors, and temperature on the driver's side. Based on limitations associated with transmitter/driver, the system may limit where the driver is allowed to travel (geofencing or route limitations), and how fast the driver can accelerate, or not allow the driver to go over any posted speed limit without an emergency. If no limitations are provided for the driver/borrower, the system can create limitations based on the passengers being added. For example, the driver is not provided limitations at the beginning of the borrowing period, but when adding passengers of a certain age to become occupants of the vehicle, such as elderly or minors becoming passengers, the system determines when elderly or minors are passengers and automatically places limitations on acceleration or maximum speed. In an aspect, the limitation may be modified based on the number of passengers added after the initial connection with the smart vehicle system. For example, the driver was allowed to go to location X, but now with additional passengers added, the driver is not allowed to go to location X. Also, if a minor enters the vehicle, the vehicle can no longer be driven over a certain speed limit, or the sport mode option is not available.") obtaining a linear velocity data of the vehicle; (Par. 0113; See "The term "computer vision module" or "computer vision system" allows the vehicle to "see" and interpret the world around it. This system uses a combination of cameras, sensors, and other technologies such as Radio Detection and Ranging (RADAR), Light Detection and Ranging (LID AR), Sound Navigation and Ranging (SONAR), Global Positioning System (GPS), and Machine learning algorithms, etc. to collect visual data about the vehicle's surroundings and to analyze that data in real-time. The computer vision system is designed to perform a range of tasks, including object detection, lane detection, and pedestrian recognition. It uses deep learning algorithms and other machine learning techniques to analyze visual data and make decisions about how to control the vehicle. For example, the computer vision system may use object detection algorithms to identify other vehicles, pedestrians, and obstacles in the vehicle's path. It can then use this information to calculate the vehicle's speed and direction, adjust its trajectory to avoid collisions, and apply the brakes or accelerate as needed. It allows the vehicle to navigate safely and efficiently in a variety of driving conditions." Measuring the vehicle speed and direction is synonymous with measuring the linear velocity data of the vehicle.) and estimating the current position by fusing the three-dimensional angular velocity, the acceleration data and the linear velocity data during a time period between consecutively obtaining the location information. (Par. 0150; See "FIG. 1 is an illustration of a vehicle with various sensors, actuators, and systems according to an embodiment. The system comprises various sensors, such as ultrasonic sensor, LIDAR sensors, radar sensors, etc. , actuators such as brake actuators, steering actuators, etc., and various subsystems such as propulsion system, steering system, brake sensor system, communication system, etc. FIG. 1 is depicted as an example system; neither is it limited by the systems depicted nor is it an exhaustive list of the sensors, actuators, and systems/subsystems, and/or features of the autonomous vehicle." & Par. 0097; See "The term "autonomous vehicle" also referred to as self-driving vehicle, driverless vehicle, robotic vehicle as used herein refers to a vehicle incorporating vehicular automation, that is, a vehicle that can sense its environment and move safely with little or no human input. Self-driving vehicles combine a variety of sensors to perceive their surroundings, such as thermographic cameras, Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR), Sound Navigation and Ranging (SONAR), Global Positioning System (GPS), odometry and inertial measurement unit. Control systems are designed for the purpose of interpreting sensor information to identify appropriate navigation paths, as well as obstacles and relevant signage." & Par. 0113; See "The term "computer vision module" or "computer vision system" allows the vehicle to "see" and interpret the world around it. This system uses a combination of cameras, sensors, and other technologies such as Radio Detection and Ranging (RADAR), Light Detection and Ranging (LID AR), Sound Navigation and Ranging (SONAR), Global Positioning System (GPS), and Machine learning algorithms, etc. to collect visual data about the vehicle's surroundings and to analyze that data in real-time. The computer vision system is designed to perform a range of tasks, including object detection, lane detection, and pedestrian recognition. It uses deep learning algorithms and other machine learning techniques to analyze visual data and make decisions about how to control the vehicle. For example, the computer vision system may use object detection algorithms to identify other vehicles, pedestrians, and obstacles in the vehicle's path. It can then use this information to calculate the vehicle's speed and direction, adjust its trajectory to avoid collisions, and apply the brakes or accelerate as needed. It allows the vehicle to navigate safely and efficiently in a variety of driving conditions." The vehicle combines all of the sensor data which measures the three-dimensional angular velocity, the acceleration data, and the linear velocity data, specifically using the inertial measurement unit along with additional sensors on the vehicle. The current position is then estimated throughout the course of travel when combining all of the data received and within the vehicle itself.) but fails to teach obtaining a three-dimensional angular velocity and an acceleration data of the vehicle at a higher frequency than obtaining the location information;
Hou makes up for the deficiencies in Singh. Hou teaches obtaining a three-dimensional angular velocity and an acceleration data of the vehicle at a higher frequency than obtaining the location information; (Par. 0094; See “In some embodiments, the GPS device may receive geographic locations with a first data receiving frequency. The first data receiving frequency of the GPS device may refer to the location updating count ( or times) per second. The first data receiving frequency may be 1 0 Hz, 20 Hz, etc., that means the GPS device may receive one geographic location every 0.1 s, 0.05 s, etc., respectively. The IMU sensor may receive IMU information with a second data receiving frequency. The second data receiving frequency of the IMU sensor may refer to the IMU information (e.g., poses of a subject) updating count ( or times) per second. The second data receiving frequency of the IMU sensor may be 100 Hz, 200 Hz, etc., that means the IMU sensor may receive IMU data for one time every 0.01 s, 0.005 s, etc., respectively. Accordingly, the first data receiving frequency may be lower than the second data receiving frequency that means during a same time period, the IMU sensor may receive more poses than geographic locations received by the GPS device. In some embodiments, the processing engine 122 may obtain a plurality of geographic locations and a plurality of poses during the time period. The processing engine 122 may further match one of the plurality of geographic locations and a pose based on the time information to obtain a first group of pose data. As used herein, the matching between a geographic location with a pose may refer to determine the geographic location where the pose is acquired. In some embodiments, the processing engine 122 may perform an interpolation operation on the plurality of geographic locations to match poses and geographic locations. Exemplary interpolation operations may include using a spherical linear interpolation (Slerp) algorithm, a Geometric Slerp algorithm, a Quaternion Slerp algorithm, etc.”)
Singh teaches obtaining a three-dimensional angular velocity and an acceleration data of the vehicle along with location information; but fails to teach obtaining the three-dimensional angular velocity and an acceleration data of the vehicle at a higher frequency than the location information. It is common, however, to receive the internal vehicle sensor data at a higher frequency than the GPS data so that the vehicle may operate independently from the GPS data. Hou proves this by obtaining a three-dimensional angular velocity and an acceleration data of the vehicle at a higher frequency than obtaining the location information as shown above with reference to Par. 0094.
Singh and Hou are both directed to vehicle positioning control systems and methods and are obvious to combine because Singh is improved with the frequency rate at which both the three-dimensional angular velocity and an acceleration data of the vehicle along with location information is received, specifically obtaining a three-dimensional angular velocity and an acceleration data of the vehicle at a higher frequency than obtaining the location information as taught by Hou which was well known before the effective filing date of the claimed invention. Thus, 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 based on Singh and Hou.
Conclusion
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/A.M.G./ Examiner, Art Unit 3661
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661
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