DETAILED ACTION
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over ICHIKI (US 20230267746 A1), in view of Igarashi (US 11763675 B2)
Re Claim 1, ICHIKI discloses an object recognition apparatus (see ICHIKI: e.g., -- an information processing device, an information processing method, and a program capable of reducing a load of object recognition using sensor fusion. The information processing device includes an object region detection unit that detects an object region indicating ranges in an azimuth direction and an elevation angle direction in which there is an object within a sensing range of the distance measurement sensor on the basis of three-dimensional data indicating a direction of and a distance to each measurement point measured by a distance measurement sensor, and associates information within a captured image captured by a camera whose imaging range at least partially overlaps the sensing range with the object region. This technology can be applied, for example, to a system that performs object recognition.--, in abstract) comprising:
a ranging sensor configured to measure at least distances to reflection points based on reflected waves of applied irradiation waves (see ICHIKI: e.g., Fig. 1, and, -- [0119] The LiDAR 212 constitutes, for example, a part of the LiDAR 53 in FIG. 1, and performs sensing in the region in front of the vehicle 1, and at least part of the sensing range overlaps an imaging range of the camera 211. For example, the LiDAR 212 scans a region in front of the vehicle 1 with laser pulses, which is measurement light, in an azimuth direction (a horizontal direction) and an elevation angle direction (a height direction), and receives reflected light of the laser pulses. The LiDAR 212 calculates a direction and distance of a measurement point, which is a reflection point on the object that reflects the laser pulses, on the basis of a scanning direction of the laser pulses and a time required for reception of the reflected light. The LiDAR 212 generates point cloud data (point cloud), which is three-dimensional data indicating the direction and distance of each measurement point on the basis of a calculated result. The LiDAR 212 supplies the point cloud data to the information processing unit 213.--, in [0117]-[0119]);
a camera configured to generate image data on an imaging range (see ICHIKI: e.g., -- [0117] The information processing system 201 includes a camera 211, a LiDAR 212, and an information processing unit 213. [0118] The camera 211 constitutes, for example, a part of the camera 51 in FIG. 1, images a region in front of the vehicle 1, and supplies an obtained image (hereinafter referred to as a captured image) to the information processing unit 213.--, in [0117]-[0118]);
and one or more processing devices configured to perform an object recognition process based on measurement data of the ranging sensor and the image data of the camera (see ICHIKI: e.g., -- an information processing device, an information processing method, and a program capable of reducing a load of object recognition using sensor fusion. The information processing device includes an object region detection unit that detects an object region indicating ranges in an azimuth direction and an elevation angle direction in which there is an object within a sensing range of the distance measurement sensor on the basis of three-dimensional data indicating a direction of and a distance to each measurement point measured by a distance measurement sensor, and associates information within a captured image captured by a camera whose imaging range at least partially overlaps the sensing range with the object region. This technology can be applied, for example, to a system that performs object recognition.--, in abstract; and, -- [0117] The information processing system 201 includes a camera 211, a LiDAR 212, and an information processing unit 213. [0118] The camera 211 constitutes, for example, a part of the camera 51 in FIG. 1, images a region in front of the vehicle 1, and supplies an obtained image (hereinafter referred to as a captured image) to the information processing unit 213.--, in [0117]-[0119]);
although ICHIKI discloses {vehicle inside sensor 26 includes various sensors for detecting information on the inside of the vehicle} detects various types of biological information of a passenger such as a driver (see ICHIKI: e.g., --[0062] The vehicle inside sensor 26 includes various sensors for detecting information on the inside of the vehicle, and supplies sensor data from each sensor to each unit of the vehicle control system 11. A type and number of sensors included in the vehicle inside sensor 26 are arbitrary.
[0063] For example, the vehicle inside sensor 26 includes a camera, a radar, a seating sensor, a steering wheel sensor, a microphone, a biosensor, and the like. For the camera, for example, any photographing type of camera such as a ToF camera, a stereo camera, a monocular camera, or an infrared camera may be used. The biosensor is provided, for example, in a seat or a steering wheel, and detects various types of biological information of a passenger such as a driver.--, in [0062]-[0063]; and, --[0087] The DMS 30 performs driver authentication processing, driver state recognition processing, and the like on the basis of sensor data from the vehicle inside sensor 26, input data input to the HMI 31, and the like. As a state of the driver as a recognition target, for example, a physical condition, wakefulness, concentration, fatigue, line of sight direction, drunkenness, driving operation, and posture are assumed.--, in [0087]);
ICHIKI however does not does not explicitly disclose the one or more processing devices are configured to perform a line-of-sight detection process of detecting a line-of-sight direction of a driver of a mobile body
Igarashi discloses one or more processing devices are configured to perform a line-of-sight detection process of detecting a line-of-sight direction of a driver of a mobile body (see Igarashi: e.g., -- (81) The vehicle-interior-information detector 142 performs a process of detecting information regarding the inside of a vehicle on the basis of data or a signal from each structural element of the vehicle control system 100. For example, the vehicle-interior-information detector 142 performs processes of authenticating and recognizing a driver, a process of detecting a state of the driver, a process of detecting a person on board, and a process of detecting a vehicle interior environment. Examples of the detection-target state of a driver include a physical condition, a degree of arousal, a degree of concentration, a degree of fatigue, and a direction of a line of sight. Examples of the detection-target vehicle interior environment include temperature, humidity, brightness, and odor. The vehicle-interior-information detector 142 supplies data indicating a result of the detection process to, for example, the state recognition section 153 of the state analyzer 133 and the emergency event avoiding section 171 of the movement controller 135.--, in lines 43-60, col 10),
ICHIKI and Igarashi are combinable as they are in the same field of endeavor: vehicle’s processing of range sensors measurements and camera image information and object detection/recognition. Therefore it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify ICHIKI’s apparatus using Igarashi’s teachings by including one or more processing devices are configured to perform a line-of-sight detection process of detecting a line-of-sight direction of a driver of a mobile body to ICHIKI’s vehicle inside sensor 26 and information processing unit in order to perform detecting a state of the driver, a process of detecting a person on board, and a process of detecting a vehicle interior environment. Examples of the detection-target state of a driver include a physical condition, a degree of arousal, a degree of concentration, a degree of fatigue, and a direction of a line of sight (see Igarashi: e.g., in 43-60, col 10),
ICHIKI as modified by Igarashi further disclose a range setting process of setting a first range including the line-of-sight direction and a second range other than the first range, in a measurement range viewed from the mobile body (see ICHIKI: e.g., -- [0038] FIG. 28 is a schematic diagram illustrating an example of setting a plurality of recognition ranges--, in [0038]; and,
--[0062] The vehicle inside sensor 26 includes various sensors for detecting information on the inside of the vehicle, and supplies sensor data from each sensor to each unit of the vehicle control system 11. A type and number of sensors included in the vehicle inside sensor 26 are arbitrary.
[0063] For example, the vehicle inside sensor 26 includes a camera, a radar, a seating sensor, a steering wheel sensor, a microphone, a biosensor, and the like. For the camera, for example, any photographing type of camera such as a ToF camera, a stereo camera, a monocular camera, or an infrared camera may be used. The biosensor is provided, for example, in a seat or a steering wheel, and detects various types of biological information of a passenger such as a driver.--, in [0062]-[0063]; and, --[0087] The DMS 30 performs driver authentication processing, driver state recognition processing, and the like on the basis of sensor data from the vehicle inside sensor 26, input data input to the HMI 31, and the like. As a state of the driver as a recognition target, for example, a physical condition, wakefulness, concentration, fatigue, line of sight direction, drunkenness, driving operation, and posture are assumed.--, in [0087]; and, -- [0150] The elevation angle θ0 is set according to the attachment angle of the LiDAR 212, and is set to, for example, an angle at which a position a predetermined reference distance away from the vehicle 1 is irradiated with a laser pulse on a horizontal road surface in front of the vehicle 1. The reference distance is set, for example, to a maximum value of a distance at which an object as a recognition target (for example, a preceding vehicle) is desired to be recognized in front of the vehicle 1.--, in [0150]; and, Fig. 14, -- [0176] It is preferable to set the number of detectable object regions in each unit region to two or more so that object regions corresponding to different objects can be separated in each unit region. On the other hand, in order to reduce a processing load, it is preferable to set an upper limit of the number of detected object regions in each unit region. For example, the upper limit of the number of detected object regions in each unit region is set within a range of 2 to 4.
[0177] Returning to FIG. 5, in step S3, the object region detection unit 221 detects a target object region on the basis of the object region.
[0178] First, the object region detection unit 221 associates each object region with the captured image. Specifically, an attachment position and attachment angle of the camera 211 and the attachment position and attachment angle of the LiDAR 212 are known, and a positional relationship between the imaging range of the camera 211 and the sensing range of the LiDAR 212 is known. Therefore, a relative relationship between the virtual plane and each unit region, and the region within the captured image is also known. Using such known information, the object region detection unit 221 calculates the region corresponding to each object region within the captured image on the basis of a position of each object region within the virtual plane, to associate each object region with the captured image.--, in [0176]-[0178]; and, -- [0193] Returning to FIG. 5, in step S4, the object recognition unit 222 sets a recognition range on the basis of the target object region.
[0194] For example, as illustrated in FIG. 21, a recognition range R31 is set on the basis of a detection result of the target object region illustrated in FIG. 20. In this example, a width and height of the recognition range R31 are set to ranges obtained by adding predetermined margins to respective ranges in the horizontal direction and the vertical direction in which there is the target object region.
[0195] In step S5, the object recognition unit 222 recognizes objects within the recognition range.--, in [0193]-[0195]; and, Fig. 28, -- [0219] For example, the object region may be associated with information other than the region within the captured image. For example, the object region may be associated with information (for example, pixel information or metadata) on a region corresponding to the object region in the captured image.
[0220] For example, a plurality of recognition ranges may be set within the captured image. For example, when positions of the detected target object regions are far apart, the plurality of recognition ranges may be set such that each target object region is included in any one of the recognition ranges.
[0221] Further, for example, classification of classes of the respective recognition ranges may be performed on the basis of a shape, size, position, distance, or the like of the target object region included in each recognition range, and the object recognition may be performed by using a method according to the class of each recognition range.
[0222] For example, in an example of FIG. 28, recognition ranges R51 to R53 are set. The recognition range R51 includes a preceding vehicle and is classified into a class requiring precise object recognition. The recognition range R52 is classified into a class including high objects such as road signs, traffic lights, street lamps, utility poles, and overpasses. The recognition range R53 is classified into a class including a region that is a distant background. An object recognition algorithm suitable for the class of each recognition range is applied to the recognition ranges R51 to R53, and object recognition is performed. This improves the accuracy or speed of the object recognition.
[0223] For example, the recognition range may be set on the basis of the object region before the coupling processing or the object region after the coupling processing without performing detection of the target object region.
[0224] For example, the object recognition may be performed on the basis of the object region before the coupling processing or the object region after the coupling processing without setting the recognition range.--, in [0219]-[0224];
also see Igarashi: e.g., -- (81) The vehicle-interior-information detector 142 performs a process of detecting information regarding the inside of a vehicle on the basis of data or a signal from each structural element of the vehicle control system 100. For example, the vehicle-interior-information detector 142 performs processes of authenticating and recognizing a driver, a process of detecting a state of the driver, a process of detecting a person on board, and a process of detecting a vehicle interior environment. Examples of the detection-target state of a driver include a physical condition, a degree of arousal, a degree of concentration, a degree of fatigue, and a direction of a line of sight. Examples of the detection-target vehicle interior environment include temperature, humidity, brightness, and odor. The vehicle-interior-information detector 142 supplies data indicating a result of the detection process to, for example, the state recognition section 153 of the state analyzer 133 and the emergency event avoiding section 171 of the movement controller 135.--, in lines 43-60, col 10);
the object recognition process of performing a visual field recognition process using one of the measurement data and the image data for the first range, and performing a surrounding recognition process using another of the measurement data and the image data for the second range (see ICHIKI: e.g., -- an information processing device, an information processing method, and a program capable of reducing a load of object recognition using sensor fusion. The information processing device includes an object region detection unit that detects an object region indicating ranges in an azimuth direction and an elevation angle direction in which there is an object within a sensing range of the distance measurement sensor on the basis of three-dimensional data indicating a direction of and a distance to each measurement point measured by a distance measurement sensor, and associates information within a captured image captured by a camera whose imaging range at least partially overlaps the sensing range with the object region. This technology can be applied, for example, to a system that performs object recognition.--, in abstract; and, -- [0038] FIG. 28 is a schematic diagram illustrating an example of setting a plurality of recognition ranges--, in [0038]; and,
--[0062] The vehicle inside sensor 26 includes various sensors for detecting information on the inside of the vehicle, and supplies sensor data from each sensor to each unit of the vehicle control system 11. A type and number of sensors included in the vehicle inside sensor 26 are arbitrary.
[0063] For example, the vehicle inside sensor 26 includes a camera, a radar, a seating sensor, a steering wheel sensor, a microphone, a biosensor, and the like. For the camera, for example, any photographing type of camera such as a ToF camera, a stereo camera, a monocular camera, or an infrared camera may be used. The biosensor is provided, for example, in a seat or a steering wheel, and detects various types of biological information of a passenger such as a driver.--, in [0062]-[0063]; and, --[0087] The DMS 30 performs driver authentication processing, driver state recognition processing, and the like on the basis of sensor data from the vehicle inside sensor 26, input data input to the HMI 31, and the like. As a state of the driver as a recognition target, for example, a physical condition, wakefulness, concentration, fatigue, line of sight direction, drunkenness, driving operation, and posture are assumed.--, in [0087]; and, -- [0150] The elevation angle θ0 is set according to the attachment angle of the LiDAR 212, and is set to, for example, an angle at which a position a predetermined reference distance away from the vehicle 1 is irradiated with a laser pulse on a horizontal road surface in front of the vehicle 1. The reference distance is set, for example, to a maximum value of a distance at which an object as a recognition target (for example, a preceding vehicle) is desired to be recognized in front of the vehicle 1.--, in [0150]; and, Fig. 14, -- [0176] It is preferable to set the number of detectable object regions in each unit region to two or more so that object regions corresponding to different objects can be separated in each unit region. On the other hand, in order to reduce a processing load, it is preferable to set an upper limit of the number of detected object regions in each unit region. For example, the upper limit of the number of detected object regions in each unit region is set within a range of 2 to 4.
[0177] Returning to FIG. 5, in step S3, the object region detection unit 221 detects a target object region on the basis of the object region.
[0178] First, the object region detection unit 221 associates each object region with the captured image. Specifically, an attachment position and attachment angle of the camera 211 and the attachment position and attachment angle of the LiDAR 212 are known, and a positional relationship between the imaging range of the camera 211 and the sensing range of the LiDAR 212 is known. Therefore, a relative relationship between the virtual plane and each unit region, and the region within the captured image is also known. Using such known information, the object region detection unit 221 calculates the region corresponding to each object region within the captured image on the basis of a position of each object region within the virtual plane, to associate each object region with the captured image.--, in [0176]-[0178]; and, -- [0193] Returning to FIG. 5, in step S4, the object recognition unit 222 sets a recognition range on the basis of the target object region.
[0194] For example, as illustrated in FIG. 21, a recognition range R31 is set on the basis of a detection result of the target object region illustrated in FIG. 20. In this example, a width and height of the recognition range R31 are set to ranges obtained by adding predetermined margins to respective ranges in the horizontal direction and the vertical direction in which there is the target object region.
[0195] In step S5, the object recognition unit 222 recognizes objects within the recognition range.--, in [0193]-[0195]; and, Fig. 28, -- [0219] For example, the object region may be associated with information other than the region within the captured image. For example, the object region may be associated with information (for example, pixel information or metadata) on a region corresponding to the object region in the captured image.
[0220] For example, a plurality of recognition ranges may be set within the captured image. For example, when positions of the detected target object regions are far apart, the plurality of recognition ranges may be set such that each target object region is included in any one of the recognition ranges.
[0221] Further, for example, classification of classes of the respective recognition ranges may be performed on the basis of a shape, size, position, distance, or the like of the target object region included in each recognition range, and the object recognition may be performed by using a method according to the class of each recognition range.
[0222] For example, in an example of FIG. 28, recognition ranges R51 to R53 are set. The recognition range R51 includes a preceding vehicle and is classified into a class requiring precise object recognition. The recognition range R52 is classified into a class including high objects such as road signs, traffic lights, street lamps, utility poles, and overpasses. The recognition range R53 is classified into a class including a region that is a distant background. An object recognition algorithm suitable for the class of each recognition range is applied to the recognition ranges R51 to R53, and object recognition is performed. This improves the accuracy or speed of the object recognition.
[0223] For example, the recognition range may be set on the basis of the object region before the coupling processing or the object region after the coupling processing without performing detection of the target object region.
[0224] For example, the object recognition may be performed on the basis of the object region before the coupling processing or the object region after the coupling processing without setting the recognition range.--, in [0219]-[0224];
also see Igarashi: e.g., -- (81) The vehicle-interior-information detector 142 performs a process of detecting information regarding the inside of a vehicle on the basis of data or a signal from each structural element of the vehicle control system 100. For example, the vehicle-interior-information detector 142 performs processes of authenticating and recognizing a driver, a process of detecting a state of the driver, a process of detecting a person on board, and a process of detecting a vehicle interior environment. Examples of the detection-target state of a driver include a physical condition, a degree of arousal, a degree of concentration, a degree of fatigue, and a direction of a line of sight. Examples of the detection-target vehicle interior environment include temperature, humidity, brightness, and odor. The vehicle-interior-information detector 142 supplies data indicating a result of the detection process to, for example, the state recognition section 153 of the state analyzer 133 and the emergency event avoiding section 171 of the movement controller 135.--, in lines 43-60, col 10).
Re Claim 2, ICHIKI as modified by Igarashi further disclose the one or more processing devices are configured to:
further perform a traveling environment determination process of determining, based on information on a traveling environment of the mobile body, whether the mobile body is in a situation of high necessity for object recognition in a short-distance region less than a minimum distance that allows for the object recognition by the object recognition process based on the image data (see ICHIKI:e.g., -- [0205] FIG. 25 illustrates an example of output information in which rectangular parallelepipeds corresponding to the respective object regions are two-dimensionally disposed on the basis of the distribution of the measurement points in each object region. Specifically, a length in the depth direction of each object region is obtained on the basis of the distance to the measurement point within each object region before the coupling processing. A length in the depth direction of each object region is calculated, for example, on the basis of a difference in distance between the measurement point closest to the vehicle 1 and the measurement point furthest from the vehicle 1 among the measurement points in each object region. Further, positions of each object region in the azimuth direction, the elevation angle direction, and the distance direction are obtained on the basis of a direction (an azimuth and an elevation angle) of the measurement point within each object region and the distance to the measurement point. Rectangular parallelepipeds indicating a width in the azimuth direction, a height in the elevation angle direction, and a length in the depth direction of the respective object regions are two-dimensionally disposed on the basis of the positions of the respective object regions, so that the output information illustrated in FIG. 25 is generated.--, in [0225]), and
perform the visual field recognition process using the measurement data for the first range including the line-of-sight direction in the object recognition process, when the one or more processing devices determine that the mobile body is in the situation of high necessity for the object recognition in the short-distance region (see ICHIK: e.g., -- [0150] The elevation angle θ0 is set according to the attachment angle of the LiDAR 212, and is set to, for example, an angle at which a position a predetermined reference distance away from the vehicle 1 is irradiated with a laser pulse on a horizontal road surface in front of the vehicle 1. The reference distance is set, for example, to a maximum value of a distance at which an object as a recognition target (for example, a preceding vehicle) is desired to be recognized in front of the vehicle 1.
[0151] Accordingly, when a region is closer to the reference distance, the scanning interval of the LiDAR 212 becomes smaller, and an interval in the distance direction between the measurement points becomes smaller.
[0152] On the other hand, when a region is farther from the reference distance, the scanning interval of the LiDAR 212 increases, and the interval in the distance direction between the measurement points increases. Therefore, the interval in the distance direction between the measurement points on the road surface in front of and near the vehicle 1 or in the region above the vehicle 1 increases.--, in [0150]-[0152]. and, -- [0038] FIG. 28 is a schematic diagram illustrating an example of setting a plurality of recognition ranges--, in [0038]; and,
--[0062] The vehicle inside sensor 26 includes various sensors for detecting information on the inside of the vehicle, and supplies sensor data from each sensor to each unit of the vehicle control system 11. A type and number of sensors included in the vehicle inside sensor 26 are arbitrary.
[0063] For example, the vehicle inside sensor 26 includes a camera, a radar, a seating sensor, a steering wheel sensor, a microphone, a biosensor, and the like. For the camera, for example, any photographing type of camera such as a ToF camera, a stereo camera, a monocular camera, or an infrared camera may be used. The biosensor is provided, for example, in a seat or a steering wheel, and detects various types of biological information of a passenger such as a driver.--, in [0062]-[0063]; and, --[0087] The DMS 30 performs driver authentication processing, driver state recognition processing, and the like on the basis of sensor data from the vehicle inside sensor 26, input data input to the HMI 31, and the like. As a state of the driver as a recognition target, for example, a physical condition, wakefulness, concentration, fatigue, line of sight direction, drunkenness, driving operation, and posture are assumed.--, in [0087]; and, Fig. 14, -- [0176] It is preferable to set the number of detectable object regions in each unit region to two or more so that object regions corresponding to different objects can be separated in each unit region. On the other hand, in order to reduce a processing load, it is preferable to set an upper limit of the number of detected object regions in each unit region. For example, the upper limit of the number of detected object regions in each unit region is set within a range of 2 to 4.
[0177] Returning to FIG. 5, in step S3, the object region detection unit 221 detects a target object region on the basis of the object region.
[0178] First, the object region detection unit 221 associates each object region with the captured image. Specifically, an attachment position and attachment angle of the camera 211 and the attachment position and attachment angle of the LiDAR 212 are known, and a positional relationship between the imaging range of the camera 211 and the sensing range of the LiDAR 212 is known. Therefore, a relative relationship between the virtual plane and each unit region, and the region within the captured image is also known. Using such known information, the object region detection unit 221 calculates the region corresponding to each object region within the captured image on the basis of a position of each object region within the virtual plane, to associate each object region with the captured image.--, in [0176]-[0178]; and, -- [0193] Returning to FIG. 5, in step S4, the object recognition unit 222 sets a recognition range on the basis of the target object region.
[0194] For example, as illustrated in FIG. 21, a recognition range R31 is set on the basis of a detection result of the target object region illustrated in FIG. 20. In this example, a width and height of the recognition range R31 are set to ranges obtained by adding predetermined margins to respective ranges in the horizontal direction and the vertical direction in which there is the target object region.
[0195] In step S5, the object recognition unit 222 recognizes objects within the recognition range.--, in [0193]-[0195]; and, Fig. 28, -- [0219] For example, the object region may be associated with information other than the region within the captured image. For example, the object region may be associated with information (for example, pixel information or metadata) on a region corresponding to the object region in the captured image.
[0220] For example, a plurality of recognition ranges may be set within the captured image. For example, when positions of the detected target object regions are far apart, the plurality of recognition ranges may be set such that each target object region is included in any one of the recognition ranges.
[0221] Further, for example, classification of classes of the respective recognition ranges may be performed on the basis of a shape, size, position, distance, or the like of the target object region included in each recognition range, and the object recognition may be performed by using a method according to the class of each recognition range.
[0222] For example, in an example of FIG. 28, recognition ranges R51 to R53 are set. The recognition range R51 includes a preceding vehicle and is classified into a class requiring precise object recognition. The recognition range R52 is classified into a class including high objects such as road signs, traffic lights, street lamps, utility poles, and overpasses. The recognition range R53 is classified into a class including a region that is a distant background. An object recognition algorithm suitable for the class of each recognition range is applied to the recognition ranges R51 to R53, and object recognition is performed. This improves the accuracy or speed of the object recognition.
[0223] For example, the recognition range may be set on the basis of the object region before the coupling processing or the object region after the coupling processing without performing detection of the target object region.
[0224] For example, the object recognition may be performed on the basis of the object region before the coupling processing or the object region after the coupling processing without setting the recognition range.--, in [0219]-[0224];
also see Igarashi: e.g., -- (81) The vehicle-interior-information detector 142 performs a process of detecting information regarding the inside of a vehicle on the basis of data or a signal from each structural element of the vehicle control system 100. For example, the vehicle-interior-information detector 142 performs processes of authenticating and recognizing a driver, a process of detecting a state of the driver, a process of detecting a person on board, and a process of detecting a vehicle interior environment. Examples of the detection-target state of a driver include a physical condition, a degree of arousal, a degree of concentration, a degree of fatigue, and a direction of a line of sight. Examples of the detection-target vehicle interior environment include temperature, humidity, brightness, and odor. The vehicle-interior-information detector 142 supplies data indicating a result of the detection process to, for example, the state recognition section 153 of the state analyzer 133 and the emergency event avoiding section 171 of the movement controller 135.--, in lines 43-60, col 10).
Re Claim 3, claim 3 is the corresponding method claim to claim 1, respectively. Claim 3 thus is rejected for the similar reasons for claim 1. See above discussions with regard to claim 1 respectively. Furthermore, ICHIKI as modified by Igarashi further disclose an object recognition processing method (see ICHIKI: e.g., -- an information processing device, an information processing method, and a program capable of reducing a load of object recognition using sensor fusion. The information processing device includes an object region detection unit that detects an object region indicating ranges in an azimuth direction and an elevation angle direction in which there is an object within a sensing range of the distance measurement sensor on the basis of three-dimensional data indicating a direction of and a distance to each measurement point measured by a distance measurement sensor, and associates information within a captured image captured by a camera whose imaging range at least partially overlaps the sensing range with the object region. This technology can be applied, for example, to a system that performs object recognition.--, in abstract).
Re Claim 4, claim 4 is the corresponding medium claim to claim 1, respectively. Claim 4 thus is rejected for the similar reasons for claim 1. See above discussions with regard to claim 1 respectively. ICHIKI as modified by Igarashi further disclose a non-transitory tangible recording medium containing a computer program, the computer program causing a computer to perform the method (see ICHIKI: e.g., --[0236] The input unit 1006 includes input switches, buttons, a microphone, an imaging device, or the like. The output unit 1007 includes a display, a speaker, or the like. The recording unit 1008 includes a hard disk, a nonvolatile memory, or the like. The communication unit 1009 includes a network interface or the like. The drive 1010 drives a removable medium 1011 such as a magnetic disk, optical disc, magneto-optical disc, or semiconductor memory.
[0237] In the computer 1000 configured as described above, the CPU 1001 loads, for example, a program recorded in the recording unit 1008 into the RAM 1003 via the input and output interface 1005 and the bus 1004, and executes the program so that the series of processing described above are performed.
[0238] A program executed by the computer 1000 (the CPU 1001) can be provided by being recorded on the removable medium 1011 such as a package medium, for example. Further, the program can be provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital satellite broadcasting.
[0239] In the computer 1000, the program can be installed in the recording unit 1008 via the input and output interface 1005 by the removable medium 1011 being mounted in the drive 1010. Further, the program can be received by the communication unit 1009 via the wired or wireless transmission medium and installed in the recording unit 1008. Further, the program can be installed in the ROM 1002 or the recording unit 1008 in advance.
[0240] The program executed by the computer may be a program that is processed in chronological order in an order described in the present specification, or may be a program in which processing is performed in parallel or at a necessary timing such as when a call is made.--, in [0236]-[0240]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEIWEN YANG whose telephone number is (571)270-5670. The examiner can normally be reached on Monday-Friday 8:30am-4:30pm east.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on 571-272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WEI WEN YANG/Primary Examiner, Art Unit 2662
