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 1-5 are rejected under 35 U.S.C. 103 as being patentable over KITAHARA (WO 2022030379 A1), and in view of SUGIURA (JP 2014120110 A), and further in view of AGRAWAL (US 20230061784 A1).
Re Claim 1, KITAHARA discloses a traffic light identification device for a host vehicle (see KITAHARA : e.g., --an operation assistance (ECU 20) acquires, from a front camera, as an observed light position, the position of a traffic signal candidate which is a light device for which the probability of being a traffic signal is at least a prescribed value. The operation assistance (ECU 20) also acquires, as a map information traffic signal position, the position of a traffic signal which exists in front of a host vehicle, by referencing map data around a current position of the host vehicle. The operation assistance (ECU 20) then determines that the light device is a traffic signal on the basis of a degree of proximity (δ) being less than a prescribed presumed threshold (Dth), the degree of proximity being the difference between the traffic signal position on the map and the position of the observed light device.--, in abstract) comprising a processor configured to:
KITAHARA although discloses the calculations of the positions and directions of various detection objects from the image (see KITAHARA: e.g., in the 1st para. of page 7/26 of the English version of WO 2022030379 A1, as provided as NPL with this Office Action);
KITAHARA however does not explicitly disclose identify a driver gaze position that is a position on a front camera image captured by a front camera corresponding to a position in front of the host vehicle which a driver of the host vehicle is gazing at based on a positional relationship between a driver monitor camera and the front camera and a driver monitor camera image including a face of the driver captured by the driver monitor camera;
SUGIURA discloses identify a driver gaze position that is a position on a front camera image captured by a front camera corresponding to a position in front of the host vehicle which a driver of the host vehicle is gazing at based on a positional relationship between a driver monitor camera and the front camera and a driver monitor camera image including a face of the driver captured by the driver monitor camera (see SUGIURA: e.g., -- The guide means acquires the front feature detected by the feature detection means by the feature object image information acquisition means based on the captured image captured by the front camera 19 and the line-of-sight position specified by the line-of-sight position specification means. Guidance information on the front feature is guided. The visual recognition mode specifying unit specifies the visual mode of the occupant on the front feature based on the captured image captured by the front camera 19 and the visual line position specified by the visual line position specifying unit. The guiding means performs guidance related to the feature based on the visual aspect of the occupant to the front characteristic specified by the visual recognition aspect specifying means. The type identifying means identifies the type of the forward feature. The gaze determining means determines whether or not the occupant is gazing at the front feature based on the visual aspect specified by the visual aspect specifying means. The visual recognition determination unit determines whether or not the occupant is visually recognizing the front feature based on the visual recognition mode specified by the visual recognition mode specification unit.--, in page 3/19, and --the driver camera 20 uses a solid-state image sensor such as a CCD, for example, and is attached to the upper surface of the instrument panel 53 of the vehicle 51 as shown in FIG. The Then, the face of the driver 52 sitting on the driver's seat is imaged. Further, the navigation ECU 13 detects the eye position (gaze start point) and the gaze direction of the driver 52 from the captured image captured by the driver camera 20 as described later.--, in page 4/19, of English version of JP 2014120110 A, as provided as NPL with this Office Action);
KITAHARA and SUGIURA are combinable as they are in the same field of endeavor: vehicle driving assistant system and devices. Therefore 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 KITAHARA’s device using SUGIURA’s teachings by including identify a driver gaze position that is a position on a front camera image captured by a front camera corresponding to a position in front of the host vehicle which a driver of the host vehicle is gazing at based on a positional relationship between a driver monitor camera and the front camera and a driver monitor camera image including a face of the driver captured by the driver monitor camera to KITAHARA ’s traffic light identification device in order to determines whether or not the occupant is visually recognizing the front feature based on the visual recognition mode specified by the visual recognition mode specification unit (see SUGIURA: e.g. in in pages 4-5/19, of English version of JP 2014120110 A, as provided as NPL with this Office Action);
KITAHARA as modified by SUGIURA further disclose detect at least one traffic light included in the front camera image (see KITAHARA : e.g., --an operation assistance (ECU 20) acquires, from a front camera, as an observed light position, the position of a traffic signal candidate which is a light device for which the probability of being a traffic signal is at least a prescribed value. The operation assistance (ECU 20) also acquires, as a map information traffic signal position, the position of a traffic signal which exists in front of a host vehicle, by referencing map data around a current position of the host vehicle. The operation assistance (ECU 20) then determines that the light device is a traffic signal on the basis of a degree of proximity (δ) being less than a prescribed presumed threshold (Dth), the degree of proximity being the difference between the traffic signal position on the map and the position of the observed light device.--, abstract);
KITAHARA as modified by SUGIURA however still do not explicitly disclose identify a traffic light for the host vehicle that is a traffic light which the host vehicle should obey among a plurality of traffic lights when the plurality of traffic lights included in the front camera image is detected;
AGRAWAL discloses identify a traffic light for the host vehicle that is a traffic light which the host vehicle should obey among a plurality of traffic lights when the plurality of traffic lights included in the front camera image is detected (see AGRAWAL: e.g., Fig. 6, --the visual environment around a driver may inform a characterization of driver behavior. Typically, running a red light may be considered an unsafe driving behavior. In some contexts, however, such as when a traffic guard is standing at an intersection and using hand gestures to instruct a driver to move through a red light, driving through a red light would be considered an appropriate driving behavior.--, in [0033]; --n FIG. 6. Here, a driver is approaching a wide intersection controlled by a traffic light. At the first time, the light is illuminated red. The driver 614 reaches a maximum braking force at the second time, just before a crosswalk of the intersection. The vertical bar 626 indicates that the driver came to a complete stop by the third time, at which time the driver 624 is still looking forward in the direction of the intersection but can additionally be seen reaching in the direction of a central console between the driver and passenger seats. At the fourth time, a lid 636 of a cooler becomes visible in a location similar to where the driver 624 was reaching at the third time, and the driver 634 is at the fourth time looking away from the road and in the direction of the cooler. In subsequent frames, not shown, the driver can be seen drinking from a water bottle.
[0095] In comparison to the driver illustrated in FIGS. 5A and 5B, the driver illustrated in FIG. 6 took his eyes off of the road for a longer duration and diverted his gaze from the road to a larger extent. Comparing these two events, however, it may be appreciated that the situation illustrated in FIGS. 5A and 5B was riskier. In the event illustrated in FIG. 6, the driver waited until the vehicle came to a complete stop before diverting his gaze from the road. In contrast, in the event illustrated in FIGS. 5A and 5B the driver diverted her gaze from the road when she had started braking, but was still travelling in excess of twenty miles per hour.--, in [0094] and [0095]);
KITAHARA (as modified by SUGIURA) and AGRAWAL are combinable as they are in the same field of endeavor: vehicle driving assistant system and devices. Therefore 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 KITAHARA (as modified by SUGIURA)’s device using AGRAWAL’s teachings by including identify a traffic light for the host vehicle that is a traffic light which the host vehicle should obey among a plurality of traffic lights when the plurality of traffic lights included in the front camera image is detected to KITAHARA ’s traffic lights recognitions in order to determine the driving risks related to the traffic lights (see AGRAWAL: e.g. in Fig. 6, {0333], and [0094]-[0095], and [0097]);
KITAHARA as modified by SUGIURA and AGRAWAL further disclose determine whether the host vehicle is entering an intersection (see KITAHARA: e.g., -- as a result of image recognition, in a situation where the distance to the direction signboard existing in front of the own vehicle is specified as 40 m, the distance from the position coordinates of the direction signboard registered in the map data is shifted to the rear of the vehicle by 40 m. It is determined that the own vehicle exists at the position. Vertical position estimation corresponds to the process of specifying the position of the own vehicle in the road extension direction. Vertical position estimation can also be called vertical localization processing. By performing such vertical position estimation, characteristic points on the road such as intersections, curve entrances / exits, tunnel entrances / exits, and the end of traffic jams, in other words, detailed remaining distances to POIs can be identified--, in page 11/26 of the English version of WO 2022030379 A1, as provided as NPL with this Office Action; and, also see AGRAWAL: e.g., -- in FIG. 6. Here, a driver is approaching a wide intersection controlled by a traffic light. At the first time, the light is illuminated red. The driver 614 reaches a maximum braking force at the second time, just before a crosswalk of the intersection. The vertical bar 626 indicates that the driver came to a complete stop by the third time, at which time the driver 624 is still looking forward in the direction of the intersection but can additionally be seen reaching in the direction of a central console between the driver and passenger seats.--, in [0094]);
determine whether a preceding vehicle exists in front of the host vehicle (see KITAHARA: e.g., -- as a result of image recognition, in a situation where the distance to the direction signboard existing in front of the own vehicle is specified as 40 m, the distance from the position coordinates of the direction signboard registered in the map data is shifted to the rear of the vehicle by 40 m. It is determined that the own vehicle exists at the position. Vertical position estimation corresponds to the process of specifying the position of the own vehicle in the road extension direction. Vertical position estimation can also be called vertical localization processing. By performing such vertical position estimation, characteristic points on the road such as intersections, curve entrances / exits, tunnel entrances / exits, and the end of traffic jams, in other words, detailed remaining distances to POIs can be identified--, in page 11/26 of the English version of WO 2022030379 A1, as provided as NPL with this Office Action; and, also see AGRAWAL: e.g., -- in FIG. 6. Here, a driver is approaching a wide intersection controlled by a traffic light. At the first time, the light is illuminated red. The driver 614 reaches a maximum braking force at the second time, just before a crosswalk of the intersection. The vertical bar 626 indicates that the driver came to a complete stop by the third time, at which time the driver 624 is still looking forward in the direction of the intersection but can additionally be seen reaching in the direction of a central console between the driver and passenger seats.--, in [0094], and Fig. 8, --By the fifth frame the driver of the monitored vehicle has begun to apply his brakes to a degree that triggers a Hard Braking alert. At this time, the pickup truck 844 is squarely in front of the monitored driver and angled nearly perpendicularly to the path of travel of the monitored driver. By the sixth frame, owing to the continued braking of the monitored driver, the pickup truck 854 has nearly cleared the monitored driver's path of travel. Finally, in the sixth frame, the pickup truck may be observed in the left view, indicating that there was no collision.--, in [0107]); and
detect a braking operation by the driver (see AGRAWAL: e.g., Fig. 8, --By the fifth frame the driver of the monitored vehicle has begun to apply his brakes to a degree that triggers a Hard Braking alert. At this time, the pickup truck 844 is squarely in front of the monitored driver and angled nearly perpendicularly to the path of travel of the monitored driver. By the sixth frame, owing to the continued braking of the monitored driver, the pickup truck 854 has nearly cleared the monitored driver's path of travel. Finally, in the sixth frame, the pickup truck may be observed in the left view, indicating that there was no collision.--, in [0107]),
wherein the processor identifies a traffic light existing at the driver gaze position among the plurality of traffic lights included in the front camera image as the traffic light for the host vehicle, when the preceding vehicle does not exist in front of the host vehicle, when the host vehicle is entering the intersection, and when the braking operation by the driver is detected (see AGRAWAL: e.g., Fig. 6, --the visual environment around a driver may inform a characterization of driver behavior. Typically, running a red light may be considered an unsafe driving behavior. In some contexts, however, such as when a traffic guard is standing at an intersection and using hand gestures to instruct a driver to move through a red light, driving through a red light would be considered an appropriate driving behavior.--, in [0033]; --n FIG. 6. Here, a driver is approaching a wide intersection controlled by a traffic light. At the first time, the light is illuminated red. The driver 614 reaches a maximum braking force at the second time, just before a crosswalk of the intersection. The vertical bar 626 indicates that the driver came to a complete stop by the third time, at which time the driver 624 is still looking forward in the direction of the intersection but can additionally be seen reaching in the direction of a central console between the driver and passenger seats. At the fourth time, a lid 636 of a cooler becomes visible in a location similar to where the driver 624 was reaching at the third time, and the driver 634 is at the fourth time looking away from the road and in the direction of the cooler. In subsequent frames, not shown, the driver can be seen drinking from a water bottle.
[0095] In comparison to the driver illustrated in FIGS. 5A and 5B, the driver illustrated in FIG. 6 took his eyes off of the road for a longer duration and diverted his gaze from the road to a larger extent. Comparing these two events, however, it may be appreciated that the situation illustrated in FIGS. 5A and 5B was riskier. In the event illustrated in FIG. 6, the driver waited until the vehicle came to a complete stop before diverting his gaze from the road. In contrast, in the event illustrated in FIGS. 5A and 5B the driver diverted her gaze from the road when she had started braking, but was still travelling in excess of twenty miles per hour.--, in [0094] and [0097], and, Fig. 8, --By the fifth frame the driver of the monitored vehicle has begun to apply his brakes to a degree that triggers a Hard Braking alert. At this time, the pickup truck 844 is squarely in front of the monitored driver and angled nearly perpendicularly to the path of travel of the monitored driver. By the sixth frame, owing to the continued braking of the monitored driver, the pickup truck 854 has nearly cleared the monitored driver's path of travel. Finally, in the sixth frame, the pickup truck may be observed in the left view, indicating that there was no collision.--, in [0107]).
Re Claim 2, KITAHARA as modified by SUGIURA and AGRAWAL further disclose the host vehicle comprising the traffic light identification device for the host vehicle according to claim 1 and a warning device, wherein a processor provided in the warning device is configured to:
determine whether a display of the traffic light for the host vehicle changed from a red light to a blue light or a green light (see AGRAWAL: e.g., -- [0024] FIG. 11 illustrates examples of a Hard Braking alert combined with a detection of a green traffic light--, in [0024], Fig. 6, Fig. 8, in [0094]-[0097], and [0119]-[0120]; and, -- [0048] As an example of a family of combinations of driving alerts that have been identified, a family of driving alerts may be related in that each combination includes a traffic sign or a traffic light. In this example, modifying co-occurring events may be distracted driving in combination with a traffic light violation; distracted driving in combination with a stop sign violation; speeding in combination with a traffic light violation (which may tend to occur on major suburban roads); and hard turning violations combined with an otherwise compliant traffic light event (which may occur when a driver makes a left turn guarded by a green or yellow arrow, but does so in a way that may be unsafe and/or cause excess wear to a vehicle. In this example, by virtue of relating these various combination alerts, a driver may be instructed in ways to improve safety around intersections, based on video data of the driver captured at a time when she was exposed to various heightened risks. Such feedback may be more effective at changing a driver's behavior than would be similar time spent on intersection violations that are associated with average risk (no modification by a co-occurring event) or un-clustered combination alerts.--, in [0048]);
determine whether the driver depressed an accelerator pedal before a predetermined time elapses from the time point when the display of the traffic light for the host vehicle changed from the red light to the blue light or the green light (see AGRAWAL: e.g., -- [0024] FIG. 11 illustrates examples of a Hard Braking alert combined with a detection of a green traffic light--, in [0024], Fig. 6, Fig. 8, in [0094]-[0097], and [0119]-[0120]; and, -- [0048] As an example of a family of combinations of driving alerts that have been identified, a family of driving alerts may be related in that each combination includes a traffic sign or a traffic light. In this example, modifying co-occurring events may be distracted driving in combination with a traffic light violation; distracted driving in combination with a stop sign violation; speeding in combination with a traffic light violation (which may tend to occur on major suburban roads); and hard turning violations combined with an otherwise compliant traffic light event (which may occur when a driver makes a left turn guarded by a green or yellow arrow, but does so in a way that may be unsafe and/or cause excess wear to a vehicle. In this example, by virtue of relating these various combination alerts, a driver may be instructed in ways to improve safety around intersections, based on video data of the driver captured at a time when she was exposed to various heightened risks. Such feedback may be more effective at changing a driver's behavior than would be similar time spent on intersection violations that are associated with average risk (no modification by a co-occurring event) or un-clustered combination alerts.--, in [0048]); and
perform processing for outputting a warning when the driver did not depress the accelerator 30 pedal before the predetermined time elapses from the time point when the display of the traffic light for the host vehicle changed from the red light to the blue light or the green light (see AGRAWAL: e.g., -- [0024] FIG. 11 illustrates examples of a Hard Braking alert combined with a detection of a green traffic light--, in [0024], Fig. 6, Fig. 8, in [0094]-[0097], and [0119]-[0120]; and, -- [0048] As an example of a family of combinations of driving alerts that have been identified, a family of driving alerts may be related in that each combination includes a traffic sign or a traffic light. In this example, modifying co-occurring events may be distracted driving in combination with a traffic light violation; distracted driving in combination with a stop sign violation; speeding in combination with a traffic light violation (which may tend to occur on major suburban roads); and hard turning violations combined with an otherwise compliant traffic light event (which may occur when a driver makes a left turn guarded by a green or yellow arrow, but does so in a way that may be unsafe and/or cause excess wear to a vehicle. In this example, by virtue of relating these various combination alerts, a driver may be instructed in ways to improve safety around intersections, based on video data of the driver captured at a time when she was exposed to various heightened risks. Such feedback may be more effective at changing a driver's behavior than would be similar time spent on intersection violations that are associated with average risk (no modification by a co-occurring event) or un-clustered combination alerts.--, in [0048]).
Re Claim 3, KITAHARA as modified by SUGIURA and AGRAWAL further disclose the host vehicle comprising the traffic light identification device for the host vehicle according to claim 1 and a communication device, wherein
the communication device is configured to send data of the front camera image and information showing the traffic light for the host vehicle included in the front camera image identified by the processor of the traffic light identification device for the host vehicle to a server device (see AGRAWAL: e.g., -- [0062] The example illustrated in FIG. 3 is also of a type that may be simply communicated to drivers in the context of a coaching program. Many drivers would understand the logic that the risk of collision with a vehicle from cross traffic is effectively only likely at intersections of roads or driveways. Crossing through an intersection, therefore, is one of the riskiest times for a driver to be distracted from the attentional demands of driving. Likewise, a driver would understand and appreciate that distracted driving events in which the driver crossed through an intersection without looking would be considered serious in nature even if they did not happen to result in a collision.--, in [0062]; and, --the determination of how environmental context may be used to modify a level of risk may be based on observed correlations between behaviors and frequency of accidents. For example, it may be determined that failing to come to a complete stop is only weakly predictive of a collision when considered in the aggregate, but that failing to come to a complete stop in urban settings in which there is not a clear line of sight to cross traffic is strongly predictive of collision risk. By associating different levels of risk with similar behaviors that occur in different environmental contexts, more of the collision-predictive events may be brought to the attention of an interested party, while events may be less strongly correlated with collision risk may be automatically ignored or deprioritized. Accordingly, the criteria for stop sign alerts may be effectively refined through the consideration of a select number of environmental factors. In some embodiments, these additional criteria may operate to modify the likelihood that recorded video associated with an alert is transmitted off the device and to a remote server via a cellular connection, WiFi connection, and the like.--, in [0071]);
the server device is configured to perform machine learning of a machine learning model for estimating a traffic light for an autonomous vehicle that is a traffic light which is included in an image captured by a front camera mounted on the autonomous vehicle and which the autonomous vehicle should obey (see AGRAWAL: -- Model Training Based on Combination Alerts
[0100] Certain aspects of the present disclosure may be directed to training a machine learning model, such as a neural network model, based at least in part on examples of combination alerts. In one example, combination alerts for which a detected Hard Braking event is preceded by Driver Distraction may be used to train a model to learn to predict when control of the vehicle should be taken from the driver. Such a model may learn to detect patterns of complex relationships between detectable elements, such as the elements identified in reference to FIGS. 5A and 5B above (slowing traffic in an adjacent lane, a construction barrel, momentary redirection of gaze, and the like), which together may indicate that the driver is failing to respond appropriately to a developing unsafe situation. Such situations, if detected, may correspond to avoidable collisions if hard braking is immediately applied.
[0101] Likewise, in accordance with certain aspect of the present disclosure, a machine learning model may be trained to identify combinations of events that precede a detected Hard Braking alert, but in which the driver is determined to be attentive to the road. In this way, the model may be trained to detect a variety of circumstances that may be surprising, even to an alert driver. In such cases, an enabled system may prime an evasive maneuver so that when the driver responds to the situation, as expected, the evasive maneuver may be more likely to result in a successfully avoided collision.--, in [0100]-[0101]);
he data of the front camera image sent by the communication device and the information showing the traffic light for the host vehicle included in the front camera image identified by the processor of the traffic light identification device for the host vehicle are used as correct answer data in the machine learning of the machine learning model (see AGRAWAL: -- Model Training Based on Combination Alerts
[0100] Certain aspects of the present disclosure may be directed to training a machine learning model, such as a neural network model, based at least in part on examples of combination alerts. In one example, combination alerts for which a detected Hard Braking event is preceded by Driver Distraction may be used to train a model to learn to predict when control of the vehicle should be taken from the driver. Such a model may learn to detect patterns of complex relationships between detectable elements, such as the elements identified in reference to FIGS. 5A and 5B above (slowing traffic in an adjacent lane, a construction barrel, momentary redirection of gaze, and the like), which together may indicate that the driver is failing to respond appropriately to a developing unsafe situation. Such situations, if detected, may correspond to avoidable collisions if hard braking is immediately applied.
[0101] Likewise, in accordance with certain aspect of the present disclosure, a machine learning model may be trained to identify combinations of events that precede a detected Hard Braking alert, but in which the driver is determined to be attentive to the road. In this way, the model may be trained to detect a variety of circumstances that may be surprising, even to an alert driver. In such cases, an enabled system may prime an evasive maneuver so that when the driver responds to the situation, as expected, the evasive maneuver may be more likely to result in a successfully avoided collision.--, in [0100]-[0101]).
Re Claim 4, claim 4 is the corresponding method claim to claim 1 respectively. Thus, claim 4 is rejected for the similar reasons as for claim 1. Furthermore, KITAHARA as modified by SUGIURA and AGRAWAL further disclose a traffic light identification method for a host vehicle (see KITAHARA : e.g., --an operation assistance (ECU 20) acquires, from a front camera, as an observed light position, the position of a traffic signal candidate which is a light device for which the probability of being a traffic signal is at least a prescribed value. The operation assistance (ECU 20) also acquires, as a map information traffic signal position, the position of a traffic signal which exists in front of a host vehicle, by referencing map data around a current position of the host vehicle. The operation assistance (ECU 20) then determines that the light device is a traffic signal on the basis of a degree of proximity (δ) being less than a prescribed presumed threshold (Dth), the degree of proximity being the difference between the traffic signal position on the map and the position of the observed light device.--, in abstract).
Re Claim 5, claim 5 is the corresponding medium claim to claim 1 respectively. Thus, claim 5 is rejected for the similar reasons as for claim 1. Furthermore, KITAHARA as modified by SUGIURA and AGRAWAL further disclose non-transitory recording medium having recorded thereon a computer program for causing a processor to execute a process (see KITAHARA : e.g., --an operation assistance (ECU 20) acquires, from a front camera, as an observed light position, the position of a traffic signal candidate which is a light device for which the probability of being a traffic signal is at least a prescribed value. The operation assistance (ECU 20) also acquires, as a map information traffic signal position, the position of a traffic signal which exists in front of a host vehicle, by referencing map data around a current position of the host vehicle. The operation assistance (ECU 20) then determines that the light device is a traffic signal on the basis of a degree of proximity (δ) being less than a prescribed presumed threshold (Dth), the degree of proximity being the difference between the traffic signal position on the map and the position of the observed light device.--, in abstract; also see AGRAWAL: e.g., -- a computer program product for performing the operations presented herein. For example, such a computer program product may comprise a computer-readable medium having instructions stored (and/or encoded) thereon, the instructions being executable by one or more processors to perform the operations described herein--, in [0174]-[0175]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEI WEN YANG whose telephone number is (571)270-5670. The examiner can normally be reached on 8:00 - 5:00 pm.
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
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