DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 26-27, 29 and 34-44 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Kim US 20210319694.
Regarding claim 26, Kim discloses A system for controlling an infrastructure item located proximate to a crossing, the system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform actions comprising: receiving environmental data, the environmental data capturing behavior of a crossing user, (Kim US 20210319694 abstract; paragraphs [0018]-[0022]; [0047]; [0056]; [0058]-[0069]; [0073]-[0078]; [0080]; [0094]; [0102]-[0104]; [0111]-[0115]; figures 1-4)
In this case, the traffic light controller 100 receives position information from the pedestrian terminal 200, photographs a surrounding area of the crosswalk using a camera, or receives information about a pedestrian from the vehicle 300 around the crosswalk (Kim par. 64). In this case, the traffic light controller 100 receives current walking speed information acquired using a sensor of the waiting vehicle 300 or receives the current walking speed information acquired using a camera disposed at an intersection (S120) and determines whether the lighting maintenance time of the green light needs to be changed in consideration of complexity of the crosswalk through pedestrian detection (S125) (Kim par. 69).
According to the cited passages and figures, examiner interprets traffic light as the infrastructure that received pedestrian maneuver information like walking speed information from the camera (data capturing behavior of a crossing user).
determining an intent of the crossing user based on the environmental data, and changing a setting of the infrastructure item based on the intent of the crossing user.
The processor may analyze information of pedestrians positioned in a surrounding area of the crosswalk, may determine a group of the pedestrians, who are subject to cross for a corresponding lighting time, and may set the lighting maintenance time in consideration of a minimum value among pieces of walking speed information of the pedestrians who are subject to cross (Kim par. 22). For example, when three pedestrians at the crosswalk are the pedestrians who are subject to cross, one of the pedestrians is an elderly person, and as a result of consideration of average walking speed information of the elderly person, when it is expected that the elderly person takes 50 seconds to cross the crosswalk, the traffic light controller 100 sets the lighting maintenance time of the green light to 60 seconds including a time allowance (Kim par. 67).
According to the cited passages and figure, examiner interprets setting a crossing time from 50 seconds to 60 second at the changing in setting of the infrastructure based on the pedestrian crossing a road.
Regarding claim 27, Kim discloses The system of claim 26, wherein receiving the environmental data comprises receiving route data from a mobile device of the crossing user.
In addition, when a pedestrian crosses, the traffic light controller 100 acquires movement trajectory information using the pedestrian terminal 200, the vehicle 300, and a camera disposed at the crosswalk, and accordingly, when the pedestrian needs to be selected as the pedestrian to be warned, as described above, the traffic light controller 100 selects the pedestrian to be warned, provides a warning, and shares information (Kim par. 80).
According to the cited passages and figures, examiner interprets the pedestrian terminal 200 as the mobile device and movement trajectory information as the route data.
Regarding claim 29, Kim discloses The system of claim 26, wherein receiving the environmental data comprises receiving images of the crossing user from one or more cameras located proximate the crossing.
In this case, the traffic light controller 100 receives position information from the pedestrian terminal 200, photographs a surrounding area of the crosswalk using a camera, or receives information about a pedestrian from the vehicle 300 around the crosswalk (Kim par. 64).
According to the cited passages and figures, examiner interprets photographs as the images.
Regarding claim 34, Kim discloses The system of claim 26, wherein changing the setting of the infrastructure item comprises transmitting a command to at least one of a traffic signal and a crosswalk signal.
The processor may analyze information of pedestrians positioned in a surrounding area of the crosswalk, may determine a group of the pedestrians, who are subject to cross for a corresponding lighting time, and may set the lighting maintenance time in consideration of a minimum value among pieces of walking speed information of the pedestrians who are subject to cross (Kim par. 22). For example, when three pedestrians at the crosswalk are the pedestrians who are subject to cross, one of the pedestrians is an elderly person, and as a result of consideration of average walking speed information of the elderly person, when it is expected that the elderly person takes 50 seconds to cross the crosswalk, the traffic light controller 100 sets the lighting maintenance time of the green light to 60 seconds including a time allowance (Kim par. 67). In this case, the traffic light controller 100 uses the current walking speed information to change the lighting maintenance time of the green light of the pedestrian traffic light 410 from 30 seconds to 50 seconds and transmits information about a change in the lighting maintenance time of the green light, information about the remaining lighting time, and the like to the vehicle 300 (Kim par. 73) In addition, the traffic light controller 100 controls the vehicle traffic light 420 according to the change in the lighting maintenance time of the green light of the pedestrian traffic light 410 (Kim par. 74).
According to the cited passages and figures, examiner interprets the system sent at least once command to set the pedestrian traffic light.
Regarding claim 35, Kim discloses The system of claim 26, wherein changing the setting of the infrastructure item comprises changing a phase of at least one of a traffic signal and a crosswalk signal.
In this case, the traffic light controller 100 uses the current walking speed information to change the lighting maintenance time of the green light of the pedestrian traffic light 410 from 30 seconds to 50 seconds and transmits information about a change in the lighting maintenance time of the green light, information about the remaining lighting time, and the like to the vehicle 300 (Kim par. 73) In addition, the traffic light controller 100 controls the vehicle traffic light 420 according to the change in the lighting maintenance time of the green light of the pedestrian traffic light 410 (Kim par. 74).
According to the cited passages and figures, examiner interprets vehicle traffic light 420 (traffic signal) and pedestrian traffic light 410 (crosswalk signal). Examiner interpret the changing in the duration of the pedestrian traffic light would also affect the changing in the duration of the vehicle traffic light.
Regarding claim 36, Kim discloses The system of claim 26, wherein changing the setting of the infrastructure item comprises changing a duration of a lighting phase.
In this case, the traffic light controller 100 uses the current walking speed information to change the lighting maintenance time of the green light of the pedestrian traffic light 410 from 30 seconds to 50 seconds and transmits information about a change in the lighting maintenance time of the green light, information about the remaining lighting time, and the like to the vehicle 300 (Kim par. 73) In addition, the traffic light controller 100 controls the vehicle traffic light 420 according to the change in the lighting maintenance time of the green light of the pedestrian traffic light 410 (Kim par. 74).
According to the cited passages and figures, examiner interprets vehicle traffic light 420 (traffic signal) and pedestrian traffic light 410 (crosswalk signal). Examiner interpret the changing in the duration of the pedestrian traffic light would also affect the changing in the duration of the vehicle traffic light.
Regarding claim 37, Kim discloses The system of claim 26, wherein the actions further comprise transmitting a message to at least one of a mobile device and a wearable device of the crossing user.
The pedestrian terminal 200 is a smart phone or a wearable device having a communication function and shares the walking speed information through a service application (Kim par. 56). The traffic light controller 100 provides a warning message to the corresponding pedestrian to be warned through a sound alarm or the like or transmits a warning to the terminal 200 of the pedestrian to be warned and transmits information about the pedestrian to be warned to the vehicle 300 (S315) (Kim par. 78).
Regarding claim 38, Kim discloses The system of claim 26, wherein the actions further comprise: transmitting a message to one or more vehicles operating proximate the crossing;
The processor may transmit information about the pedestrian to be warned to surrounding vehicles (Kim par. 21). The method may further include operation (e) of transmitting information about the pedestrian to be warned to surrounding vehicles (Kim par. 31). The traffic light controller 100 provides a warning message to the corresponding pedestrian to be warned through a sound alarm or the like or transmits a warning to the terminal 200 of the pedestrian to be warned and transmits information about the pedestrian to be warned to the vehicle 300 (S315) (Kim par. 78).
and receiving an acknowledgement from each of the one or more vehicles in response to receiving the message.
The vehicle 300 receiving the information about the pedestrian to be warned may provide a warning to the pedestrian to be warned through a headlight or klaxon and may perform autonomous driving to prevent a collision with the pedestrian in consideration of the information about the pedestrian to be warned or provide a collision avoidance warning to a driver (Kim par. 79).
According to the cited passages and figures, examiner interpret the vehicle warn the pedestrian through a headlight as the acknowledgement from the warning message about pedestrian information.
Regarding claim 39, Kim discloses The system of claim 26, wherein the system is a component of the infrastructure item.
Meanwhile, the method of controlling a traffic light according to the embodiment of the present invention may be implemented in a computer system or recorded in a recording medium. The computer system may include at least one processor, a memory, a user input device, a data communication bus, a user output device, and a storage. Each of the above-described components performs data communication through the data communication bus (Kim par. 111).
Regarding claim 40, Kim discloses The system of claim 26, wherein the infrastructure item is a traffic signal, a crosswalk signal, or a roadside unit.
In this case, the traffic light controller 100 receives position information from the pedestrian terminal 200, photographs a surrounding area of the crosswalk using a camera, or receives information about a pedestrian from the vehicle 300 around the crosswalk (Kim par. 64). In this case, the traffic light controller 100 receives current walking speed information acquired using a sensor of the waiting vehicle 300 or receives the current walking speed information acquired using a camera disposed at an intersection (S120) and determines whether the lighting maintenance time of the green light needs to be changed in consideration of complexity of the crosswalk through pedestrian detection (S125) (Kim par. 69). Therefore, when it is determined that a waiting vehicle cannot recognize the vehicle traffic light (for example, when the waiting vehicle stops beyond a stop line, a driver of the waiting vehicle cannot visually recognize the vehicle traffic light and can recognize only the pedestrian traffic light), the traffic light controller 100 does not perform a process of changing the pedestrian traffic light 410 to the “red light” as described above and maintains turning-on of the green light of the pedestrian traffic light 410 and also maintains the vehicle traffic light 420 as a “red light” until the crossing is completed (Kim par. 102)
According to the cited passages and figures, examiner interprets pedestrian traffic light 410 and vehicle traffic light 420 as the infrastructure item.
Regarding claim 41, Kim discloses An infrastructure item located proximate a crossing, the infrastructure item comprising: a traffic light configured to direct a flow of traffic proximate the crossing; at least one processor in electrical communication with the traffic light; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform actions comprising: receiving environmental data, the environmental data capturing behavior of a crossing user, (Kim US 20210319694 abstract; paragraphs [0018]-[0022]; [0047]; [0056]; [0058]-[0069]; [0073]-[0078]; [0080]; [0094]; [0102]-[0104]; [0111]-[0115]; figures 1-4)
In this case, the traffic light controller 100 receives position information from the pedestrian terminal 200, photographs a surrounding area of the crosswalk using a camera, or receives information about a pedestrian from the vehicle 300 around the crosswalk (Kim par. 64). In this case, the traffic light controller 100 receives current walking speed information acquired using a sensor of the waiting vehicle 300 or receives the current walking speed information acquired using a camera disposed at an intersection (S120) and determines whether the lighting maintenance time of the green light needs to be changed in consideration of complexity of the crosswalk through pedestrian detection (S125) (Kim par. 69).
According to the cited passages and figures, examiner interprets traffic light as the infrastructure that received pedestrian maneuver information like walking speed information from the camera (data capturing behavior of a crossing user).
determining an intent of the crossing user based on the environmental data, and changing the traffic light from a first state to a second state based on the intent of the crossing user.
The processor may analyze information of pedestrians positioned in a surrounding area of the crosswalk, may determine a group of the pedestrians, who are subject to cross for a corresponding lighting time, and may set the lighting maintenance time in consideration of a minimum value among pieces of walking speed information of the pedestrians who are subject to cross (Kim par. 22). In this case, the traffic light controller 100 uses the current walking speed information to change the lighting maintenance time of the green light of the pedestrian traffic light 410 from 30 seconds to 50 seconds and transmits information about a change in the lighting maintenance time of the green light, information about the remaining lighting time, and the like to the vehicle 300. (Kim par. 73). In addition, the traffic light controller 100 controls the vehicle traffic light 420 according to the change in the lighting maintenance time of the green light of the pedestrian traffic light 410 (Kim par. 74). In this case, when the pedestrian traffic light 410 is changed to the red light, a driver of a vehicle starts the vehicle by predicting that the vehicle traffic light 420 has changed to the green light. Thus, the vehicle may collide with a pedestrian who has not yet completed the crossing (Kim par. 101). Therefore, when it is determined that a waiting vehicle cannot recognize the vehicle traffic light (for example, when the waiting vehicle stops beyond a stop line, a driver of the waiting vehicle cannot visually recognize the vehicle traffic light and can recognize only the pedestrian traffic light), the traffic light controller 100 does not perform a process of changing the pedestrian traffic light 410 to the “red light” as described above and maintains turning-on of the green light of the pedestrian traffic light 410 and also maintains the vehicle traffic light 420 as a “red light” until the crossing is completed (Kim par. 102).
According to the cited passages and figure, examiner interprets setting a crossing time from 30 seconds to 50 second at the changing in setting of the infrastructure based on the pedestrian crossing a road. Examiner interprets the pedestrian light will go from red (a first state) to green (a second state) when determine the pedestrian at the intersection want to cross. In the same time the vehicle traffic light will go from green (a first state) to red (a second state) for stopping all the vehicle for pedestrian crossing.
Regarding claim 42, Kim discloses The infrastructure item of claim 41, wherein the first state comprises a red phase and the second state comprises a green phase.
The processor may analyze information of pedestrians positioned in a surrounding area of the crosswalk, may determine a group of the pedestrians, who are subject to cross for a corresponding lighting time, and may set the lighting maintenance time in consideration of a minimum value among pieces of walking speed information of the pedestrians who are subject to cross (Kim par. 22). In this case, the traffic light controller 100 uses the current walking speed information to change the lighting maintenance time of the green light of the pedestrian traffic light 410 from 30 seconds to 50 seconds and transmits information about a change in the lighting maintenance time of the green light, information about the remaining lighting time, and the like to the vehicle 300. (Kim par. 73). In addition, the traffic light controller 100 controls the vehicle traffic light 420 according to the change in the lighting maintenance time of the green light of the pedestrian traffic light 410 (Kim par. 74). In this case, when the pedestrian traffic light 410 is changed to the red light, a driver of a vehicle starts the vehicle by predicting that the vehicle traffic light 420 has changed to the green light. Thus, the vehicle may collide with a pedestrian who has not yet completed the crossing (Kim par. 101). Therefore, when it is determined that a waiting vehicle cannot recognize the vehicle traffic light (for example, when the waiting vehicle stops beyond a stop line, a driver of the waiting vehicle cannot visually recognize the vehicle traffic light and can recognize only the pedestrian traffic light), the traffic light controller 100 does not perform a process of changing the pedestrian traffic light 410 to the “red light” as described above and maintains turning-on of the green light of the pedestrian traffic light 410 and also maintains the vehicle traffic light 420 as a “red light” until the crossing is completed (Kim par. 102).
According to the cited passages and figure, examiner interprets setting a crossing time from 30 seconds to 50 second at the changing in setting of the infrastructure based on the pedestrian crossing a road. Examiner interprets the pedestrian light will go from red (a first state) to green (a second state) when determine the pedestrian at the intersection want to cross. In the same time the vehicle traffic light will go from green (a first state) to red (a second state) for stopping all the vehicle for pedestrian crossing.
Regarding claim 43, Kim discloses The infrastructure item of claim 41, wherein the first state comprises a green phase and the second state comprises a red phase.
The processor may analyze information of pedestrians positioned in a surrounding area of the crosswalk, may determine a group of the pedestrians, who are subject to cross for a corresponding lighting time, and may set the lighting maintenance time in consideration of a minimum value among pieces of walking speed information of the pedestrians who are subject to cross (Kim par. 22). In this case, the traffic light controller 100 uses the current walking speed information to change the lighting maintenance time of the green light of the pedestrian traffic light 410 from 30 seconds to 50 seconds and transmits information about a change in the lighting maintenance time of the green light, information about the remaining lighting time, and the like to the vehicle 300. (Kim par. 73). In addition, the traffic light controller 100 controls the vehicle traffic light 420 according to the change in the lighting maintenance time of the green light of the pedestrian traffic light 410 (Kim par. 74). In this case, when the pedestrian traffic light 410 is changed to the red light, a driver of a vehicle starts the vehicle by predicting that the vehicle traffic light 420 has changed to the green light. Thus, the vehicle may collide with a pedestrian who has not yet completed the crossing (Kim par. 101). Therefore, when it is determined that a waiting vehicle cannot recognize the vehicle traffic light (for example, when the waiting vehicle stops beyond a stop line, a driver of the waiting vehicle cannot visually recognize the vehicle traffic light and can recognize only the pedestrian traffic light), the traffic light controller 100 does not perform a process of changing the pedestrian traffic light 410 to the “red light” as described above and maintains turning-on of the green light of the pedestrian traffic light 410 and also maintains the vehicle traffic light 420 as a “red light” until the crossing is completed (Kim par. 102).
According to the cited passages and figure, examiner interprets setting a crossing time from 30 seconds to 50 second at the changing in setting of the infrastructure based on the pedestrian crossing a road. Examiner interprets the pedestrian light will go from red (a first state) to green (a second state) when determine the pedestrian at the intersection want to cross. In the same time the vehicle traffic light will go from green (a first state) to red (a second state) for stopping all the vehicle for pedestrian crossing.
Regarding claim 44, Kim discloses The infrastructure item of claim 41, wherein receiving the environmental data comprises receiving images of the crossing user from one or more cameras located proximate the crossing.
In this case, the traffic light controller 100 receives position information from the pedestrian terminal 200, photographs a surrounding area of the crosswalk using a camera, or receives information about a pedestrian from the vehicle 300 around the crosswalk (Kim par. 64). In this case, the traffic light controller 100 receives current walking speed information acquired using a sensor of the waiting vehicle 300 or receives the current walking speed information acquired using a camera disposed at an intersection (S120) and determines whether the lighting maintenance time of the green light needs to be changed in consideration of complexity of the crosswalk through pedestrian detection (S125) (Kim par. 69).
According to the cited passages and figures, examiner interprets traffic light as the infrastructure that received pedestrian maneuver information like walking speed information from the camera (data capturing behavior of a crossing user).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 28, 32-33 and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Kim US 20210319694 in view of Gwin et al. US 20190126921.
Regarding claim 28, Kim teaches all the limitation in the claim 26.
Kim does not explicitly teaches The system of claim 26, wherein receiving the environmental data comprises receiving a projected course of the crossing user.
Gwin et al. teach The system of claim 26, wherein receiving the environmental data comprises receiving a projected course of the crossing user. (Gwin et al. US 20190126921 abstract; paragraphs [0016]-[0019]; [0026]-[0032]; [0034]-[0038]; [0053]-[0062]; [0094]-[0095]; [0104]-[0108]; figures 1-12)
In various embodiments, the technology further includes at least one computer-readable medium (CRM) having instructions stored therein, to cause a computing device, in response to execution of the instruction by the computing device, to: receive, from a personal system of a pedestrian or a bicyclist, sensor data collected by sensors of the personal system for routes or paths traveled by the pedestrian or bicyclist; store the received sensor data collected for routes or paths traveled by the pedestrian or bicyclist; generate a current intended or projected path of the pedestrian or bicyclist, based at least in part on the stored sensor data for routes or paths previously traveled by the pedestrian or bicyclist; and output the generated current intended or projected path of the pedestrian or bicyclist to assist a computer assisted or autonomous driving (CA/AD) vehicle in responding to detection of the pedestrian or bicyclist proximally moving near the CA/AD vehicle (Gwin et al. par. 18). At block 706, a determination to respond to the detection or observance of the nearby travelers are made. As described earlier, the navigation subsystem of the CA/AD vehicle may be provided with machine learning trained to make the determination factoring into consideration the intended or projected paths of the nearby travelers received. For example, a no response may be determined if the traveler is detected or observed within certain threshold or confidence boundaries, and the response might be progressive relative to the degree the traveler is detected or observed outside certain threshold or confidence boundaries. In various embodiments, operations at 706 may also include providing feedback to the navigation subsystem with machine learning. An example neural network used by the navigation subsystem will be further described below with references to FIG. 9 (Gwin et al. par. 62).
According to the cited passages and figures, examiner interprets the projected path of the pedestrian as the projected course like illustrate in the figures 2, the travel 272 as the pedestrian with the projected path 204 in the proximity with the vehicle 252.
Therefore, it would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Kim by apply a learning model that are trained to make the determination factoring into consideration the intended or projected paths of the nearby travelers received as taught by Gwin et al. reference in order to avoid collision and improve traffic safety.
Regarding claim 32, the combination of Kim and Gwin et al. disclose The system of claim 26, wherein determining the intent of the crossing user comprises determining a projected course of the crossing user based on the environmental data.
In various embodiments, the technology further includes at least one computer-readable medium (CRM) having instructions stored therein, to cause a computing device, in response to execution of the instruction by the computing device, to: receive, from a personal system of a pedestrian or a bicyclist, sensor data collected by sensors of the personal system for routes or paths traveled by the pedestrian or bicyclist; store the received sensor data collected for routes or paths traveled by the pedestrian or bicyclist; generate a current intended or projected path of the pedestrian or bicyclist, based at least in part on the stored sensor data for routes or paths previously traveled by the pedestrian or bicyclist; and output the generated current intended or projected path of the pedestrian or bicyclist to assist a computer assisted or autonomous driving (CA/AD) vehicle in responding to detection of the pedestrian or bicyclist proximally moving near the CA/AD vehicle (Gwin et al. par. 18). At block 706, a determination to respond to the detection or observance of the nearby travelers are made. As described earlier, the navigation subsystem of the CA/AD vehicle may be provided with machine learning trained to make the determination factoring into consideration the intended or projected paths of the nearby travelers received. For example, a no response may be determined if the traveler is detected or observed within certain threshold or confidence boundaries, and the response might be progressive relative to the degree the traveler is detected or observed outside certain threshold or confidence boundaries. In various embodiments, operations at 706 may also include providing feedback to the navigation subsystem with machine learning. An example neural network used by the navigation subsystem will be further described below with references to FIG. 9 (Gwin et al. par. 62).
According to the cited passages and figures, examiner interprets the projected path of the pedestrian as the projected course like illustrate in the figures 2, the travel 272 as the pedestrian with the projected path 204 in the proximity with the vehicle 252.
Regarding claim 33, the combination of Kim and Gwin et al. disclose The system of claim 26, wherein the environmental data includes images of the crossing user, and wherein determining the intent of the crossing user comprises determining a projected course of the crossing user using object tracking within the images of the crossing user.
In various embodiments, the technology further includes at least one computer-readable medium (CRM) having instructions stored therein, to cause a computing device, in response to execution of the instruction by the computing device, to: receive, from a personal system of a pedestrian or a bicyclist, sensor data collected by sensors of the personal system for routes or paths traveled by the pedestrian or bicyclist; store the received sensor data collected for routes or paths traveled by the pedestrian or bicyclist; generate a current intended or projected path of the pedestrian or bicyclist, based at least in part on the stored sensor data for routes or paths previously traveled by the pedestrian or bicyclist; and output the generated current intended or projected path of the pedestrian or bicyclist to assist a computer assisted or autonomous driving (CA/AD) vehicle in responding to detection of the pedestrian or bicyclist proximally moving near the CA/AD vehicle (Gwin et al. par. 18). Sensors 110 include in particular one or more cameras (not shown) to capture images of surrounding area 80 of CA/AD vehicles 52 (Gwin et al. par. 30). At block 706, a determination to respond to the detection or observance of the nearby travelers are made. As described earlier, the navigation subsystem of the CA/AD vehicle may be provided with machine learning trained to make the determination factoring into consideration the intended or projected paths of the nearby travelers received. For example, a no response may be determined if the traveler is detected or observed within certain threshold or confidence boundaries, and the response might be progressive relative to the degree the traveler is detected or observed outside certain threshold or confidence boundaries. In various embodiments, operations at 706 may also include providing feedback to the navigation subsystem with machine learning. An example neural network used by the navigation subsystem will be further described below with references to FIG. 9 (Gwin et al. par. 62).
According to the cited passages and figures, examiner interprets the projected path of the pedestrian as the projected course like illustrate in the figures 2, the travel 272 as the pedestrian with the projected path 204 in the proximity with the vehicle 252.
Regarding claim 45, the combination of Kim and Gwin et al. disclose The infrastructure item of claim 41, wherein the environmental data includes images of the crossing user, and wherein determining the intent of the crossing user comprises determining a projected course of the crossing user using object tracking within the images of the crossing user.
In various embodiments, the technology further includes at least one computer-readable medium (CRM) having instructions stored therein, to cause a computing device, in response to execution of the instruction by the computing device, to: receive, from a personal system of a pedestrian or a bicyclist, sensor data collected by sensors of the personal system for routes or paths traveled by the pedestrian or bicyclist; store the received sensor data collected for routes or paths traveled by the pedestrian or bicyclist; generate a current intended or projected path of the pedestrian or bicyclist, based at least in part on the stored sensor data for routes or paths previously traveled by the pedestrian or bicyclist; and output the generated current intended or projected path of the pedestrian or bicyclist to assist a computer assisted or autonomous driving (CA/AD) vehicle in responding to detection of the pedestrian or bicyclist proximally moving near the CA/AD vehicle (Gwin et al. par. 18). Sensors 110 include in particular one or more cameras (not shown) to capture images of surrounding area 80 of CA/AD vehicles 52 (Gwin et al. par. 30). At block 706, a determination to respond to the detection or observance of the nearby travelers are made. As described earlier, the navigation subsystem of the CA/AD vehicle may be provided with machine learning trained to make the determination factoring into consideration the intended or projected paths of the nearby travelers received. For example, a no response may be determined if the traveler is detected or observed within certain threshold or confidence boundaries, and the response might be progressive relative to the degree the traveler is detected or observed outside certain threshold or confidence boundaries. In various embodiments, operations at 706 may also include providing feedback to the navigation subsystem with machine learning. An example neural network used by the navigation subsystem will be further described below with references to FIG. 9 (Gwin et al. par. 62).
According to the cited passages and figures, examiner interprets the projected path of the pedestrian as the projected course like illustrate in the figures 2, the travel 272 as the pedestrian with the projected path 204 in the proximity with the vehicle 252.
Claims 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Kim US 20210319694 in view of Rothschild et al. US 12243420.
Regarding claim 30, Kim teaches all the limitation in the claim 26.
Kim does not explicitly teaches The system of claim 26, wherein receiving the environmental data comprises receiving telemetry data from a modality of transportation operated by the crossing user.
Rothschild et al. teach The system of claim 26, wherein receiving the environmental data comprises receiving telemetry data from a modality of transportation operated by the crossing user. (Rothschild et al. US 12243420 abstract; col. 8 lines 18-38, 53-67; col. 9 lines 1-23; col. 17 lines 10-46; col. 18 lines 6-39 figures 1-11)
As previously mentioned, in addition to the vehicles 118a,b, various other telemetry consumers 106a-d may be configured to receive feeds of geo-converged telemetry data 122. For example, various traffic control systems, such as traffic lights, pedestrian crossing signals, swing lane controllers, etc., may receive relevant geo-converged telemetry data 122, and use the received data for controlling traffic signals. For example, a decision to control a traffic signal based on geo-converged telemetry data 122 may be associated with optimizing vehicle safety and/or traffic flow, increasing pedestrian traffic safety and/or traffic flow, avoiding hazards, etc. Depending on configuration settings, traffic flow may be optimized for a particular type of traffic (e.g., pedestrian traffic versus vehicular traffic), or in some example aspects, for particular individual vehicles 118 or other telemetry consumers 106. For example, based on geo-converged telemetry data 122, a traffic control system (other telemetry consumer 106) may be able to know that no pedestrians are sensed near an intersection, and thus can keep pedestrian cross walks set to “no crossing” to optimize vehicular traffic flow until one or more pedestrians are detected at or near the intersection. The traffic control system may be configured to optimize pedestrian traffic such that when a pedestrian is detected in an area, the traffic control system may adjust the timing of traffic control signals to stop vehicular traffic and provide a “walk” signal to pedestrians to allow the detected pedestrian to cross the intersection without having to stop and wait for the “walk” signal. Additionally, geo-converged telemetry data 122 may be provided to vehicles 118 approaching the intersection that may include known information about the presence of pedestrians and the timing of the traffic control signal, enabling a vehicle to adjust its speed such that the vehicle may not to come to a complete stop or to adjust its speed such that the vehicle can safely come to a stop for allowing the pedestrians to cross (Rothschild et al. col. 17 lines 10-46).
Therefore, it would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Kim by apply a telemetry data share within a traffic control system as taught by Rothschild et al. reference in order to improve traffic safety.
Regarding claim 31, the combination of Kim and Rothschild et al. disclose The system of claim 26, wherein receiving the environmental data comprises receiving telemetry data from one or more vehicles operating in a roadway proximate the crossing.
In example aspects, telemetry data 120 may be pushed by a vehicle 118 or another telemetry source 112 to a GCT processing node 110. For example, a vehicle 118 or another telemetry source 112 may transmit telemetry data 120 to a GCT processing node 110 continually, when the data are sensed or collected (e.g., in real time or near-real time), or based on an event threshold. As described above, the telemetry data 120 may be communicated to a GCT processing node 110 via a network node 104 via which the vehicle 118 or another telemetry source 112 is connected. Depending on how a telemetry source 112 is configured, telemetry data 120 may be collected by the telemetry source 112 periodically or responsive to a triggering event (e.g., when motion is sensed, when an object is detected) (Rothschild et al. col. 8 lines 53-66). See figure 4 one or more of vehicle 118 transmit the telemetry data to the node 110.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THANG D TRAN whose telephone number is (408)918-7546. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm (pacific time).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian A Zimmerman can be reached at 571-272-3059. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/THANG D TRAN/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686
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