DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s amendments to the independent claims have overcome the rejections under 35 U.S.C. 101. The rejections under 101 have been withdrawn.
Applicant's arguments filed 10/30/2025 have been fully considered but they are not persuasive. Applicant asserts that during the interview conducted on 9/25/2025, Examiner agreed that amendments substantially similar to the current amendments to independent claims 1, 11, and 16 overcome the rejections under prior art. However, the subject matter which has been incorporated into the independent claims is not the subject matter that was indicated as allowable. Applicant has incorporated only subject matter which was already rejected in the previous office action, so the claims stand rejected.
On page 12, Applicant states that claim 1 has been amended to include features of claim 3 and certain features of claim 4. Examiner recognizes that features of claim 3 have been included in currently amended claim 1, but there does not appear to be any subject matter from claim 4 incorporated into claim 1. The subject matter from original claim 7 has been incorporated into claim 1, but Examiner indicated during the telephone interview that this proposed amendment remained unpatentable in view of the prior art made of record. Examiner notes that analogous amendments were made for claims 11 and 16.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 8, 10-11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nagano (JP 2020166702 A) in view of Eoh (US 11614747 B2) and Yoo (US 20190227545 A1).
Claim 1
Nagano teaches
at least one memory storing at least one instruction;
(Nagano - [p.18, para. 1] The memory 14b is a volatile storage device that stores a computer program executed by the microcomputer 14a. The memory 14b can also be used as a work memory when the microcomputer 14a and the Position estimation device 14e perform calculations.)
a sensor configured to detect an environment of the robot device and output detection data;
(Nagano - [p. 4, para. 1-3] … The automatic guided vehicle according to the embodiment of the present disclosure includes a self-position estimation device and can travel in a guideless manner.
The "self-position estimation device" is a device that estimates the self-position on the environment map based on the sensor data acquired by an external sensor such as a laser range finder.
The "outside world sensor" is a sensor that senses the external state of a moving body. External world sensors include, for example, a laser range finder (also called a range finder), a camera (or image sensor), a lidar (Light Detection and Ranging), a millimeter wave radar, and a magnetic sensor.)
(Nagano - [p.6, para. 5] The position estimation device 105 collates the sensor data output from the external world sensor 103 with the map data, and estimates the position and orientation of the moving body based on the collation result. The position estimation device 105 sequentially outputs information (referred to as “position information” in the present specification) indicating the estimated position and orientation of the moving body.)
and at least one processor configured to execute the at least one instruction
(Nagano - [p.15, para. 2] The travel control device 14 … includes an integrated circuit including a microcomputer (described later), electronic components, and a substrate on which they are mounted …)
EXAMINER NOTE: See fig. 7A. The position estimation device 14e (cited below) is included in the travel control device.
(Nagano - [p.20, para. 1] In the present embodiment, the microcomputer 14a and the position estimation device 14e are considered to be separate components, but this is an example. It may be one chip circuit or a semiconductor integrated circuit capable of independently performing each operation of the microcomputer 14a and the position estimation device 14e. FIG. 7A shows a chip circuit 14g including the microcomputer 14a and the position estimation device 14e.)
wherein the at least one instruction, when executed by the at least one processor individually or collectively, causes the robot device to:
acquire a map of a space where the robot device is positioned based on the detection data received from the sensor, and a reliability value of each of a plurality of areas of the map,
(Nagano - [p. 19, para. 4] The position estimation device 14e can perform a map creation process and a self-position estimation process during traveling. For example, the position estimation device 14e creates a map of the moving space S based on the position and orientation of the AGV 10 and the scan result of the laser range finder. During traveling, the position estimation device 14e receives the sensor data from the laser range finder 15 and reads out the map data M stored in the storage device 14c. By matching the local map data (sensor data) created from the scan results of the laser range finder 15 with the map data M in a wider range, the self-position (x, y, θ) on the map data M can be determined. The position estimation device 14e generates "reliability" data indicating the degree to which the local map data matches the map data M. ... )
store the map and the reliability value of each of the plurality of areas in the at least one memory,
(Nagano - [p.9, ln 1-2] … In step S22, the arithmetic circuit 207 stores the generated reliability map data in the storage device 203.)
identify at least one area having a reliability value greater than or equal to a critical value, based on the reliability value of each of the plurality of areas,
identify a movement path of the robot device in the space, based on the at least one area, and
(Nagano - [p. 10, para. 3] In step S30, the arithmetic circuit 207 of the operation management device 251 reads the reliability map 205b from the storage device 203. In step S32, the arithmetic circuit 207 refers to the reliability map data to determine a high reliability region whose reliability is equal to or higher than a predetermined reliability threshold. In step S34, the arithmetic circuit 207 generates a path passing through the high reliability region. In step S36, the arithmetic circuit 207 transmits the data of the route generated via the communication circuit 209.)
cause the robot device to move along the movement path,
(Nagano - [page 10, para. 4] In step S40, the controller 107 of the mobile body 101 receives the route data via the communication circuit 111. In step S42, the moving body 101 controls the driving device 109 and moves along the path.
…obtain an updated reliability value corresponding to the new position of the robot device, …
(Nagano - [p. 28, para.2] The CPU 51 periodically updates the reliability map 42, for example, by using the position information and the reliability received from the AGV 10. The reliability map 42 can be updated periodically as one or more AGVs 10 move in the moving space S.)
Nagano may not explicitly teach the following limitations in combination. However, Eoh teaches
wherein the at least one instruction, when executed by the at least one processor individually or collectively, further causes the robot device to:
acquire movement information of the robot device comprising
a movement direction of the robot device,
a movement distance of the robot device,
or a movement speed of the robot device
based on first detection data and second detection data that are consecutively received from the sensor,
(Eoh - [col 6, ln 62-64] The robot may calculate a distance moved by the robot on the basis of the odometry information as well as the information generated using the sensors.
[col 12, ln 39-46] Additionally, the controller 250 generates LiDAR odometry information using two LiDAR frames registered between the two nodes (S57). The LiDAR odometry information may include a differential value of the two LiDAR frames (each LiDAR frame registered in each node) between the two nodes, or differential values changed according to a distance between the two nodes may be stored as LiDAR odometry information.
[col 12, ln 47-54] Further, the controller 250 generates visual odometry information using two visual frames registered between the two nodes (S58). The visual odometry information may include a differential value of the two visual frames (each visual frame registered in each node) between the two nodes, or differential values changed according to a distance between the two nodes may be stored as visual odometry information.
[col 12, ln 47-54] Further, the controller 250 generates visual odometry information using two visual frames registered between the two nodes (S58). The visual odometry information may include a differential value of the two visual frames (each visual frame registered in each node) between the two nodes, or differential values changed according to a distance between the two nodes may be stored as visual odometry information.
[col 5, ln 65-67] That is, the camera sensor 230 photographs an object outside the robot and generates a visual frame including vision information.)
EXAMINER NOTE: The camera data and/or the LIDAR data qualify as detection data.
estimate a new position of the robot device corresponding to the second detection data, based on the movement information, and
(Eoh - [col 17, ln 17-22] The controller 250 calculates the current position of the robot by comparing wheel odometry information (WO) and a distance moved by the robot and sensed by the wheel encoder, and by comparing LiDAR odometry information (LO) or visual odometry information (VO) and LiDAR/visual frames sensed by the robot.)
Neither Nagano nor Eoh teaches the following limitations in the proposed combination. However, Yoo teaches
based on identifying an inability to maintain at least one of the movement direction of the robot device, the movement distance of the robot device, and the movement speed of the robot device, obtain a … reliability value corresponding to the new position of the robot device, wherein the … reliability value is less than the critical value.
(Yoo - [0077] According to an embodiment, when the map reliability is determined based on the danger degree of the current location, the device 100 may identify the danger degree (e.g., the road curvature, the accident danger, etc.) of the current location from the map information stored in the vehicle 110 and/or from the information sensed from the sensor of the vehicle 110. When it is determined that the danger degree of the current location is high, the map information may be determined to have a low map reliability.
[0093] In operation S720, when the map reliability is equal to or less than the threshold value, this indicates that that the accuracy or reliability of map information that the vehicle 110 is using is low. )
EXAMINER NOTE: The presence of a curve is an indication of an inability to maintain a movement direction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to apply the odometry techniques taught by Eoh to Nagano's robot in order to accurately identify the position of the robot.
(Eoh - [col 1, ln 56-58] Additionally, the present disclosure is to implement fusion SLAM in which the position of a robot is identified using various sensors in a space.
[col 3, ln 4-7] The present disclosure according to embodiments allows a robot to use a correlation while the robot moves on the basis of a map comprised of various sensors and to accurately calculate the current position of the robot.)
Nagano teaches the updating of reliability information as the AGV moves throughout the environment. Yoo teaches a beneficial measure of calculating reliability based on dangers such as curves, which would force a direction change. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to further modify Nagano’s robot by implementing Yoo’s suggestion to calculate reliability based on the presence of dangers in order to improve the functioning of the robot.
(Yoo - [0036] … The map information with improved map reliability as provided according to various embodiments of the present disclosure may directly improve the functioning of the autonomous vehicle, e.g., by directly improving the location estimation performance of the autonomous vehicle.)
Claim 8
The combination of Nagano, Eoh, and Yoo teaches the limitations of claim 1 as outlined above. Nagano further teaches
wherein the at least one instruction, when executed by the at least one processor individually or collectively, further causes the robot device to update, in the at least one memory, the reliability value corresponding to an area of the plurality of areas, based on the acquired reliability value.
(Nagano - [p. 28, para.2] The CPU 51 periodically updates the reliability map 42, for example, by using the position information and the reliability received from the AGV 10. The reliability map 42 can be updated periodically as one or more AGVs 10 move in the moving space S.
Claim 10
The combination of Nagano, Eoh, and Yoo teaches the limitations of claim 1 as outlined above. Nagano further teaches
wherein the at least one instruction, when executed by the at least one processor individually or collectively, further causes the robot device to: based on identifying an area of the plurality of areas having a reliability value less than the critical value, modify the movement path to bypass the identified area.
(Nagano - [p.29, para. 3] After the route is generated once, the reliability map 42 is updated, and as a result, a part of the generated route may not pass through the high reliability region 94. In such a case, the CPU 51 may regenerate the route and transmit it to the AGV 10. The procedure at that time is the same as the above-mentioned procedure)
Claim 11
Nagano teaches
acquiring a map of a space where the robot device is positioned, wherein the map comprises a plurality of areas and each area of the plurality of areas has a corresponding reliability value;
(Nagano - [p. 19, para. 4] The position estimation device 14e can perform a map creation process and a self-position estimation process during traveling. For example, the position estimation device 14e creates a map of the moving space S based on the position and orientation of the AGV 10 and the scan result of the laser range finder. During traveling, the position estimation device 14e receives the sensor data from the laser range finder 15 and reads out the map data M stored in the storage device 14c. By matching the local map data (sensor data) created from the scan results of the laser range finder 15 with the map data M in a wider range, the self-position (x, y, θ) on the map data M can be determined. The position estimation device 14e generates "reliability" data indicating the degree to which the local map data matches the map data M. ... )
identifying at least one area, among the plurality of areas, having a reliability value greater than or equal to a critical value, based on the reliability value of each of the plurality of areas; identifying a movement path of the robot device in the space, based on the at least one area,
(Nagano - [p. 10, para. 3] In step S30, the arithmetic circuit 207 of the operation management device 251 reads the reliability map 205b from the storage device 203. In step S32, the arithmetic circuit 207 refers to the reliability map data to determine a high reliability region whose reliability is equal to or higher than a predetermined reliability threshold. In step S34, the arithmetic circuit 207 generates a path passing through the high reliability region. In step S36, the arithmetic circuit 207 transmits the data of the route generated via the communication circuit 209.)
and causing the robot device to move along the movement path, and
(Nagano - [page 10, para. 4] In step S40, the controller 107 of the mobile body 101 receives the route data via the communication circuit 111. In step S42, the moving body 101 controls the driving device 109 and moves along the path.
…obtain an updated reliability value corresponding to the new position of the robot device, …
(Nagano - [p. 28, para.2] The CPU 51 periodically updates the reliability map 42, for example, by using the position information and the reliability received from the AGV 10. The reliability map 42 can be updated periodically as one or more AGVs 10 move in the moving space S.)
Nagano alone may not explicitly teach the following limitations in combination. However, Eoh teaches
acquire movement information of the robot device comprising
a movement direction of the robot device, a movement distance of the robot device, or a movement speed of the robot device based on first detection data and second detection data that are consecutively received from a sensor of the robot device
(Eoh - [col 6, ln 62-64] The robot may calculate a distance moved by the robot on the basis of the odometry information as well as the information generated using the sensors.
[col 12, ln 39-46] Additionally, the controller 250 generates LiDAR odometry information using two LiDAR frames registered between the two nodes (S57). The LiDAR odometry information may include a differential value of the two LiDAR frames (each LiDAR frame registered in each node) between the two nodes, or differential values changed according to a distance between the two nodes may be stored as LiDAR odometry information.
[col 12, ln 47-54] Further, the controller 250 generates visual odometry information using two visual frames registered between the two nodes (S58). The visual odometry information may include a differential value of the two visual frames (each visual frame registered in each node) between the two nodes, or differential values changed according to a distance between the two nodes may be stored as visual odometry information.
[col 12, ln 47-54] Further, the controller 250 generates visual odometry information using two visual frames registered between the two nodes (S58). The visual odometry information may include a differential value of the two visual frames (each visual frame registered in each node) between the two nodes, or differential values changed according to a distance between the two nodes may be stored as visual odometry information.
[col 5, ln 65-67] That is, the camera sensor 230 photographs an object outside the robot and generates a visual frame including vision information.)
EXAMINER NOTE: The camera data and/or the LIDAR data qualify as detection data.
estimate a new position of the robot device corresponding to the second detection data, based on the movement information;
(Eoh - [col 17, ln 17-22] The controller 250 calculates the current position of the robot by comparing wheel odometry information (WO) and a distance moved by the robot and sensed by the wheel encoder, and by comparing LiDAR odometry information (LO) or visual odometry information (VO) and LiDAR/visual frames sensed by the robot.)
Neither Nagano nor Eoh teaches the following limitations in the proposed combination. However, Yoo teaches
and based on identifying an inability to maintain at least one of the movement direction of the robot device, the movement distance of the robot device, and the movement speed of the robot device, obtain a … reliability value corresponding to the new position of the robot device, wherein the … reliability value is less than the critical value.
(Yoo - [0077] According to an embodiment, when the map reliability is determined based on the danger degree of the current location, the device 100 may identify the danger degree (e.g., the road curvature, the accident danger, etc.) of the current location from the map information stored in the vehicle 110 and/or from the information sensed from the sensor of the vehicle 110. When it is determined that the danger degree of the current location is high, the map information may be determined to have a low map reliability.
[0093] In operation S720, when the map reliability is equal to or less than the threshold value, this indicates that that the accuracy or reliability of map information that the vehicle 110 is using is low. )
EXAMINER NOTE: The presence of a curve is an indication of an inability to maintain a movement direction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to apply the odometry techniques taught by Eoh to Nagano's robot in order to accurately identify the position of the robot.
(Eoh - [col 1, ln 56-58] Additionally, the present disclosure is to implement fusion SLAM in which the position of a robot is identified using various sensors in a space.
[col 3, ln 4-7] The present disclosure according to embodiments allows a robot to use a correlation while the robot moves on the basis of a map comprised of various sensors and to accurately calculate the current position of the robot.)
Nagano teaches the updating of reliability information as the AGV moves throughout the environment. Yoo teaches a beneficial measure of calculating reliability based on dangers such as curves, which would force a direction change. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to further modify Nagano’s robot by implementing Yoo’s suggestion to calculate reliability based on the presence of dangers in order to improve the functioning of the robot.
(Yoo - [0036] … The map information with improved map reliability as provided according to various embodiments of the present disclosure may directly improve the functioning of the autonomous vehicle, e.g., by directly improving the location estimation performance of the autonomous vehicle.)
Claim 16
Nagano teaches
A non-transitory computer readable medium having instructions stored therein, which when executed by a processor cause the processor to execute a method of controlling a robot device,
(Nagano - [p.18, para. 1] The memory 14b is a volatile storage device that stores a computer program executed by the microcomputer 14a. The memory 14b can also be used as a work memory when the microcomputer 14a and the Position estimation device 14e perform calculations.)
(Nagano - [p.20, para. 1] In the present embodiment, the microcomputer 14a and the position estimation device 14e are considered to be separate components, but this is an example. It may be one chip circuit or a semiconductor integrated circuit capable of independently performing each operation of the microcomputer 14a and the position estimation device 14e. FIG. 7A shows a chip circuit 14g including the microcomputer 14a and the position estimation device 14e.)
the method comprising:
acquiring a map of a space where the robot device is positioned, wherein the map comprises a plurality of areas, and each area of the plurality of areas has a corresponding reliability value;
(Nagano - [p. 19, para. 4] The position estimation device 14e can perform a map creation process and a self-position estimation process during traveling. For example, the position estimation device 14e creates a map of the moving space S based on the position and orientation of the AGV 10 and the scan result of the laser range finder. During traveling, the position estimation device 14e receives the sensor data from the laser range finder 15 and reads out the map data M stored in the storage device 14c. By matching the local map data (sensor data) created from the scan results of the laser range finder 15 with the map data M in a wider range, the self-position (x, y, θ) on the map data M can be determined. The position estimation device 14e generates "reliability" data indicating the degree to which the local map data matches the map data M. ... )
identifying at least one area among the plurality of areas having a reliability value greater than or equal to a critical value, based on the reliability value of each of the plurality of areas; identifying a movement path of the robot device in the space, based on the at least one area,
(Nagano - [p. 10, para. 3] In step S30, the arithmetic circuit 207 of the operation management device 251 reads the reliability map 205b from the storage device 203. In step S32, the arithmetic circuit 207 refers to the reliability map data to determine a high reliability region whose reliability is equal to or higher than a predetermined reliability threshold. In step S34, the arithmetic circuit 207 generates a path passing through the high reliability region. In step S36, the arithmetic circuit 207 transmits the data of the route generated via the communication circuit 209.)
and causing the robot device to move along the movement path, and
(Nagano - [page 10, para. 4] In step S40, the controller 107 of the mobile body 101 receives the route data via the communication circuit 111. In step S42, the moving body 101 controls the driving device 109 and moves along the path.
…obtain an updated reliability value corresponding to the new position of the robot device, …
(Nagano - [p. 28, para.2] The CPU 51 periodically updates the reliability map 42, for example, by using the position information and the reliability received from the AGV 10. The reliability map 42 can be updated periodically as one or more AGVs 10 move in the moving space S.)
Nagano may not explicitly teach the following limitations in combination. However, Eoh teaches
acquire movement information of the robot device comprising
a movement direction of the robot device, a movement distance of the robot device, or a movement speed of the robot device based on first detection data and second detection data that are consecutively received from a sensor of the robot device;
(Eoh - [col 6, ln 62-64] The robot may calculate a distance moved by the robot on the basis of the odometry information as well as the information generated using the sensors.
[col 12, ln 39-46] Additionally, the controller 250 generates LiDAR odometry information using two LiDAR frames registered between the two nodes (S57). The LiDAR odometry information may include a differential value of the two LiDAR frames (each LiDAR frame registered in each node) between the two nodes, or differential values changed according to a distance between the two nodes may be stored as LiDAR odometry information.
[col 12, ln 47-54] Further, the controller 250 generates visual odometry information using two visual frames registered between the two nodes (S58). The visual odometry information may include a differential value of the two visual frames (each visual frame registered in each node) between the two nodes, or differential values changed according to a distance between the two nodes may be stored as visual odometry information.
[col 12, ln 47-54] Further, the controller 250 generates visual odometry information using two visual frames registered between the two nodes (S58). The visual odometry information may include a differential value of the two visual frames (each visual frame registered in each node) between the two nodes, or differential values changed according to a distance between the two nodes may be stored as visual odometry information.
[col 5, ln 65-67] That is, the camera sensor 230 photographs an object outside the robot and generates a visual frame including vision information.)
EXAMINER NOTE: The camera data and/or the LIDAR data qualify as detection data.
estimate a new position of the robot device corresponding to the second detection data, based on the movement information;
(Eoh - [col 17, ln 17-22] The controller 250 calculates the current position of the robot by comparing wheel odometry information (WO) and a distance moved by the robot and sensed by the wheel encoder, and by comparing LiDAR odometry information (LO) or visual odometry information (VO) and LiDAR/visual frames sensed by the robot.)
Neither Nagano nor Eoh teaches the following limitations in the proposed combination. However, Yoo teaches
and based on identifying an inability to maintain at least one of the movement direction of the robot device, the movement distance of the robot device, and the movement speed of the robot device, obtain a … reliability value corresponding to the new position of the robot device, wherein the … reliability value is less than the critical value.
(Yoo - [0077] According to an embodiment, when the map reliability is determined based on the danger degree of the current location, the device 100 may identify the danger degree (e.g., the road curvature, the accident danger, etc.) of the current location from the map information stored in the vehicle 110 and/or from the information sensed from the sensor of the vehicle 110. When it is determined that the danger degree of the current location is high, the map information may be determined to have a low map reliability.
[0093] In operation S720, when the map reliability is equal to or less than the threshold value, this indicates that that the accuracy or reliability of map information that the vehicle 110 is using is low. )
EXAMINER NOTE: The presence of a curve is an indication of an inability to maintain a movement direction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to apply the odometry techniques taught by Eoh to Nagano's robot in order to accurately identify the position of the robot.
(Eoh - [col 1, ln 56-58] Additionally, the present disclosure is to implement fusion SLAM in which the position of a robot is identified using various sensors in a space.
[col 3, ln 4-7] The present disclosure according to embodiments allows a robot to use a correlation while the robot moves on the basis of a map comprised of various sensors and to accurately calculate the current position of the robot.)
Nagano teaches the updating of reliability information as the AGV moves throughout the environment. Yoo teaches a beneficial measure of calculating reliability based on dangers such as curves, which would force a direction change. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to further modify Nagano’s robot by implementing Yoo’s suggestion to calculate reliability based on the presence of dangers in order to improve the functioning of the robot.
(Yoo - [0036] … The map information with improved map reliability as provided according to various embodiments of the present disclosure may directly improve the functioning of the autonomous vehicle, e.g., by directly improving the location estimation performance of the autonomous vehicle.)
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Nagano, Eoh, and Yoo as applied to claim 1 above, and further in view of Yu (US 11006039 B1)
Claim 6
The combination of Nagano, Eoh, and Yoo teaches the limitations of claim 1 as outlined above. As evidenced by the above rejections, Nagano and Eoh teach
wherein the sensor comprises a camera,
wherein the detection data comprises image data received through the camera,
(Nagano - [p.4, para. 3] The "outside world sensor" is a sensor that senses the external state of a moving body. External world sensors include, for example, a laser range finder (also called a range finder), a camera (or image sensor), a lidar (Light Detection and Ranging), a millimeter wave radar, and a magnetic sensor.)
(Eoh - [col 5, ln 65-67] That is, the camera sensor 230 photographs an object outside the robot and generates a visual frame including vision information.)
The combination cited in claim 3 may not explicitly teach the following limitations in combination. However, Yu teaches
and wherein the at least one instruction, when executed by the at least one processor individually or collectively, further causes the robot device to:
identify a field of view of the camera based on the image data,
(Yu - [col 2, ln 48-49] Embodiments described herein relate to determining occlusion within a camera field of view, …)
and based on an angle of the field of view being less than a critical angle, obtain a second updated reliability value corresponding to the new position of the robot device, wherein the second updated reliability value is less than the critical value.
(Yu - [col 3, ln 30-42] (13) In an embodiment, the occlusion analysis may be used, for instance, to determine whether to re-perform object recognition, or to adjust a manner in which the object recognition is performed. For example, if the level of confidence for an object recognition operation is below a defined threshold (e.g., a defined confidence threshold), the object recognition operation may be re-performed. The level of confidence for the object recognition may be below the defined threshold as a result of, e.g., an amount of occlusion being too high, such as when a ratio between the size of the occluding region and a size of the 2D region surrounding the target feature exceeds a defined occlusion threshold, or when the size of the occluding region exceeds the defined occlusion threshold.)
EXAMINER NOTE: An occlusion threshold being too high indicates that the viewing angle of the camera is too small for the object to be identified. A high occlusion threshold results in low confidence (reliability) for the object to be identified.
Nagano's locating method depends on recognition of the environment using sensors (such as cameras) to locate a robot. Yu teaches a system in which confidence in recognition of objects in an environment is determined based on a degree in which the objects are occluded (angle of field of view of the camera is reduced). If the occlusion reaches a defined occlusion threshold (angle of the field of view being less than a critical angle), then the confidence may be below a defined threshold (reliability threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to further modify Nagano's robotic system by utilizing Yu's suggestion to account for usable field of view in order to improve how the robot interacts with its environment based on detected objects.
(Yu - [col 9, ln 37-52] (34) In an embodiment, occlusion of one or more locations in a camera's field of view may affect robot interaction with objects in the field of view, because the robot interaction may depend on camera data that describe, e.g., location, size, and/or orientations of the objects relative to a robot. In some cases, the robot interaction may entail performing object recognition to recognize the objects in the field of view, and the occlusion may affect an accuracy of object recognition. Thus, some aspects of the embodiments herein relate to detecting or otherwise determining occlusion within a camera field of view. Such a determination may be used to, e.g., evaluate object recognition that may have been performed while there was occlusion in the camera field of view, to control how object recognition is performed, and/or to control robot interaction with objects in the camera field of view. )
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Nagano in view of Eoh and Yoo as applied to claim 1 above, and further in view of Ohira (US 20230032367 A1)
Claim 9
Nagano teaches the limitations of claim 1 as outlined above. Nagano further teaches
wherein the sensor comprises a light detection and ranging (LiDAR) sensor,
(Nagano - [p.4, para. 3] The "outside world sensor" is a sensor that senses the external state of a moving body. External world sensors include, for example, a laser range finder (also called a range finder), a camera (or image sensor), a lidar (Light Detection and Ranging), a millimeter wave radar, and a magnetic sensor.)
Nagano may not explicitly teach the following limitations in combination. However, Ohira teaches
and wherein at least one instruction, when executed by the at least one processor individually or collectively, further causes the robot device to:
identify an error value based on an orientation of the LiDAR sensor in an area of the plurality of areas based on the detection data received from the LiDAR sensor,
and adjust the orientation of the LiDAR sensor based on the error value while the robot device moves along the movement path in the area of the plurality of areas.
(Ohira - [0015] A method for enabling stable traveling according to the present exemplary embodiment will be described below. Specifically, the method includes generating a reliability map for each orientation at a position in a space and determining an orientation of a moving object such that a reliability of position and orientation estimation is higher than or equal to a predetermined threshold value, during control of a movement of the moving object using the reliability map.
[0028] In step S102, a position and an orientation and a reliability that are estimated by the position/orientation estimation unit 102 based on measurement values acquired from the sensor 101 by the position/orientation information acquisition unit 111 are acquired, ... According to the present exemplary embodiment, the sensor 101 is fixed to the moving object, and the movement and turn of the sensor 101 are linked with the movement and turn of the moving object, …
[0035] In step S201, an orientation of the moving object at a position (x, y) on the two-dimensional plane are divided by an angle. Specifically, the orientation is divided into eight regions formed by dividing each quadrant between X- and Y-axes by 45 degrees as illustrated in FIG. 5A. In FIG. 5B, an area in a dotted frame indicates an angle range of the orientation obtained as the positive Y-axis direction by the dividing.
[0036] In step S202, a representative value of reliabilities is calculated for each divided orientation. First, from among combinations of a position and an orientation and a reliability stored in the storage unit 112, combinations of orientations and reliabilities of positions that is within a predetermined range from the position (x, y) are acquired. Then, with respect to each orientation, an average value of the target reliabilities is calculated.
[0041] An orientation of the moving object is determined based on the route information read into the RAM H13 and the reliability maps generated by the generation unit 113 in step S103.
[0042] With respect to each position on a route through which the moving object is to travel, the reliability of a predetermined orientation and the threshold value stored in the RAM H13 are compared based on the reliability map for orientations. In a case where the reliability is higher than the threshold value, the predetermined orientation is not changed. On the other hand, in a case where the reliability is lower than the threshold value, the predetermined orientation is changed to an orientation based on the reliability map such that the reliability becomes higher than or equal to the threshold value.)
EXAMINER NOTE: The reliability of each possible orientation is considered, and the orientation which has reliability higher than or equal to the threshold value is used for the route.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Nagano's robot with Ohira's suggestion to optimize the detection angle of the sensor in order to ensure reliability remains greater than or equal to the critical value during travel.
Allowable Subject Matter
As indicated in the previous office action, claims 4-5, 14-15, and 19-20 appear to contain allowable subject matter, but are objected to for being dependent on a rejected base claim.
The following is a statement of reasons for the indication of allowable subject matter: The prior art of record fails to teach or suggest at least the following limitations in combination with the limitations found in claims from which the above claims depend:
“… and based on a failure of the operation to combine the plurality of point clouds, obtain an updated reliability value corresponding to the new position of the robot device, wherein the updated reliability value is less than the critical value…” (claims 4, 14, and 19)
“… based on a number of dynamic objects being greater than or equal to a critical number, obtain an updated reliability value corresponding to the new position of the robot device, wherein the updated reliability value is less than the critical value.” (claims 5, 15, and 20)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES MILLER WATTS whose telephone number is (703)756-1249. The examiner can normally be reached 7:30-5:30 M-TH.
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, Adam Mott can be reached at 571-270-5376. 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.
/JAMES MILLER WATTS III/Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
Prosecution Insights