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 .
Claims 1-18 are pending in the application. Claims 1, 9 and 18 have been amended.
Response to Arguments
Applicant’s arguments, filed 7/2/26, with respect to claim(s) 1-18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Independent claims 1, 9 and 18 have been amended to introduce new limitations that have not been examined before.
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.
Claim(s) 1-2, 7-10 and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liao (US Patent 11,403,860 B1), in view of Cho et al. (US 20230326060 A1, hereafter Cho).
As per claim 1, Liao teaches a method (Abstract), comprising:
capturing a three-dimensional image depicting an object (Abstract “The system uses a LiDAR system and a monocular camera to obtain point cloud data and image data of an object, respectively”; col. 2 ln 46-51);
capturing a two-dimensional image depicting the object (Abstract “The system uses a LiDAR system and a monocular camera to obtain point cloud data and image data of an object, respectively”; col. 2 ln 46-51);
determining a region of interest in the two-dimensional image, the region of interest containing the object (col. 4 ln 36-41);
determining, based on the region of interest from the two-dimensional image, a quality indicator corresponding to the three-dimensional image (Liao detects 2D bounding box in 2D image data and 3D bounding box in point cloud data (See above). Liao then transforms a point cloud data coordinate in the 3D object bounding box to a 2D pixel coordinate in the pixel coordinate system. Liao further calculates the distance between the centroid of the 3D bounding box that is transformed into 2D coordinate, and the center of the 2D bounding box. See col. 5 ln 40-col. 6 ln 45. The distance is considered the quality indicator.);
comparing the quality indicator to a predetermined threshold; and
when the quality indicator does not satisfy the predetermined threshold, generating a feedback notification (col. 6 ln 53-col. 7 ln 3).
Liao does not further teach the notification is a positional notification, wherein generating the positional notification comprises presenting, to an operator on a display of a device configured to capture the three-dimensional image and the two-dimensional image, a directional instruction to reposition the device relative to the object.
Cho in an analogous field discloses a method for aligning multiple three-dimensional (3D) point clouds into a common 3D point cloud (Abstract). Specifically, Cho discloses a sensor system comprising TOF sensor, camera, display device etc. (FIG. 1). The TOF sensor (FIG. 1 #110) captures distance data from the sensor to an object (FIG. 2; para. [0027]), and the camera (FIG. 1 #130) captures image frames of the environment (para. [0029]). The field of view of the camera 130 and TOF sensor 110 are partially or fully overlapping, e.g., the field of view of the camera 130 may be slightly larger than the field of view of the TOF sensor 110 (para. [0029]). The display device (FIG. 1 #140) displays an image obtained by the camera 130 and overlays visual imagery indicating one or more features identified in the field of view of the camera 130 and TOF sensor 110 based on the distance data. For example, the processor 120 may instruct the display device 140 to display an outline of a box over an image of the box obtained by the camera 130. A user can use this display to determine whether the sensor system 100 has correctly identified the box and the box’s edges (para. [0030]). Cho’s sensor system further comprises a capture verifying component (FIG. 1 #158) for verifying that images received from the TOF sensor 110 are taken at desired poses for performing object dimensioning. In some examples, capture verifying component 158 can ensure that a view pair has enough distinction to provide at least a threshold improvement in SNR for the view pair over a single one of the views. In another example, capture verifying component 158 can ensure that an object is discernable in the image, e.g., that an object has a large enough number of associated points in a point cloud to facilitate identification and accurate measurement of the object from the image. For example, the size of the object in the picture can be configurable, and capture verifying component 158 may detect whether the image includes an object of at least the configured size in determining whether to keep the image (para. [0046]). That is to say, the capture verifying component 158 determines if the captured TOF image satisfies a quality requirement. If the captured TOF image does not satisfy the quality requirement, the capture verifying component 158 can provide feedback to guide a user of the sensor system 100 to the desired or acceptable position (FIG. 4 #412). For example, capture verifying component 158 can provide the feedback as an arrow displayed on the display device 140 indicating a direction to move the sensor system 100 to be at the desired position, a box or other indicator displayed on the display device 140 showing where to align a center of the display device 140, etc. (para. [0048).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider modify the teaching of Liao to incorporate the teaching of Cho to generate a positional notification, wherein generating the positional notification comprises presenting, to an operator on a display of a device configured to capture the three-dimensional image and the two-dimensional image, a directional instruction to reposition the device relative to the object. Doing so would validate or otherwise ensure the captured images are captured at desired positions for effective object dimensioning, as recognized by Cho (para. [0036]).
As per claim 2, dependent upon claim 1, Liao in view of Cho teaches determining the quality indicator comprises:
mapping the region of interest to a portion of the three-dimensional image (Liao col. 4 ln 22-36); and
determining a distance from a depth sensor to the object, based on the portion of the three-dimensional image (Cho FIG. 1 #110; FIG. 2; para. [0035] “As such, collecting multiple TOF frames at each pose can increase the number of returned photons to yield a more dense aggregated point cloud at the position. The images can also include, or be referred to as, range maps, depth maps, or depth images”) .
As per claim 7, dependent upon claim 1, Liao in view of Cho teaches: when the quality indicator satisfies the predetermined threshold, determining dimensions of the object from the three-dimensional image (Cho FIG. 4 #416; FIG. 5 #508; para. [0052], [0060]).
As per claim 8, dependent upon claim 1, Liao in view of Cho teaches capturing the three-dimensional image includes capturing a plurality of depth measurements, and generating a point cloud from the depth measurements (Liao FIG. 1; col. 2 ln 46-49 “In a second step at 600, according to some embodiments, the system 10 acquires point cloud data using the LiDAR assembly 100 and image data using the camera assembly 200 of one or more objects.”).
As per claim 9, an independent device claim, Liao teaches a computing device (Liao Abstract), comprising:
a sensor assembly (Liao FIG. 1 LiDAR assembly 100, a camera assembly 200; col. 2 ln 10-18); and
a processor (FIG. 8) configured to:
capture, via the sensor assembly, a three-dimensional image depicting an object (Abstract “The system uses a LiDAR system and a monocular camera to obtain point cloud data and image data of an object, respectively”; col. 2 ln 46-51);
capture, via the sensor assembly, a two-dimensional image depicting the object (Abstract “The system uses a LiDAR system and a monocular camera to obtain point cloud data and image data of an object, respectively”; col. 2 ln 46-51);
determine a region of interest in the two-dimensional image, the region of interest containing the object (col. 4 ln 36-41);
determine, based on the region of interest from the two-dimensional image, a quality indicator corresponding to the three-dimensional image (Liao detects 2D bounding box in 2D image data and 3D bounding box in point cloud data (See above). Liao then transforms a point cloud data coordinate in the 3D object bounding box to a 2D pixel coordinate in the pixel coordinate system. Liao further calculates the distance between the centroid of the 3D bounding box that is transformed into 2D coordinate, and the center of the 2D bounding box. See col. 5 ln 40-col. 6 ln 45. The distance is considered the quality indicator.);
compare the quality indicator to a predetermined threshold; and
when the quality indicator does not satisfy the predetermined threshold, generate a feedback notification (col. 6 ln 53-col. 7 ln 3).
Liao does not further teach generating the feedback notification by presenting, to an operator on a display of the computing device, a directional instruction to reposition the computing device relative to the object.
Cho in an analogous field discloses a method for aligning multiple three-dimensional (3D) point clouds into a common 3D point cloud (Abstract). Specifically, Cho discloses a sensor system comprising TOF sensor, camera, display device etc. (FIG. 1). The TOF sensor (FIG. 1 #110) captures distance data from the sensor to an object (FIG. 2; para. [0027]), and the camera (FIG. 1 #130) captures image frames of the environment (para. [0029]). The field of view of the camera 130 and TOF sensor 110 are partially or fully overlapping, e.g., the field of view of the camera 130 may be slightly larger than the field of view of the TOF sensor 110 (para. [0029]). The display device (FIG. 1 #140) displays an image obtained by the camera 130 and overlays visual imagery indicating one or more features identified in the field of view of the camera 130 and TOF sensor 110 based on the distance data. For example, the processor 120 may instruct the display device 140 to display an outline of a box over an image of the box obtained by the camera 130. A user can use this display to determine whether the sensor system 100 has correctly identified the box and the box’s edges (para. [0030]). Cho’s sensor system further comprises a capture verifying component (FIG. 1 #158) for verifying that images received from the TOF sensor 110 are taken at desired poses for performing object dimensioning. If it is determined that the captured image is not qualified for dimensioning (FIG. 4 “NO”), feedback can be indicated for device positioning to assist in capturing an image that is useful for dimensioning (FIG. 4 #412; para. [0050]). For example, capture verifying component 158 can provide the feedback as an arrow displayed on the display device 140 indicating a direction to move the sensor system 100 to be at the desired position (para. [0048]). If it is determined that the captured image is qualified for dimensioning (FIG. 4 “YES”), Cho’s system performs object dimensioning (FIG. 4 #416).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider modify the teaching of Liao to incorporate the teaching of Cho to generate a positional notification, wherein generating the positional notification comprises presenting, to an operator on a display of a device configured to capture the three-dimensional image and the two-dimensional image, a directional instruction to reposition the device relative to the object. Doing so would validate or otherwise ensure the captured images are captured at desired positions for effective object dimensioning, as recognized by Cho (para. [0036]).
Claim 10, dependent upon claim 9, is rejected as applied to claim 2 above.
Claim 15, dependent upon claim 9, is rejected as applied to claim 7 above.
As per claim 16, dependent upon claim 9, Liao in view of Cho teaches the sensor assembly comprises a depth sensor configured to capture the three-dimensional image (Liao FIG. 1 LiDAR assembly 100; col. 2 ln 46-51), and an image sensor configured to capture the two-dimensional image (Liao FIG. 1 camera assembly 200; col. 2 ln 46-51).
As per claim 17, dependent upon claim 9, Liao in view of Cho teaches the sensor assembly includes a sensor configured to capture the three-dimensional image and the two-dimensional image (See rejections applied to claim 16).
As per claim 18, an independent claim, Liao teaches a method (Abstract), comprising:
obtaining a point cloud depicting an object (Liao Abstract “The system uses a LiDAR system and a monocular camera to obtain point cloud data and image data of an object, respectively”; col. 2 ln 46-51);
obtaining a two-dimensional image of the object (Abstract “The system uses a LiDAR system and a monocular camera to obtain point cloud data and image data of an object, respectively”; col. 2 ln 46-51);
detecting the object in the two-dimensional image (col. 4 ln 36-41);
based on the detected object in the two-dimensional image, determining a quality indicator corresponding to the three-dimensional image (Liao detects 2D bounding box in 2D image data and 3D bounding box in point cloud data (See above). Liao then transforms a point cloud data coordinate in the 3D object bounding box to a 2D pixel coordinate in the pixel coordinate system. Liao further calculates the distance between the centroid of the 3D bounding box that is transformed into 2D coordinate, and the center of the 2D bounding box. See col. 5 ln 40-col. 6 ln 45. The distance is considered the quality indicator.); and
generating a notification when the quality indicator does not satisfy a threshold (col. 6 ln 53-col. 7 ln 3).
Liao does not teach determining a quality indicator configured to indicate a likelihood that dimensions of the object can be obtained from the point cloud; and selecting, based on the quality indicator, between (i) generating feedback, wherein generating the feedback comprises presenting, to an operator on a display of a device configured to capture the point cloud and the two-dimensional image, a directional instruction to reposition the device relative to the object and (ii) obtaining dimensions of the object from the point cloud.
Cho in an analogous field discloses a method for aligning multiple three-dimensional (3D) point clouds into a common 3D point cloud (Abstract). Specifically, Cho discloses a sensor system comprising TOF sensor, camera, display device etc. (FIG. 1). The TOF sensor (FIG. 1 #110) captures distance data from the sensor to an object (FIG. 2; para. [0027]), and the camera (FIG. 1 #130) captures image frames of the environment (para. [0029]). The field of view of the camera 130 and TOF sensor 110 are partially or fully overlapping, e.g., the field of view of the camera 130 may be slightly larger than the field of view of the TOF sensor 110 (para. [0029]). The display device (FIG. 1 #140) displays an image obtained by the camera 130 and overlays visual imagery indicating one or more features identified in the field of view of the camera 130 and TOF sensor 110 based on the distance data. For example, the processor 120 may instruct the display device 140 to display an outline of a box over an image of the box obtained by the camera 130. A user can use this display to determine whether the sensor system 100 has correctly identified the box and the box’s edges (para. [0030]). Cho’s sensor system further comprises a capture verifying component (FIG. 1 #158) for verifying that images received from the TOF sensor 110 are taken at desired poses for performing object dimensioning. Cho specifically teaches evaluating if the captured image is qualified for object dimensioning, i.e., determining the likelihood that the captured image can be used for dimensioning. See para. [0036] “In some examples, the capture verifying component 158 can validate or otherwise ensure the view pairs are captured at desired positions for effective object dimensioning”; para. [0046] “For example, capture verifying component 158, e.g., in conjunction with processor 120, memory 150, etc., can determine whether the image complies with parameters for effective object dimensioning. In some examples, capture verifying component 158 can ensure that a view pair has enough distinction to provide at least a threshold improvement in SNR for the view pair over a single one of the views. In another example, capture verifying component 158 can ensure that an object is discernable in the image, e.g., that an object has a large enough number of associated points in a point cloud to facilitate identification and accurate measurement of the object from the image. For example, the size of the object in the picture can be configurable, and capture verifying component 158 may detect whether the image includes an object of at least the configured size in determining whether to keep the image”; para. [0049] “In some examples, capture verifying component 158 can determine whether the images are taken at desired positions/orientations around the object to allow for object dimensioning”. If it is determined that the captured image is not qualified for dimensioning (FIG. 4 “NO”), feedback can be indicated for device positioning to assist in capturing an image that is useful for dimensioning (FIG. 4 #412; para. [0050]). For example, capture verifying component 158 can provide the feedback as an arrow displayed on the display device 140 indicating a direction to move the sensor system 100 to be at the desired position (para. [0048]). If it is determined that the captured image is qualified for dimensioning (FIG. 4 “YES”), Cho’s system performs object dimensioning (FIG. 4 #416).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider modify the teaching of Liao to incorporate the teaching of Cho to determine a quality indicator configured to indicate a likelihood that dimensions of the object can be obtained from the point cloud; and select, based on the quality indicator, between (i) generating feedback, wherein generating the feedback comprises presenting, to an operator on a display of a device configured to capture the point cloud and the two-dimensional image, a directional instruction to reposition the device relative to the object and (ii) obtaining dimensions of the object from the point cloud. Doing so would validate or otherwise ensure the captured images are captured at desired positions for effective object dimensioning, as recognized by Cho (para. [0036]).
Claim(s) 3 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liao (US Patent 11,403,860 B1), in view of Cho et al. (US 20230326060 A1, hereafter Cho), and further in view of KIMURA (US Publication 2025/0033899 A1).
As per claim 3, Liao in view of Cho teaches that the quality indicator satisfies a distance threshold (Liao col. 6 ln 53-col. 7 ln 3), but does not teach the predetermined threshold includes a lower distance threshold, and an upper distance threshold; and wherein the quality indicator satisfies the predetermined threshold when the distance is between the lower distance threshold and the upper distance threshold.
KIMURA in an analogous field discloses a method for object loading recognition associated with a plurality of objects (Abstract). KIMURA’s system includes a sensor for measuring distances between the sensor and the plurality of objects, and a linear slider, which can be moved linearly by the linear slider (FIG. 8). KIMURA’s system is configured to: measure surfaces of the plurality of objects using the sensor; recognize dimensions, positions, and orientations of the plurality of objects based on the measured surfaces to identify recognized objects; calculate a confidence of each of the recognized objects; identify undistinguishable objects from the recognized objects based on the calculated confidences; calculate an approachable distance for each of the undistinguishable objects; and move the sensor towards the plurality of objects by a distance that corresponds to a minimum approachable distance from the calculated approachable distances (Abstract; FIG. 8-9). The calculated confidence of each of the recognized objects is considered a quality indicator. Upon determining the calculated confidence is below a confidence threshold, the object is considered as “undistinguishable”. The system then controls the vision sensor to move to a new position in order to better recognize the object. See FIG. 8-9, para. [0036]-[0040].
KIMURA further teaches the quality indicator is correlated with distance (para. [0038]), and the distance is between a minimum approachable distance and a maximum approachable distance (FIG. 10; para. [0037]-[0040]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider modify the teaching of Liao and Cho to incorporate the teaching of KIMURA to set a lower distance threshold and an upper distance threshold for the quality indicator. Setting such a distance threshold limits would guarantee the vision sensor moves in an effective range as recognized by KIMURA (para. [0039]-[0040]).
Claim 11, dependent upon claim 10, is rejected as applied to claim 3 above.
Claim(s) 4 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liao (US Patent 11,403,860 B1), in view of Cho et al. (US 20230326060 A1, hereafter Cho) and KIMURA (US Publication 2025/0033899 A1), and further in view of Yu et al. (US Publication 2022/0371200 A1, hereafter Yu) and Sanchez (US Patent 10,643,441 B1).
As per claim 4, Liao in view of Cho and KIMURA teaches estimating dimension of the object (Cho FIG. 4 #416); and selecting the predetermined threshold based on the estimated dimension (KIMURA FIG. 10; para. [0036]-[0040]).
Yu in an analogous field discloses a robotic system for object size measurement (Abstract). Specifically, Yu teaches determining, based on the two-dimensional image and the motion data, an estimated dimension of the object (para. [0128], [0132]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider modify the teaching of Liao, Cho and KIMURA to incorporate the teaching of Yu to determine based on the two-dimensional image and the motion data, an estimated dimension of the object. The motivation of doing so is that object dimension is closely related to motion data, such as movement distance as recognized by Yu (para. [00121]).
Liao in view of Cho, KIMURA and Yu, does not further teach obtaining motion data via a motion sensor.
Sanchez is evidenced that obtaining motion data via a motion sensor is
well-known and practiced (Abstract).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider modify the teaching of Liao, Cho, KIMURA and Yu to incorporate the teaching of Sanchez to obtain motion data via a motion sensor. The motivation of doing so is to track an object’s motion (Sanchez col. 1 ln 34-39).
Claim 12, dependent upon claim 10, is rejected as applied to claim 4 above.
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liao (US Patent 11,403,860 B1), in view of Cho et al. (US 20230326060 A1, hereafter Cho), and further in view of Yao (US Publication 2020/0211542 A1).
As per claim 5, Liao in view of Cho does not teach determining a fraction of a field of view of an image sensor occupied by the region of interest.
Yao teaches calculating a fraction of a face occupies an image, i.e., a fraction of a field of view of the image sensor occupied by the region of interest (para. [0020] “In embodiments including a single camera, the depth calculation module 120 may approximate an objects distance to the camera by determining a portion of the image occupied by the object. For example, if a person's face occupies 80% of an image, then the depth calculation module 120 may approximate that the person is in close proximity to the camera”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider modify the teaching of Liao and Cho to incorporate the teaching of Yao to determine a fraction of a field of view of the image sensor occupied by the region of interest. Doing so would allow the estimation of distance between image sensor and the object to be available as recognized by Yao (para. [0020]).
Claim 13, dependent upon claim 9, is rejected as applied to claim 5 above.
Claim(s) 6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liao (US Patent 11,403,860 B1), in view of Cho et al. (US 20230326060 A1, hereafter Cho) and Yao (US Publication 2020/0211542 A1), and further in view of and KIMURA (US Publication 2025/0033899 A1).
As per claim 6, dependent upon claim 5, Liao in view of Cho and Yao teaches the quality indicator satisfies a distance threshold (Liao col. 6 ln 53-col. 7 ln 3), but does not teach the predetermined threshold includes a lower threshold, and an upper threshold.
KIMURA in an analogous field discloses a method for object loading recognition associated with a plurality of objects (Abstract). KIMURA’s system includes a sensor for measuring distances between the sensor and the plurality of objects, and a linear slider, which can be moved linearly by the linear slider (FIG. 8). KIMURA’s system is configured to: measure surfaces of the plurality of objects using the sensor; recognize dimensions, positions, and orientations of the plurality of objects based on the measured surfaces to identify recognized objects; calculate a confidence of each of the recognized objects; identify undistinguishable objects from the recognized objects based on the calculated confidences; calculate an approachable distance for each of the undistinguishable objects; and move the sensor towards the plurality of objects by a distance that corresponds to a minimum approachable distance from the calculated approachable distances (Abstract; FIG. 8-9). The calculated confidence of each of the recognized objects is considered a quality indicator. Upon determining the calculated confidence is below a confidence threshold, the object is considered as “undistinguishable”. The system then controls the vision sensor to move to a new position in order to better recognize the object. See FIG. 8-9, para. [0036]-[0040].
KIMURA further teaches the quality indicator is correlated with distance (para. [0038]), and the distance is between a minimum approachable distance and a maximum approachable distance (FIG. 10; para. [0037]-[0040]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider modify the teaching of Liao, Cho and Yao to incorporate the teaching of KIMURA to set a lower threshold and an upper threshold for the quality indicator. Setting such a threshold limits would guarantee the vision sensor moves in an effective range as recognized by KIMURA (para. [0039]-[0040]).
KIMURA does not expressly teach a lower threshold and a upper threshold with respect to a fraction. However, Yao teaches that the fraction is related to the distance. Therefore, a person with ordinary skill in the art would have appreciated that fraction threshold can be derived from the distance threshold as an obvious variation.
Claim 14, dependent upon claim 13, is rejected as applied to claim 6 above.
Conclusion
Additional prior art Rothberg et al. (US 20190130554 A1, hereafter Rothberg) discloses system and method for calculating, during imaging, a quality of a sequence of images collected during the imaging (Abstract). Rothberg further discloses an graphic interface for an interactive operation when calculating a quality indicator. Specifically, a computing device may generate for display the live quality indicator simultaneously with instructions for positioning the ultrasound device that captures the sequence of images. The instructions includes positional/directional instructions, such as “MOVE UP,” “MOVE LEFT,” “MOVE RIGHT,” “ROTATE CLOCKWISE,” “ROTATE COUNTER-CLOCKWISE,” or “MOVE DOWN (FIG. 11; para. [0063]).
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUEMEI G CHEN whose telephone number is (571)270-3480. The examiner can normally be reached Monday-Friday 9am-6pm.
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/XUEMEI G CHEN/Primary Examiner, Art Unit 2661
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