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 .
Response to Arguments
This office action is in response to amendments filed 05/18/2026. Claims 1-3, 5, 7-16 are pending.
Applicant’s arguments and amendments to the claims with respect to prior art rejections of Claims 1-3, 5, 7-13 under 35 USC 103 have been fully considered and are persuasive. The rejections of Claims 1-3, 5, 7-13 under 35 USC 103 have been withdrawn. However, upon further consideration, a new rejection is made in view of Greiner et al (US 20230418288, hereinafter Greiner)
With respect to applicant’s arguments that Vu fails to teach “"the simulation point cloud is installed in a straight line connecting a position of a tip of the robot and a position of a joint of the robot.”, examiner respectfully disagrees. Cited par. 0092 discloses that static 3D models of each link between joints are added together to form the 3D model of the entire robot at a given time. The figures show the links between the tip of the robot and the end effector joint as well as between arm joints being cylindrical in nature, and forming an axis straight from the tip and the end effector to the closest joint. Par. 0023 clarifies that cubes or voxels of another shape are used to model the robot and its environment, so the cubes modeled along the end effector axis would fall within the scope of the claim.
With respect to applicant’s arguments that Vu fails to teach "first classification method" of Claim 2 is used when it is determined that the nearby object has moved, and the "second classification method" of Claim 2 is used when it is determined that the nearby object has not moved., examiner respectfully disagrees. The claims do not require that the first classification method is only ever used when it is determined that the nearby object has moved and the second classification method is only ever used when it is determined that the nearby object has not moved. Par. 0045, 0092-0093, and 0100 of Vu cited below disclose using both clustering and robot point cloud subtraction on objects regardless of whether they are determined to be moving or static, which reads on the scope of the claim. Additionally, the first and second classification methods are required by the claims not to be mutually exclusive as the first classification is used as part of the second classification methods, so both classification methods are used on non-moving objects, just as is described in Vu.
With respect to applicant’s arguments that Vu fails to teach "classifying, as the point cloud data corresponding to the robot, a point cloud corresponding to the tip portion of the robot among point clouds overlapping with a three-dimensional model of the robot when the three-dimensional model of the robot is arranged at the predicted position." examiner respectfully disagrees. Par. 0045, 0092-0093 cited below make clear that the disclosed system compares the point clouds around the robot generated from sensor data and compares them to 3D models of the end effector to determine whether they are just expected points on the end effector or the workpiece it is holding, or a nearby obstacle that warrants the safety system to intervene. This comparison falls within the scope of the claim limitation.
With respect to applicant’s arguments that Vu and Ikeguchi fail to teach “determining whether there is an overlap between the point cloud data and the voxel, or between the voxels, where the overlap exists, counting a number of points included in the voxel or the voxels, deriving an evaluation value based on the number of points, and where the evaluation value is equal to or greater than a preset threshold, determining that a collision is about to occur”, examiner agrees. A new rejection is provided relying on Greiner to teach this limitation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5, 7-10, 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vu et al (US 20220088787, hereinafter Vu) in view of Greiner et al (US 20230418288, hereinafter Greiner)
Regarding Claim 1, Vu teaches:
a collision prediction method for predicting a collision between a multi-joint robot and a nearby object (see Claim 18), the collision prediction method comprising:
a first acquisition step of acquiring configuration information of the robot and posture information of the robot (see at least "For example, these data may be provided by the robot 402 or the robot controller 407 via a safety-rated communication protocol providing access to safety-rated data. The 3D pose of the robot may then be determined by combining provided joint positions with a static 3D model of each link and any end effectors to obtain the 3D shape of the entire robot 402.” In par. 0092) ;
a second acquisition step of acquiring point cloud data including the robot and the nearby object (see at least "In many cases, however, it is desirable to monitor an area in which there are at least some objects during normal operation, such as one or more machines and workpieces on which the machine is operating. In these cases, analysis module 342 may be configured to identify intruding objects that are unexpected or that may be humans. One suitable approach to such classification is to cluster individual occupied voxels into objects that can be analyzed at a higher level." in par. 0042 and “To achieve this, analysis module 342 may implement any of several conventional, well-known clustering techniques such as Euclidean clustering, K-means clustering and Gibbs-sampling clustering. Any of these or similar algorithms can be used to identify clusters of occupied voxels from 3D point cloud data” in par. 0043) ;
a classification step of classifying the point cloud data acquired in the second acquisition step into point cloud data corresponding to the robot and point cloud data corresponding to the nearby object, based on the configuration information of the robot and the posture information of the robot (see at least "In other cases, however, it may be necessary to further classify objects into one or more of four categories: (1) elements of the machinery being controlled by system 112, (2) the workpiece or workpieces that the machinery is operating on, and (3) other foreign objects, including people, that may be moving in unpredictable ways and that can be harmed by the machinery. It may or may not be necessary to conclusively classify people versus other unknown foreign objects. It may be necessary to definitively identify elements of the machinery as such, because by definition these will always be in a state of “collision” with the machinery itself and thus will cause the system to erroneously stop the machinery if detected and not properly classified. Similarly, machinery typically comes into contact with workpieces, but it is typically hazardous for machinery to come into contact with people. Therefore, analysis module 342 should be able to distinguish between workpieces and unknown foreign objects, especially people.df" in par. 0045) ; and
a prediction step of predicting a collision between the robot and the nearby object, based on a classification result obtained in the classification step (see at least "A further refinement is for SADM 425 to modulate maximum velocity proportionally to the minimum possible time to collision—that is, to project the robot's current state forward in time, project the intrusions toward the robot trajectory, and identify the nearest potential collision or violation of an object-specified protective separation distance. This refinement has the advantage that the robot will move more quickly away from an obstacle than toward it, which maximizes throughput while still correctly preserving safety." in par. 0099) .
an installation step of installing a simulation point cloud for point cloud data classified as the point cloud data corresponding to the robot in the classification step (see at least “Reliable state analysis typically requires an accurate model of each robot link. This model can be obtained a priori, e.g., from 3D CAD files provided by the robot manufacturer or generated by industrial engineers for a specific project.” In par. 0095 and " In this case, it is possible for RSDM 420 to create the model itself, e.g., using sensors 102. This may be done in a separate training mode where robot 402 runs through a set of motions, e.g., the motions that are intended for use in the given application and/or a set of motions designed to provide sensors 102 with appropriate views of each link and end effector. It is possible, but not necessary, to provide some basic information about the robot a priori, such as the lengths and rotational axes of each link. During this training mode, RSDM 420 generates a 3D model of each link, complete with all necessary attachments. This model can then be used by RSDM 420 in conjunction with sensor images to determine the robot state." in par. 0096) ,
wherein the prediction step predicts a collision between the robot and the nearby object by using point cloud data including the simulation point cloud installed in the installation step (see at least " A refinement of this technique is for SADM 425 to control maximum velocity proportionally to the square root of the minimum distance, which reflects the fact that in a constant-deceleration scenario, velocity changes proportionally to the square root of the distance traveled, resulting in a smoother and more efficient, but still equally safe, result. A further refinement is for SADM 425 to modulate maximum velocity proportionally to the minimum possible time to collision—that is, to project the robot's current state forward in time, project the intrusions toward the robot trajectory, and identify the nearest potential collision or violation of an object-specified protective separation distance. This refinement has the advantage that the robot will move more quickly away from an obstacle than toward it, which maximizes throughput while still correctly preserving safety. Since the robot's future trajectory depends not just on its current velocity but on subsequent commands, SADM 425 may consider all points reachable by robot 402 within a certain reaction time given its current joint positions and velocities, and cause control signals to be issued based on the minimum collision time among any of these states. Yet a further refinement is for SADM 425 to take into account the entire planned trajectory of the robot when making this calculation, rather than simply the instantaneous joint velocities." in par. 0099) , and
and the simulation point cloud is installed in a straight line connecting a position of a tip of the robot and a position of a joint of the robot or is installed in a predetermined shape with respect to a position corresponding to a predetermined part of the robot. (see at least " The 3D pose of the robot may then be determined by combining provided joint positions with a static 3D model of each link and any end effectors to obtain the 3D shape of the entire robot 402." in par. 0092) .
Vu does not appear to explicitly teach all of the following, but Greiner does teach:
determining whether there is an overlap between the point cloud data and the voxel, or between the voxels, where the overlap exists, counting a number of points included in the voxel or the voxels, deriving an evaluation value based on the number of points, and where the evaluation value is equal to or greater than a preset threshold, determining that a collision is about to occur (see at least " Once the points from the complete point cloud data are isolated using axis-aligned culling, processing unit 26 further check each of the points in P.sub.AABB for occupancy in each of the individual voxels in the voxel grid representation. processing unit 26 create a collision data structure to count the number of points contained within a voxel. In this method, similar to the axis-aligned culling, processing unit 26 test each point of the isolated point cloud P.sub.AABB within the extent of each voxel. A collision count for a voxel is updated if a point lies within the bounds of that voxel." in par. 0063 and “The point cloud data can have noise in terms of random scanned points in the space, which does not belong to any surface. These points can be due to scanning errors or inaccurate clean-up during postprocessing. This noise can produce faulty collision along the path, as shown in FIGS. 12A, 12B, 12C and 12D. FIG. 12 a illustrates noise in the scan data in the form of stranded points and corresponding collision with the vehicle model. To avoid this, processing unit 26 set a minimum number of points that a voxel should contain to be classified as a colliding voxel. Processing unit 26 applies a point cloud threshold for a voxel to eliminate such cases.” in par. 0071)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Vu to incorporate the teachings of Greiner wherein the obstacle points within the voxels of the vehicle model are counted for each voxel and there is a minimum number of points threshold that needs to be exceeded for a voxel to considered about to collide with an obstacle, in order to arrive at the same collision determination logic used with the robot model taught by Vu. The motivation to incorporate the teachings of Greiner would be to avoid false positive collision detections (see par. 00071)
Regarding Claim 2, Vu as modified by Greiner (references to Vu) teaches:
the collision prediction method according to claim 1, wherein
in the classification step, at least one of a first classification method, a second classification method, a third classification method, or a fourth classification method is executable,
the first classification method being a method of performing clustering on the point cloud data acquired in the second acquisition step (see at least " One simple way clustering can be used is to eliminate small groups of occupied or potentially occupied voxels that are too small to possibly contain a person. Such small clusters may arise from occupation and occlusion analysis, as described above, and can otherwise cause control system 112 to incorrectly identify a hazard. Clusters can be tracked over time by simply associating identified clusters in each image frame with nearby clusters in previous frames or using more sophisticated image-processing techniques. The shape, size, or other features of a cluster can be identified and tracked from one frame to the next. Such features can be used to confirm associations between clusters from frame to frame, or to identify the motion of a cluster. This information can be used to enhance or enable some of the classification techniques described below. Additionally, tracking clusters of points can be employed to identify incorrect and thus potentially hazardous situations." in par. 0044) and
classifying the point cloud data into the point cloud data corresponding to the robot and the point cloud data corresponding to the nearby object, based on a result of the clustering and a position of the robot, the position of the robot being identified by the configuration information and the posture information acquired in the first acquisition step (see at least “In some cases, it may be sufficient filter out clusters below a certain size and to identify cluster transitions that indicate error states. In other cases, however, it may be necessary to further classify objects into one or more of four categories: (1) elements of the machinery being controlled by system 112, (2) the workpiece or workpieces that the machinery is operating on, and (3) other foreign objects, including people, that may be moving in unpredictable ways and that can be harmed by the machinery.” In par. 0045 and "For example, these data may be provided by the robot 402 or the robot controller 407 via a safety-rated communication protocol providing access to safety-rated data. The 3D pose of the robot may then be determined by combining provided joint positions with a static 3D model of each link and any end effectors to obtain the 3D shape of the entire robot 402.” In par. 0092) ;
the second classification method being a method of acquiring first point cloud data corresponding to the robot from the point cloud data acquired in the second acquisition step by using the first classification method (see at least “In some cases, it may be sufficient filter out clusters below a certain size and to identify cluster transitions that indicate error states. In other cases, however, it may be necessary to further classify objects into one or more of four categories: (1) elements of the machinery being controlled by system 112, (2) the workpiece or workpieces that the machinery is operating on, and (3) other foreign objects, including people, that may be moving in unpredictable ways and that can be harmed by the machinery.” In par. 0045 and "For example, these data may be provided by the robot 402 or the robot controller 407 via a safety-rated communication protocol providing access to safety-rated data. The 3D pose of the robot may then be determined by combining provided joint positions with a static 3D model of each link and any end effectors to obtain the 3D shape of the entire robot 402.” In par. 0092),
comparing the first point cloud data with second point cloud data stored in advance (see at least "In some cases, the robot may provide an interface to obtain joint positions that are not safety-rated, in which case the joint positions can be verified against images from sensors 102 (using, for example, safety-rated software). For example, received joint positions may be combined with static 3D models of each link and any end effectors to generate a 3D model of the entire robot 402. This 3D image can be used to remove any objects in the sensing data that are part of the robot itself If the joint positions are correct, this will fully eliminate all object data attributed to the robot 402. If, however, the joint positions are incorrect, the true position of robot 402 will diverge from the model, and some parts of the detected robot will not be removed. Those points will then appear as a foreign object in the new cycle. In the previous cycle, it can be assumed that the joint positions were correct because otherwise robot 402 would have been halted. Since the base joint of the robot does not move, at least one of the divergent points must be close to the robot. The detection of an unexpected entity close to robot 402 can then be used to trigger an error condition, which will cause control system 112 (see FIG. 1) to transition robot 402 to a safe state. Alternatively, sensor data can be used to identify the position of the robot using a correlation algorithm, such as described above in the section on registration, and this detected position can be compared with the joint position reported by the robot. " in par. 0093) ,
removing, from the first point cloud data, a point cloud to obtain point cloud data, and classifying the obtained point cloud data as the point cloud data corresponding to the robot (see at least "This 3D image can be used to remove any objects in the sensing data that are part of the robot itself If the joint positions are correct, this will fully eliminate all object data attributed to the robot 402." in par. 0093) ,
the third classification method being a method of predicting a position of a tip portion of the robot in the point cloud data acquired in the second acquisition step, based on the configuration information and the posture information, and classifying, as the point cloud data corresponding to the robot, a point cloud corresponding to the tip portion of the robot among point clouds overlapping with a three-dimensional model of the robot when the three-dimensional model of the robot is arranged at the predicted position (see at least "The 3D pose of the robot may then be determined by combining provided joint positions with a static 3D model of each link and any end effectors to obtain the 3D shape of the entire robot 402." in par. 0092 and “Alternatively, sensor data can be used to identify the position of the robot using a correlation algorithm, such as described above in the section on registration, and this detected position can be compared with the joint position reported by the robot. If the joint position information provided by robot 402 has been validated in this manner, it can be used to validate joint velocity information, which can then be used to predict future joint positions. If these positions are inconsistent with previously validated actual joint positions, the program can similarly trigger an error condition.” In par. 0093) ,
the fourth classification method being a method of acquiring first point cloud data corresponding to the robot from the point cloud data acquired in the second acquisition step by using the first classification method, comparing the first point cloud data with past point cloud data corresponding to the robot and past point cloud data corresponding to the nearby object, and classifying the first point cloud data as closer point cloud data of the past point cloud data corresponding to the robot and the past point cloud data corresponding to the nearby object (see at least "In some cases, the robot may provide an interface to obtain joint positions that are not safety-rated, in which case the joint positions can be verified against images from sensors 102 (using, for example, safety-rated software). For example, received joint positions may be combined with static 3D models of each link and any end effectors to generate a 3D model of the entire robot 402. This 3D image can be used to remove any objects in the sensing data that are part of the robot itself If the joint positions are correct, this will fully eliminate all object data attributed to the robot 402. If, however, the joint positions are incorrect, the true position of robot 402 will diverge from the model, and some parts of the detected robot will not be removed. Those points will then appear as a foreign object in the new cycle. In the previous cycle, it can be assumed that the joint positions were correct because otherwise robot 402 would have been halted. Since the base joint of the robot does not move, at least one of the divergent points must be close to the robot. The detection of an unexpected entity close to robot 402 can then be used to trigger an error condition, which will cause control system 112 (see FIG. 1) to transition robot 402 to a safe state. Alternatively, sensor data can be used to identify the position of the robot using a correlation algorithm, such as described above in the section on registration, and this detected position can be compared with the joint position reported by the robot. If the joint position information provided by robot 402 has been validated in this manner, it can be used to validate joint velocity information, which can then be used to predict future joint positions. If these positions are inconsistent with previously validated actual joint positions, the program can similarly trigger an error condition." in par. 0093) .
Vu does not appear to explicitly teach all of the following, but Greiner does teach:
removing, from the first point cloud data, a point cloud for which a shortest distance from the second point cloud data is equal to or less than a predetermined threshold to obtain point cloud data, and classifying the obtained point cloud data as the point cloud data corresponding to the robot (see at least “The voxelization approach described in the “Voxelization” section computes voxels using a tight bounding box of the vehicle model. Any valid collision with the voxels is considered a model collision. However, processing unit 26 cannot use this voxel grid to identify if a point lies within a certain distance of the model. Processing unit 26 may define this region as the Clearance Zone, which is useful to ensure there is enough clearance around the model while navigating along the desired path." in par. 0051)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Vu to incorporate the teachings of Greiner wherein a clearance zone of a specified distance is added to the voxel model. The motivation to incorporate the teachings of Greiner would be to improve the chances there is enough clearance for the robot to carry out a path (see par. 0051)
Regarding Claim 3, Vu as modified by Greiner (references to Vu) teaches:
the collision prediction method according to claim 2,
Vu further teaches: wherein the first classification method and the second classification method are executable and the method further comprising a determination step of determining whether the nearby object has moved (see at least "The set of relevant entities can include all entities in the workspace, including both static background such as walls and tables, and moving entities such as workpieces and human workers. Either from prior configuration or run-time detection, sensors 102 and analysis module 342 may be able to infer which entities could possibly be moving. In this case, any of the algorithms described above can be refined to leave additional margins to account for entities that might be moving, but to eliminate those margins for entities that are known to be static, so as not to reduce throughput unnecessarily but still automatically eliminate the possibility of collisions with static parts of the work cell." in par. 0100) , wherein
in the classification step, the first classification method is used when it is determined that the nearby object has moved (see at least “In some cases, it may be sufficient filter out clusters below a certain size and to identify cluster transitions that indicate error states. In other cases, however, it may be necessary to further classify objects into one or more of four categories: (1) elements of the machinery being controlled by system 112, (2) the workpiece or workpieces that the machinery is operating on, and (3) other foreign objects, including people, that may be moving in unpredictable ways and that can be harmed by the machinery.” In par. 0045 and "For example, these data may be provided by the robot 402 or the robot controller 407 via a safety-rated communication protocol providing access to safety-rated data. The 3D pose of the robot may then be determined by combining provided joint positions with a static 3D model of each link and any end effectors to obtain the 3D shape of the entire robot 402.” In par. 0092) , and
the second classification method is used when it is determined that the nearby object has not moved (see at least " This 3D image can be used to remove any objects in the sensing data that are part of the robot itself If the joint positions are correct, this will fully eliminate all object data attributed to the robot 402. If, however, the joint positions are incorrect, the true position of robot 402 will diverge from the model, and some parts of the detected robot will not be removed. Those points will then appear as a foreign object in the new cycle. In the previous cycle, it can be assumed that the joint positions were correct because otherwise robot 402 would have been halted. Since the base joint of the robot does not move, at least one of the divergent points must be close to the robot. The detection of an unexpected entity close to robot 402 can then be used to trigger an error condition, which will cause control system 112 (see FIG. 1) to transition robot 402 to a safe state." in par. 0093) .
Regarding Claim 5, Vu as modified by Greiner (references to Vu) teaches:
the collision prediction method according to claim 1, further comprising a preprocessing step of performing predetermined preprocessing on the point cloud data acquired in the second acquisition step, wherein
the predetermined preprocessing includes a process of removing point cloud data located out of a movable range of the robot (see at least “in particular, at each cycle, object analysis module 415 identifies the precise 3D location and extent of all entities in workspace 400 that are either within the robot's reach or that could move into the robot's reach at conservative expected velocities….That is, a workspace may have a plurality of entry points, not all of which are necessarily monitored; an operator may, for example, enter the workspace 400 via an unguarded entry point (which may also be unmonitored). When the sensors 102 detect the presence of an object (which could be the entering operator), object-analysis module 415 assumes the object is a human and computes a trajectory and based thereon, a minimum estimated time before the entering operator could come within an unsafe distance of robot 402.” In par. 0062 " The separation of tracking and safeguarding also allows for the tracking of objects and humans outside the safeguarded space, which we define as the volume that is being actively monitored for objects and humans. In general, the safeguarded space corresponds to the space occupied by the workcell. This eliminates the need for tracking entry points for objects smaller than a human, as the tracking may be performed outside the safeguarded volume, knowing where the humans are outside the safeguarded volume. Once a human enters the safeguarded volume, it is known to be a human as it has been tracked as such by the workcell's vision system or one associated with an adjacent workcell and in communication with the workcell's vision system" in par. 0091) , and
the classification step classifies the point cloud data on which the predetermined preprocessing is performed in the preprocessing step (see at least "The separation of tracking and safeguarding also allows for the tracking of objects and humans outside the safeguarded space, which we define as the volume that is being actively monitored for objects and humans. In general, the safeguarded space corresponds to the space occupied by the workcell. This eliminates the need for tracking entry points for objects smaller than a human, as the tracking may be performed outside the safeguarded volume, knowing where the humans are outside the safeguarded volume. Once a human enters the safeguarded volume, it is known to be a human as it has been tracked as such by the workcell's vision system or one associated with an adjacent workcell and in communication with the workcell's vision system" in par. 0091 ) .
Regarding Claim 7, Vu as modified by Greiner (references to Vu) teaches:
the collision prediction method according to claim 1, further comprising:
a step of acquiring information on a movement direction of a predetermined point on the robot (see at least " Since the robot's future trajectory depends not just on its current velocity but on subsequent commands, SADM 425 may consider all points reachable by robot 402 within a certain reaction time given its current joint positions and velocities, and cause control signals to be issued based on the minimum collision time among any of these states. Yet a further refinement is for SADM 425 to take into account the entire planned trajectory of the robot when making this calculation, rather than simply the instantaneous joint velocities. " in par. 0099 ) ; and
an extraction step of setting a predefined range for the point cloud data corresponding to the robot, the predefined range being defined in advance to extend along a movement direction of the robot with respect to the predetermined point, and extracting point cloud data corresponding to the nearby object within the predefined range wherein the prediction step predicts a collision between the robot and the nearby object by using the point cloud data corresponding to the nearby object extracted in the extraction step and the point cloud data corresponding to the robot. (see at least " A further refinement is for SADM 425 to modulate maximum velocity proportionally to the minimum possible time to collision—that is, to project the robot's current state forward in time, project the intrusions toward the robot trajectory, and identify the nearest potential collision or violation of an object-specified protective separation distance. This refinement has the advantage that the robot will move more quickly away from an obstacle than toward it, which maximizes throughput while still correctly preserving safety. Since the robot's future trajectory depends not just on its current velocity but on subsequent commands, SADM 425 may consider all points reachable by robot 402 within a certain reaction time given its current joint positions and velocities, and cause control signals to be issued based on the minimum collision time among any of these states." in par. 0099)
Regarding Claim 8, Vu as modified by Greiner (references to Vu) teaches:
the collision prediction method according to claim 1, wherein
the posture information includes at least one of a position of a tip portion of the robot, an angle of the tip portion of the robot, a position of a joint portion of the robot, or an angle of the joint portion of the robot (see at least " Similarly, the instantaneous joint positions associated with the pose and the velocity of the end effector can be used to predict a future end-effector state. The state of an end effector may also include the size and/or weight of a workpiece, which can affect kinematics and maintenance of a safe distance from inanimate entities and humans." in par. 0061 and “The 3D pose of the robot may then be determined by combining provided joint positions with a static 3D model of each link and any end effectors to obtain the 3D shape of the entire robot 402.” In par. 0092) .
Regarding Claim 9, Vu as modified by Greiner (references to Vu) teaches:
the collision prediction method according to claim 1, wherein
the configuration information includes a three-dimensional model indicating a shape of the robot (see at least " For example, received joint positions may be combined with static 3D models of each link and any end effectors to generate a 3D model of the entire robot 402. " in par. 0093).
Regarding Claim 10, Vu as modified by Greiner (references to Vu) also teaches:
a collision prediction device for predicting a collision between a multi-joint robot and a nearby object (see at least " FIG. 3 illustrates, in greater detail, a representative embodiment of control system 112, which may be implemented on a general-purpose computer. The control system 112 includes a central processing unit (CPU) 305, system memory 310, and one or more non-volatile mass storage devices (such as one or more hard disks and/or optical storage units) 312. " in par. 0024) ) , the collision prediction device comprising:
processing circuitry for implementing the method of Claim 1 (see Claim 1 analysis for rejection of the method)
Regarding Claim 12, Vu as modified by Greiner (references to Vu) also teaches:
the collision prediction method according to claim 1,
wherein the classification step includes acquiring first point cloud data corresponding to the robot from the point cloud data acquired in the second acquisition step by using the first classification method, (see at least “In some cases, it may be sufficient filter out clusters below a certain size and to identify cluster transitions that indicate error states. In other cases, however, it may be necessary to further classify objects into one or more of four categories: (1) elements of the machinery being controlled by system 112, (2) the workpiece or workpieces that the machinery is operating on,” in par. 0045)
comparing the first point cloud data with second point cloud data stored in advance, removing, from the first point cloud data, a point cloud for which a shortest distance from the second point cloud data is equal to or less than a predetermined threshold to obtain point cloud data, and classifying the obtained point cloud data as the point cloud data corresponding to the robot. (see at least “In some cases, it may be sufficient filter out clusters below a certain size and to identify cluster transitions that indicate error states.” In par. 0045 and “An offsetting tool may be used to adjust the boundary of a represented entity by a configurable number of voxels. This boundary offsetting step may be performed to account, for example, for multiple variants, part-to-part tolerance stack up or variance, or to influence the sensitivity of object analysis module 415 to the sensed entity or entities. Alternatively, conventional techniques of image data augmentation can be employed to translate and rotate by small amounts the entity images captured by the different sensors, thereby establishing the range of acceptable variations in position and orientation.” In par. 0065)
Regarding Claim 13, Vu as modified by Greiner (references to Vu) also teaches:
the collision prediction method according to claim 1,
wherein the classification step includes predicting a position of a tip portion of the robot in the point cloud data acquired in the second acquisition step. based on the configuration information and the posture information, and classifying, as the point cloud data corresponding to the robot, a point cloud corresponding to the tip portion of the robot among point clouds overlapping with a three-dimensional model of the robot when the three- dimensional model of the robot is arranged at the predicted position. (see at least “In some cases, it may be sufficient filter out clusters below a certain size and to identify cluster transitions that indicate error states. In other cases, however, it may be necessary to further classify objects into one or more of four categories: (1) elements of the machinery being controlled by system 112, (2) the workpiece or workpieces that the machinery is operating on, and (3) other foreign objects, including people, that may be moving in unpredictable ways and that can be harmed by the machinery.” In par. 0045 and “In cases where the machinery changes shape, elements of the machinery can be identified and classified, e.g., by supplying analysis module 342 with information about these elements (e.g., as scalable 3D representations), and in some cases (such as industrial robot arms) providing a source of instantaneous information about the state of the machinery. Analysis module 342 may be “trained” by operating machinery, conveyors, etc. in isolation under observation by the sensors 102, allowing analysis module 342 to learn their precise regions of operation resulting from execution of the full repertoire of motions and poses. Analysis module 342 may classify the resulting spatial regions as occupied.” In par. 0046 and "For example, these data may be provided by the robot 402 or the robot controller 407 via a safety-rated communication protocol providing access to safety-rated data. The 3D pose of the robot may then be determined by combining provided joint positions with a static 3D model of each link and any end effectors to obtain the 3D shape of the entire robot 402.” In par. 0092)
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vu et al (US 20220088787, hereinafter Vu) in view of Greiner et al (US 20230418288, hereinafter Greiner) and Ikeguchi (US 20240402674, hereinafter Ikeguchi)
Regarding Claim 11, Vu as modified by Greiner (references to Vu) also teaches:
the collision prediction device according to claim 10 (see Claim 10 analysis)
Vu and Greiner does not appear to explicitly teach all of the following, but Ikeguchi does teach:
A welding system (see at least "The welding robot system 100 includes the interference determination device 1, a robot controller 2, and a manipulator (robot) 3, for example. " in par. 0035) comprising:
a welding robot, the welding robot comprising the robot (see at least "The manipulator 3 is a welding robot, which performs arc welding to a workpiece that is the welding target in accordance with the operation conditions set in the robot controller 2. " in par. 0037) .
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Vu as modified by Greiner to incorporate the teachings of Ikeguchi wherein the system is a welding robot system with an interference determination device, in order to arrive at the system taught by Vu used to monitor a welding robot. The motivation to incorporate the teachings of Ikeguchi would be to more efficiently detect collisions between a welding robot and the surrounding environment (see par. 0050).
Claim(s) 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vu et al (US 20220088787, hereinafter Vu) in view of Greiner et al (US 20230418288, hereinafter Greiner) and Murugan (US 20240208060, hereinafter Murugan)
Regarding Claim 14, Vu as modified by Greiner teaches:
the collision prediction method according to claim 1,
Vu and Greiner do not appear to explicitly teach all of the following, but Murugan does teach:
wherein the simulation point cloud is set in a predetermined shape corresponding to a cable located behind an arm of the robot in accordance with a posture of the robot. (see at least " A processor-based system models a respective volume occupied by one or more cables 119 (FIG. 1) based on one or more of a geometry of the cable(s) 119, a current set of joint positions of the current robot I (e.g., first robot 102), a position and orientation of the cable(s) 119 with respect to a portion of current robot I, an effect of gravity on the cable(s) 119, an amount of slack, sag, droop, or tension in the cable(s) 119, and/or a current velocity of at least a part of the portion of the current robot I or the cable(s) 119, for instance accounting for inertial effects on the cable(s) 119." in par. 0100)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Vu as modified by Greiner to incorporate the teachings of Murugan wherein the system models the space occupied by cables routed around the outside of the robot and extends the 3D model of the robot to include this space. The motivation to incorporate the teachings of Murugan would be to more accurately model the space taken up by the robot for collision avoidance (see par. 0007), which reduces the probability of collisions.
Regarding Claim 15, Vu as modified by Greiner teaches:
the collision prediction method according to claim 1,
Vu and Greiner do not appear to explicitly teach all of the following, but Murugan does teach:
wherein the simulation point cloud is set in a predetermined shape corresponding to a cable located below an arm of the robot. (see at least "A processor-based system models a respective volume occupied by one or more cables 119 (FIG. 1) based on one or more of a geometry of the cable(s) 119, a current set of joint positions of the current robot I (e.g., first robot 102), a position and orientation of the cable(s) 119 with respect to a portion of current robot I, an effect of gravity on the cable(s) 119, an amount of slack, sag, droop, or tension in the cable(s) 119, and/or a current velocity of at least a part of the portion of the current robot I or the cable(s) 119, for instance accounting for inertial effects on the cable(s) 119." in par. 0100)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Vu as modified by Greiner to incorporate the teachings of Murugan wherein the system models the space occupied by cables routed around the outside of the robot and extends the 3D model of the robot to include this space. The motivation to incorporate the teachings of Murugan would be to more accurately model the space taken up by the robot for collision avoidance (see par. 0007), which reduces the probability of collisions.
Allowable Subject Matter
Claim 16 allowed.
The following is a statement of reasons for the indication of allowable subject matter: The closest prior art comes from Vu, Greiner, and Murugan but the prior art does not appear to teach “predicting the collision, based on a difference in the number of points between current point cloud data corresponding to the robot and previous point cloud data corresponding to the robot” in combination with all of the other limitations in the claim.
Conclusion
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/DYLAN M KATZ/Examiner, Art Unit 3657
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