DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20220016763 A1) in view of Meier (US 20180117761 A1) in further view of Perreault (US 7313464 B1)
Regarding claim 1, Chen teaches A computer implemented method for providing observational support in a conventional robotic pick cell comprising a robot, an imaging system, and a vision system, the computer implemented method comprising: ([0027] example implementations involve the use of deep reinforcement learning in a simulation environment 102 to provide a control strategy model 108 to be deployed on a robot 110, so that the robot 110 can understand situations through sensors 111 and execute operations accordingly, such as object pickup or locating an object in the case for robotic arms. Sensors 111 can be any kind of sensor utilized to facilitate the operations, such as cameras, stereo cameras, and so on in accordance with the desired implementation.)
obtaining a trigger signal, the trigger signal obtained by a processor associated with an observational support system, the observational support system positioned in relation to a robotic pick cell such that the observational support system can obtain pick scene data associated with the robotic pick cell; ([0027] the robot 110 can understand situations through sensors 111 and execute operations accordingly, such as object pickup or locating an object in the case for robotic arms…The control strategy model 108 will be executed by control system 113 and the operations will continue running even if the physical robot fails to accomplish the potential task. Monitoring system 115 will conduct the recording of the performance of the robot. If a robot fails to accomplish a task, the system will send that failed condition or failure scenario back to the training server 101 to control strategy trainer 106. )
obtaining, in response to the trigger signal, auxiliary pick scene data associated with the robotic pick cell, wherein the auxiliary pick scene data is obtained by an auxiliary sensor module associated with the observational support system, wherein the auxiliary pick scene data is obtained in addition to pick scene data obtained by a first sensor module associated with a first vision system ([0025] On training server 101, the control strategy trainer 106 manages the simulation environment 102, control system 107 and the training agent 109, wherein the simulation environment 102 is responsible for simulating the actual motion of the robot and the physics of the objects found in the real-world environment; the control system 107 generates a robot control strategy based on inputs from virtual sensor 104 and control strategy model 108; the training agent 109 evaluates the performance of the robot and helps the control strategy trainer 106 update the control strategy model 108 iteratively in a training process.), wherein both the first vision system and the observational support system are configured to provide pick control information to a robot, ([0026] The control strategy model 114 is a copy of the same model 108 generated on training server. In addition, the robot performance monitoring system continuously records outputs from sensor(s) 111 and the performance of robot 110..)
sending the auxiliary pick scene data obtained by the auxiliary sensor module, wherein the intervention system is operable to communicate with the observational support system; (Claim 11 A first server communicatively coupled to a second server configured to manage a real world environment comprising one or more robots and one or more sensors, the first server configured to manage a simulation environment comprising one or more virtual robots corresponding to the one or more robots, and one or more virtual sensors corresponding to the one or more sensors, [0026] In case the robot 110 fails to accomplish a job, the failure data is uploaded to control strategy training 106, which will regenerate the failed scenario on the training server 101 and launch an additional training process to further update the control strategy model 108. In the end, the updated control strategy model 108 is deployed back to the real-world environment as an update to control strategy model 114.)
obtaining response data from the intervention system via network communication, wherein the response data comprises pick control information which the first vision system did not provide to the robot; and ([0027] Once the model is updated by the control strategy trainer 106, it is redeployed as an updated model 108 to update control strategy model 114.)
sending the response data to the robot for use in controlling the robot, ([0040] In step 503, the control system 107 drives the robot 103 to complete the operation based on such control strategy.)
Chen does not expressly disclose but Meier discloses to a remote intervention system via network communication ([0087] In some implementations, the data (e.g., the digitized frame 416) obtained responsive to the spot-light operation, may be offloaded via a communications link to a remote processing resource (e.g., a remote server).)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Meier with a reasonable expectation of success by providing contextual instruction to a robotic apparatus as taught by Meier ([0006]).
Chen does not expressly disclose but Perreault discloses wherein the trigger signal is associated with the first vision system being unable to determine appropriate pick control information for the robot based on the data obtained by the first sensor module; (Col 6 Line 62-64 In step 102, a 3D image of the contents of the bin 12 is acquired using the vision system 20. Col 8 66-67 – Col 9 Line 1 Line In step 124, if there is no reachable object, the bin is tilted and typically shaken in step 126 before going back to step 102)
wherein the robot is configured to accept instructions from the observational support system when the first vision system is unable to determine appropriate pick control information for the robot. (Col 8 Line 36-43 In step 118, if there is no prehensible object, the robot 14 not being able to grasp any object, the bin is shaken in step 120 and the method goes back to step 102. In step 120, the bin is shaken horizontally on the conveyer. In one embodiment, the bin is shaken by the robot 14 which grasp one side wall 13 of the bin and translates it into a jerky back in forth motion. When the bin 12 is tilted or flat shaken by the robot 14, a prior check of the position of the bin 12 is performed)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Perreault with a reasonable expectation of success by picking up objects randomly arranged in a bin using a robot as taught by Perreault (Abstract).
Regarding claim 2, Chen teaches The computer implemented method according to claim 1, wherein the trigger signal is associated with an error code associated with at least one of the first vision system and first sensor module. ([0026] In addition, the robot performance monitoring system continuously records outputs from sensor(s) 111 and the performance of robot 110. In case the robot 110 fails to accomplish a job, the failure data is uploaded to control strategy training 106, which will regenerate the failed scenario on the training server 101 and launch an additional training process to further update the control strategy model 108)
Regarding claim 3, Chen teaches The computer implemented method according to claim 1, wherein the trigger signal is associated with a robot generated trigger signal in response to an error code associated with at least one of the first vision system and first sensor module. ([0026] In addition, the robot performance monitoring system continuously records outputs from sensor(s) 111 and the performance of robot 110. In case the robot 110 fails to accomplish a job, the failure data is uploaded to control strategy training 106, which will regenerate the failed scenario on the training server 101 and launch an additional training process to further update the control strategy model 108)
Regarding claim 4, Chen does not expressly disclose but Meier discloses The computer implemented method according to claim 1, wherein the trigger signal is associated with a manually activated trigger, the manually activated trigger initiated via interaction with a component of a housing associated with the auxiliary sensor module. ([0077] Beams of different light may be used, for example, to attract attention (e.g., guide) of a particular robotic cars 110, 120. Individual robots 110, 120 may be characterized by a sensing field of view 112, associated, for example, with the aperture of the robot's sensor. In some implementations, the sensor (not shown) may comprise a digital camera comprising a lens and an imaging array. [0076] (iii) one or more buttons configured to provide supplementary contextual information to the robot; (iv) a wireless communication block configured to communicate to the robot non-visual information (e.g., button presses);)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Meier with a reasonable expectation of success by providing contextual instruction to a robotic apparatus as taught by Meier ([0006]).
Regarding claim 5, Chen teaches The computer implemented method according to claim 1, wherein the first vision system being unable to determine appropriate pick control information for the robot comprises at least one of a failed pick resulting from information provided by the first vision system and the first vision system failing to provide pick information usable by the robot in executing an operation. ([0031] Monitoring system 115 is configured to continuously record the input from the sensors 111 and also the performance of the robot 110. Should any failure in the robot operation occur, the monitoring system will send the failure scenario back to the training server 101 which will then train a virtual robot 103 under the same failure scenario until the virtual robot 103 learns how to do conduct the operation without incurring failure. The control strategy model 108 is then updated and redeployed back to the real world environment.)
Regarding claim 6, Chen teaches The computer implemented method according to claim 1, wherein the obtained data associated with the scene comprises at least one of image data, video data, and depth data. ([0032] The failure scenario can be in the form of images of the workspace and data involving the operation to be completed, sensor measurements that are used as inputs for the virtual sensors, and any other data describing the condition for the physical robot 110, and including data indicating that the operation has failed.)
Regarding claim 7, Chen teaches The computer implemented method according to claim 1, wherein the auxiliary sensor module comprises at least one of a camera and a depth sensor. ([0027] Sensors 111 can be any kind of sensor utilized to facilitate the operations, such as cameras, stereo cameras, and so on in accordance with the desired implementation.)
Regarding claim 8, Chen teaches The computer implemented method according to claim 1, wherein the auxiliary sensor module gathers pick scene data that is substantially the same as pick scene data captured by the first sensor module or at least partially different than or at least partially overlapping with the pick scene data captured by the first sensor module. ([0040] In step 501, the control system 107 loads the current/latest control strategy model 108. In step 502, the control system 107 passes the inputs from sensor 104 into the control strategy model 108 and acquires a control strategy. )
Regarding claim 9, Chen does not expressly disclose but Meier discloses The computer implemented method according to claim 1, wherein the intervention system is associated with at least one of a human-in-the-loop operator and a remote artificial intelligence based intervention system. ([0074] Exemplary implementations are now described in detail. It will be appreciated that while described substantially in the context of autonomous robotic devices, the present disclosure is in no way so limited. Rather, the innovation is contemplated for use with any number of different artificial intelligence, robotic, and/or automated control systems)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Meier with a reasonable expectation of success by providing contextual instruction to a robotic apparatus as taught by Meier ([0006]).
Regarding claim 10, Chen teaches The computer implemented method according to claim 1, further comprising sending query information to the remote intervention system via network communication ([0043] For example, to understand if an object is picked up by the robot, the training agent 109 will examine the coordinates of each object and robot gripper. If one object is no longer sitting on the workplace and is co-located with the center of the robot gripper, then this object is successfully picked up by the robot. ), the query information comprising information to be displayed to a user via the intervention system including at least an indication of expected response information to be provided via the intervention system. ([0044] For the convenience of maintenance, a monitor 807 and keyboard 808 can also be installed through the display interface 805 and USB interface 806. In addition, the robot performance monitoring system server has an Input/Output (I/O) interface 810 to communication with external devices, such as sensors 111. All mentioned components communicate through a bus 809.)
Regarding claim 11, Chen does not expressly disclose but Meier discloses The computer implemented method according to claim 1, the query information obtained from a database of previously configured query requests and associated expected response information. ([0098] The error signal may enable the robot to incrementally improve an estimation of the “correct” region to attend to in an online manner. Off-line learning may be used to minimize attentional error across a database of actions and/or contexts associated with individual actions.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Meier with a reasonable expectation of success by providing contextual instruction to a robotic apparatus as taught by Meier ([0006]).
Regarding claim 12, Chen does not expressly disclose but Meier discloses The computer implemented method according to claim 1, the query information obtained from a database storing query information in association with an identifier associated with the auxiliary sensor module. ([0098] In some implementations, learning by a robot may be aided by an error signal. The error signal may convey a difference in the robots internal attention algorithm, the region selected by the user, and/or other information. The error signal may enable the robot to incrementally improve an estimation of the “correct” region to attend to in an online manner. Off-line learning may be used to minimize attentional error across a database of actions and/or contexts associated with individual actions)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Meier with a reasonable expectation of success by providing contextual instruction to a robotic apparatus as taught by Meier ([0006]).
Regarding claim 13, Chen teaches The computer implemented method according to claim 1, the response data comprising at least one of coordinates associated with an object, object classification information, and binary response data. (Fig. 1 [0037] robot to accomplish the task in workspace 105)
Regarding claim 14, Chen teaches The computer implemented method according to claim 1, wherein the pick control information comprises at least one of pick instructions, pick coordinates, and identification of a pick object. ([0034] The gripper 203 can open and close so as to pick up and drop off an object.)
Regarding claim 15, Chen teaches The computer implemented method according to claim 1, wherein sending the response data to the robot comprises converting the response data to pick instructions usable by the robot to execute a picking operation. ([0039] Referring to step 405, if only incremental updates to the control strategy model 108 are needed (e.g. real robot failed in an action in real-world) (No), the control strategy trainer 106 will load the current model and then re-generate failure scenarios in step 406. The rest of the training process from step 407 through 412 remains the same. In step 413, once the end condition is met, the updated model is then copied to its counterpart 114 in the real-world environment.)
Regarding claim 16, Chen teaches The computer implemented method according to claim 1, the robot comprising a robotic picking system. ([0034] The gripper 203 can open and close so as to pick up and drop off an object.)
Regarding claim 17, Chen teaches The computer implemented method according to claim 1, wherein the robot is configured to accept instructions from the second vision system if there is an error code associated with the first vision system. ([0026] In addition, the robot performance monitoring system continuously records outputs from sensor(s) 111 and the performance of robot 110. In case the robot 110 fails to accomplish a job, the failure data is uploaded to control strategy training 106, which will regenerate the failed scenario on the training server 101 and launch an additional training process to further update the control strategy model 108)
Regarding claim 18, Chen teaches The computer implemented method according to claim 1, wherein the first vision system and first sensor module are not configured for communication with a remote intervention system. ([0023] FIG. 1 illustrates an overview of the self-learning robotic system, in accordance with an example implementation. In the example of FIG. 1, the self-learning robotic system can involve two aspects: the training server 101 and the real-world environment.)
Regarding claim 19, Chen teaches A computing system for providing observational support in a conventional robotic pick cell comprising a robot, an imaging system, and a vision system, the computing system comprising: ([0027] example implementations involve the use of deep reinforcement learning in a simulation environment 102 to provide a control strategy model 108 to be deployed on a robot 110, so that the robot 110 can understand situations through sensors 111 and execute operations accordingly, such as object pickup or locating an object in the case for robotic arms. Sensors 111 can be any kind of sensor utilized to facilitate the operations, such as cameras, stereo cameras, and so on in accordance with the desired implementation.)
at least one computing processor; and (Claim 11 a processor)
memory comprising instructions that, when executed by the at least one computing processor, enable the computing system to: ([0044] FIG. 8, which can involve one or more CPUs 801, memory modules 802)
obtain a trigger signal, the trigger signal obtained by a processor associated with an observational support system, the observational support system positioned in relation to a robotic pick cell such that the observational support system can obtain pick scene data associated with the robotic pick cell; ([0027] the robot 110 can understand situations through sensors 111 and execute operations accordingly, such as object pickup or locating an object in the case for robotic arms…The control strategy model 108 will be executed by control system 113 and the operations will continue running even if the physical robot fails to accomplish the potential task. Monitoring system 115 will conduct the recording of the performance of the robot. If a robot fails to accomplish a task, the system will send that failed condition or failure scenario back to the training server 101 to control strategy trainer 106. )
obtain, in response to the trigger signal, auxiliary pick scene data associated with the robotic pick cell, wherein the auxiliary pick scene data is obtained by an auxiliary sensor module associated with the observational support system, wherein the auxiliary pick scene data is obtained in addition to pick scene data obtained by a first sensor module associated with a first vision system ([0025] On training server 101, the control strategy trainer 106 manages the simulation environment 102, control system 107 and the training agent 109, wherein the simulation environment 102 is responsible for simulating the actual motion of the robot and the physics of the objects found in the real-world environment; the control system 107 generates a robot control strategy based on inputs from virtual sensor 104 and control strategy model 108; the training agent 109 evaluates the performance of the robot and helps the control strategy trainer 106 update the control strategy model 108 iteratively in a training process.), wherein both the first vision system and the observational support system are configured to provide pick control information to a robot; ([0026] The control strategy model 114 is a copy of the same model 108 generated on training server. In addition, the robot performance monitoring system continuously records outputs from sensor(s) 111 and the performance of robot 110..)
send the auxiliary pick scene data obtained by the auxiliary sensor module, wherein the intervention system is operable to communicate with the observational support system; (Claim 11 A first server communicatively coupled to a second server configured to manage a real world environment comprising one or more robots and one or more sensors, the first server configured to manage a simulation environment comprising one or more virtual robots corresponding to the one or more robots, and one or more virtual sensors corresponding to the one or more sensors, [0026] In case the robot 110 fails to accomplish a job, the failure data is uploaded to control strategy training 106, which will regenerate the failed scenario on the training server 101 and launch an additional training process to further update the control strategy model 108. In the end, the updated control strategy model 108 is deployed back to the real-world environment as an update to control strategy model 114.)
obtain response data from the intervention system via network communication, wherein the response data comprises pick control information which the first vision system did not provide to the robot; and ([0027] Once the model is updated by the control strategy trainer 106, it is redeployed as an updated model 108 to update control strategy model 114.)
send the response data to the robot for use in controlling the robot,. ([0040] In step 503, the control system 107 drives the robot 103 to complete the operation based on such control strategy.)
Chen does not expressly disclose but Meier discloses to a remote intervention system via network communication ([0087] In some implementations, the data (e.g., the digitized frame 416) obtained responsive to the spot-light operation, may be offloaded via a communications link to a remote processing resource (e.g., a remote server).)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Meier with a reasonable expectation of success by providing contextual instruction to a robotic apparatus as taught by Meier ([0006]).
Chen does not expressly disclose but Perreault discloses wherein the trigger signal is associated with the first vision system being unable to determine appropriate pick control information for the robot based on the data obtained by the first sensor module(Col 6 Line 62-64 In step 102, a 3D image of the contents of the bin 12 is acquired using the vision system 20. Col 8 66-67 – Col 9 Line 1 Line In step 124, if there is no reachable object, the bin is tilted and typically shaken in step 126 before going back to step 102)
wherein the robot is configured to accept instructions from the observational support system when the first vision system is unable to determine appropriate pick control information for the robot(Col 8 Line 36-43 In step 118, if there is no prehensible object, the robot 14 not being able to grasp any object, the bin is shaken in step 120 and the method goes back to step 102. In step 120, the bin is shaken horizontally on the conveyer. In one embodiment, the bin is shaken by the robot 14 which grasp one side wall 13 of the bin and translates it into a jerky back in forth motion. When the bin 12 is tilted or flat shaken by the robot 14, a prior check of the position of the bin 12 is performed)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Perreault with a reasonable expectation of success by picking up objects randomly arranged in a bin using a robot as taught by Perreault (Abstract).
Regarding claim 20, Chen teaches A computer readable medium comprising instructions that when executed by a processor enable the processor to: ([0027] example implementations involve the use of deep reinforcement learning in a simulation environment 102 to provide a control strategy model 108 to be deployed on a robot 110, so that the robot 110 can understand situations through sensors 111 and execute operations accordingly, such as object pickup or locating an object in the case for robotic arms. Sensors 111 can be any kind of sensor utilized to facilitate the operations, such as cameras, stereo cameras, and so on in accordance with the desired implementation.)
obtain a trigger signal, the trigger signal obtained by a processor associated with an observational support system, the observational support system positioned in relation to a robotic pick cell such that the observational support system can obtain pick scene data associated with the robotic pick cell; ([0027] the robot 110 can understand situations through sensors 111 and execute operations accordingly, such as object pickup or locating an object in the case for robotic arms…The control strategy model 108 will be executed by control system 113 and the operations will continue running even if the physical robot fails to accomplish the potential task. Monitoring system 115 will conduct the recording of the performance of the robot. If a robot fails to accomplish a task, the system will send that failed condition or failure scenario back to the training server 101 to control strategy trainer 106. )
obtain, in response to the trigger signal, auxiliary pick scene data associated with the robotic pick cell, wherein the auxiliary pick scene data is obtained by an auxiliary sensor module associated with the observational support system, wherein the auxiliary pick scene data is obtained in addition to pick scene data obtained by a first sensor module associated with a first vision system([0025] On training server 101, the control strategy trainer 106 manages the simulation environment 102, control system 107 and the training agent 109, wherein the simulation environment 102 is responsible for simulating the actual motion of the robot and the physics of the objects found in the real-world environment; the control system 107 generates a robot control strategy based on inputs from virtual sensor 104 and control strategy model 108; the training agent 109 evaluates the performance of the robot and helps the control strategy trainer 106 update the control strategy model 108 iteratively in a training process.), wherein both the first vision system and the observational support system are configured to provide pick control information to a robot; ([0026] The control strategy model 114 is a copy of the same model 108 generated on training server. In addition, the robot performance monitoring system continuously records outputs from sensor(s) 111 and the performance of robot 110..)
send the auxiliary pick scene data obtained by the auxiliary sensor module, wherein the intervention system is operable to communicate with the observational support system; (Claim 11 A first server communicatively coupled to a second server configured to manage a real world environment comprising one or more robots and one or more sensors, the first server configured to manage a simulation environment comprising one or more virtual robots corresponding to the one or more robots, and one or more virtual sensors corresponding to the one or more sensors, [0026] In case the robot 110 fails to accomplish a job, the failure data is uploaded to control strategy training 106, which will regenerate the failed scenario on the training server 101 and launch an additional training process to further update the control strategy model 108. In the end, the updated control strategy model 108 is deployed back to the real-world environment as an update to control strategy model 114.)
obtain response data from the intervention system via network communication, wherein the response data comprises pick control information which the first vision system did not provide to the robot; and ([0027] Once the model is updated by the control strategy trainer 106, it is redeployed as an updated model 108 to update control strategy model 114.)
send the response data to the robot for use in controlling the robot,. ([0040] In step 503, the control system 107 drives the robot 103 to complete the operation based on such control strategy.)
Chen does not expressly disclose but Meier discloses to a remote intervention system via network communication ([0087] In some implementations, the data (e.g., the digitized frame 416) obtained responsive to the spot-light operation, may be offloaded via a communications link to a remote processing resource (e.g., a remote server).)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Meier with a reasonable expectation of success by providing contextual instruction to a robotic apparatus as taught by Meier ([0006]).
Chen does not expressly disclose but Perreault discloses wherein the trigger signal is associated with the first vision system being unable to determine appropriate pick control information for the robot based on the data obtained by the first sensor module(Col 6 Line 62-64 In step 102, a 3D image of the contents of the bin 12 is acquired using the vision system 20. Col 8 66-67 – Col 9 Line 1 Line In step 124, if there is no reachable object, the bin is tilted and typically shaken in step 126 before going back to step 102)
wherein the robot is configured to accept instructions from the observational support system when the first vision system is unable to determine appropriate pick control information for the robot(Col 8 Line 36-43 In step 118, if there is no prehensible object, the robot 14 not being able to grasp any object, the bin is shaken in step 120 and the method goes back to step 102. In step 120, the bin is shaken horizontally on the conveyer. In one embodiment, the bin is shaken by the robot 14 which grasp one side wall 13 of the bin and translates it into a jerky back in forth motion. When the bin 12 is tilted or flat shaken by the robot 14, a prior check of the position of the bin 12 is performed)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Chen with the teachings of Perreault with a reasonable expectation of success by picking up objects randomly arranged in a bin using a robot as taught by Perreault (Abstract).
Response to Arguments
Applicants arguments filed 12/18/2025 have been fully considered as follows:
Applicant argues that the 35 USC 103 rejections to the claims should not be maintained in view of “the claimed invention relates to two real world vision systems wherein, when one system is unable to determine appropriate pick control information for the robot, a trigger signal is sent to the second system to obtain data of the same real world scenario that the first vision system had issue with. During the interview, it was agreed that Chen did not read on the claims and that the search and office action would be updated accordingly.” This argument is persuasive. Therefore, a new ground of rejection is above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH TRAN whose telephone number is (313)446-6642. The examiner can normally be reached 8am-5pm M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Khoi Tran can be reached at (571) 272-6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.A.T./Examiner, Art Unit 3656
/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656
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