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 .
Status of Claims
Claims 1, 3-11, 13-16, and 18-20 remain pending. Claims 1, 11, and 16 have been amended.
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.
Claims 1, 3-4, 6, 8, 11, 13, 15-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnasamy et al (US 20140249675 A1) in view of Graciano et al (US 20220059383 A1), Hosek et al (US 20180233397 A1), and Tan et al (US 20230021486 A1) (Hereinafter referred to as Krishnasamy, Graciano, Hosek, and Tan respectively)
Regarding Claims 1, 11, and 16, Krishnasamy discloses a method (See at least Krishnasamy Paragraph 0038), a non-transitory computer-readable storage medium storing instructions which, when executed, cause a processing device to perform operations (See at least Krishnasamy Paragraph 0022, “The controller of the processing tool, such as controller 691, or any other suitable controller connected to the substrate processing tool may include a processor and/or a memory configured to generate the optimal trajectories as described herein”), a system (See at least Krishnasamy Figure 1) comprising:
a memory; and a processing device coupled to the memory (See at least Krishnasamy Paragraph 0022, “The controller of the processing tool, such as controller 691, or any other suitable controller connected to the substrate processing tool may include a processor and/or a memory configured to generate the optimal trajectories as described herein”), the processing device to:
identifying a sequence of robot configurations associated with processing a plurality of substrates (See at least Krishnasamy Paragraphs 0027-0028 and Figure 3c, the path for transporting/processing substrates includes segments and points, which are interpreted as sequence of robot configurations), the sequence of robot configurations being of a robot arm disposed in a chamber (See at least Krishnasamy Paragraphs 0025, and Figure 1, the transfer robot/robot arm is located within the transport chamber), the chamber coupled to one or more additional chambers (See at least Krishnasamy Paragraph 0025 and Figure 1, the transport chamber is coupled to the load lock and processing stations, which are interpreted as one or more additional chambers);
…
generating, based on the sequence of robot configurations…, motion planning data comprising corresponding velocity data and corresponding acceleration data for…each portion of a trajectory associated with the processing of the plurality of substrates (See at least Krishnasamy Paragraphs 0027, 0030 and Figures 5a and 6b, the time optimal trajectory for each segment of the path/sequence of robot configurations includes velocity data and acceleration data)…; and
causing, based on the motion planning data, the robot arm to be actuated for processing substrates in a substrate processing system (See at least Krishnasamy Paragraph 0022 and Figure 2, “a "bang-bang" controller that controls the robotic transport such that all available power is used (e.g. max torque) to effect motion of the robotic transport as otherwise further described below”; See at least Krishnasamy Paragraph 0025, the robot arm is actuated to transfer/process substrates).
Even though Krishnasamy teaches a chamber and one or more additional chambers, Krishnasamy fails to explicitly disclose identifying position map data associated with locations of the chamber and the one or more additional chambers and
generating motion planning data…based on the position map data.
However, Graciano teaches identifying position map data associated with locations of the chamber and the one or more additional chambers (See at least Graciano Paragraphs 0041, 0110, and Figure 1, the transport robot workspace, which includes the transport chamber, load lock, and processing modules/stations, is mapped) and
generating motion planning data…based on the position map data (See at least Graciano Paragraph 0110, the map, which includes the spatial positions of each processing apparatus features/components, is used for trajectory planning).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in Krishnasamy with Graciano to generate the motion planning data based on the position map data. Graciano teaches mapping out the robot workspace and then using the map for trajectory planning (See at least Graciano Paragraph 0110). This modification allows the system to utilize the position map data to issue movement commands for the robot (See at least Graciano Paragraph 0110), which would improve the accuracy of the motion planning data.
Even though Krishnasamy teaches generating motion planning data comprising corresponding velocity data and corresponding acceleration data for each portion of a trajectory, modified Krishnasamy fails to disclose each robot configuration comprises a corresponding joint angle for each joint of a plurality of joints of the robot arm to position an end effector of the robot arm in a corresponding location and
the corresponding velocity data and corresponding acceleration data are for…each joint of the plurality of joints of the robot arm for each portion of a trajectory,
wherein the corresponding velocity data and the corresponding acceleration data for two or more joints of the plurality of joints comprise ramping up after starting at a starting location and ramping down until coming to a stop at the ending location.
However, Hosek teaches each robot configuration comprises a corresponding joint angle for each joint of a plurality of joints of the robot arm to position an end effector of the robot arm in a corresponding location (See at least Hosek Paragraphs 0009 and Figure 11E, each motion setpoint/robot configuration includes joint positions/angles to position an end effector in a corresponding location) and
corresponding velocity data and corresponding acceleration data are for…each joint of the plurality of joints of the robot arm for each portion of a trajectory (See at least Hosek Paragraphs 0009 and Figure 11E, the desired joint velocities and accelerations to form motion setpoints for the trajectory of the robot arm are interpreted as corresponding velocity data and corresponding acceleration data for each joint),
wherein the corresponding velocity data and the corresponding acceleration data for two or more joints of the plurality of joints comprise ramping up after starting at a starting location and ramping down until coming to a stop at the ending location (See at least Hosek Paragraphs 0029, 0052-0053, 0082, Table 1, and Figure 11e, the velocity data for two joints comprises ramping up after starting and ramping down until coming to a stop, and the acceleration data for two joints comprises ramping up after starting, and ramping down to decelerate the robot to a stop) .
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Hosek to have each robot configuration comprise a corresponding joint angle for each joint of a plurality of joints of the robot arm to position an end effector of the robot arm in a corresponding location and have the corresponding velocity data and corresponding acceleration data be for each joint of the plurality of joints of the robot arm for each portion of a trajectory, wherein at least two joints are ramped up after starting and ramped down until coming to a stop. This modification, as taught by Hosek, would allow the system to generate the joint positions, velocities, and accelerations needed to move the end effector to an end position from a starting location by using a modified formulation of inverse kinematics to form motion setpoints along the trajectory (See at least Hosek Paragraph 0009), which would improve the motion planning of the system.
Modified Krishnasamy fails to disclose the motion planning data being generated via a combination of shortest path planning and non-linear optimization.
However, Tan teaches the motion planning data being generated via a combination of shortest path planning and non-linear optimization (See at least Tan Paragraphs 0134 and 0139, the path/motion planning data is generated via a shortest path planning algorithm and nonlinear optimization).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Tan to generate the motion planning data via a combination of shortest path planning and non-linear optimization. This modification, as taught by Tan, would find the shortest path of the end effector very efficiently by using nonlinear optimization solvers that return the best joint solutions for the shortest path (See at least Tan Paragraphs 0134 and 0139), which would improve the motion planning of the system.
Regarding Claims 3-4, 13, and 18, modified Krishnasamy teaches identifying the/a plurality of locations associated with the processing of the plurality of substrates (See at least Krishnasamy Paragraph 0025 and Figure 1, the load lock and the processing stations are interpreted as the/a plurality of locations), wherein the sequence of robot configurations are based on the plurality of locations (See at least Krishnasamy Paragraphs 0025 and 0027, the transport paths, which includes the sequence of robot configurations, are from the load lock to the processing stations),
wherein the chamber is a transfer chamber (See at least Krishnasamy Paragraph 0025 and Figure 1, the transport chamber is interpreted as a transfer chamber), wherein the locations comprise a first location of a load lock coupled to the transfer chamber (See at least Krishnasamy Paragraph 0025 and Figure 1, load lock “610”) and a second location of a processing chamber coupled to the transfer chamber (See at least Krishnasamy Paragraph 0025 and Figure 1, processing stations/chambers “630”), and wherein the one or more additional chambers comprise the load lock and the processing chamber (See at least Krishnasamy Paragraph 0025 and Figure 1, the load lock and processing stations/chamber are interpreted as the one or more additional chambers)
Regarding Claims 6, 15, and 20, modified Krishnasamy teaches the generating of the motion planning data is based on constraint data comprising an initial robot configuration, a final robot configuration, kinematic constraints of the robot arm, and robot dynamics of the robot arm (See at least Krishnasamy Paragraph 0030 and Figure 5a, the motion data is generated based on a starting point/initial configuration, end point/final configuration, acceleration and velocity constraints, and a smoothing bridge segment, which is interpreted as robot dynamics).
Regarding Claim 8, modified Krishnasamy fails to disclose the position map data is based on sensor data associated with actuation of the robot arm.
However, Graciano teaches this limitation (See at least Graciano Paragraphs 0108, 0110-0111, the map is generated using the sensor/wayfinding system on the robot arm while the robot arm is actuated/moved).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Graciano to identify position map data based on sensor data associated with actuation of the robot arm. This modification, as taught by Graciano, allows the system to teach the robot arm the locations of the processing apparatus features/components by scanning the workspace while the robot arm is actuated (See at least Graciano Paragraphs 0110-0111), which would improve the awareness of the robot arm.
Claims 5, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnasamy in view of Graciano, Hosek, and Tan and in further view of Griffiths et al (US 20220000571 A1) (Hereinafter referred to as Griffiths)
Regarding Claims 5, 14, and 19, modified Krishnasamy fails to disclose the generating of the motion planning data comprises:
identifying joint limits of the robot arm; and
minimizing distance of the trajectory between corresponding robot configurations of the sequence of robot configurations based on the joint limits of the robot arm.
However, Griffiths teaches identifying joint limits of the robot arm (See at least Griffiths Paragraph 0058, the joints have range of motion limits); and
minimizing distance of the trajectory between corresponding robot configurations of the sequence of robot configurations based on the joint limits of the robot arm (See at least Griffiths Paragraph 0058, the distance is minimized when moving to a desired configuration to avoid the range of motion limits of the joints).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Griffiths to minimize distance of the trajectory between corresponding robot configurations of the sequence of robot configurations based on the joint limits of the robot arm. This modification, as taught by Griffiths, would minimize a cost function while avoiding range of motion limits of the joints when moving to a desired configuration (See at least Griffiths Paragraph 0058), which would improve the safety of the robot arm in Krishnasamy.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Krishnasamy in view of Graciano, Hosek, and, Tan, and in further view of Ames (US 20210053220 A1) and Jaekel et al (US 20170190052 A1) (Hereinafter referred to as Ames and Jaekel respectively)
Regarding Claim 7, modified Krishnasamy fails to disclose wherein the generating of the motion planning data via the non-linear optimization is based on:
one or more constraints comprising start state.
However, Tan teaches wherein the generating of the motion planning data via the non-linear optimization is based on:
one or more constraints comprising start state (See at least Tan Paragraphs 0134-0135 and 0139, nonlinear optimization is used to generate the motion planning/path data from the start state, which is interpreted as the start state is a constraint).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Tan to generate the motion planning data via non-linear optimization based on the start state being a constraint. This modification, as taught by Tan, would allow the system to generate a path from the starting position to the goal location by using nonlinear optimization solvers that return the best solutions (See at least Tan Paragraphs 0134-0135 and 0139), which would improve the motion planning data.
Modified Krishnasamy fails to disclose the generating of the motion planning data is via the non-linear optimization using a graphics processing unit (GPU)
However, Ames teaches generating of the motion planning data is via the non-linear optimization (See at least Ames Paragraphs 0003-0004, conventional approaches employ non-linear optimization methods to optimize velocity while maintaining limits on acceleration), and using a graphics processing unit (GPU) for motion planning (See at least Ames Paragraph 0124, the motion planner includes a GPU).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Ames to generate the motion planning data via non-linear optimization using a graphics processing unit (GPU). Ames teaches that conventional approaches employ non-linear optimization methods to optimize velocity while maintaining limits on acceleration and minimizing jerk by constraining velocity, acceleration, and jerk (See at least Ames Paragraphs 0003-0004), and using GPUs as a motion planner (See at least Ames Paragraph 0124). Thus, one of ordinary skill in the art would be motivated to use a GPU to generate the motion planning data via non-linear optimization since this teaching is routine and well-understood in the art for robotic motion planning.
Modified Krishnasamy fails to disclose the generating of the motion planning data via the non-linear optimization is based on:
one or more boundary conditions comprising blade acceleration.
However, Jaekel teaches the generating of the motion planning data via the non-linear optimization is based on:
one or more boundary conditions comprising blade acceleration (See at least Jaekel Paragraphs 0060-0061, and 0153, the nonlinear optimization for motion planning uses the constraint function f, which includes the acceleration of the end effector/blade).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Jaekel to have the motion planning data via the non-linear optimization be based on one or more boundary conditions comprising blade acceleration. This modification, as taught by Jaekel, would constrain the acceleration of the blade/end effector when generating a motion path (See at least Jaekel Paragraphs 0060-0061, and 0153), which would improve the safety of the system by preventing the blade/end effector from accelerating too fast.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Krishnasamy in view of Graciano, Hosek, and Tan, and in further view of Yang et al (US 20210205996 A1) (Hereinafter referred to as Yang)
Regarding Claim 9, modified Krishnasamy fails to disclose the position map data is associated with output of a trained machine learning model responsive to providing the sensor data as input to the trained machine learning model.
However, Yang teaches this limitation (See at least Yang Paragraphs 0075-0076, the trained model outputs the position map data in response to the received sensor data input).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Yang to have a trained machine learning model output position map data in response to sensor data input. This modification, as taught by Yang, would allow the system to localize the robot by using the sensor data of the surrounding environment (See at least Yang Paragraphs 0075-0076), which would improve the awareness of the system.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Krishnasamy in view of Graciano, Hosek, Tan, and Yang, and in further view of Kaehler et al (US 11787050 B1) (Hereinafter referred to as Kaehler)
Regarding Claim 10, modified Krishnasamy fails to disclose the trained machine learning model is trained based on data input comprising…sensor data and target output comprising…position map data.
However, Yang teaches the trained machine learning model is trained based on data input comprising…sensor data and target output comprising…position map data (See at least Yang Paragraphs 0060-0061 and 0076, the model is trained using sensor data input to output position map data corresponding to the sensor input).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Yang to train the machine learning model based on data input comprising sensor data and target output comprising position map data. This modification, as taught by Yang, would allow the system to train the model to localize the robot using the sensor data of the surrounding environment (See at least Yang Paragraphs 0061 and 0075-0076), which would improve the awareness of the system.
Even though Yang teaches training the model using sensor data input and position map data output, modified Krishnasamy fails to disclose that the training data comprises historical sensor data input and historical position map data output.
However, Kaehler teaches this limitation (See at least Kaehler Column 23 line 66-Column 24 line 23, the neural network/machine learning model is trained using reinforcement learning to output position data based on sensor data input, which is interpreted as the training data comprises historical sensor data input and historical position data output).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings disclosed in modified Krishnasamy with Kaehler to have the training data comprise historical sensor data input and historical position map data output. Reinforcement learning uses previous/historical data to reinforce positive desired outputs, and discard negative undesired outputs, which is well-understood and routine when training machine learning models. Kaehler teaches using reinforcement learning to train the machine learning model used to determine the position data from the sensor data (See at least Kaehler Column 23 line 66-Column 24 line 23), which would improve the results of the position map data output.
Response to Arguments
Applicant's arguments filed 02/02/2026 have been fully considered but they are not persuasive. Applicant has amended the independent claims to include the limitation “wherein the corresponding velocity data and the corresponding acceleration data for two or more joints of the plurality of joints comprise ramping up after starting at a starting location and ramping down until coming to a stop at the ending location”. This limitation is taught by Hosek in Figure 11E. Both the velocity and acceleration are ramped up for two joints after starting and then the two joints are decelerated to come to a stop. Regarding the interview, Examiner proposed further clarifying how the velocity and acceleration data ramp up/down for each joint, not just two joints, as stated in the interview summary dated 09/15/2025. Applicant has velocity and acceleration data for four different joints in Figure 5O, and corresponding Paragraphs 0237-0239. Hosek only teaches the velocity data profile and acceleration data profile for two joints, not four.
Thus, for these reasons, the claims still stand rejected under 103.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ESVINDER SINGH/Examiner, Art Unit 3657