DETAILED ACTION
This is a Final Office Action on the Merits in response to communications filed by applicant on January 28th, 2026. Claims 12-23 are currently pending and examined below.
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 Amendment
The Amendments to the Claims, filed on January 28th, 2026, have been filed. Claims 12 and 22 are currently amended and pending, claims 13-21 and 23 are as previously presented and pending, and claims 1-11 have been canceled. The amendments to the Drawings, filed on January 28th, 2026, have been entered and have overcome each and every objection set forth in the previous Non-Final office Action mailed July 28th, 2025. The Notice of Non-Compliant Amendment, mailed February 17th, 2026m is hereby withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 12-16 and 20-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8296064 B2 ("Lee") in view of US 12017364 B2 ("Floyd-Jones") in further view of JP 2009131913 A ("Fukaura").
Regarding claim 12, Lee teaches a method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on an injection molding machine for processing plastics and other plasticizable materials or a 3D printing machine, configured for at least one of removing or transferring or depositing a molding, comprising the steps of (Lee: Column 3 lines 49-54, “Referring to FIG. 1, the path search apparatus, which is generally installed to a mobile object, e.g., a robot, a unit, an unmanned surveillance plane, and the like, includes a preprocessing module 100, a block map generating module 110, a block path search module 120, and a final path search module 130..”. One of ordinary skill in the art would see that the path clearly comprises a starting and ending point. Furthermore, one of ordinary skill in the art would see that the robot is recited at a high level of generality and that the method taught in Lee could easily be used in the control of an injection molding machine or a 3D printing machine.):
c) determining a current position of the at least one movable component and the machine and the molding in the space (Lee: Column 5 lines 36-50, “The block map generated through the above process is input to the block path search module 120, which finds a block path from a starting position to a destination position in the block map. To be specific, when the centers of the mobile object at the starting position and at the destination position are respectively located on cell S and cell D in the grid map, the block path search module 120 sets the starting block and the destination block in the block map to a block including the cell S and a block including the cell D, respectively.”. One of ordinary skill in the art would see that the system is configured to determine the current position of the moving obstacle because it sets the starting position to the center of the moving object.),
d) bringing the geometry information and the current position of the at least one movable component and of the machine and of the molding in the space into relation with one another to generate at least one graph of the space (Lee: Column 4 lines 36-49, “The block map generating module 110 receives the preprocessed map, i.e., the map having the extended blocked region, and generates a block map. The block map generating module 110 merges a specific number of neighboring cells in the preprocessed map into one block to thereby generate the block map. In generating the block map, a block including at least one empty cell is set as an empty block, and a block including only blocked cells is set as a blocked block. At this time, the block size is set to be equal to or smaller than the extended size of the blocked region in the preprocessing, i.e., the difference of the size of the blocked regions in the grid map and in the preprocessed map. For example, when the mobile object occupies mxm grid cells, the extended size of the blocked region would be (m+ 1 )/2x(m+ 1 )/2 cells.”, Column 4 lines 53-59, “The map shown in FIG. 3 is the preprocessed map generated using the grid map of FIG. 2 in which the mobile object has a size of 5x5 cells. Thus, the block map generating module 110 sets a block size to 3x3 cells, and then, sets a block including at least one empty cell (i.e., unshaded cell) as an empty block, and a block including only blocked cells as a blocked block, thereby generating a block map of FIG. 4.”. One of ordinary skill in the art would see that the representation of the moving object have a size of mxm cells comprises a basic 2D model of said moving object.),
e) calculating the path by applying at least one algorithm to the graph , wherein at least one optimization is additionally carried out for calculating the path (Lee: Column 5 lines 36-50, “The block map generated through the above process is input to the block path search module 120, which finds a block path from a starting position to a destination position in the block map. To be specific, when the centers of the mobile object at the starting position and at the destination position are respectively located on cell S and cell D in the grid map, the block path search module 120 sets the starting block and the destination block in the block map to a block including the cell Sand a block including the cell D, respectively. After that, the block path search module 120 obtains a block path from the starting block to the destination block using one of path search algorithms which secure an optimal solution, such as the A* algorithm, the Dijkstra D* algorithm, a breadth-first graph search algorithm, a depth-first graph search algorithm, and the like.”),
g) autonavigating in collision-free manner the at least one movable component along the path, wherein the at least one movable component moves relative to the machine (Lee: Column 3 lines 7-12, “Further, in a robot movement control using the path search method in accordance with the embodiments of the present invention, the path of the robot can be changed in real time when the robot encounters an obstacle while moving, thereby minimizing a likelihood of a collision between the robot and the obstacle which allows a flexible motion control.”)
Lee does not teach a) providing at least one model of the at least one movable component and the machine and the molding
b) gathering geometry information of the space and the at least one movable component and the machine and the molding
f) performing at least one collision check along the path between
1. the at least one movable component,
2. the machine and
3. the molding
and the algorithm is applied while dynamically changing the graph as a result of movements of at least one of the at least one movable component or the machine or the molding,
when adjusting a machine cycle, wherein a sequence of the at least one movable component is adapted to the machine cycle automatically and dynamically,
wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence
and the algorithm reacts to at least one of changed positions or changed speeds of at least one of the at least one movable component or the machine or the molding by carrying out at least one further collision check
and by calculating at least one new path and applying the algorithm again, using a current actual position as the starting point
wherein the calculation, collision check and autonavigation are performed predictively
Floyd-Jones, in the same field of endeavor, teaches a) providing at least one model of the at least one movable component and the machine and the molding (Floyd-Jones: Column 30 lines 15-16, “At 506, a robot model is received or specified via the processor-based system or application.”),
b) gathering geometry information of the space and the at least one movable component and the machine and the molding (Floyd-Jones: Column 6 lines 45-67, “The robot 102 of FIG. 1 has sensors, such as sensors 282 shown in FIG. 2, which send perception data to one or more processors, such as processor 212a. The perception data can be provided as a stream of which voxels or boxes or other representations are occupied in the current environment. This data is generated by (or provided to) one or more processors, such as processor 212a, in the form of an occupancy grid or other digital representation. In particular, when representing either a robot or an object in the environment 100 (e.g., an obstacle), one may represent their surfaces as either voxels (3D pixels) or meshes of polygons (often triangles). Each discretized region of space is termed a "voxel," equivalent to a 3D (volumetric) pixel.”)
f) performing at least one collision check along the path between
1. the at least one movable component,
2. the machine and
3. the molding (Floyd: Column 30 lines 51-64, “At 520, the processor-based system or application autonomously performs kinematic checking and/or collision checking of the autonomously generated additional poses and/or edges. For example, the processor-based system or application autonomously determines whether any kinematic constraints for the robot being modeled are violated by the additional poses or transitions ( e.g., edges) between the seed pose and additional poses or transitions between additional poses. Also for example, the processor-based system or application autonomously determines whether any collisions with objects (e.g., known static objects in the environment) would result from the additional poses or transitions between the seed pose and additional poses or transitions between additional poses”, Column 31 lines 9-23, “At 528, the system or application autonomously performs kinematic checking and/or collision checking based on the specified adjustments. For example, the system 200 autonomously determines whether any kinematic constraints for the robot being modeled are violated by based on the adjustment(s). Also for example, the system 200 identifies any of the edges of the first planning graph for which the corresponding transition would result in a collision between at least a portion of the robot 102 and at least a portion of at least one of the one or more obstacles in the environment. The system may determine a volume swept by the robot, or a portion thereof, in transitioning between a first pose represented as a first node, and a second pose represented by a second node, the transition represented as an edge between the first and second nodes.”. One of ordinary skill in the art would see that because the system is configured to determine collision between any and all obstacles it the robot’s environment, that this could easily include a machine and mold.)
and the algorithm is applied while dynamically changing the graph as a result of movements of at least one of the at least one movable component or the machine or the molding (Floyd-Jones: Column 31 lines 24-28, “At 530, if kinematic constraints are not violated and no collisions detected, the processor-based system or application updates the motion planning graph, roadmap or lattice or to a region or sub-lattice of the motion planning graph, roadmap or lattice to represent the adjustments.”. One of ordinary skill in the art would see that the method is configured to update the graph when the transition from one pose to the next is free of kinematic constraints and collisions.),
wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence (Floyd-Jones: Column 11 lines 1-14, “As noted above, pre-runtime calculations (e.g., generation of the family of planning graphs) may be performed by a system that is separate from the robot 102 or other robot, while runtime calculations may be performed on the processor 212 that is on the robot 102 since it is important for the system 200 to be able to change planning graphs to react in real time to changing physical dimensions of the robot 102.”, Column 15 lines 1-29, “Then, at runtime, the obstacle voxels (or boxes) representing environment 100, including obstacle A 112 (and also obstacle B 104) are provided (e.g., streamed into) to processor 212a in the form of an occupancy grid or other representation and stored on the on-chip environment memory 294. The system 200 determines which voxels are occupied (based on the occupancy grid or other representation), and determines to not use any motion that would collide with any currently occupied voxel. In particular, for each edge voxel (or box) representing a portion of the swept volume of an edge, when it is streamed in from planning graph edge information memory 284, the processor determines whether it collides with any of the obstacle voxels (or boxes) that have been stored in environment memory 294 based on the occupancy grid or other representation. If the edge voxel ( or box) collides with any of the obstacle voxels (or boxes), the system 200 determines a collision with that edge in the planning graph and will determine to not use the motion of the robot 102 associated with that edge in the planning graph.”. One of ordinary skill in the art would see that the path planning and collision checking methods are run in real time.)
and the algorithm reacts to at least one of changed positions or changed speeds of at least one of the at least one movable component or the machine or the molding by carrying out at least one further collision check (Floyd-Jones: Column 30 lines 51-64, Column 31 lines 9-23. From the cited passages, one of ordinary skill in the art would see that the collision check is performed for every transition from one pose to the next. This is therefore a method of checking for collisions in response to the movement of the robot.).
and by calculating at least one new path and applying the algorithm again, using a current actual position as the starting point (Floyd-Jones: Column 31 lines 29-34, “At 532, the system or application presents results via the user interface. The method 500 of operation in a processor based system 200 to facilitate motion planning concludes at 534, for example until invoked again. In some implementations, the method 500 may execute continually or execute as a thread on a multi-threaded processor.. One of ordinary skill in the art would see that because the method can be configured to execute continually, the a new path would be determined once the robot reaches its previous destination. This is clearly done in response to a change in position.”),
wherein the calculation, collision check and autonavigation are performed predictively (Floyd: Column 30 lines 51-64, Column 31 lines 9-23. One of ordinary skill in the art would see that because the collision check and the next pose is determined prior to the robot transitioning to the next pose, that this method is performed predicatively. Additionally, the collision check being performed before a collision occurs also shows that the method is performed predicatively.).
Lee teaches a method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on a machine comprising the steps of: determining a current position of the at least one movable component and the machine and the molding in the space, bringing the geometry information and the current position of the at least one movable component and of the machine and of the molding in the space into relation with one another to generate at least one graph of the space, calculating the path by applying at least one algorithm to the graph, wherein at least one optimization is additionally carried out for calculating the path, and autonavigating in collision-free manner the at least one movable component along the path, wherein the at least one movable component moves relative to the machine. Lee does not teach providing at least one model of the at least one movable component and the machine and the molding, gathering geometry information of the space and the at least one movable component and the machine and the molding, performing at least one collision check along the path between the at least one movable component, the machine and he molding, and the algorithm is applied while dynamically changing the graph as a result of movements of at least one of the at least one movable component or the machine or the molding, wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence, and the algorithm reacts to at least one of changed positions or changed speeds of at least one of the at least one movable component or the machine or the molding by carrying out at least one further collision check and by calculating at least one new path and applying the algorithm again, using a current actual position as the starting point, wherein the calculation, collision check and autonavigation are performed predictively. Floyd-Jones teaches providing at least one model of the at least one movable component and the machine and the molding, gathering geometry information of the space and the at least one movable component and the machine and the molding, performing at least one collision check along the path between the at least one movable component, the machine and he molding, and the algorithm is applied while dynamically changing the graph as a result of movements of at least one of the at least one movable component or the machine or the molding, wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence, and the algorithm reacts to at least one of changed positions or changed speeds of at least one of the at least one movable component or the machine or the molding by carrying out at least one further collision check and by calculating at least one new path and applying the algorithm again, using a current actual position as the starting point, wherein the calculation, collision check and autonavigation are performed predictively. A person of ordinary skill in the art would have had the technological capabilities required to have modified the method of determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on a machine taught in Lee with the method of providing at least one model of the at least one movable component and the machine and the molding, gathering geometry information of the space and the at least one movable component and the machine and the molding, performing at least one collision check along the path between the at least one movable component, the machine and he molding, and the algorithm is applied while dynamically changing the graph as a result of movements of at least one of the at least one movable component or the machine or the molding, wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence, and the algorithm reacts to at least one of changed positions or changed speeds of at least one of the at least one movable component or the machine or the molding by carrying out at least one further collision check and by calculating at least one new path and applying the algorithm again, using a current actual position as the starting point, wherein the calculation, collision check and autonavigation are performed predictively. The method taught in Lee teaches that the method knows how many and what grids the moving object currently occupies (Lee: Column 4 lines 21-24, “That is, when the mobile object has a size of mxm cells, the preprocessing module 100 generates a map having a blocked region increased by (m+ 1 )/2 cells compared to the input grid map.”, Column 4 lines 25-31, “For example, when the preprocessing module 100 receives an input grid map in which shaded cells form a blocked region and unshaded cells form a non-blocked region as shown in FIG. 2 and a mobile object has a size of 5x5 cells, the preprocessing module 100 changes the input grid map into a map in which the blocked region is extended as shown in FIG. 3, i.e., the preprocessed map.”), but does not explicitly describe using a model of said moving object. Due to the fact that basic geometric data regarding the moving object is known, modifying the method such that it uses a more detailed model as taught in Floyd-jones would be within the capabilities of a person of ordinary skill in the art. Furthermore, the method in Lee teaches a grid map of the environment that is already generated and does not detail the steps of creating said grid map. One of ordinary skill in the art would have known that geometric information regarding the environment and the objects in said environment would be required to create such a grid map and would have been able to modify the method with the step of gathering the geometric data as taught in Floyd-Jones. Additionally, the method taught in Lee teaches updating the path in real-time when the possibility of a collision is determined (Lee: Column 3 lines 7-12) but does not disclose the method by which this is done. A person of ordinary skill in the art would therefore have easily been able to modify the method taught in Lee with the collision checking and avoidance method taught in Floyd-Jones. Though the method of Lee does not teach dynamically updating the graph, a person of ordinary skill in the art would have easily been able to modify the method of Lee to perform such a dynamic update. Furthermore, the additional limitations of wherein the method is simulated in real time in the event of a change in the production sequence, and the algorithm reacts to at least one of changed positions or changed speeds of at least one of the at least one movable component or the machine or the molding by carrying out at least one further collision check and by calculating at least one new path and applying the algorithm again, using a current actual position as the starting point, wherein the calculation, collision check and autonavigation are performed predictively are all simple modifications that can be implemented by modifying the method in Lee to be performed iteratively while the moving object moves along its path. None of the modifications discussed above would change or introduce new functionality to either the method taught in Lee or those taught in Floyd-Jones. No inventive effort would have been required. The combination would have yielded the predictable result of a method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on a machine comprising the steps of: providing at least one model of the at least one movable component and the machine and the molding, gathering geometry information of the space and the at least one movable component and the machine and the molding, performing at least one collision check along the path between the at least one movable component, the machine and he molding, and the algorithm is applied while dynamically changing the graph as a result of movements of at least one of the at least one movable component or the machine or the molding, wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence, and the algorithm reacts to at least one of changed positions or changed speeds of at least one of the at least one movable component or the machine or the molding by carrying out at least one further collision check and by calculating at least one new path and applying the algorithm again, using a current actual position as the starting point, wherein the calculation, collision check and autonavigation are performed predictively.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on a machine taught in Lee with the method of providing at least one model of the at least one movable component and the machine and the molding, gathering geometry information of the space and the at least one movable component and the machine and the molding, performing at least one collision check along the path between the at least one movable component, the machine and the molding, the algorithm is applied while dynamically changing the graph as a result of movements of at least one of the at least one movable component or the machine or the molding, wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence, and the algorithm reacts to at least one of changed positions or changed speeds of at least one of the at least one movable component or the machine or the molding by carrying out at least one further collision check, and by calculating at least one new path and applying the algorithm again, using a current actual position as the starting point, wherein the calculation, collision check and autonavigation are performed predictively taught in Floyd-Jones with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Lee in view of Floyd-Jones does not teach when adjusting a machine cycle, wherein a sequence of the at least one movable component is adapted to the machine cycle automatically and dynamically.
Fukaura, in the same field of endeavor, teaches when adjusting a machine cycle, wherein a sequence of the at least one movable component is adapted to the machine cycle automatically and dynamically (Fukaura: Abstract, “he processing device and the processing method of the resin component is provided with an articulated robot 11 which moves an arm 11a at an optional position in an optional direction, a spindle 12 mounted on an arm tip of the articulated robot and rotationally driven by a motor, an end mill 13 interlocking with the rotation of the spindle to rotate, and a control part 15 for controlling the drive of the articulated robot and the motor. Cutting of the resin component is performed by adjusting a feed rate V of the robot arm to the rotation speed R of the end mill 13 so that the control part 15 can stabilize the cutting property and perform high-quality processing.”, ¶ 0007, “According to the first configuration of the present invention, the above object is achieved by an articulated robot that moves an arm to an arbitrary position and in an arbitrary direction, and a spindle that is attached to the arm tip of the articulated robot and is driven to rotate by a motor. An end mill that rotates in conjunction with the rotation of the spindle, and a control unit that drives and controls the robot and the motor, and the control unit adjusts the feed speed of the robot arm with respect to the rotation speed of the end mill, This is achieved by a resin component machining apparatus characterized by cutting a resin component.”, ¶ 0021, “Furthermore, the control unit 15 according to the embodiment of the present invention appropriately adjusts the feed speed V of the arm 11a of the multi-joint robot 11 with respect to the rotation speed R of the spindle 12 and the end mill 13, and is under appropriate machining conditions. Control is performed so that the cutting by the end mill 13 is reliably performed. This processing condition is set as follows. That is, if the feed speed V is slow with respect to the rotational speed R of the end mill 13, the processed surface is deteriorated due to melting of the resin during cutting, the durability of the end mill 13 is reduced due to the adhesion of chips to the end mill 13, Burr residue occurs. Further, when the feed speed V is high with respect to the rotation speed R of the end mill 13, a reduction in machining accuracy due to an increase in cutting resistance, a decrease in durability of the end mill, a displacement of the work due to the cutting resistance, and a burr residue on the product occur.”. The cited passages clearly shows that the speed of the robot arm is adjusted based on the operational speed of the mill. One of ordinary skill in the art would recognize that when the speed of the mill is changed the speed of the robot arm would also be adjusted. Therefore, Fukaura clearly teaches the limitation “when adjusting a machine cycle, wherein a sequence of the at least one movable component is adapted to the machine cycle automatically and dynamically”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the method taught in Lee in further view of Floyd-Jones with when adjusting a machine cycle, wherein a sequence of the at least one movable component is adapted to the machine cycle automatically and dynamically taught in Fukaura with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because changing a sequence of the robot when the machine cycle has been adjusted allows for the machining process to be performed in a more accurate, stabile, and high-quality fashion (Fukaura: ¶ 0021, “Furthermore, the control unit 15 according to the embodiment of the present invention appropriately adjusts the feed speed V of the arm 11a of the multi-joint robot 11 with respect to the rotation speed R of the spindle 12 and the end mill 13, and is under appropriate machining conditions. Control is performed so that the cutting by the end mill 13 is reliably performed. This processing condition is set as follows. That is, if the feed speed V is slow with respect to the rotational speed R of the end mill 13, the processed surface is deteriorated due to melting of the resin during cutting, the durability of the end mill 13 is reduced due to the adhesion of chips to the end mill 13, Burr residue occurs. Further, when the feed speed V is high with respect to the rotation speed R of the end mill 13, a reduction in machining accuracy due to an increase in cutting resistance, a decrease in durability of the end mill, a displacement of the work due to the cutting resistance, and a burr residue on the product occur.”, ¶ 0030, “As described above, according to the present invention, it is possible to deal with various hole shapes, and it is possible to drill a workpiece having a curved surface. Further, since the resin parts are cut by adjusting the feed rate of the robot arm with respect to the rotation speed of the end mill, the cutting performance can be stabilized and high-quality machining can be performed.”).
Regarding claim 13, Lee in view of Floyd-Jones in further view of Fukaura teaches further comprising providing at least one contact point each of the at least one movable component and the machine with respect to a position of the at least one contact point in the space (Floyd-Jones: Column 6 lines 45-67, “The robot 102 of FIG. 1 has sensors, such as sensors 282 shown in FIG. 2, which send perception data to one or more processors, such as processor 212a. The perception data can be provided as a stream of which voxels or boxes or other representations are occupied in the current environment. This data is generated by (or provided to) one or more processors, such as processor 212a, in the form of an occupancy grid or other digital representation. In particular, when representing either a robot or an object in the environment 100 (e.g., an obstacle), one may represent their surfaces as either voxels (3D pixels) or meshes of polygons (often triangles). Each discretized region of space is termed a "voxel," equivalent to a 3D (volumetric) pixel.”, Column 7 lines 21-36, “Each edge of a planning graph for the robot 102 also has some number of voxels ( or boxes) corresponding to the volume in 3D space swept by the robot 102 when making the transition in the planning graph from one state to another state represented by that edge. Those voxels or boxes swept by the robot 102 when making the transition in the planning graph from one state to another state represented by that edge may be stored for each edge of the planning graph in off-chip memory devoted to the planning graph, such as in planning graph edge information memory 284.”, Column 7 lines 37-57, “In one embodiment, the collision assessment is performed by first streaming in all of the obstacle voxels (or boxes) onto a processor, such as processor 212a. For example, the obstacle voxels (or boxes) representing environment 100, including obstacle A 112 and obstacle B 104, may be streamed into processor 212a and stored on environment memory 294. … . Then the edge information for each edge of the planning graph for the robot 102 is streamed from the off-chip memory devoted to the planning graph, such as from planning graph edge information memory 284. For each edge voxel (or box), when it is streamed in from the swept volume of an edge, if it collides with any of the obstacle voxels (or boxes), the processor 212a determines a collision with that edge in the planning graph.”. One of ordinary skill in the art would see from the cited passages that the voxels or boxes used to represent the obstacles and the area swept by the robot are used to determine collisions and therefore function as contact points.),
wherein the contact points are logically coupled to one another for providing the model of the at least one movable component and the machine (Floyd-Jones: Column 7 lines 21-36, “Each edge of a planning graph for the robot 102 also has some number of voxels ( or boxes) corresponding to the volume in 3D space swept by the robot 102 when making the transition in the planning graph from one state to another state represented by that edge. Those voxels or boxes swept by the robot 102 when making the transition in the planning graph from one state to another state represented by that edge may be stored for each edge of the planning graph in off-chip memory devoted to the planning graph, such as in planning graph edge information memory 284.”. One of ordinary skill in the art would see that because the voxels or boxes represent a the area swept by the robot when transitioning from one pose to the next, these voxels (or boxes) are logically coupled together to represent the robot.).
Regarding claim 14, Lee in view of Floyd-Jones in further view of Fukaura teaches wherein at least one list is associated with the at least one contact point, on the basis of which list couplable models are described or listed (Floyd-Jones: Column 16 line 48 – Column 17 line 7, “The processor 212a compares the voxels or boxes currently in the environment based on the information stored in environment memory 294 to the voxels or boxes listed for each edge of the planning graph edge information stored in planning graph edge information memory 284 to determine which edges are currently in collision.”. One of ordinary skill in the art would see that the voxels or boxes (which function as contact points) representing the robot as it transitions from pose to pose (this is the “edges” of the graph) are stored in a list.).
Regarding claim 15, Lee in view of Floyd-Jones in further view of Fukaura teaches, wherein the space is divided into a grid of cubes which is used for generating the at least one graph (Floyd-Jones: Column 6 lines 45-67, “In some cases, it is advantageous to represent the objects instead as boxes (rectangular prisms). Due to the fact that objects are not randomly shaped, there may be a significant amount of structure in how the voxels are organized; many voxels in an object are immediately next to each other in 3D space. Thus, representing objects as boxes may require far fewer bits (i.e., may require just the x, y, z Cartesian coordinates for two opposite corners of the box).”. One of ordinary skill in the art would see from the cited passages that the space can be divide into boxes (rectangular prisms). Furthermore, one of ordinary skill in the art would know that a cube is simply a specific type of rectangular prism.).
Regarding claim 16, Lee in view of Floyd-Jones in further view of Fukaura teaches wherein at least one of Greedy Search or Dijkstra or A* algorithm with at least one open list is used as the algorithm (Lee: Column 5 lines 36-50, “After that, the block path search module 120 obtains a block path from the starting block to the destination block using one of path search algorithms which secure an optimal solution, such as the A* algorithm, the Dijkstra D* algorithm, a breadth-first graph search algorithm, a depth-first graph search algorithm, and the like.”).
Regarding claim 19, Lee in view of Floyd-Jones in further view of Fukaura teaches wherein the method is simulated in advance (Lee: Column 5 lines 36-50, “The block map generated through the above process is input to the block path search module 120, which finds a block path from a starting position to a destination position in the block map. To be specific, when the centers of the mobile object at the starting position and at the destination position are respectively located on cell S and cell D in the grid map, the block path search module 120 sets the starting block and the destination block in the block map to a block including the cell Sand a block including the cell D, respectively. After that, the block path search module 120 obtains a block path from the starting block to the destination block using one of path search algorithms which secure an optimal solution, such as the A* algorithm, the Dijkstra D* algorithm, a breadth-first graph search algorithm, a depth-first graph search algorithm, and the like.”, Column 5 lines-51-59, “The block path obtained by the block path search module 120 is input to the final path search module 130, and the final path search module 130 performs path search on cells in the blocks on the obtained block path to thereby obtain a cell based final path. More specifically, the final path search module 130 limits searching target cells to the cells in the blocks on the block path, and performs a path search on the searching target cells in the preprocessed grid map using one of the above-described path search algorithms.”. It is clear from the cited passages describing the process of determining the path for the moving object that this process is performed before the object begins following said path/ In other words the process is performed in advance.).
Regarding claim 21, wherein at least one of the graph or the model of at least one of the at least one movable component or the machine or the molding is represented graphically (Floyd-Jones: Figures 4A-B, Column 29 lines 9-28, “FIG. 4A is an example motion planning graph 400 for the robot 102 of FIG. 1, including edges of the planning graph 400 for which it has been determined the corresponding transition would result in a collision between the robot 102 and one or more obstacles in the environment 100, according to one illustrated embodiment. FIG. 4B is an example motion planning graph 400 in which edges of the planning graph 400 have been removed for which it has been determined the corresponding transition would result in a collision between the robot 102 and one or more obstacles in the environment 100, according to one illustrated embodiment. Each node of the planning graph 400 represents a state of the robot 102 and each edge of the planning graph 400 represents a transition of the robot 102 from one state to another state.”. The graph of the robot and its environment (including other objects/obstacles) is clearly represented graphically.).
Regarding claim 22, Lee in view of Floyd-Jones in further view of Fukaura teaches a machine control for an injection molding machine for processing plastics and other plasticizable materials wherein the machine control is configured, set up or constructed to carry out the method in accordance with claim 12 (Floyd-Jones: Column 8 lines17-27, “In various embodiments, such operations may be performed entirely in hardware circuitry or as software stored in a memory storage, such as system memory 214, and executed by one or more hardware processors 212a, such as one or more microprocessors, digital signal processors (DSPs), field programmable gate arrays (FPGAs), application specific integrated circuits (AS I Cs), graphics processing units (GPUs) processors, programmed logic controllers (PLCs ), electrically programmable read only memories (EEPROMs), or as a combination of hardware circuitry and software stored in the memory storage.”. The method is clearly implemented using a computer controller. Furthermore, one of ordinary skill in the art would see that the robot is recited at a high level of generality and that the method taught in Lee in view of Floyd-Jones in further view of Fukaura could easily be used in the control of an injection molding machine or a 3D printing machine.).
Regarding claim 23, Lee in view of Floyd-Jons a computer-program product comprising a program code stored on a computer-readable medium for carrying out a method in accordance with claim 12 (Floyd-Jones: Column 8 lines17-27, “In various embodiments, such operations may be performed entirely in hardware circuitry or as software stored in a memory storage, such as system memory 214, and executed by one or more hardware processors 212a, such as one or more microprocessors, digital signal processors (DSPs), field programmable gate arrays (FPGAs), application specific integrated circuits (AS I Cs), graphics processing units (GPUs) processors, programmed logic controllers (PLCs ), electrically programmable read only memories (EEPROMs), or as a combination of hardware circuitry and software stored in the memory storage.”).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8296064 B2 ("Lee") in view of US 12017364 B2 ("Floyd-Jones") in further view of JP 2009131913 A ("Fukaura") in further view of CN 106774347 A ("Chen").
Regarding claim 17, Lee in view of Floyd-Jones in further view of Fukaura does not teach wherein at least one of at least one jump point search or at least one open list management is used as an optimization.
Chen, in the same field of endeavor, teaches wherein at least one of at least one jump point search or at least one open list management is used as an optimization (Chen: ¶ 0067, “Step 15: Calculate the global path with the minimum sum of path cost values between the starting point and the target point using a jump point search algorithm;”, ¶ 0068, “Specifically, the global path with the minimum sum of path cost values includes a number of local target points connected in sequence; the number of nodes generated and expanded by the Jump Point Search (JPS) algorithm is small, and the speed of reaching the target is very fast.”, ¶ 0082 – 0089, “Step 31, create an open list and a close list; the open list is used to store nodes that have not been traversed and visited; the close list is used to store nodes that have been traversed and visited. Step 32: Put the robot's target point into the open list. Step 33, traverse the child nodes in eight directions of the target point: up, down, left, right, upper left, lower left, upper right and lower right. Step 34: Determine whether the child node is already in the open list or the close list. If the child node is already in the open list, recalculate the f(n) value of the child node to determine whether the f(n) value of the child node decreases; if the f(n) value of the child node decreases, update the f(n) value of the child node and the parent node in the open list. If the child node is already in the close list, recalculate the f(n) value of the child node to determine whether the f(n) value of the child node decreases; if the f(n) value of the child node decreases, remove the child node from the close list, re-update the f(n) value of the child node and the parent node, and add the child node to the open list. If the child node is not in the open list or the close list, put the child node in the open list. Step 35: Select the node with the smallest f(n) value from the open list, put the parent node of the node with the smallest f(n) value into the close list, and jump to step 36 for execution.”. The cited passages clearly teach using the jump point search to optimize the method of determining a path for the robot, as well as managing the nodes of the graph by managing them in an open list.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on a machine taught in Lee in view of Floyd-Jones in further view of Fukaura with wherein at least one of at least one jump point search or at least one open list management is used as an optimization taught in Chen with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the jump point search eliminates symmetric paths and reduces the number of intermediate nodes added to the open list, improving the search speed and amount of data stored (Chen: ¶ 0068, “Because skip-point search can eliminate the symmetry between paths and identify successors by pruning nodes in straight and diagonal directions, a large number of intermediate nodes that may be added to the open list and closed list and other calculations are skipped during the search, which greatly improves the search speed and reduces the amount of node storage in the process.”).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8296064 B2 ("Lee") in view of US 12017364 B2 ("Floyd-Jones") in further view of JP 2009131913 A ("Fukaura") in further view of CN 106774347 A ("Chen") in further view of CN 102155942 A ("Zhou").
Regarding claim 18, Lee in view of Floyd-Jones in further view of Fukaura in further view of Chen does not teach wherein the open list is managed with a binary heap.
Zhou, in the same field of endeavor, teaches wherein the open list is managed with a binary heap (Zhou: ¶ 0031, “The heuristic algorithm uses the established fuzzy topology map to apply for storage space for the following attributes of each node: parent node data, distance cost G from this node to the starting node, estimated distance cost value H from this node to the target node, and the sum of possible distance costs F for actions to this node; at the same time, a single table is used to store the nodes to be used in order of priority. The table uses a binary heap method to store data for quick sorting.”, ¶ 0061, “The heuristic algorithm uses the established fuzzy topological map to apply for storage space for the following attributes of each node: parent node data (used to trace back from the target node to the starting node after completing the search), the distance cost G from the current node to the starting node (obtained by continuously accumulating distance data in the algorithm), the estimated distance cost H from the current node to the target node (H uses the sum of the weighted difference in the two perpendicular directions of the distance from the current node to the target node. This simple estimated cost calculation function is sufficient for this algorithm because the robot can only run along corridors perpendicular to each other when running on the floor), the sum of possible distance costs F to the current node, that is, F=G+H; at the same time, a single table is used to store the nodes to be used in order of priority. The table uses a binary heap method to store data for quick sorting.”. The cited passage clearly shows storing the nodes of the graph in a list using a binary heap.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on a machine taught in Lee in view of Floyd-Jones in further view of Fukaura in further view of Chen with wherein the open list is managed with a binary heap taught in Zhou with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the binary heap allows the data to be quickly sorted (Zhou: ¶ 0061, “The table uses a binary heap method to store data for quick sorting.”).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8296064 B2 ("Lee") in view of US 12017364 B2 ("Floyd-Jones") in further view of JP 2009131913 A ("Fukaura") in further view of US 2011/0153080 A1 ("Shapiro").
Regarding claim 20, Lee in view of Floyd-Jones in further view of Fukaura does not teach wherein at least two different variants of the method are simulated and compared with one another with respect to different criteria.
Shapiro, in the same field of endeavor, teaches wherein at least two different variants of the method are simulated and compared with one another with respect to different criteria (Shapiro: ¶ 0078, “In an initial pass through step 604, a zone selection process according to the disclosure is performed for each path in the initial population. The process selects for each path a set of zones that are collision-free in simulated robot motion and provide a minimal( or near-minimal) cycle time for the simulated motion. The selected set of zones and simulated cycle time associated with each path are stored along with the path
in the population database 606,”, ¶ 0079, “In step 608, one or more termination criteria are
evaluated and, if satisfied, a path and associated zones having a lowest cycle time are returned for use by a robot programmer. As described with reference to FIG. 5, termination criteria may relate to a diminishing measure of improvement in cycle time, a maximum number of iterations of the process 600, or other suitable criterion indicating that further iterations of the process 600 are not needed. While minimum cycle time is one criterion for selecting a candidate path to return upon termination, it will be understood that in other embodiments other criteria may be used, such as simulated dynamic loads on drive and brake mechanisms of the robot's joints, maximum levels of acceleration/deceleration experienced by the robot during motion, and other suitable selection criteria.”, ¶ 0080, “If the termination criteria are not satisfied, in step 612 a new path is generated from paths in the population database 606. The process 600 generates new candidate paths from a population of paths using techniques of genetic algorithms. In other embodiments, a new path may be generated solely from the previous path, without the use of a population of paths, by techniques such as simulated annealing or other metaheuristic processes. Unlike the generation of a new path in step 512 of the process 500, in step 612 only waypoint locations (position and orientation) are modified in generating a new path and the new path has no associated zones. The new path, having only via, locations at this stage, is stored in step 614.”, ¶ 0082, “The new path may also be validated in step 616 by simulating the path using a largest possible set of zones, without testing for collisions. Applying Assumption 1, the cycle time for this path with largest zones is assumed to be the best possible cycle time achievable for the new path. If this cycle time does not meet a threshold criterion when compared to cycle times for paths already in the population database 606, then the new path may be discarded on the assumption that it will not advance the search for an optimum combination of locations and zones. In such a case, the process 600 returns to step 612 to generate another new path,”. One of ordinary skill in the art would see that the paths are generated using at least two different methods, generating an initial population of paths and using a genetic algorithm to further generate new paths, and compares these paths to different criteria. Such criteria includes the paths being free from collision and the cycle time of each respective path.).
Lee in view of Floyd-Jones in further view of Fukaura teaches a method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on a machine, but does not teach wherein at least two different variants of the method are simulated and compared with one another with respect to different criteria. Shapiro teaches wherein at least two different variants of the method are simulated and compared with one another with respect to different criteria. A person of ordinary skill in the art would have had the technological capabilities required to have modified the method taught in Lee in view of Floyd-Jones in further view of Fukaura with wherein at least two different variants of the method are simulated and compared with one another with respect to different criteria taught in Shapiro. Furthermore, the method taught in Lee in view of Floyd-Jones in further view of Fukaura teaches different methods by which the path can be generated (Lee: Column 5 lines 36-50, “After that, the block path search module 120 obtains a block path from the starting block to the destination block using one of path search algorithms which secure an optimal solution, such as the A* algorithm, the Dijkstra D* algorithm, a breadth-first graph search algorithm, a depth-first graph search algorithm, and the like.”) and also that the paths generated are compared to a criteria, i.e. that the paths are free of collisions. Therefore, one of ordinary skill in the art would have been able to have modified the method in Lee in view of Floyd-Jones in further view of Fukaura to calculate the path using two or more of the different algorithms without changing or introducing new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on a machine wherein at least two different variants of the method are simulated and compared with one another with respect to different criteria.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on a machine taught in Lee in view of Floyd-Jones in further view of Fukaura with wherein at least two different variants of the method are simulated and compared with one another with respect to different criteria taught in Shapiro with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Response to Arguments
Applicant’s arguments with respect to claim(s) 12, specifically regarding the arguments that the combination of Lee in view of Floyd-Jones does not teach the limitation “when adjusting a machine cycle, wherein a sequence of the at least one movable component is adapted to the machine cycle automatically and dynamically” on Pages 6-8, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments filed January 28th, 2026, have been fully considered but they are not persuasive.
Regarding Applicant’s arguments on Pages 6-10, Applicant argues that the prior art does not teach the limitations of the amended independent claim 12.
Specifically on Page 6-7, Applicant argues that the combination of Lee in view of Floyd-Jones does not teach or suggest the limitation “a method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on an injection molding machine for processing plastics and other plasticizable materials or a 3D printing machine, configured for at least one of removing or transferring or depositing a molding, comprising the steps of”. The Examiner respectfully disagrees. Applicant’s arguments rely on language solely recited in preamble recitations in claim(s) 12. When reading the preamble in the context of the entire claim, the recitation “… an injection molding machine for processing plastics and other plasticizable materials or a 3D printing machine …” is not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02. Additionally, under the broadest reasonable interpretation of the limitation, the movable device comprises a device that moves relative to the machine, such as a robot. Therefore, the recitation that the robot autonavigates “through the space on an injection molding machine for processing plastics and other plasticizable materials or a 3D printing machine” is merely a recitation of the intended environment the robot is to be used in. As can clearly be seen in the 35 U.S.C. § 103 rejection section, the combination of Lee in view of Floyd-Jones teaches a method of autonavigating a robot which is capable of meeting the intended use of the limitation in question. Therefore, the combination of Lee in view of Floyd-Jones teaches the limitation “a method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on an injection molding machine for processing plastics and other plasticizable materials or a 3D printing machine, configured for at least one of removing or transferring or depositing a molding, comprising the steps of”.
Specifically on Pages 9-10, Applicant argues that the combination of Lee in view of Floyd-Jones does not teach or suggest the limitation “wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence”. The Examiner respectfully disagrees. As was stated in the previous Non-Final Office Action mailed July 28th, 2025, and above in the 35 U.S.C. § 103 rejection section, the primary reference Lee teaches Lee teaches a method for determining at least part of a path for connecting at least one starting point to at least one finishing point in a space for at least one autonavigation of at least one movable component through the space on an injection molding machine for processing plastics and other plasticizable materials or a 3D printing machine, configured for at least one of removing or transferring or depositing a molding, comprising the steps of (Lee: Column 3 lines 49-54): c) determining a current position of the at least one movable component and the machine and the molding in the space (Lee: Column 5 lines 36-50), d) bringing the geometry information and the current position of the at least one movable component and of the machine and of the molding in the space into relation with one another to generate at least one graph of the space (Lee: Column 4 lines 36-49, Column 4 lines 53-59), e) calculating the path by applying at least one algorithm to the graph , wherein at least one optimization is additionally carried out for calculating the path (Lee: Column 5 lines 36-50), g) autonavigating in collision-free manner the at least one movable component along the path, wherein the at least one movable component moves relative to the machine (Lee: Column 3 lines 7-12). The secondary reference Floyd-Jones teaches a) providing at least one model of the at least one movable component and the machine and the molding (Floyd-Jones: Column 30 lines 15-16), b) gathering geometry information of the space and the at least one movable component and the machine and the molding (Floyd-Jones: Column 6 lines 45-67) f) performing at least one collision check along the path between 1. the at least one movable component, 2. the machine and 3. the molding (Floyd: Column 30 lines 51-64, Column 31 lines 9-23) and the algorithm is applied while dynamically changing the graph as a result of movements of at least one of the at least one movable component or the machine or the molding (Floyd-Jones: Column 31 lines 24-28), wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence (Floyd-Jones: Column 11 lines 1-14, “As noted above, pre-runtime calculations (e.g., generation of the family of planning graphs) may be performed by a system that is separate from the robot 102 or other robot, while runtime calculations may be performed on the processor 212 that is on the robot 102 since it is important for the system 200 to be able to change planning graphs to react in real time to changing physical dimensions of the robot 102.”, Column 15 lines 1-29, “Then, at runtime, the obstacle voxels (or boxes) representing environment 100, including obstacle A 112 (and also obstacle B 104) are provided (e.g., streamed into) to processor 212a in the form of an occupancy grid or other representation and stored on the on-chip environment memory 294. The system 200 determines which voxels are occupied (based on the occupancy grid or other representation), and determines to not use any motion that would collide with any currently occupied voxel. In particular, for each edge voxel (or box) representing a portion of the swept volume of an edge, when it is streamed in from planning graph edge information memory 284, the processor determines whether it collides with any of the obstacle voxels (or boxes) that have been stored in environment memory 294 based on the occupancy grid or other representation. If the edge voxel ( or box) collides with any of the obstacle voxels (or boxes), the system 200 determines a collision with that edge in the planning graph and will determine to not use the motion of the robot 102 associated with that edge in the planning graph.”. One of ordinary skill in the art would see that the path planning and collision checking methods are run in real time.) and the algorithm reacts to at least one of changed positions or changed speeds of at least one of the at least one movable component or the machine or the molding by carrying out at least one further collision check (Floyd-Jones: Column 30 lines 51-64, Column 31 lines 9-23), and by calculating at least one new path and applying the algorithm again, using a current actual position as the starting point (Floyd-Jones: Column 31 lines 29-34), wherein the calculation, collision check and autonavigation are performed predictively (Floyd: Column 30 lines 51-64, Column 31 lines 9-23). The cited passages of Floyd-Jones shows that, at runtime, the system is configured to generate an occupancy grid using a 3D voxel representation of the environment and uses this occupancy grid to check for collisions along the robot’s path. This simulation is performed at runtime, at each time the robot performs a task. One of ordinary skill in the art would recognize that this clearly teaches that the motion of the robot is simulated in real time during a current cycle. As such, the combination of Jones in view of Floyd-Jones teaches the limitation “wherein the method is simulated in real time during a current cycle in the event of a change in the production sequence”.
Therefore, for the reasons stated herein and above in the 35 U.S.C. § 103 rejection section, the 35 U.S.C. § 103 rejection section of claim 12 is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Noah W Stiebritz whose telephone number is (571)272-3414. The examiner can normally be reached Monday thru Friday 7-5 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramon Mercado can be reached at (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.W.S./ Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
Prosecution Insights