DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The amendment filed 5/12/2026 has been entered. Claims 1 and 3-4 are amended. Claims 1-4 and 7-9 remain pending in the application. Applicant’s amendments to the specification and claims have overcome each and every 112(b) rejection set forth in the Final Office Action mailed 3/25/2026.
Applicant’s arguments, see pages 5-6, with respect to the cited prior art not specifically teaching the amended features have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Bradski (US 20160221187 A1), Johnson (US 20200086487 A1), Bai (US 20220203535 A1), and Maeda (US 20220281103 A1).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “model storing unit” in claims 1 and 7; “workpiece detecting unit” in claims 1-2; “workpiece model positioning unit” in claims 1, 3, and 4; “path setting unit” in claims 1 and 7-8; “target selecting unit” in claim 2; “hand model positioning unit” in claims 7; “program generating unit” in claims 8-9; “program executing unit” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. According to paragraph [0015], the control device includes all of the ‘units’ and the control device “includes, for example, a memory, a CPU, an input/output interface, and the like, and may be realized by one or more computer devices that execute appropriate programs.”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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) 1-4 and 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bradski (US 20160221187 A1) in view of Johnson (US 20200086487 A1), Bai (US 20220203535 A1), and Maeda (US 20220281103 A1).
Regarding Claim 1,
Bradski teaches
A robot system comprising: a robot; (“The system includes a robotic manipulator” See at least [0006])
a three-dimensional sensor configured to measure a surface shape of a target area in which workpieces can exist; (“a system including one or more sensors … The sensors may scan an environment containing one or more objects in order to capture visual data and/or three-dimensional (3D) depth information.” See at least [0037]; “scans from one or more 2D or 3D sensors with fixed mounts on a mobile base, such as a front navigation sensor 116 and a rear navigation sensor 118, and one or more sensors mounted on a robotic arm, such as sensor 106 and sensor 108, may be integrated to build up a digital model of the environment, including the sides, floor, ceiling, and/or front wall of a truck or other container.” See at least [0043])
a hand provided at a distal end of the robot; (“A robotic device may include a robotic arm that may be equipped with a gripper, such as a suction gripper, in order to move objects to specified locations.” See at least [0004] and fig. 1A)
and a control device configured to generate a removal path for removing at least one of the workpieces by the robot based on the surface shape measured by the three-dimensional sensor, (“a control system configured to perform functions. The functions include identifying one or more characteristics of a physical object within a physical environment. The functions also include, based on the identified one or more characteristics, determining one or more potential grasp points on the physical object corresponding to points at which the gripper is operable to grip the physical object. Additionally, the functions also include determining a motion path for the gripper to follow in order to move the physical object to a drop-off location for the physical object.” See at least [0006]; “Data from the scans may then be integrated into a representation of larger areas in order to provide digital environment reconstruction. In additional examples, the reconstructed environment may then be used for identifying objects to pick up, determining pick positions for objects, and/or planning collision-free trajectories for the one or more robotic arms and/or a mobile base.” See at least [0037]; Examiner Interpretation: The path/trajectories to move the object are removal paths.)
wherein the control device has: a model storing unit configured to store a workpiece model obtained by modeling a three-dimensional shape of the workpieces; a workpiece detecting unit configured to detect (“known templates of certain shapes can be used to refine detected features of objects within the environment that appear to match a particular shape.” See at least [0059]; “identified characteristics of the physical object may include a set of geometric characteristics from a particular perspective viewpoint of the physical object, which may then be used to train templates for recognition. In particular, a comparison can be made between the set of geometric characteristics and one or more virtual geometric shapes from the particular perspective viewpoint of the physical object. Based on an output of the comparison indicating that at least one of the geometric characteristics substantially matches a given virtual geometric shape, a virtual object may be generated that is representative of the physical object and associated with the matching virtual geometric shape. As a result, the identified characteristics of the physical object may be adjusted based on characteristics of the virtual object.” See at least [0105-0106]; Examiner Interpretation: The detected features/characteristics of the objects are from the 3D sensors.)
and a path setting unit configured to set the removal path by moving the (“a 3D model of a stack of boxes may be constructed and used as a model to help plan and track progress for loading/unloading boxes to/from a stack or pallet. … the 3D model may be used for collision avoidance. Within examples, planning a collision-free path may involve determining the 3D location of objects and surfaces in the environment. A path optimizer may make use of the 3D information provided by environment reconstruction to optimize paths in the presence of obstacles.” See at least [0064]; “Planning a collision-free path may involve determining the “virtual” location of objects and surfaces in the environment. For example, a path optimizer may make use of the 3D information provided by environment reconstruction to optimize paths in the presence of obstacles, such as bin 610 as shown in FIGS. 6A-6B.” See at least [0136]; “To set up the path optimization problem … (b) collision checking with objects in the environment which may be carried out for a swept volume between each waypoint,” See at least [0141])
Bradski does not explicitly teach, but Johnson teaches
a workpiece detecting unit configured to detect (“detecting the location of the object at 331 includes processing the image of the object through a convolutional neural network to predict one or more parts of the object forming a two-dimensional (2D) position of the object in the image. Next, as part of determining the location 331, such an embodiment determines the 6DOF pose using (i) the 2D position of the object in the image, (ii) pixels of the object, and (iii) a depth map corresponding to the image of the object. In such an embodiment, determining the 6DOF pose using (i) the 2D position of the object, (ii) the depth map corresponding to the image of the object, and (iii) the pixels of the object may include fitting the depth map to a candidate three-dimensional (3D) model of the object, where dimensions of the 3D model match dimensions of the object.” See at least [0057])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Bradski to further include the teachings of Johnson with a reasonable expectation of success to acquire more detailed pose information of objects with limited sensor information. (See at least [0057])
Johnson also does not explicitly teach, but Bai teaches
a workpiece model positioning unit configured to position, in a virtual space, object workpiece models each being a copy of the workpiece model in the positions and the poses of the workpieces detected by the workpiece detecting unit; (“The configuration engine 142 utilizes the vision data 112A, the vision data 171A, and/or the pose data and/or object identifier data 112, in generated a configured simulated environment 143. The configuration engine 142 can also utilize object model(s) database 152 in generating the configured simulated environment 143. For example, object identifiers from pose data and/or object identifier data 112 and/or determined based on vision data 112A or 117A, can be utilized to retrieve corresponding 3D models of objects from the object model(s) database 152. Those 3D models can be included in the configured simulated environment, and can be included at corresponding poses from pose data and/or object identifier data 112 and/or determined based on vision data 112A or 117A.” See at least [0069])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Bradski and Johnson to further include the teachings of Bai with a reasonable expectation of success for “more robust and/or more accurate determination of a sequence of robotic actions in various scenarios.” (See at least [0007])
Bai also does not explicitly teach, but Maeda teaches
and a hand model obtained by modeling a three-dimensional shape of the hand; (“In the virtual space R, a virtual stand 500A and a virtual robot 100A are defined. The virtual stand 500A and the virtual robot 100A are pieces of three-dimensional model data obtained by simulating the stand 500 and the robot 100, which are illustrated in FIG. 1. … The virtual robot 100A includes, as a plurality of portions, a virtual base 110A, a plurality of virtual links 111A to 116A, and a virtual robot hand 102A.” See at least [0046] and fig. 4)
a hand model positioning unit configured to position the hand model in a position and a pose that hold a first workpiece model of the object workpiece models in the virtual space, (“the CPU 311 accepts the setting of a teach point that has been inputted by a user. … The teach point is a target position and posture of the tool center point. … The teach point P1 is a first teach point. The teach point P1 indicates a position at which the virtual workpiece W11A is held, and is provided with a name of “TP_1”.” See at least [0068-0069]; “the virtual robot 100A is moved from the teach point P0 to the teach point P1, then the virtual workpiece W11A is linked with the virtual robot 100A at the teach point P1, and then the virtual robot 100A is moved from the teach point P1 to the teach point P2. Thus, the robot 100 conveys the workpiece W11 to a position above the workpiece W21 and assembles the workpiece W11 to the workpiece W21 by moving from the teach point P0, which is set as a start point, to the teach point P1, then holding the workpiece W11 at the teach point P1, and then moving from the teach point P1 to the teach point P2.” See at least [0078])
and a path setting unit configured to set the removal path by moving the first workpiece model and the hand model so as not to interfere with the object workpiece models other than the first workpiece model without changing a relative positional relationship between the workpiece model and the hand model in the virtual space. (“linking the virtual workpiece W11A with the virtual robot 100A means causing the virtual robot hand 102A, which is one example of a predetermined portion of the virtual robot 100A, to hold the virtual workpiece W11A in simulation. Thus, if the virtual workpiece W11A is linked with the virtual robot 100A, the position and posture of the virtual workpiece W11A relative to the virtual robot hand 102A is kept even when the posture of the virtual robot 100A changes. In the first embodiment, the CPU 311 links the virtual workpiece W11A with the virtual robot 100A by keeping the relative position and posture between the virtual robot hand 102A and the virtual workpiece W11A. In this manner, the state where the virtual robot hand 102A is holding the virtual workpiece W11 A can be achieved in simulation.” See at least [0079]; “In the search process of Step S600, the CPU 311 links the virtual workpiece W11A with the virtual robot 100A, and searches for the motion of the virtual robot 100A in which the virtual robot 100A and the virtual workpiece W11A do not contact the detection targets. … FIG. 12 is a diagram illustrating one example of motion of the virtual robot 100A, determined in the search process in the first embodiment.” See at least [0099-0100] and fig. 12 (provided below))
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It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Bradski, Johnson, and Bai to further include the teachings of Maeda with a reasonable expectation of success such that “the CPU 311 links the virtual workpiece W11A with the virtual robot 100A, and automatically determines a motion of the virtual robot 100A in which the virtual robot 100A and the virtual workpiece W11A do not contact obstacles. Thus, a user can easily perform the simulation work even if the user has no expertise. In addition, since a user can obtain a motion of the virtual robot 100A, in which the virtual robot 100A and the virtual workpiece W11A do not contact obstacles, without visually checking the motion of the virtual robot 100A one by one, the user workability for the simulation can be improved. In addition, the teaching for the robot 100 can be easily performed, based on the simulation work.” (See at least [0121])
Regarding Claim 2,
Bradski further teaches
wherein the control device further has a target selecting unit configured to select any one of the workpieces detected by the workpiece detecting unit as a removal target. (“a facade may be constructed from boxes, for instance to plan in what order the boxes should be picked up. For instance, as shown in FIG. 2C, box 222 may be identified by the robotic device as the next box to pick up. Box 222 may be identified within a facade representing a front wall of the stack of boxes 220 constructed based on sensor data collected by one or more sensors, such as sensor 106 and 108. A control system may then determine that box 222 is the next box to pick, possibly based on its shape and size, its position on top of the stack of boxes 220, and/or based on characteristics of a target container or location for the boxes.” See at least [0061])
Regarding Claim 3,
Bradski further teaches
wherein the control device selects one of the object workpiece (“a facade may be constructed from boxes, for instance to plan in what order the boxes should be picked up. For instance, as shown in FIG. 2C, box 222 may be identified by the robotic device as the next box to pick up. Box 222 may be identified within a facade representing a front wall of the stack of boxes 220 constructed based on sensor data collected by one or more sensors, such as sensor 106 and 108. A control system may then determine that box 222 is the next box to pick, possibly based on its shape and size, its position on top of the stack of boxes 220, and/or based on characteristics of a target container or location for the boxes.” See at least [0061])
Bradski does not explicitly teach, but Bai teaches
the one workpiece model positioned by the workpiece model positioning unit (“The configuration engine 142 utilizes the vision data 112A, the vision data 171A, and/or the pose data and/or object identifier data 112, in generated a configured simulated environment 143. The configuration engine 142 can also utilize object model(s) database 152 in generating the configured simulated environment 143. For example, object identifiers from pose data and/or object identifier data 112 and/or determined based on vision data 112A or 117A, can be utilized to retrieve corresponding 3D models of objects from the object model(s) database 152. Those 3D models can be included in the configured simulated environment, and can be included at corresponding poses from pose data and/or object identifier data 112 and/or determined based on vision data 112A or 117A.” See at least [0069]; “Once the simulated environment is configured to reflect the real environment, the robotic simulator can be used to determine a sequence of robotic actions for use by the real world robot(s) in performing at least part of a robotic task. The robotic task can be one that is specified by a higher-level planning component of the robotic simulator or by real world robot(s), or can be one that is specified based on user interface input. As one non-limiting example, the robotic task can include grasping an object and placing the object in a container.” See at least [0005])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Bradski and Johnson to further include the teachings of Bai with a reasonable expectation of success for “more robust and/or more accurate determination of a sequence of robotic actions in various scenarios.” (See at least [0007])
Regarding Claim 4,
Bradski further teaches
wherein the workpiece model positioning unit registers the object workpiece models other than the first workpiece model as an obstacle, and the first workpiece model is excluded from being registered as an obstacle. (“a 3D model of a stack of boxes may be constructed and used as a model to help plan and track progress for loading/unloading boxes to/from a stack or pallet. … the 3D model may be used for collision avoidance. Within examples, planning a collision-free path may involve determining the 3D location of objects and surfaces in the environment. A path optimizer may make use of the 3D information provided by environment reconstruction to optimize paths in the presence of obstacles.” See at least [0064]; “Planning a collision-free path may involve determining the “virtual” location of objects and surfaces in the environment. For example, a path optimizer may make use of the 3D information provided by environment reconstruction to optimize paths in the presence of obstacles, such as bin 610 as shown in FIGS. 6A-6B.” See at least [0136]; “To set up the path optimization problem … (b) collision checking with objects in the environment which may be carried out for a swept volume between each waypoint,” See at least [0141]; Examiner Interpretation: The workpiece model of the object being carried is not considered as an object in the environment to be avoided and therefore is not registered as an obstacle.)
Regarding Claim 7,
Bradski does not explicitly teach, but Maeda teaches
wherein the model storing unit further stores a robot model obtained by modeling a three- dimensional shape of at least a distal end portion of the robot, (“The RAM 313 is a storage device that is temporarily used in a computing process, which is performed by the CPU 311” See at least [0042]; “In the virtual space R, a virtual stand 500A and a virtual robot 100A are defined. The virtual stand 500A and the virtual robot 100A are pieces of three-dimensional model data obtained by simulating the stand 500 and the robot 100, which are illustrated in FIG. 1. … The virtual robot 100A includes, as a plurality of portions, a virtual base 110A, a plurality of virtual links 111A to 116A, and a virtual robot hand 102A.” See at least [0046] and fig. 4; “the CPU 311 accepts the information on a virtual object that is set by a user.” See at least [0058] and fig. 6, wherein the user sets the virtual robot and its portions including the 3D models. Examiner Interpretation: The robot model that was input (as illustrated in fig. 6) includes Robot1_Hand as the distal end portion of the robot and the input information is stored in at least the RAM.)
the hand model positioning unit positions the robot model together with the hand model, and the path setting unit sets the removal path so that the object workpiece models, the hand model, and the robot model do not interfere with each other. (“The virtual robot 100A includes, as a plurality of portions, a virtual base 110A, a plurality of virtual links 111A to 116A, and a virtual robot hand 102A.” See at least [0046]; “In Step S607, the CPU 311 determines whether the plurality of portions of the virtual robot 100A and the virtual workpiece W11A linked with the virtual robot 100A contact the respective detection targets at each position, which is determined by performing the interpolation. … If the plurality of portions of the virtual robot 100A and the virtual workpiece W11A do not contact the detection targets (S610: NO), then the CPU 311 proceeds to Step S611. In Step S611, the CPU 311 extracts intermediate points from the node group. The intermediate points are extracted by tracing the nearest nodes sequentially in the order from the last new node added. The above-described steps S601 to S611 are the search process performed for the case where the virtual workpiece W11A is linked with the virtual robot 100A.” See at least [0111-0118])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Bradski to further include the teachings of Maeda with a reasonable expectation of success such that “the CPU 311 links the virtual workpiece W11A with the virtual robot 100A, and automatically determines a motion of the virtual robot 100A in which the virtual robot 100A and the virtual workpiece W11A do not contact obstacles. Thus, a user can easily perform the simulation work even if the user has no expertise. In addition, since a user can obtain a motion of the virtual robot 100A, in which the virtual robot 100A and the virtual workpiece W11A do not contact obstacles, without visually checking the motion of the virtual robot 100A one by one, the user workability for the simulation can be improved. In addition, the teaching for the robot 100 can be easily performed, based on the simulation work.” (See at least [0121])
Regarding Claim 8,
Bradski further teaches
wherein the control device further has a program generating unit configured to generate an operation program for moving the robot along the removal path set by the path setting unit. (“the robotic arm motion may go through N poses, each of which may have multiple joint space solutions. Additionally, there may be multiple criteria at each pose or between poses. For example, the system may be configured to minimize the joint rotations necessary to go from one pose to the next. To solve this problem, the system may be configured to set up the problem as a dynamic programming problem. In particular, at each Cartesian goal pose, the system may define the set of joint space solutions corresponding to that pose. Additionally, for each pair of joint space solutions in neighboring goals, the system may compute an optimal path between the joint configurations. Each such path may have a weight or cost assigned to it, based on criteria previously stated above (e.g., how long the path is, how close the joints are to zero at the end). Subsequently, using dynamic programming, the system may select the desirable connected path from the start position to the end goal. As such, dynamic programming may determine the most appropriate path, in the sense that the path has the smallest cost of all the connected paths from start to goal.” See at least [0140])
Regarding Claim 9,
Bradski further teaches
wherein the control device further has a program executing unit configured to operate the robot in accordance with the operation program generated by the program generating unit. (“Referring back to FIG. 5, at block 510, method 500 involves providing instructions to cause the robotic manipulator to grip the physical object at the selected grasp point with the gripper and move the physical object through the determined motion path to the drop-off location.” See at least [0152])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Watanabe (US 20180290307 A1) is pertinent because it discusses storing the relative position and relative posture when the workpieces of a unique coordinate system (hand coordinate system) set in the hand model of the storage unit and the workpiece coordinate system are gripped.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Karston G Evans whose telephone number is (571)272-8480. The examiner can normally be reached Mon-Fri 9:00-5:00.
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, Abby Lin can be reached at (571)270-3976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KARSTON G. EVANS/Examiner, Art Unit 3657
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