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
Applicant’s arguments, filed 10/17/2025 with respect to claims 1, 7 and 13 have been considered but are not persuasive.
For claim 1, applicant stated:
Twigg at least fails to teach or suggest using "a neural network to output a set of keypoints, each keypoint of the set comprising a label of a feature of a hand, and a wrist pose of the hand based, at least in part, on image data depicting a wrist and the hand," and "determin[ing] a target pose of a robot hand based, at least in part, on the set of keypoints and the wrist pose output by the neural network," as recited in claim 1
Examiner respectfully disagrees. Twig is not relied on to teach all the limitations above. Instead, Sills teaches output a set of keypoints, each keypoint of the set comprising a label of a feature of a hand (Column 17 lines 1-20, disclosing number of capsuloids 372, e.g. five (5), are used to represent fingers on a hand while a number of radial solids 374 are used to represent the shapes of the palm and wrist. And capsule hand 300B is created using stereo matching, depth maps, or by finding contours and/or feature points. Figure 6A and column 21, disclosing determining several features of a hand);
and a wrist pose of the hand based, at least in part, on image data depicting a wrist and the hand (column 6 lines 8-40, disclosing motion capture system coupled to image analysis, motion capture, and gesture recognition system. Column 17 lines 1-20, disclosing number of capsuloids 372, e.g. five (5), are used to represent fingers on a hand while a number of radial solids 374 are used to represent the shapes of the palm and wrist. Column 4 lines 55-65, disclosing, technology disclosed generates a 3D solid model that includes joints with locations and orientations. Orientation of palm includes wrist pose. Column 16-17 and figures 3B-C, disclosing capsuloid hands with hand and wrist. Wrist is merely a joint of forearm and hand. As forearm and hand are generated, image of hand with forearm (including) is necessarily captured and utilized);
Therefore, Sills teaches all the limitations argued. Except, does not disclose using a neural.
Twig is relied on only to output features of hand using a neural network (abstract, disclosing hand tracking unit applies the single depth image data to a neural network model to generate heat maps indicating locations of hand features. The locations of hand features are used to generate a user hand shape model).
As Sills and Twig both use image data to generate model of a user hand, both are analogous arts. And it would have been obvious to one having ordinary skill in the art before effective filing date of claimed invention to modify art of Sill to use a neural network to generate a set of key points, each comprising a label associated with a feature of a hand as taught by Twigg for fast calibration of hand shape model.
Therefore, amendment of Sills through teaching of Twig teaches the limitations argued by applicant. Same arguments are presented for claims 7 and 13. Hence claims 1, 7 and 13 are taught by Sills modified through Twig. Hence the rejection is maintained.
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.
Claims 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Sills (US 10768708, disclosed in IDS submitted on 12/08/2023) in view of Twigg (US 10803616).
For claim 1, Sills teaches: A system (abstract, disclosing motion capture and recognition system), comprising:
at least one processor (column 11 lines 6-26, disclosing a computer implementing gesture recognition system having a processor); and
at least one memory comprising instructions that, in response to being performed by the at least one processor, cause the system to (column 25 lines 60-65, disclosing a non-transitory computer readable storage medium storing instructions executable by a processor to perform any of the methods described) at least:
output a set of keypoints, each keypoint of the set comprising a label of a feature of a hand (Column 17 lines 1-20, disclosing number of capsuloids 372, e.g. five (5), are used to represent fingers on a hand while a number of radial solids 374 are used to represent the shapes of the palm and wrist. And capsule hand 300B is created using stereo matching, depth maps, or by finding contours and/or feature points. Figure 6A and column 21, disclosing determining several features of a hand);
and a wrist pose of the hand based, at least in part, on image data depicting a wrist and the hand (column 6 lines 8-40, disclosing motion capture system coupled to image analysis, motion capture, and gesture recognition system. Column 17 lines 1-20, disclosing number of capsuloids 372, e.g. five (5), are used to represent fingers on a hand while a number of radial solids 374 are used to represent the shapes of the palm and wrist. Column 4 lines 55-65, disclosing, technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm, clenching of the fist, and the curling of each finger. The technology disclosed generates a 3D solid model that includes joints with locations and orientations. Orientation of palm includes wrist pose and 3D model and capsuloids are keypoints. Column 16-17 and figures 3B-C, disclosing capsuloid hands with hand and wrist. Wrist is merely a joint of forearm and hand. As forearm and hand are generated, image of hand with forearm (including) is necessarily captured and utilized);
determine a target pose of a robot hand based, at least in part, on the set of keypoints and the wrist pose (column 4 lines 55-65, disclosing technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm, clenching of the fist, and the curling of each finger. Column 2 lines 23-37, disclosing generating corresponding robotic commands that replicate the motion and contact of the human hand on a workpiece through a robotic tool. Replicating the user gesture by control of robot requires determining target pose of robotic tool); and
generate a set of control commands based, at least in part, on the target pose, wherein the set of control commands is to cause at least the robot hand to move in accordance with the target pose (column 4 lines 55-65, disclosing technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm).
Sills teaches of generating a set of keypoints of a hand but does not disclose using a neural network
Twigg teaches of use a neural network to generate a set of key points, each comprising a label associated with a feature of a hand (abstract, disclosing hand tracking unit applies the single depth image data to a neural network model to generate heat maps indicating locations of hand features. The locations of hand features are used to generate a user hand shape model)
Sills and Twigg are analogous arts as they are in same field of endeavor i.e., generating hand model. It would have been obvious to one having ordinary skill in the art before effective filing date of claimed invention to modify art of Sill to use a neural network to generate a set of key points, each comprising a label associated with a feature of a hand as taught by Twigg for fast calibration of hand shape model.
Method of claim 7 recites limitations similar in scope to claim 1, hence is similarly rejected. Furthermore, Sills teaches determining a pose of a robot hand based, at least in part, on the one or more keypoints, the wrist pose, and one or more components of the robot hand (column 4 lines 55-65, disclosing technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm, clenching of the fist, and the curling of each finger. As user motion of replicated as spreading pal, clenching fist and curling each finger. Pose of robot hand in including fingers of robot is determined)
For claim 13, Sills teaches: A non-transitory computer-readable medium comprising instructions that, when performed by at least one processor of a computing device (column 25 lines 60-65, disclosing a non-transitory computer readable storage medium storing instructions executable by a processor to perform any of the methods described), cause the computing device to at least: LIMITATIONS SIMILAR IN SCOPE TO CLAIM 1.
For claim 2, modified Sills teaches: The system of claim 1, wherein the at least one memory comprises further instructions that, in response to being performed by the at least one processor, cause the system to at least:
provide the set of control commands to a robot that includes the robot hand (Column 2 lines 23-37, disclosing generating corresponding robotic commands that replicate the motion and contact of the human hand on a workpiece through a robotic tool. Robotic tool is interpreted as robot hand); and
cause the robot to perform the set of control commands (Column 2 lines 23-37, disclosing generating corresponding robotic commands that replicate the motion and contact of the human hand on a workpiece through a robotic tool).
For claim 3, modified Sill teaches: The system of claim 1, wherein the target pose indicates at least a position and an orientation of the robot hand based, at least in part, on the hand as depicted in the image data (Column 2 lines 23-37, disclosing generating corresponding robotic commands that replicate the motion and contact of the human hand on a workpiece through a robotic tool. And Column 4 lines 55-65, disclosing, technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm, clenching of the fist, and the curling of each finger. And column 13 lines 60-65, disclosing user performs a gesture that is captured by the cameras 102, 104 as a series of temporally sequential images).
For claim 5, modified Sills teaches: The system of claim 1, wherein the at least one memory comprises further instructions that, in response to being performed by the at least one processor, cause the system to at least:
calculate the set of keypoints and the wrist pose based, at least in part, on depth information associated with the image data (column 19 lines 33-47, disclosing 3D hand model can be aligned to the observed information using any of a variety of techniques. Aligning techniques bring model portions (e.g., capsules, capsuloids, capsulitis) into alignment with the information from the image source (e.g., edge samples, edge rays, interior points, 3D depth maps, and so forth)).
For claim 6, modified Sills teaches: The system of claim 1, wherein each keypoint of the set of keypoints indicates a respective feature of the hand as depicted in the image data (Column 17 lines 1-20, disclosing number of capsuloids 372, e.g. five (5), are used to represent fingers on a hand while a number of radial solids 374 are used to represent the shapes of the palm and wrist. Column 4 lines 55-65, disclosing, technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm, clenching of the fist, and the curling of each finger. The technology disclosed generates a 3D solid model that includes joints with locations and orientations. Capsuloids and 3D model indicate fingers palm and wrist and each of them are a feature of the hand).
For claim 8, modified Sills teaches: The method of claim 7, further comprising:
causing the one or more components of the robot hand to be oriented in accordance with the pose of the robot hand based, at least in part, on the set of commands (column 16 lines 11-20, disclosing implementation of a 3D solid hand model 300A with capsule representation of predictive information of the hand 114. Examples of predictive information of the hand include finger segment length, distance between finger tips, joint angles between fingers, and finger segment orientation. Column 24 lines 60-67, disclosing parameters of robotic tool actuation include at least one of path, trajectory, velocity, angular velocity, orientation).
For claim 10, modified Sills teaches: The method of claim 7, wherein the set of commands further causes at least a wrist of the robot hand to be oriented in accordance with a wrist pose of the hand of the user (column 16 lines 11-20, disclosing implementation of a 3D solid hand model 300A with capsule representation of predictive information of the hand 114. Examples of predictive information of the hand include finger segment length, distance between finger tips, joint angles between fingers, and finger segment orientation. Column 24 lines 60-67, disclosing parameters of robotic tool actuation include at least one of path, trajectory, velocity, angular velocity, orientation. As orientation of robotic tool follows orientation of human hand, a joint of robot is necessarily actuated to match the orientation and the joint for tool orientation is wrist of the robot hand).
For claim 11, modified Sills teaches: The method of claim 7, wherein the set of frames are generated in connection with two or more cameras (column 5 lines 63-67, disclosing For 3D motion capture, at least two cameras are typically used. See also column 6 lines 20-30).
For claim 12, modified Sills teaches: The method of claim 7, wherein the robot hand is part of a simulated robot or real robot (abstract, disclosing generating corresponding robotic commands that replicate the motion and contact of the human hand on a workpiece through a robotic tool. Column 25 lines 15-36, disclosing a real manipulator performs work on workpiece based on gesture of human hand).
Examiner’s NOTE: Current claim limitation requires only one (simulated or real) robot, therefore simulated robot is mapped through Sills.
For claim 14, modified Sills teaches: The non-transitory computer-readable medium of claim 13, comprising further instructions that when performed by the at least one processor of the computing device, cause the computing device to at least:
cause the robot hand to perform one or more tasks, based, at least in part, on the one or more control commands (abstract, disclosing generating corresponding robotic commands that replicate the motion and contact of the human hand on a workpiece through a robotic tool. As control commands are generated to control the robotic work tool, it will perform the task(s) observed by human hand).
For claim 16, modified Sills teaches: The non-transitory computer-readable medium of claim 13, wherein the one or more control commands, when performed by a robot that includes the robot hand, causes a pose of the robot hand to match the target pose (Column 2 lines 23-37, disclosing generating corresponding robotic commands that replicate the motion and contact of the human hand on a workpiece through a robotic tool. column 4 lines 55-65, disclosing technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm, clenching of the fist, and the curling of each finger. column 16 lines 11-20, disclosing implementation of a 3D solid hand model 300A with capsule representation of predictive information of the hand 114. Examples of predictive information of the hand include finger segment length, distance between finger tips, joint angles between fingers, and finger segment orientation).
For claim 17, modified Sills teaches: The non-transitory computer-readable medium of claim 13, comprising further instructions that when performed by the at least one processor of the computing device, cause the computing device to at least:
Sills teaches of determining location of hand (column 21, disclosing state (e.g., position, an orientation, and a location of a portion of the hand) is extracted from model of hand). However, of does not teach use the neural network to calculate one or more locations associated with the hand that correspond to the one or more keypoints
Twigg teaches calculate one or more locations associated with the hand that corresponds to the one or more keypoints (abstract, disclosing locations of hand features are used to generate hand shape model)
As hand features are utilized in determining hand model and as hand model is used to cause a robot to replicate hand gestures and movements. It would have been obvious to one having ordinary skill in the art before effective filing date of claimed invention to further modify art of Sills to use the neural network to calculate one or more locations associated with the hand that correspond to the one or more keypoints as taught by Twigg to accurately identify posture of hand for replication.
For claim 18, modified Sills teaches: The non-transitory computer-readable medium of claim 13, comprising further instructions that when performed by the at least one processor of the computing device, cause the computing device to at least:
calculate a wrist pose based, at least in part, on the set of frames and depth information (column 19 lines 33-47, disclosing 3D hand model can be aligned to the observed information using any of a variety of techniques. Aligning techniques bring model portions (e.g., capsules, capsuloids, capsuloids) into alignment with the information from the image source (e.g., edge samples, edge rays, interior points, 3D depth maps, and so forth. column 16 lines 11-20, disclosing implementation of a 3D solid hand model 300A with capsule representation of predictive information of the hand 114. Examples of predictive information of the hand include finger segment length, distance between finger tips, joint angles between fingers, and finger segment orientation. Column 24 lines 60-67, disclosing parameters of robotic tool actuation include at least one of path, trajectory, velocity, angular velocity, orientation. As orientation of robotic tool follows orientation of human hand, a joint of robot is necessarily actuated to match the orientation and the joint for tool orientation is wrist of the robot hand); and
determine the target pose based, at least in part, on the wrist pose (Column 4 lines 55-65, disclosing, technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm, clenching of the fist, and the curling of each finger. The technology disclosed generates a 3D solid model that includes joints with locations and orientations. Capsuloids and 3D model indicate fingers palm and wrist and each of them are a feature of the hand).
For claim 19, modified Sills teaches: The non-transitory computer-readable medium of claim 13, wherein:
the set of frames depicts at least the hand performing a task (column 6 lines 8-40, disclosing motion capture system coupled to image analysis, motion capture, and gesture recognition system. Column 17 lines 1-20, disclosing number of capsuloids 372, e.g. five (5), are used to represent fingers on a hand while a number of radial solids 374 are used to represent the shapes of the palm and wrist. Column 4 lines 55-65, disclosing, technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm, clenching of the fist, and the curling of each finger); and
the one or more control commands cause the robot hand to perform the task (column 4 lines 55-65, disclosing technology disclosed allows for advance control of a robotic arm that emulates and replicates user gesture control such as spread of the palm).
21. The system of claim 1, wherein the wrist pose comprises data generated from at least one of a position or orientation of the wrist depicted in the image data (column 16-17 and figures 3B-C, disclosing A number of capsuloids 372, e.g. five (5), are used to represent fingers on a hand while a number of radial solids 374 are used to represent the shapes of the palm and wrist. And capturing hand to reproduce on display screen. As wrist is reproduced on screen, it is necessarily captured in image data).
Claims 4 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sills in view of Twigg and Wang (US 20220080581).
For claim 4, modified Sills teaches: The system of claim 1, wherein the at least one memory comprises further instructions that, in response to being performed by the at least one processor, cause the system to at least:
Sills does not teach: determine another target pose of another robot hand based, at least in part, on the set of keypoints and the wrist pose output by the neural network; and
generate another set of control commands based, at least in part, on the other target pose, wherein the other set of control commands causes at least the other robot hand to move in accordance with the other target pose.
Wang teaches determine another target pose of another robot hand based, at least in part, on the set of keypoints and the wrist pose (abstract, disclosing method for dual arm robot teaching from dual hand detection in human demonstration. A camera image of the demonstrator's hands and workpieces is provided to a first neural network which determines the identity of the left and right hand from the image); and
generate another set of control commands based, at least in part, on the other target pose, wherein the other set of control commands causes at least the other robot hand to move in accordance with the other target pose (abstract, disclosing dual hand pose data for an entire operation is converted to robot gripper pose data and used for teaching two robot arms to perform the operation on the workpieces, where each hand's motion is assigned to one robot arm)
Sills and Wang are analogous arts as they are in same field of endeavor i.e., controlling robot arm based on user hand demonstration. It would have been obvious to one having ordinary skill in the art before effective filing date of claimed invention to modify art of Sills to determine another target pose of another robot hand based, at least in part, on the set of keypoints and the wrist pose; and generate another set of control commands based, at least in part, on the other target pose, wherein the other set of control commands causes at least the other robot hand to move in accordance with the other target pose as taught by Wang to use two robot arms, where the two robot arms perform two different operations at the same time. See Wang [0007].
For claim 20, modified Sills teaches: The non-transitory computer-readable medium of claim 13, comprising further instructions that when performed by the at least one processor of the computing device, cause the computing device to at least:
Sills does not teach: obtain another set of frames depicting at least another hand;
determine another target pose based, at least in part, on the other set of frames;
and generate one or more other control commands for another robot hand based, at least in part, on the other target pose, wherein the other robot hand and the robot hand are in a same environment.
Wang teaches: obtain another set of frames depicting at least another hand (abstract, disclosing method for dual arm robot teaching from dual hand detection in human demonstration. A camera image of the demonstrator's hands and workpieces is provided to a first neural network which determines the identity of the left and right hand from the image, and also provides cropped sub-images of the identified hands. The cropped sub-images are provided to a second neural network which detects the poses of both the left and right hand from the images);
determine another target pose based, at least in part, on the other set of frames (abstract, disclosing dual hand pose data for an entire operation is converted to robot gripper pose data and used for teaching two robot arms to perform the operation on the workpieces, where each hand's motion is assigned to one robot arm);
and generate one or more other control commands for another robot hand based, at least in part, on the other target pose, wherein the other robot hand and the robot hand are in a same environment ([0083], disclosing At box 890, the refined motion traces from the human demonstrator's left and right hands are provided to a dual-arm robot system, where the “left” robot arm performs the motions and tasks of the human's left hand, and the “right” robot arm performs the motions and tasks of the human's right hand. Figure 8, disclosing two robot hands in same environment)
Wills and Wang are analogous arts as they are in same field of endeavor i.e., controlling robots through hand gestures. It would have been obvious to one having ordinary skill in the art before effective filing date of claimed invention to modify art of Sills to obtain another set of frames depicting at least another hand; determine another target pose based, at least in part, on the other set of frames; and generate one or more other control commands for another robot hand based, at least in part, on the other target pose, wherein the other robot hand and the robot hand are in a same environment as taught by Wang to use two robot arms, where the two robot arms perform two different operations at the same time. See Wang [0007].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Sills in view of Twigg and Xu (US 20240180624).
For claim 9, modified Sills teaches: The method of claim 7, further comprising:
Sills does not teach: generating a visualization of results of a robot performing the set of commands;
and providing the visualization to one or more users.
Xu teaches: generating a visualization of results of a robot performing the set of commands (abstract, disclosing a method performed by a surgical system. [0022], disclosing performing an operation by a remote operator during teleoperation. [0027], disclosing remote operator 9 holds and moves the UID 14 to provide an input command to drive (move) one or more robotic arm actuators 17 (or driving mechanism) in the system 1 for teleoperation. [0029], disclosing system 1 may provide video output to one or more displays, including displays within the operating room as well as remote displays that are accessible via the Internet or other networks);
and providing the visualization to one or more users ([0061], disclosing first stage 50 shows video 57 captured by the camera 22 displayed on the display 25 that includes the surgical instrument, which is a grasping tool (e.g., having a distal grasping/clamping portion coupled to the surgical instrument), adjacent to the object 56 (e.g., showing the instrument entering the field of view of the camera). [0004], disclosing displaying surgical site to surgeon. And [0022], disclosing displaying surgical site inside a patient, and may also display in 3D perception. And the operator may be remote e.g., another different building, city, or country)
Sills and Xu are analogous arts as they are in same field of endeavor i.e., surgical robots (see Sills column 5 lines 7-21). It would have been obvious to one having ordinary skill in the art before effective filing date of claimed invention to modify art of Sills to generating a visualization of results of a robot performing the set of commands; and providing the visualization to one or more users as taught by Xu to provide display of surgical site to the operator to as a necessary feature to perform surgical procedure.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Sills in view of Twigg, Xu (US 20240180624) and Mandlekar (US 20230226696).
For claim 15, Sill teaches: The non-transitory computer-readable medium of claim 13,
Sills teaches of utilizing internet (i.e., web) to control a robot (column 23 lines 47-61, disclosing sensing robot control command through and one or combination of networks such as WiFi, LTE Internet etc.).
However does not teach: comprising further instructions that when performed by the at least one processor of the computing device, cause the computing device to at least:
generate a visualization of the robot hand in connection with a web browser.
Xu teaches: generate a visualization of the robot hand
Sills and Xu are analogous arts as they are in same field of endeavor i.e., teleoperating surgical robots. It would have been obvious to one having ordinary skill in the art before effective filing date of claimed invention to modify art of Sill to generate a visualization of the robot hand as taught by Xu to assist the operator in performing the surgery.
Mandlekar teaches of displaying robot environment in connection with a web browser ([0013], disclosing a teleoperation server connected to the robotic device, where the coordination server connects the user to the teleoperation server to control the robotic device. [0051-0052], disclosing users to control the robotic device using a smartphone as a motion controller and receives a real-time video stream of the robot workspace in their web browser)
Sills and Mandlekar are analogous arts as they are in same field of endeavor i.e., remotely controlling robots. It would have been obvious to one having ordinary skill in the art before effective filing date of claimed invention to further modify art of Sills to use a web browser to display robot as taught by Mandlekar to facilitate multiple users in controlling their respective robots. See Mandlekar [0051].
Conclusion
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