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 .
Detailed Action
Claims 1-20 are pending.
Response to Amendment
This action is in response to Amendment filled on 03/25/2026. The amendment has been entered. Claims 1,11 and 20 have been amended; claims 1-20 are pending, with claims 1,11 and 20 being independent in the instant application.
Response to Arguments
Applicant's Arguments/Remarks filed on page 8-10 regarding 35 U.S.C. 103 rejections have been fully considered, and found persuasive in view of Applicant's amendments to the claims. However, a new ground of rejections is necessitated by Applicant's claim amendments. Therefore, the previous rejections regarding 35 U.S.C.103 are being amended in this current office action. (See analysis below Claim Rejections-35 U.S.C. §103).
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 set forth in Graham, v. John Deere Co., 383 U.S.1.148 USPQ 459 (1966), that are applied 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 non-obviousness.
6. Claims 1-5, and 7-20 are rejected under 35 U.S.C. 103 as being unpatentable over an NPL paper “Dexterity Analysis of Three 6-DOF Continuum Robots Combining Concentric Tube Mechanisms and Cable Driven Mechanisms” Liao Wu et al. (hereinafter Wu, paper published in December, 2016), in view of a thesis/dissertation “Design Optimization for a Compliant, Continuum-Joint, Quadruped Robot” by Vallan Gray Sherrod (hereinafter Sherrod, thesis/dissertation published on 2019) and further in view of an NPL “A Novel Continuum Manipulator Design Using Serially Connected Double-Layer Planar Springs” by Peng Qi (hereinafter Qi, paper published in 2016).
Regarding claim 1, Wu teaches a computer-implemented method for generating a design for a continuum robot, (Wu disclosed in page 7 under ‘Conclusions’: “In this paper, we proposed three structures of combining concentric tube mechanisms and cable driven mechanisms, and introduced a set of indices based on the orientability to analyze the dexterity of these three continuum robots. A Monte Carlo method was used to calculate the dexterity distribution across the workspace. … the dexterity analysis performed in this paper provides good guidelines for designing this kind of continuum robots and can be adopted for evaluation of other robots as well.”).
Wu teaches determining, by an evaluation engine, the first plurality of candidate designs, a plurality of reachability factors, each reachability factor indicating an amount of a targeted workspace that is reachable based on a plurality of random locations within the targeted workspace, wherein the evaluation engine executes a kinematics solver to perform forward kinematics operations; (Wu disclosed in page 3-4 section III: “Constructing a unit sphere with its center placed at the spatial position, which is named the Service Sphere, all the possible areas on the sphere that can be orientated to by the tip of the robot are referred to as the Service Regions. Hence, the dexterity at this spatial position is defined as D(P) = AR(P)/AS … where AR(P) is the area of the Service Regions at position P and AS is the total surface area of the unit sphere. … Workspace of a robot is a set of all the possible positions that are reachable. In this paper, we use a Monte-Carlo method to calculate the workspace, that is, randomly sample the joint space, calculate the forward kinematics, and statistically analyze all the reachable positions. … Having the workspace and dexterity at any position inside the workspace, we can now define the total dexterity over the workspace as Dt… Note that this is an index between 0 and 1. The higher the index is, the more dexterity the robot has. If the robot has a total dexterity of 1, that means the robot can be oriented to all directions at any position inside its reachable workspace.” The disclosure above “the dexterity at this spatial position is defined as D(P) = AR(P)/AS … where AR(P) is the area of the Service Regions at position P and AS is the total surface area of the unit sphere” corresponds to claim limitation “reachability factor (e.g., dexterity) indicating an amount of a targeted workspace (e.g., AR(P) is the area of the Service Regions) that is reachable within the targeted workspace”.
Further, in page 4 section IV (at right col.): “we present the dexterity analysis results of the three robots. … Two groups of simulations, of which the conditions are listed in Table I, … In each group, the variables of each robot are given a certain variation range, and 100,000,000 samples are randomly collected from the variation ranges.” The disclosure dexterity index between 0 and 1 corresponds with claim element “reachability factor”. The dexterity analysis results of three robots being performed using 100,000,000 samples randomly collected from the variation ranges).
However, Wu doesn’t explicitly teach the amended limitations “generating a first plurality of candidate designs for the continuum robot in a first iteration of a generative design application, wherein a design generator module generates each candidate design included in the first plurality of candidate designs by selecting a first set of values for a set of design parameters to minimize an objective function; and generating in a second iteration of the generative design application, a second plurality of candidate designs for the continuum robot, wherein the design generator module generates each candidate design included in the second plurality of candidate designs by selecting a second set of values for the set of design parameters to minimize the objective function based on inputs comprising a first plurality of performance values generated by the kinematics solver for at least a subset of candidate designs selected in the first iteration.
Sherrod teaches generating a first plurality of candidate designs for the continuum robot in a first iteration of a generative design application, (Sherrod disclosed in page 2 under heading ‘Introduction’ (last para): “this work’s goal is to develop and validate useful metrics to aid in the design optimization of a 16-degree-of-freedom (DoF), pneumatically-actuated, continuum-joint, soft quadruped robot whose goal applications are in unstructured environments and mobile manipulation. Design variables include the base design, link geometry, and leg mounting configuration.” In page 60 section 3.2: “we chose a gradient-free evolutionary algorithm to design the quadruped robot. An evolutionary algorithm is suitable for this application for the following reasons: … An evolutionary algorithm has the ability to naturally converge on a Pareto front (the set of all non-dominated solutions in an objective space) for a multi-objective optimization problem as is the case in this work. The formulation of an evolutionary algorithm allows easy parallelization. This is because multiple designs in an iteration (known as a generation) can be evaluated independent of each other. Therefore, an evolutionary algorithm can be run on multiple processors in code.” A general evolutionary algorithm utilized in the optimization of the 16-DoF quadruped robot design to show how this general algorithm is applied to the optimization of the quadruped robot that includes the definition of the objective functions, constraints, and design generation.).
wherein Sherrod teaches a design generator module generates each candidate design included in the first plurality of candidate designs by selecting a first set of values for a set of design parameters to minimize an objective function; (Sherrod disclosed in page 63-64 section 3.2.2: “the four metrics that were used as objectives in this optimization: dexterity in walking regions, average payload in walking regions, average static stability criteria, and average desired velocity. … Table 3.3 lists the parameters that were used to calculate these metrics for this design problem. … In Table 3.3, Min Walking Clearance, Max Walking Clearance, and max are the parameters necessary to define the walking region, W, … Pmax (in Table 3.3) is the maximum pressure to which the joints can safely be filled. This value is necessary to calculate the torque limits … Fpayload,max is necessary to calculate the average payload in walking regions metric. … Since Pmax is 599844 Pa, this is believed to be a reasonable average pressure to maintain. Therefore, a max payload, Fpayload,max, of 981 N (equivalent to 100 kg) was chosen. The necessary information for masses and CoGs of the links, joints, and other parts of the legs needed for the average payload in walking regions and average static stability criteria are included in the robot description …”.
Further in page 65 section 3.2.3: “The bound constraints and perturbation bounds for each design variable are listed in Table 3.4. An additional constraint was enforced on the total length of the leg design for the quadruped … where h is the height of the joint (described in Section 3.1.3 as 0.205 m). Lmax was set to 18288m (6 ft) in the optimization. This constraint is utilized to prevent long legs. However, the results of the optimization (see Section 3.3) show that the objectives involving force output of the leg (average payload and average desired velocity metrics) seem to keep leg designs away from this constraint.”).
and Sherrod teaches generating in a second iteration of the generative design application, a second plurality of candidate designs for the continuum robot, wherein the design generator module generates each candidate design included in the second plurality of candidate designs by selecting a second set of values for the set of design parameters to minimize the objective function based on inputs comprising a first plurality of performance values generated by the kinematics solver for at least a subset of candidate designs selected in the first iteration. (Examiner would construe the claim term “objective function” as optimizing the design variable related to “continuum robot” (in light of Specification of current application para [0005]).
Sherrod disclosed in page 56-57 heading ‘Forward Kinematics’: “the Cartesian location of the foot represented in the Body Frame is needed for any given configuration. This Cartesian location can be found through the homogeneous transformation matrix gBodyFoot which is the Foot Frame expressed in the Body Frame. … We calculate gBodyFoot by combining all the transformation matrices for each component of the leg … The other four transformations … are transformations representing the rotational offsets the joints have with respect to the frames of the rest of the leg. They all are 45° offsets about the z axis of their respective frames. These offsets line up the joints such that the torques in the u and v directions combined to create a larger torque to provide a greater normal force between the foot and the ground. This helps increase the average payload in walking regions metric for a similar model without these offsets.”
Further, in page 69-70: “As in Figure 3.16, the robots with the best dexterity in walking region, average payload, average stability criteria, and averaged desired velocity are labeled as A, B, C, and D respectively. … Table 3.5 lists the design variables of the designs A, B, C, and D in Figure 3.16. These quadruped designs are visualized in their zero configurations (i.e. all joint variables u and v set to zero) in Figures 3.17a, 3.18a, 3.19a, and 3.20a. … This helps to illustrate the each quadruped’s dexterity and stability performance. … The robots with higher payload and desired velocity scores (designs B and D) tend to have smaller bases and the minimum lengths required for their legs. This is to reduce the overall weight of the robot allowing more of the force created by their joint torques to be utilized on extra payload. Another interesting trend in these designs is the last bend angle α2 tends to be around 90°. This creates a smaller moment arm for the second joint of each of the legs when applying a downwards force on the ground. Therefore, this joint can convert more of its torque into a downward force … Finally, these designs tend to utilize the inherent stiffness of the joints to provide extra torque. This is because their zero configurations (when the joints are straight with no bend) are below the actual walking region. Therefore, the configurations that are in the defined walking region are such that the joints want to spring back to their zero configurations. This provides extra torque to lift the robot in addition to the torque provided by the pressure in the joints’ bellows.”
The disclosure “quadruped’s dexterity and stability performance” in Figure 3.16 is the objective function, which is related to design variables of the continuum robot designs. The disclosures “create a larger torque to provide a greater normal force between the foot and the ground, this helps increase the average payload in walking regions metric”; “reducing the overall weight of the robot allowing more of the force created by their joint torques to be utilized on extra payload”; “designs with bend angle to be around 90°, creates a smaller moment arm for the second joint of each of the legs therefore, this joint can convert more of its torque into a downward force. And finally, these designs tend to utilize the inherent stiffness of the joints to provide extra torque”. It has already been discussed earlier (above) that during 1st iteration additional constraint was enforced on the total length of the leg design for the quadruped. In 2nd iteration the objective function is minimized or design variables get optimized (e.g., better torque as discussed above) in above disclosure based on 1st iteration’s result and using kinematic solver (or Forward Kinematics)).
and Sherrod teaches outputs comprising a torque (Sherrod disclosed in page 74-75 section 4.1: “An image of the robot design in its zero configuration is shown in Figure 4.1. The leg constructed from this design may be seen in Figure 4.2. … This change has no effect on either the optimization or experimental results since these continuum-joints are modeled to be identical in terms of torque output and range of motion when rotated by offsets of 90◦.”).
Wu and Sherrod are analogous art because they are related in optimizing of design parameters in robot design. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Wu and Sherrod before him or her, to modify generating and optimizing the design variables for continuum robot of Wu, to include selecting values for the design parameters to minimize an objective function related to continuum robot design from the teaching of Sherrod. The suggestion/motivation for doing so would have been obvious by Sherrod because “a multi-objective design optimization of a pneumatically-actuated continuum joint quadruped robot using an evolutionary algorithm was presented. This optimization used the novel metrics to explore the quadruped’s design space. To our knowledge, this optimization provides designs for the most design variables ever used in an optimization of a quadruped robot design to date. The construction of a leg design chosen from an optimized quadruped is described. To our knowledge, this is the first large-scale (1.3 m long) compliant, continuum soft leg built. The size and strength of this leg gives it the potential to perform human-sized tasks.” (Sherrod disclosed in page 94 section 5.2).
Even Sherrod teaches the claim elements “torque” and “dexterity in walking regions” (maximum reach), however, Wu and Sherrod do not explicitly teach the amended limitations “a torque factor for a configuration for where an end effector is at a maximum reach or a farthest location in the targeted workspace”.
Qi teaches a torque factor for a configuration for where an end effector is at a maximum reach or a farthest location in the targeted workspace. (Examiner would construe the claim term “torque factor” as “a summation of torques at the base of each continuum joint of the candidate design”, in light of Specification of current Application para [0038].
Qi disclosed in page 1281 under ‘Abstract’: “This paper introduces a novel continuum manipulator that integrates multiple layers of compliant planar springs—a structure that provides several notable advantages over existing designs. … With the serial connection of multiple conjoined layers, the manipulator demonstrates linear predictable bending even when executing large bends. … the reachable workspace of the end effector is enlarged by means of varying the length of the continuum manipulator via controlled contraction and expansion.” In page 1288 section V. B.: “the experiments started with single modular segment tests and then three-segment assembly tests. Based on the experimental results, the linear and decoupled behaviors of this multilayer planar spring-based design are further studied. Fig. 9 shows several snapshots of the configurations of a double layer module and a three-segment assembly when experiencing known loads applied via tendon(s). The other end of the string is tied to the Nano 17 force/torque sensor which is held on the slider and moves along the rail quasi-statically and slowly at a constant speed of 10 mm/min, thus forces are produced along the taut tendons.” The disclosures above teach the claim limitation “a torque factor for a configuration for where an end effector is connected”
In page 1289 section V. B.2): “We continue to study the bending configuration of the three- segment assembly in this test and examine its total length. … Therefore, these experiments confirm the predicted decoupling between bending and contraction. This is an important aspect and the primary contribution of our multilayer planar spring stacked concept, … Besides, we observed that each layer’s bending angle or contraction distance was about equal and uniformly distributed across the total deformations in all the four tests (see Fig. 9). The maximum reachable bending angle depends on the material properties and segment design.” Further, in page 1290 section V.C. (right col.): “Fig. 12(c)–(f) illustrates the bending motion of the manipulator. The continuum manipulator is actuated by three tendons simultaneously and this enables the manipulator to deflect in 3D space [see Fig. 12(f)]. In order to determine the workspace of the manipulator tip, the 2-D tip positions in the horizontal plane … The central length of the bending manipulator was then measured for each configuration. The results are listed in Table II. … with the bending angle increasing to values higher than 30° certain levels of contraction are exhibited. … From Fig. 13, we can see that the red trajectory indicates the tip positions of a spring backbone continuum manipulator when exerted with a bending force.”
The disclosures “The central length of the bending manipulator was measured for each configuration; the results are listed in Table II, with the bending angle increasing to values higher than 30° certain levels of contraction are exhibited; from Fig. 13, the red trajectory indicates the tip positions of a spring backbone continuum manipulator when exerted with a bending force” correspond to claim limitation “torque factor (bending force) is at maximum reach or a farthest location in the targeted workspace”).
Wu, Sherrod and Qi are analogous art because they are related in optimizing of design parameters in robot design. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Wu, Sherrod and Qi before him or her, to modify torque factor as continuum robot output design in Sherrod’s teaching, to include torque factor for the end effector with maximum reachability in Qi’s teaching. The suggestion/motivation for doing so would have been obvious by Qi because “This paper presented the design of a continuum manipulator with multiple layers of compliant planar modules linked in series. First, we reviewed frequently applied continuum manipulator constructions to date based on the distinctive backbone architecture. Through the study, we found that our structure has advantages over other existing traditional continuum manipulators. These advantages are longitudinal compliance, large linear displacement motions, effectively decoupled contraction, and bending motions, as well as an enlarged workspace” (Qi disclosed in page 1291 under ‘Conclusion’ (left col.)).
Regarding Claim 2, Wu, Sherrod and Qi teach the computer-implemented method of claim 1, wherein Wu teaches the set of design parameters includes at least one of a number of continuum joints included in a candidate design, a minimum bending radius for each continuum joint included in the candidate design, a length for a rigid portion associated with each continuum joint included in the candidate design, or a straightened length for each continuum joint included in the candidate design. (Examiner notes that the claim language includes four optional embodiments, since claim language has term “at least one of”, only one of the four embodiments need to be taught by the reference. Wu disclosed in page 2-3 section II: “In Robot 3, both Segment 2 and Segment 3 are made by cable driven mechanisms. … As a result, the two segments have only one translational DOF in total. However, each segment has two rotational DOFs as they can bend in both directions, making the total numbers of DOF six as well as the Robot 1 and 2.” Here, the disclosure “each segment has two rotational DOFs as they can bend in both directions” corresponds to the claim limitation “a minimum bending radius for each continuum joint included in the candidate design”).
Regarding Claim 3, Wu, Sherrod and Qi teach the computer-implemented method of claim 1, wherein Wu teaches the first plurality of performance values is based on at least one of an objective criterion associated with the continuum robot, a performance criterion associated with the continuum robot, the targeted workspace that is reachable by the candidate design included in the first plurality of candidate designs, or a trajectory within the targeted workspace that can be performed by the candidate design included in the first plurality of candidate designs. (Wu disclosed in page 3-4 section III: “Constructing a unit sphere with its center placed at the spatial position, which is named the Service Sphere, all the possible areas on the sphere that can be orientated to by the tip of the robot are referred to as the Service Regions. Hence, the dexterity at this spatial position is defined as D(P) = AR(P)/AS … where AR(P) is the area of the Service Regions at position P and AS is the total surface area of the unit sphere. As in this paper, we will use a discretization method to obtain the dexterity indices, … Workspace of a robot is a set of all the possible positions that are reachable. In this paper, we use a Monte-Carlo method to calculate the workspace, that is, randomly sample the joint space, calculate the forward kinematics, and statistically analyze all the reachable positions. … It is noted that all the three robots investigated in this work have rotational symmetry about the Z axis of the base frame. Therefore, we will only calculate the workspace within the right half of the X-Z plane (X+) of the base frame. By evenly discretizing the plane into patches with side lengths of x and z, the workspace can be calculated by W … Having the workspace and dexterity at any position inside the workspace, we can now define the total dexterity over the workspace as Dt … Note that this is an index between 0 and 1. The higher the index is, the more dexterity the robot has. If the robot has a total dexterity of 1, that means the robot can be oriented to all directions at any position inside its reachable workspace.”
The disclosure D(P) = AR(P)/AS … where AR(P) is the area of the Service Regions at position P and AS is the total surface area of the unit sphere.” corresponds to claim limitation “a reachability factor (e.g., dexterity) indicating an amount of a targeted workspace (e.g., AR(P) is the area of the Service Regions) that is reachable within the targeted workspace”. Further, the disclosure “robot has a total dexterity of 1, that means the robot can be oriented to all directions at any position inside its reachable works” corresponds to the claim limitation “performance value is based on objective or performance criterion associated with the continuum robot”).
Regarding Claim 4, Wu, Sherrod and Qi teach the computer-implemented method of claim 3, however, Wu doesn’t explicitly teach the limitation “the objective criterion associated with the continuum robot includes at least one of a total arm length, a torque factor, a total weight, or a number of continuum joints included in the continuum robot”.
wherein Sherrod teaches the objective criterion associated with the continuum robot includes at least one of a total arm length, a torque factor, a total weight, or a number of continuum joints included in the continuum robot. (Sherrod disclosed in page 48-49 heading ‘Torque Model’: “The torque’s dependence on the joint’s configuration is a result of the inherent stiffness from the plastic bellows in these types of joints. The torque output has been modeled by the company that designed them, Pneubotics, as follows: … Here, τx and τy are the torques about the x and y axes in the bottom frame of the joint in Figures 3.7 and 3.8; Pi is the pressure in the ith bellow; u and v are the joint configuration variables; ktp is a constant relating pressure to torque; and kp is the spring constant associated with the inherent stiffness of the joint. … Since the torque output is configuration dependent, so are the torque limits. These torque limits are necessary to calculate the average payload in walking regions and average desired velocity metrics … For example, to find the maximum torque along the x axis, we assume bellow zero (in Figure 3.7) is at max pressure, while bellow one is at zero pressure. This results in the following equation from Equation 3.12: … To find the minimum torque in the x direction, we assume bellow one is at max pressure (since it causes rotation in the negative x direction) and bellow zero is at zero pressure. This results in the following equation from Equation 3.12: …”).
Wu and Sherrod are analogous art because they are related in optimizing of design parameters in robot design. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Wu and Sherrod before him or her, to modify generating and optimizing the design variables for continuum robot of Wu, to include selecting values for the design parameters to minimize an objective function related to continuum robot design from the teaching of Sherrod. The suggestion/motivation for doing so would have been obvious by Sherrod because “a multi-objective design optimization of a pneumatically-actuated continuum joint quadruped robot using an evolutionary algorithm was presented. This optimization used the novel metrics to explore the quadruped’s design space. To our knowledge, this optimization provides designs for the most design variables ever used in an optimization of a quadruped robot design to date. The construction of a leg design chosen from an optimized quadruped is described. To our knowledge, this is the first large-scale (1.3 m long) compliant, continuum soft leg built. The size and strength of this leg gives it the potential to perform human-sized tasks.” (Sherrod disclosed in page 94 section 5.2).
Regarding Claim 5, Wu, Sherrod and Qi teach the computer-implemented method of claim 3, wherein Wu teaches the performance criterion associated with the continuum robot includes at least one of the reachability factor or a trajectory factor. (Wu disclosed in page 3-4 section III: “Constructing a unit sphere with its center placed at the spatial position, … the dexterity at this spatial position is defined as D(P) = AR(P)/AS … As in this paper, we will use a discretization method to obtain the dexterity indices, … Workspace of a robot is a set of all the possible positions that are reachable. In this paper, we use a Monte-Carlo method to calculate the workspace, that is, randomly sample the joint space, calculate the forward kinematics, and statistically analyze all the reachable positions. … Having the workspace and dexterity at any position inside the workspace, we can now define the total dexterity over the workspace as Dt … Note that this is an index between 0 and 1. The higher the index is, the more dexterity the robot has. If the robot has a total dexterity of 1, that means the robot can be oriented to all directions at any position inside its reachable workspace.”).
Regarding Claim 7, Wu, Sherrod and Qi teach the computer-implemented method of claim 1, wherein Wu teaches generating the first plurality of candidate designs comprises computing values for the first set of values that conform to at least one design parameter constraint. (Wu disclosed in page 6 section IV: “Fig. 7 and Fig. 8 display the results of simulation Group II. … Comparing the data in Table II, it can be seen that the workspaces of Robot 1 and 3 have significantly increased. The workspace of Robot 2 also expands, but relatively more moderately … With regard to the dexterity distribution along different axes, Robot 1 basically retains the same characteristics, whereas Robot 2 and 3 evince evenly distributed dexterity among the three directions in Group II. This is also supported by Fig. 8, which shows that Robot 1 has more service regions in the axial and radial directions at the position with the maximum total dexterity, while Robot 2 and 3 has their service regions almost covering all the surface of the service sphere.” In page 6 section V: “The results of simulation Group I and Group II suggest that this different allocation affects both the shape of workspace and the distribution of dexterity. Comparing Robot 1 and 2 in Group I and Group II, we can see that augmentation of Segment 2 tends to enlarge the workspace while augmentation of Segment 3 tends to improve the dexterity. In both groups, Robot 3 achieves the largest workspace and the best dexterity, which implies that evenly allocate DOFs among the segments is the best solution.”).
Regarding Claim 8, Wu, Sherrod and Qi teach the computer-implemented method of claim 7, wherein Wu teaches the at least one design parameter constraint includes at least one of a maximum number of continuum joints included in the continuum robot, a minimum number of continuum joints included in the continuum robot, a required number of continuum joints included in the continuum robot, a maximum total arm length of the continuum robot, a relative position between the continuum robot and the targeted workspace, or a shape of the targeted workspace. (Examiner notes that the claim language includes six optional embodiments, since claim language has term “at least one of”, only one of the six embodiments need to be taught by the reference.
Wu disclosed in page 3-4 section III: “Constructing a unit sphere with its center placed at the spatial position, which is named the Service Sphere, all the possible areas on the sphere that can be orientated to by the tip of the robot are referred to as the Service Regions. Hence, the dexterity at this spatial position is defined as D(P) = AR(P)/AS … where AR(P) is the area of the Service Regions at position P and AS is the total surface area of the unit sphere. As in this paper, we will use a discretization method to obtain the dexterity indices, … Workspace of a robot is a set of all the possible positions that are reachable. In this paper, we use a Monte-Carlo method to calculate the workspace, that is, randomly sample the joint space, calculate the forward kinematics, and statistically analyze all the reachable positions. … It is noted that all the three robots investigated in this work have rotational symmetry about the Z axis of the base frame. Therefore, we will only calculate the workspace within the right half of the X-Z plane (X+) of the base frame. By evenly discretizing the plane into patches with side lengths of x and z, the workspace can be calculated by W … Having the workspace and dexterity at any position inside the workspace, we can now define the total dexterity over the workspace as Dt …”.
The disclosure D(P) = AR(P)/AS … where AR(P) is the area of the Service Regions at position P and AS is the total surface area of the unit sphere.” corresponds to claim limitation “a reachability factor (e.g., dexterity) indicating an amount of a targeted workspace (e.g., AR(P) is the area of the Service Regions) that is reachable within the targeted workspace”).
Regarding Claim 9, Wu, Sherrod and Qi teach the computer-implemented method of claim 1, wherein Wu teaches generating the first plurality of candidate designs comprises computing, for each candidate design included in the first plurality of candidate designs, the first set of values based on a global optimization process. (Examiner would construe the claim term “Global optimization” as numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set. Wu disclosed in page 6 section IV: “Fig. 7 and Fig. 8 display the results of simulation Group II. … Comparing the data in Table II, it can be seen that the workspaces of Robot 1 and 3 have significantly increased. The workspace of Robot 2 also expands, but relatively more moderately … With regard to the dexterity distribution along different axes, Robot 1 basically retains the same characteristics, whereas Robot 2 and 3 evince evenly distributed dexterity among the three directions in Group II. This is also supported by Fig. 8, which shows that Robot 1 has more service regions in the axial and radial directions at the position with the maximum total dexterity, while Robot 2 and 3 has their service regions almost covering all the surface of the service sphere.” In page 6 section V: “The results of simulation Group I and Group II suggest that this different allocation affects both the shape of workspace and the distribution of dexterity. Comparing Robot 1 and 2 in Group I and Group II, we can see that augmentation of Segment 2 tends to enlarge the workspace while augmentation of Segment 3 tends to improve the dexterity. In both groups, Robot 3 achieves the largest workspace and the best dexterity, which implies that evenly allocate DOFs among the segments is the best solution.”).
Regarding Claim 10, Wu, Sherrod and Qi teach the computer-implemented method of claim 9, however, Wu doesn’t explicitly teach the limitation “the global optimization process includes an objective function that is based on a plurality of objective criteria associated with the continuum robot”.
wherein Sherrod teaches the global optimization process includes an objective function that is based on a plurality of objective criteria associated with the continuum robot. (Sherrod disclosed in page 63 section 3.2.2: “the four metrics that were used as objectives in this optimization: dexterity in walking regions, average payload in walking regions, average static stability criteria, and average desired velocity. We chose the static stability margin as the stability criteria for the average static stability criteria metric. Table 3.3 lists the parameters that were used to calculate these metrics for this design problem. … To sample the leg’s configuration space, every combination of u and v for each joint between -π/2 and π/2 at the resolution Joint Angle Res (see Table 3.3) was sampled.”).
Wu and Sherrod are analogous art because they are related in optimizing of design parameters in robot design. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Wu and Sherrod before him or her, to modify generating and optimizing the design variables for continuum robot of Wu, to include selecting values for the design parameters to minimize an objective function related to continuum robot design from the teaching of Sherrod. The suggestion/motivation for doing so would have been obvious by Sherrod because “a multi-objective design optimization of a pneumatically-actuated continuum joint quadruped robot using an evolutionary algorithm was presented. This optimization used the novel metrics to explore the quadruped’s design space. To our knowledge, this optimization provides designs for the most design variables ever used in an optimization of a quadruped robot design to date. The construction of a leg design chosen from an optimized quadruped is described. To our knowledge, this is the first large-scale (1.3 m long) compliant, continuum soft leg built. The size and strength of this leg gives it the potential to perform human-sized tasks.” (Sherrod disclosed in page 94 section 5.2).
Regarding Claim 11, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Wu, Sherrod and Qi as discussed above for substantially similar rationale. In addition, claim 11 recites following limitations:
Wu teaches a non-transitory computer readable medium storing instruction that, when executed by a processor, cause the processor to generate a design for a continuum robot, by performing the steps of: (Wu discussed in page 4 heading ‘RESULTS’ that the dexterity analysis results of the three robots has been presented. Two groups of simulations, of which the conditions are listed in Table I, are carried out on a WINDOWS 7 64-bit platform with Intel Core i7-5600U 2.60 GHz CPU and 16.0 GB RAM. Any person having skills in the art would understand that a computer system having processor (s) and memory to perform any simulation. Therefore, this disclosure teaches the limitation “computer readable medium storing instruction that, when executed by a processor”.
In page 7 under ‘Conclusions’: “In this paper, we proposed three structures of combining concentric tube mechanisms and cable driven mechanisms, and introduced a set of indices based on the orientability to analyze the dexterity of these three continuum robots. A Monte Carlo method was used to calculate the dexterity distribution across the workspace. … the dexterity analysis performed in this paper provides good guidelines for designing this kind of continuum robots and can be adopted for evaluation of other robots as well.” This disclosure teaches the limitation “generate a design for a continuum robot”).
Even Sherrod teaches the claim elements “torque” and “dexterity in walking regions” (maximum reach), however, Wu and Sherrod do not explicitly teach the amended limitations “a torque factor for a configuration for where an end effector is at a maximum reach or a farthest location in the targeted workspace”.
Qi teaches a torque factor for a configuration for where an end effector is at a maximum reach or a farthest location in the targeted workspace. (Examiner would construe the claim term “torque factor” as “a summation of torques at the base of each continuum joint of the candidate design”, in light of Specification of current Application para [0038].
Qi disclosed in page 1281 under ‘Abstract’: “This paper introduces a novel continuum manipulator that integrates multiple layers of compliant planar springs—a structure that provides several notable advantages over existing designs. … With the serial connection of multiple conjoined layers, the manipulator demonstrates linear predictable bending even when executing large bends. … the reachable workspace of the end effector is enlarged by means of varying the length of the continuum manipulator via controlled contraction and expansion.” In page 1288 section V. B.: “the experiments started with single modular segment tests and then three-segment assembly tests. Based on the experimental results, the linear and decoupled behaviors of this multilayer planar spring-based design are further studied. Fig. 9 shows several snapshots of the configurations of a double layer module and a three-segment assembly when experiencing known loads applied via tendon(s). The other end of the string is tied to the Nano 17 force/torque sensor which is held on the slider and moves along the rail quasi-statically and slowly at a constant speed of 10 mm/min, thus forces are produced along the taut tendons.” The disclosures above teach the claim limitation “a torque factor for a configuration for where an end effector is connected”
In page 1289 section V. B.2): “We continue to study the bending configuration of the three- segment assembly in this test and examine its total length. … Therefore, these experiments confirm the predicted decoupling between bending and contraction. This is an important aspect and the primary contribution of our multilayer planar spring stacked concept, … Besides, we observed that each layer’s bending angle or contraction distance was about equal and uniformly distributed across the total deformations in all the four tests (see Fig. 9). The maximum reachable bending angle depends on the material properties and segment design.” Further, in page 1290 section V.C. (right col.): “Fig. 12(c)–(f) illustrates the bending motion of the manipulator. The continuum manipulator is actuated by three tendons simultaneously and this enables the manipulator to deflect in 3D space [see Fig. 12(f)]. In order to determine the workspace of the manipulator tip, the 2-D tip positions in the horizontal plane … The central length of the bending manipulator was then measured for each configuration. The results are listed in Table II. … with the bending angle increasing to values higher than 30° certain levels of contraction are exhibited. … From Fig. 13, we can see that the red trajectory indicates the tip positions of a spring backbone continuum manipulator when exerted with a bending force.”
The disclosures “The central length of the bending manipulator was measured for each configuration; the results are listed in Table II, with the bending angle increasing to values higher than 30° certain levels of contraction are exhibited; from Fig. 13, the red trajectory indicates the tip positions of a spring backbone continuum manipulator when exerted with a bending force” correspond to claim limitation “torque factor (bending force) is at maximum reach or a farthest location in the targeted workspace”).
Wu, Sherrod and Qi are analogous art because they are related in optimizing of design parameters in robot design. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Wu, Sherrod and Qi before him or her, to modify torque factor as continuum robot output design in Sherrod’s teaching, to include torque factor for the end effector with maximum reachability in Qi’s teaching. The suggestion/motivation for doing so would have been obvious by Qi because “This paper presented the design of a continuum manipulator with multiple layers of compliant planar modules linked in series. First, we reviewed frequently applied continuum manipulator constructions to date based on the distinctive backbone architecture. Through the study, we found that our structure has advantages over other existing traditional continuum manipulators. These advantages are longitudinal compliance, large linear displacement motions, effectively decoupled contraction, and bending motions, as well as an enlarged workspace” (Qi disclosed in page 1291 under ‘Conclusion’ (left col.)).
Regarding Claim 12, Wu, Sherrod and Qi teach the non-transitory computer readable medium of claim 11, wherein Wu teaches generating the first plurality of performance values for each candidate design included in the first plurality of candidate designs comprises performing one or more operations to evaluate each candidate design with respect to a kinematics model for a robot that includes at least one continuum joint. (Wu disclosed in page 5-6 section IV: “The total dexterity of the three robots is almost the same, but its distribution across the workspace is quite different. The position with the maximum dexterity appears in the middle of the workspace of Robot1, while for Robot 2 and 3, the most dexterous point locates near the edge of the workspace. The maximum dexterity of Robot 2 and 3 is also greater than Robot 1. … For Robot 2, the circumferential direction is also the least dexterous. However, the radial dexterity outperforms the axial dexterity in terms of the maximum value, which is quite different from Robot 1. All the three directional dexterity has the summit occurring near the edge of the workspace, which is different from Robot 1 as well. The dexterity distribution of Robot 3 is most similar to Robot 2 in terms of the extreme value of each directional dexterity. … The differences of dexterity distribution among these three robots can also be found in Fig. 6, which shows the service regions of each robot at the position with the maximum total dexterity. It can be easily concluded that Robot 1 has more dexterity along the Z axis while Robot 2 and 3 have better radial dexterity.”
In page 3-4 section III: “Workspace of a robot is a set of all the possible positions that are reachable. In this paper, we use a Monte-Carlo method to calculate the workspace, that is, randomly sample the joint space, calculate the forward kinematics, and statistically analyze all the reachable positions.” This disclosure teaches the limitation: “performing one or more operations to evaluate each candidate design with respect to a kinematics model for a robot that includes at least one continuum joint”).
Regarding Claim 13, Wu, Sherrod and Qi teach the non-transitory computer readable medium of claim 11, wherein Wu teaches the first set of values for the set of design parameters includes multiple values for at least one design parameter included in the set of design parameters (Wu disclosed in page 2-3 section II: “In this paper, we investigate three 6-DOF (Degree of Freedom) continuum robots, whose structures are shown in Fig. 1. All these robots have three segments, with Segment 1 identically being a straight tube and having a translational DOF. … In this paper, we only focus on the robot independent mapping. As shown in Fig. 3, a single segment of a continuum robot can be modelled by three variables under constant curvature assumption. ĸ is the curvature of the arc, φ means the angle between the plane of the arc and the X-Z plane, and s stands for the length of the arc angle … For example, as the first segments of all the three robots are straight, ĸ1 and φ1 are constant zero. Segment 3 of Robot 1 and Segment 2 of Robot 2 have fixed curvature, and therefore ĸ3 of Robot 1 and ĸ2 of Robot 2 are constant (non-zero). In Robot 3, the lengths of Segment 2 and Segment 3 are dependent. As a result, s2 and s3 are related: if s2 is nonzero, then s3 can only be the full length, meaning that Segment 3 is fully exposed; if s3 is less than the full length, then s2 can only be zero, meaning that Segment 2 is completely invisible”).
However, Wu doesn’t explicitly teach the limitations “the second set of values for the set of design parameters includes multiple values for at least one design parameter included in the set of design parameters”.
and Sherrod teaches the second set of values for the set of design parameters includes multiple values for at least one design parameter included in the set of design parameters. (Sherrod disclosed in page 74 section 4.1: “We chose a design that was closest to the 95 percentile in the average payload and average desired velocity metrics from the optimization results in Chapter 3. … It scored 72377, 196:0 N, 0:1593 m, and 0:1263 in the dexterity, average payload, average stability margin, and average desired velocity, metrics respectively. The design variables, as defined in Section 3.1, for this chosen quadruped is shown in Table 4.1. An image of the robot design in its zero configuration is shown in Figure 4.1. …”).
Wu and Sherrod are analogous art because they are related in optimizing of design parameters in robot design. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Wu and Sherrod before him or her, to modify generating and optimizing the design variables for continuum robot of Wu, to include selecting values for the design parameters to minimize an objective function related to continuum robot design from the teaching of Sherrod. The suggestion/motivation for doing so would have been obvious by Sherrod because “a multi-objective design optimization of a pneumatically-actuated continuum joint quadruped robot using an evolutionary algorithm was presented. This optimization used the novel metrics to explore the quadruped’s design space. To our knowledge, this optimization provides designs for the most design variables ever used in an optimization of a quadruped robot design to date. The construction of a leg design chosen from an optimized quadruped is described. To our knowledge, this is the first large-scale (1.3 m long) compliant, continuum soft leg built. The size and strength of this leg gives it the potential to perform human-sized tasks.” (Sherrod disclosed in page 94 section 5.2).
Regarding claims 14-17 and 19, Wu, Sherrod and Qi teach the non-transitory computer readable medium of claim 11, are incorporating the rejections of claims 2-5 and 7 respectively, because claims 14-17 and 19 have substantially similar claim language as claims 2-5 and 7, therefore claims 14-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, Sherrod and Qi as discussed above for substantially similar rationale.
Regarding Claim 18, Wu, Sherrod and Qi teach the non-transitory computer readable medium of claim 11, wherein Wu teaches each candidate design included in the first plurality of candidate designs is further based on at least one design parameter constraint. (Wu disclosed in page 1 under ‘Abstract’: “In this paper, we investigate the dexterity of three continuum robots combining both mechanisms. Indices based on the concept of orientability are introduced to analyze the distribution of the dexterity. A Monte Carlo method is used to calculate the dexterity distribution across the workspace. Particularly, directional dexterity indices are put forward to describe the dexterity along different axes. Results imply that evenly allocating degrees of freedom (DOFs) among the segments achieves the best workspace and dexterity.” The disclosure related to the “dexterity of continuum robots” corresponds to the claim element “design parameter constraint”).
Regarding Claim 20, the same ground of rejection is made as discussed in claims 1 and 11 for substantially similar rationale, therefore claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Wu, Sherrod and Qi as discussed above for substantially similar rationale. In addition, claim 20 recites following limitations:
Wu teaches a system, comprising: a memory that stores instructions; and a processor that is communicatively coupled to the memory and is configured to, when executing the instructions, perform the steps of: (Wu discussed in page 4 heading ‘RESULTS’ that the dexterity analysis results of the three robots has been presented. Two groups of simulations, of which the conditions are listed in Table I, are carried out on a WINDOWS 7 64-bit platform with Intel Core i7-5600U 2.60 GHz CPU and 16.0 GB RAM. Any person having skills in the art would understand that a computer system having processor (s) and memory to perform any simulation. Therefore, this disclosure teaches the limitation).
Claims 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wu, Sherrod and Qi and further in view of an NPL paper “Reachability and Dexterity: Analysis and Applications for Space Robotics” by Oliver Porges (hereinafter Porges, NPL published in 2015).
Regarding Claim 6, Wu, Sherrod and Qi teach the computer-implemented method of claim 5, however Wu, Sherrod and Qi do not explicitly teach the limitation “generating the first plurality of performance values comprises performing a hybrid approach that includes determining reachability for a first portion of the targeted workspace via forward kinematics and reachability for a second portion of the targeted workspace via inverse kinematics”.
Porges teaches generating the first plurality of performance values comprises performing a hybrid approach that includes determining reachability for a first portion of the targeted workspace via forward kinematics and reachability for a second portion of the targeted workspace via inverse kinematics. (Porges disclosed in page 2 section II A. (left col.): “A straightforward method for the map generation uses a combination of forward kinematics (FK) and random joint sampling … Inverse kinematics (IK) can also be used for the map generation, as it guarantees a complete workspace exploration. Each bin in the map is represented by one TCP sample (commonly chosen at the center of the bin). If an IK solution (i.e., a combination of joint values) exists to reach this TCP, the bin is marked as reachable … A third generation method, the hybrid (HYB) method, combines the two previously mentioned strategies. The process starts by using an FK approach based on random sampling of the joint positions. At the beginning, a large number of bins is correctly set to 1, … and once it reaches a predefined threshold an IK-based generation is triggered to complete the map by verifying if there exists an IK solution for the bins that have not yet been set … In this way, the number of IK queries required to guarantee a complete exploration of the workspace is reduced, thus reducing the computational effort required for the map generation.”
A hybrid approach is performed in above disclosure that includes reachability for a first portion of the targeted workspace via forward kinematics is determined (e.g., forward kinematics (FK) approach used with map generation in above disclosure). Further, reachability for a second portion of the targeted workspace via inverse kinematics (e.g., Inverse kinematics (IK) also used for the map generation, as it guarantees a complete workspace exploration is reduced, thus reducing the computational effort for the map generation). The map is stored as a binary array where each bin represents the reachability factor (disclosed in page 1 section II (right side col.) and each bin in the map is considered as the portion of the region).
Wu, Sherrod, Qi and Porges are analogous art because they are related in optimizing of design parameters in robot design. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Wu, Sherrod, Qi and Porges before him or her, to modify determining of the performance criterion of Sherrod, to include performing hybrid approach to determine the reachability factor or map related to the region of workspace of Porges. The suggestion/motivation for doing so would have been obvious by Porges because “Reachability index essentially describes how many of the discretized directions are reachable within each voxel, and quantifies its dexterity. The index is later used to encode the color for visualization. Using the voxel coordinates in space and the associated color, the robot workspace with the dexterity information can be visualized.” (Porges disclosed in page 4 section III C.).
Conclusion
7. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. An NPL “Design of an Interactive Control System for a Multisection Continuum Robot” by Bo Ouyang et al. presents the development of a human–robot interactive control system that includes two operational modes for controlling end-effector position and the entire robot shape with image feedback. To control the end-effector position, all the desired positions of the end effector fall within a reachable workspace constructed with an approximate boundary. The proposed interactive control system ensures that the Continuum robot end effector follows the desired path, while the robot shape is regulated based on visual feedback. The proposed approach exhibits the following advantages. First, a schematic based on the proposed approximate boundary of the reachable workspace is developed to ensure that a user-prescribed set of desired positions of the robot end effector can be achieved. Finally, the proposed shape correspondence approach can directly prescribe the desired path of the continuum robot without requiring prior knowledge of the environment.
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/NUPUR DEBNATH/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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