DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The amendment filed 5/18/2026 has been entered. Claims 1-3, 5, 7-9, and 11-14 are amended. Claims 1-3, 5, 7-9, and 11-14 remain pending in the application. Applicant’s amendments to the claims have overcome each and every objection and 112(a) and 112(b) rejections set forth in the Final Office Action mailed 9/26/2025. However, a new objection and new 112(a) and 112(b) rejections are made in view of the amendments.
The claimed “imaging system” in claim 1 no longer invokes 112(f) interpretation because of the amended claim language and therefore the corresponding 112(a) and 112(b) rejections have been withdrawn. However, the amendments reintroduce the language “intelligence module” in claims 1, 12, and 14 which does invoke 112(f) claim interpretation. For example, claim 12 recites “an intelligence module configured to capture a three-dimensional image and determine waypoint coordinates.” New 112(a) and 112(b) rejections are given because the specification lack’s structure that is clearly linked to the “intelligence module” interpreted under 112(f). It remains unclear whether the “intelligence module” is a processor, part of a camera, software, or something else.
Applicant’s arguments, see pages 2-5 of the remarks, with respect to the cited prior art not teaching the amended subject matter is fully considered and is persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Lin (US 20220063099 A1), Fay (US 20210041243 A1), Casler (US 4772831 A), and Terada (US 20150100194 A1).
Applicant’s argument, see page 4 of the remarks, with respect to Lin not teaching “generating, storing, and reusing spline segments on a per waypoint-pair basis as part of a larger compositional framework” is unpersuasive. The applicant does not explicitly claim this feature, rather the claim recites “generating … a plurality of trajectory paths between neighbouring waypoint pairs using spline interpolation by fitting multiple low-degree polynomials between each pair of neighbouring waypoints.” Under broad reasonable interpretation, Lin teaches this feature in at least paragraph [0059] as cited in the rejections below.
Claim Objections
Claim 12 is objected to because of the following informalities:
Claim 12 repeats a claim limitation: “A system, comprising: an automated machine comprising a robot arm; an automated machine comprising a robot arm;”
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “intelligence module” in claims 1, 12, and 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The specification does not provide sufficient structure for the “intelligence module.” Due to the 112(b) rejection, the “intelligence module” cannot be interpreted under 112(f) because there is no supporting structure present in the specification.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 5, 7-9, and 11-14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 12, and 14 recite the “intelligence module.” The specification lack’s structure that is clearly linked to the “intelligence module” interpreted under 112(f). The specification recites: “complimenting the Planning and sequence manager with an intelligence module to capture an image and provide a mapping to the Trajectory path-managing module.” ([0010]); “there is provided a method, wherein a 3D (Three dimensional) image of the working environment is captured by the intelligence module which predicts a 3D pose or map … there is provided a method, wherein the intelligence manager handles a pipeline of imaging server actions to determine object locations and communicate the non-linear trajectory sequence to the Robot controller in real time” ([0013-0014]). The specification describes the functions of the intelligence module but lacks any disclosure of its structure. Because there is no disclosure of adequate structure to perform the claimed function, the specification does not convey with reasonable clarity to those skilled in the art that the applicant had possession of the claimed invention. Accordingly claims 1, 12, and 14 are rejected under 35 U.S.C. 112(a). Claims 2-3, 5, 7-9, 11 and 13 are also rejected for being dependent on a rejected base claim and failing to cure the deficiencies.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 5, 7-9, and 11-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 12, and 14 recite the “intelligence module.” Claim limitation “intelligence module” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification recites: “complimenting the Planning and sequence manager with an intelligence module to capture an image and provide a mapping to the Trajectory path-managing module.” ([0010]); “there is provided a method, wherein a 3D (Three dimensional) image of the working environment is captured by the intelligence module which predicts a 3D pose or map … there is provided a method, wherein the intelligence manager handles a pipeline of imaging server actions to determine object locations and communicate the non-linear trajectory sequence to the Robot controller in real time” ([0013-0014]). The specification describes the functions of the intelligence module but lacks any disclosure of its structure. Is it a processor, computer software, or something else? Therefore, the claim is indefinite because the structure of the intelligence module is unclear and claims 1, 12, and 14 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claims 2-3, 5, 7-9, 11 and 13 are also rejected for being dependent on a rejected base claim and failing to cure the deficiencies.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 20220063099 A1) in view of Fay (US 20210041243 A1), Casler (US 4772831 A), and Terada (US 20150100194 A1).
Regarding Claim 1,
Lin teaches
A method, comprising: (“The following discussion of the embodiments of the disclosure directed to a robot motion planning technique.” See at least [0015])
capturing, by an intelligence module operating an imaging system, a three-dimensional image of a working environment and predicting a three-dimensional pose to determine positional coordinates of neighbouring waypoints including a begin state, an end state, (“FIG. 3 is a block diagram of a system for robotic motion path planning using an external computer in communication with a robot controller, according to an embodiment of the present disclosure. … In FIG. 3, the sensor processing and task management module 210 operates as described above relative to FIG. 2. That is, the module 210 receives camera images or sensor data, and provides the start and goal points for the upcoming robot task, along with obstacle data. The start and goal points and the obstacle data are provided to a motion planning module 320.” See at least [0028-0029] and figs. 1, 3, and 5, wherein Camera 122 in fig. 1 is an imaging system.; “Although the diagrams shown in FIG. 4 are two-dimensional, it is to be understood that all of the calculations performed in the presently disclosed techniques are three-dimensional and include all six degrees of freedom (DOF) of an item where applicable. The start and goal points include position (three coordinates) and pose (three angular orientations). The obstacles are defined as three-dimensional objects, either in terms of surface or solid models from CAD, or by point cloud or surface data from the sensor module 120. The computed tool paths are in three-dimensional space, and include not only the tool center point location but also the orientation (six DOF).” See at least [0035])
generating, by a computing system comprising a sequence manager, a planning manager, and a trajectory path manager, a plurality of trajectory paths (“The motion planning module 320 provides a sparse sequence of planned path points to a top-level program execution module 340 on the robot controller 110. … By providing the sparse sequence of points from the computer 120 to the program execution module 340, all of the inherent capabilities of the robot controller 110 can be utilized—thereby providing robot kinematics calculations, interpolation points and motion commands which allow the robot 100 to move smoothly and without hesitation along the path computed by the motion planning module 320.” See at least [0030-0032]; “The process continues in this manner, with each planned path output on the line 530 and executed by the robot, followed by a loop back to store the planned path, then receive new input scene data and compute a new path.” See at least [0047]; Examiner Interpretation: The motion planning module 320 illustrated in at least fig. 5 functions as a sequence manager, a planning manager, and a trajectory path manager.) between neighbouring waypoint pairs using spline interpolation by fitting multiple low-degree polynomials between each pair of neighbouring waypoints, such that smooth motion between the neighbouring waypoints is obtained without (“The first calculation in the path function fitting module 440, shown at 442, is to compute a spline function s based on the planned path points (q.sub.0, . . . , q.sub.T). The spline function s may be computed using any suitable technique, such as fitting the planned path points with a series of piecewise cubic polynomials. The spline function s is represented as a continuous entity having an arc length parameter a representing the distance along the spline s. That is, a=0 at the start point q.sub.0, and a=1 at the goal point q.sub.T.” See at least [0059])
storing, in a trajectory paths database associated with the computing system, the plurality of trajectory paths between respective waypoint pairs as pre-planned trajectory paths (“At box 540, the path which was just output on the line 530 is stored in a data repository for later use in initial reference path generation, as discussed below.” See at least [0045], wherein the data repository is the database.; “Because the path currently being computed is very similar to a previously computed path (same obstacle environment, and similar start and goal points), the initial reference generation at the box 550 will be able to quickly provide an initial reference path which is a very good approximation of the path currently being computed. This in turn allows the optimization method box 410 to quickly converge on an optimized path and provide that planned path to the box 520. The process continues in this manner, with each planned path output on the line 530 and executed by the robot, followed by a loop back to store the planned path” See at least [0047])
updating the trajectory paths database with the constructed final trajectory path; (“At box 540, the path which was just output on the line 530 is stored in a data repository for later use in initial reference path generation, as discussed below.” See at least [0045], wherein the data repository is the database.; “Because the path currently being computed is very similar to a previously computed path (same obstacle environment, and similar start and goal points), the initial reference generation at the box 550 will be able to quickly provide an initial reference path which is a very good approximation of the path currently being computed. This in turn allows the optimization method box 410 to quickly converge on an optimized path and provide that planned path to the box 520. The process continues in this manner, with each planned path output on the line 530 and executed by the robot, followed by a loop back to store the planned path” See at least [0047])
and operating a robot arm from the begin state to the end state according to the final trajectory path as communicated by a robot controller, (“Motion of the robot 100 is controlled by a controller 110, which typically communicates with the robot 100 via a cable 112. The controller 110 provides joint motion commands to the robot 100 and receives joint position data from encoders in the joints of the robot 100, as known in the art. The controller 110 also provides commands to control operation of the gripper 102.” See at least [0018]; “For each workpiece 130 arriving on the conveyor 140, a new path must be computed by the computer 120 and the controller 110 which causes the robot 100 to move the gripper 102 from a home or approach position along a path segment 180 to pick up the workpiece 130 at the start point 160, and move the workpiece 130 along a path 182 to the goal point 162 while avoiding the obstacle 170” See at least [0022]; Also see at least fig. 7 illustrating the trajectory composed of waypoints.)
wherein the final trajectory path is obtained using stored trajectory paths between waypoint pairs to reduce real-time trajectory computation. (“The new path must be computed by the computer 120 and the controller 110 very quickly, because the path computation must be performed in real time as fast as the robot 110 can move one workpiece 130 and return to pick up the next.” See at least [0022]; “Because the path currently being computed is very similar to a previously computed path (same obstacle environment, and similar start and goal points), the initial reference generation at the box 550 will be able to quickly provide an initial reference path which is a very good approximation of the path currently being computed. This in turn allows the optimization method box 410 to quickly converge on an optimized path and provide that planned path to the box 520.” See at least [0047])
Lin also does not explicitly teach, but Fay teaches
neighbouring waypoints including a begin state, an end state, and one or more intermediate waypoints; (“The method includes receiving, at data processing hardware of a robot, a navigation route. The navigation route includes a series of high-level waypoints that begin at a starting location and end at a destination location and are based on high-level navigation data. The high-level navigation data is representative of locations of static obstacles in an area the robot is to navigate. The method also includes receiving, at the data processing hardware, image data of an environment about the robot from an image sensor. The method also includes generating, by the data processing hardware, at least one intermediate waypoint based on the image data.” See at least [0004])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Lin to further include the teachings of Fay with a reasonable expectation of success “to efficiently navigate around larger dynamic objects.” (See at least [0037])
Fay also does not explicitly teach, but Casler teaches
using spline interpolation by fitting multiple low-degree polynomials between each pair of neighbouring waypoints, such that smooth motion between the neighbouring waypoints is obtained without position, velocity, and acceleration discontinuities; (“Execution of a continuous path routine in the planning program provides for computation of coefficients for a stored polynomial equation to enable the position commands to be generated in joint and Cartesian moves as tool orientation and tool position commands that produce smoothed robot tool motion both in tool orientation and tool position between the initial transition point at the end of the slew portion of one path segment to the end transition point at the beginning of the slew portion of the next path segment. The trajectory program computes from the polynomial coefficients interpolated position commands that produce smoothed tool positioning and orientation motion between path segments without position, velocity and acceleration discontinuities in the operation of each of the feedback loop means.” See at least the Abstract)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Lin and Fay to further include the teachings of Casler with a reasonable expectation of success “to produce enhanced continuous path robot operation.” (See at least col. 2, lines 48-64)
Casler also does not explicitly teach, but Terada teaches
storing, in a trajectory paths database …, the plurality of trajectory paths … generated prior to execution; (“The trajectory generation device 1 includes a trajectory database 2 that stores a plurality of trajectories in advance,” See at least [0019])
in response to trajectory path being unavailable in the trajectory paths database between a given begin state and a given end state: (“the reference trajectory acquisition unit 3 selects a trajectory from the trajectory database 2. The trajectory selected in this case is a trajectory whose motion planning input (for example, the current environment such as the position/pose of robot, object type, and object position) is similar to that of the arm motion planning problem to be solved and whose motion planning output is highly evaluated. The reference trajectory acquisition unit 3 outputs the selected trajectory to the differential motion generation unit 4 as a reference trajectory.” See at least [0027]; “First, the differential motion generation unit 4 acquires a reference trajectory from the reference trajectory acquisition unit 3 (FIG. 3A). Next, the differential motion generation unit 4 applies the reference trajectory, acquired from the reference trajectory acquisition unit 3, to the arm motion planning problem to be solved (FIG. 3B). At this point, note that the robot cannot move along the reference trajectory. This is because there are new obstacles associated with the arm motion planning problem to be solved and because the start point and the end point of the acquired reference trajectory differ respectively from the start point and the end point of the arm motion planning problem to be solved.” See at least [0051]; Examiner Interpretation: The trajectory path is interpreted as being unavailable in the database because the start point and the end point of the acquired reference trajectory differ respectively from the start point and the end point of the arm motion planning problem to be solved.)
determining, by the planning manager in conjunction with the sequence manager, a first trajectory path from the given begin state to a nearest waypoint for which a pre-planned trajectory path exists; and determining a second trajectory path from a nearest waypoint to the given end state; constructing a final trajectory path by concatenating trajectory paths corresponding to the waypoint pairs including the first trajectory path, the second trajectory path, and one or more pre-planned trajectory paths retrieved from the trajectory paths database, to form a continuous trajectory path in a chain-movement mode; (“the differential motion generation unit 4 extends the branches, one extending from the start point and the other extending from the end point, using the BiRRT method and connects them respectively to the ends of the calculated trajectory part T'longest (FIG. 4A). The differential motion generation unit 4 determines the trajectory, generated by the connection described above, as a temporarily solved trajectory (FIG. 4B). Finally, the differential motion generation unit 4 smoothes the temporarily solved trajectory to make it shorter and determines the smoothed trajectory as the finally solved trajectory (FIG. 4C).” See at least [0053] and figs 4a-4b (provided below))
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It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Lin, Fay, and Casler to further include the teachings of Terada with a reasonable expectation of success to “generate a reliable trajectory quickly.” (See at least [0053])
Regarding Claim 11,
Lin further teaches
wherein the final trajectory path is formed by combining multiple trajectory paths corresponding to successive waypoint pairs such that motion is executed as a continuous trajectory without intermediate stop-start transitions. (“The first calculation in the path function fitting module 440, shown at 442, is to compute a spline function s based on the planned path points (q.sub.0, . . . , q.sub.T). The spline function s may be computed using any suitable technique, such as fitting the planned path points with a series of piecewise cubic polynomials. The spline function s is represented as a continuous entity having an arc length parameter a representing the distance along the spline s. That is, a=0 at the start point q.sub.0, and a=1 at the goal point q.sub.T.” See at least [0059])
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable Lin (US 20220063099 A1) in view of Fay (US 20210041243 A1), Casler (US 4772831 A), Terada (US 20150100194 A1), and Hong (US 20220363273 A1).
Regarding Claim 2,
Lin further teaches
wherein generating the plurality of trajectory paths further comprises generating the plurality of trajectory paths prior to execution using (“By using a combination of optimization and sampling algorithms, the path selector module 430 can utilize the strengths of both methods—and avoid the weaknesses of each method—in order to obtain the best possible planned tool path.” See at least [0036]) each trajectory path corresponding to different motion durations and distances. (“On input lines 602 and 604, candidate planned paths are received from the optimization module 410 and the sampling module 420. The optimization module 410 and the sampling module 420 each independently compute a candidate path based on the data from the camera or sensor module 210 (start and goal points, and obstacle data). … At the box 650, the quality of the candidate paths is evaluated to select one path to use. The quality may be determined as a combination of robot joint path travel distance, path curvature, path execution time, smoothness of the path, and/or other factors.” See at least [0050-0051] and fig. 4 which illustrates the plurality of trajectories with begin and end states.)
Modified Lin does not explicitly teach, but Hong teaches
generating the plurality of trajectory paths prior to execution using PID, MPC, (“an example hybrid motion control system for controlling mobile robots utilizes different kinds of control schemes (e.g., Model Predictive Control (MPC), simplified MPC, and conventional (PID) control) and switches among those control schemes depending on operation scenarios for energy efficiency. An MPC scheme can predict, future trajectories and finds a control command generating an optimal trajectory and is beneficial in dealing with collision-free motion control in a dynamic environment.” See at least [0009]; “PID 134 represents an upfront motion controller to create motion commands based on the position and orientation errors with respect to a goal along with pre-defined control gain values. The PID 134 provides the most energy efficient control scheme.” See at least [0016])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Lin to further include the teachings of Hong with a reasonable expectation of success to use different algorithms, including MPC and PID, depending on operation scenarios for energy efficiency because different algorithms provide different advantages such as better motion performance or better energy efficiency. (See at least [0009] and [0016])
Regarding Claim 3,
Lin further teaches
wherein the plurality of trajectory paths are generated according to degrees of freedom corresponding to controllable joints of the robot arm. (“A position and orientation of a start point 160 are defined based on information about the position of the workpiece 130 on the conveyor 140 and the speed of the conveyor 140. Orientation of the workpiece 130 at the start point 160 is needed in order to determine an orientation for the gripper 102. Similarly, a position and orientation of a goal point 162 are defined based on information about the next available compartment or location within the container 150.” See at least [0020]; “Given surface/solid or point cloud data representing the obstacles, and the initial and final workpiece configurations (start and goal position and pose), RRT can be used to find a motion sequence if one exists.” See at least [0024]; “Although the diagrams shown in FIG. 4 are two-dimensional, it is to be understood that all of the calculations performed in the presently disclosed techniques are three-dimensional and include all six degrees of freedom (DOF) of an item where applicable. The start and goal points include position (three coordinates) and pose (three angular orientations). … The computed tool paths are in three-dimensional space, and include not only the tool center point location but also the orientation (six DOF).” See at least [0035])
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 20220063099 A1) in view of Fay (US 20210041243 A1), Casler (US 4772831 A), Terada (US 20150100194 A1), Hong (US 20220363273 A1), and Tunyasuvunakool (US 20190126472 A1).
Regarding Claim 5,
Lin further teaches
further comprising refining the plurality of trajectory paths stored in the trajectory paths database (“As outlined above, the disclosed techniques for a robot motion planning using a combination of optimization and sampling methods and path function fitting improve the speed and quality of robot path planning. The combination of optimization and sampling path planning methods provide the best quality path that can be computed in the allotted cycle time, and the path function fitting allows the robot controller to use its inherent capability for computing robot joint motions and interpolation points which provide smooth and fast execution of the robot tool motion.” See at least [0063]; “If path quality is insufficient at the box 520 (lacks smoothness, includes jerky motions), then a system fault is declared and no part is picked by the robot.” See at least [0043])
Lin does not explicitly teach, but Tunyasuvunakool teaches
using artificial intelligence, machine learning, and reinforcement learning techniques to minimise jerking and improve speed and positional accuracy over repeated operations. (“the training of the neural network benefits both from whatever expert performances of the task are available, and from the results of the experimentation associated with reinforcement learning.” See at least [0019]; “Compared to a system which just uses imitation learning, the trained neural network of the present disclosure will typically learn to control the agent to perform the task more accurately because it is trained from a larger database of examples.” See at least [0025]; “Compared to a system which just learns by reinforcement learning, the trained neural network may make more natural motions (e.g., less jerky ones) because it benefits from examples of performing the task well.” See at least [0026])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Lin to further include the teachings of Tunyasuvunakool with a reasonable expectation of success to mor efficiently generate more successful and less jerky motions. (See at least [0025-0027])
Claim(s) 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 20220063099 A1) in view of Fay (US 20210041243 A1), Casler (US 4772831 A), Terada (US 20150100194 A1), and Bellicoso (US 20230117928 A1).
Regarding Claim 7,
Lin further teaches
wherein the robot arm (“collision avoidance calculations are performed not just between the robot tool and the obstacles, but between all parts/arms of the robot and the obstacles.” See at least [0035] and fig. 1; “The process then branches based on whether the current robot task is expected to have a similar motion pattern to the previous task (only slightly different start and/or goal points). At decision diamond 510, it is determined whether a process reset has occurred. A process reset could be an event such as providing a new bin of parts to be picked … If the process reset answer is yes at the decision diamond 510, which means that the current robot task might not have a very similar motion to the previous task” See at least [0040-41], wherein not having a very similar motion to the previous task demonstrates non-repetitive motion.; “If there has been no significant reset of the process (such as a new bin of input parts, or a new output shipping container, or a new obstacle), then at the decision diamond 510 the answer is no. … the initial reference path is based on the shape of a previously planned path adjusted (scaled and shifted) to match the start and goal points of the path currently being calculated.” See at least [0046], wherein the path based on the shape of a previously planned path demonstrates a repetitive motion.)
Lin does not explicitly teach, but Bellicoso teaches
the robot arm is mounted on an industrial machine (“The robotic arm 130 is operatively coupled to the mobile base 110.” See at least [0041] and figs. 1-3)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Lin to further include the teachings of Bellicoso with a reasonable expectation of success because “an integrated mobile manipulator robot may be able to perform complex and/or dynamic motions that are unable to be achieved by conventional, loosely integrated mobile manipulator systems. As a result, this type of robot may be well suited to perform a variety of different tasks (e.g., within a warehouse environment) with speed, agility, and efficiency.” (See at least [0039]; Also see at least [0041] and [0045])
Regarding Claim 8,
Lin further teaches
wherein a repetitive motion trajectory path is retrieved directly from the trajectory paths database without recomputation. (“If there has been no significant reset of the process (such as a new bin of input parts, or a new output shipping container, or a new obstacle), then at the decision diamond 510 the answer is no. The process then moves to box 550 for initial reference generation. Initial reference generation involves providing an initial reference path for the optimization motion calculations, where the initial reference path is based on the shape of a previously planned path adjusted (scaled and shifted) to match the start and goal points of the path currently being calculated.” See at least [0046]; “the initial reference generation process includes selecting a candidate path from a repository of previously computed paths.” See at least claim 8)
Regarding Claim 9,
Lin further teaches
wherein a non-repetitive motion trajectory path is determined using three-dimensional positional coordinates of the waypoints obtained from the imaging system; (“the module 210 receives camera images or sensor data, and provides the start and goal points for the upcoming robot task, along with obstacle data. The start and goal points and the obstacle data are provided to a motion planning module 320.” See at least [0029]; “wherein providing input information includes providing camera images or sensor data depicting a workspace, and determining the start point and the goal point from the camera images or sensor data, where the start point and the goal point each include a three-dimensional (3D) location and an orientation.” See at least claim 2)
and rotational coordinates including roll, pitch, and yaw to generate a six-dimensional trajectory path. (“all of the calculations performed in the presently disclosed techniques are three-dimensional and include all six degrees of freedom (DOF) of an item where applicable. The start and goal points include position (three coordinates) and pose (three angular orientations).” See at least [0035], wherein the three angular orientations are roll, pitch, and yaw.)
Claim(s) 12 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 20220063099 A1) in view of Terada (US 20150100194 A1).
Regarding Claim 12,
Lin teaches
A system, comprising: an automated machine comprising a robot arm; an automated machine comprising a robot arm; a robot controller (“A robot 100 having a gripper 102 operates within a workspace 104. Motion of the robot 100 is controlled by a controller 110” See at least [0018] and fig. 1) configured for automatic, repetitive, and non-repetitive motion; (“The process then branches based on whether the current robot task is expected to have a similar motion pattern to the previous task (only slightly different start and/or goal points). At decision diamond 510, it is determined whether a process reset has occurred. A process reset could be an event such as providing a new bin of parts to be picked … If the process reset answer is yes at the decision diamond 510, which means that the current robot task might not have a very similar motion to the previous task” See at least [0040-41], wherein not having a very similar motion to the previous task demonstrates non-repetitive motion.; “If there has been no significant reset of the process (such as a new bin of input parts, or a new output shipping container, or a new obstacle), then at the decision diamond 510 the answer is no. … the initial reference path is based on the shape of a previously planned path adjusted (scaled and shifted) to match the start and goal points of the path currently being calculated.” See at least [0046], wherein the path based on the shape of a previously planned path demonstrates a repetitive motion.)
a computing system comprising a sequence manager, a planning manager, and a trajectory path manager; (“The motion planning module 320 provides a sparse sequence of planned path points to a top-level program execution module 340 on the robot controller 110. … By providing the sparse sequence of points from the computer 120 to the program execution module 340, all of the inherent capabilities of the robot controller 110 can be utilized—thereby providing robot kinematics calculations, interpolation points and motion commands which allow the robot 100 to move smoothly and without hesitation along the path computed by the motion planning module 320.” See at least [0030-0032]; “The process continues in this manner, with each planned path output on the line 530 and executed by the robot, followed by a loop back to store the planned path, then receive new input scene data and compute a new path.” See at least [0047]; Examiner Interpretation: The motion planning module 320 illustrated in at least fig. 5 functions as a sequence manager, a planning manager, and a trajectory path manager.)
an intelligence module configured to capture a three-dimensional image and determine waypoint coordinates; (“FIG. 3 is a block diagram of a system for robotic motion path planning using an external computer in communication with a robot controller, according to an embodiment of the present disclosure. … In FIG. 3, the sensor processing and task management module 210 operates as described above relative to FIG. 2. That is, the module 210 receives camera images or sensor data, and provides the start and goal points for the upcoming robot task, along with obstacle data. The start and goal points and the obstacle data are provided to a motion planning module 320.” See at least [0028-0029] and figs. 1, 3, and 5; “Although the diagrams shown in FIG. 4 are two-dimensional, it is to be understood that all of the calculations performed in the presently disclosed techniques are three-dimensional and include all six degrees of freedom (DOF) of an item where applicable. The start and goal points include position (three coordinates) and pose (three angular orientations). The obstacles are defined as three-dimensional objects, either in terms of surface or solid models from CAD, or by point cloud or surface data from the sensor module 120. The computed tool paths are in three-dimensional space, and include not only the tool center point location but also the orientation (six DOF).” See at least [0035])
and a trajectory paths database, wherein: the computing system is configured to generate and store a plurality of pre-planned trajectory paths between neighbouring waypoint pairs using spline interpolation; the trajectory paths database stores the plurality of trajectory paths for retrieval during execution; the robot controller retrieves a trajectory path when available between a begin state and an end state; (“At box 540, the path which was just output on the line 530 is stored in a data repository for later use in initial reference path generation, as discussed below.” See at least [0045], wherein the data repository is the database.; “Because the path currently being computed is very similar to a previously computed path (same obstacle environment, and similar start and goal points), the initial reference generation at the box 550 will be able to quickly provide an initial reference path which is a very good approximation of the path currently being computed. This in turn allows the optimization method box 410 to quickly converge on an optimized path and provide that planned path to the box 520. The process continues in this manner, with each planned path output on the line 530 and executed by the robot, followed by a loop back to store the planned path” See at least [0047]; See at least [0059] for spline interpolation.; “selecting a candidate path from a repository of previously computed paths,” See at least Claim 8)
Lin does not explicitly teach, but Terada teaches
and when unavailable, the computing system determines trajectory paths using nearest waypoint linkage and concatenates stored trajectory paths corresponding to waypoint pairs to construct a final trajectory path in a chain-movement mode.(“the reference trajectory acquisition unit 3 selects a trajectory from the trajectory database 2. The trajectory selected in this case is a trajectory whose motion planning input (for example, the current environment such as the position/pose of robot, object type, and object position) is similar to that of the arm motion planning problem to be solved and whose motion planning output is highly evaluated. The reference trajectory acquisition unit 3 outputs the selected trajectory to the differential motion generation unit 4 as a reference trajectory.” See at least [0027]; “First, the differential motion generation unit 4 acquires a reference trajectory from the reference trajectory acquisition unit 3 (FIG. 3A). Next, the differential motion generation unit 4 applies the reference trajectory, acquired from the reference trajectory acquisition unit 3, to the arm motion planning problem to be solved (FIG. 3B). At this point, note that the robot cannot move along the reference trajectory. This is because there are new obstacles associated with the arm motion planning problem to be solved and because the start point and the end point of the acquired reference trajectory differ respectively from the start point and the end point of the arm motion planning problem to be solved.” See at least [0051]; “the differential motion generation unit 4 extends the branches, one extending from the start point and the other extending from the end point, using the BiRRT method and connects them respectively to the ends of the calculated trajectory part T'longest (FIG. 4A). The differential motion generation unit 4 determines the trajectory, generated by the connection described above, as a temporarily solved trajectory (FIG. 4B). Finally, the differential motion generation unit 4 smoothes the temporarily solved trajectory to make it shorter and determines the smoothed trajectory as the finally solved trajectory (FIG. 4C).” See at least [0053] and figs 4a-4b (provided below); Examiner Interpretation: The trajectory path is interpreted as being unavailable in the database because the start point and the end point of the acquired reference trajectory differ respectively from the start point and the end point of the arm motion planning problem to be solved.)
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It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Lin to further include the teachings of Terada with a reasonable expectation of success to “generate a reliable trajectory quickly.” (See at least [0053])
Regarding Claim 14,
Lin teaches
A system, comprising: an automated machine comprising a robot arm; a robot controller; (“A robot 100 having a gripper 102 operates within a workspace 104. Motion of the robot 100 is controlled by a controller 110” See at least [0018] and fig. 1)
a computing system comprising a sequence manager, a planning manager, and a trajectory path manager; (“The motion planning module 320 provides a sparse sequence of planned path points to a top-level program execution module 340 on the robot controller 110. … By providing the sparse sequence of points from the computer 120 to the program execution module 340, all of the inherent capabilities of the robot controller 110 can be utilized—thereby providing robot kinematics calculations, interpolation points and motion commands which allow the robot 100 to move smoothly and without hesitation along the path computed by the motion planning module 320.” See at least [0030-0032]; “The process continues in this manner, with each planned path output on the line 530 and executed by the robot, followed by a loop back to store the planned path, then receive new input scene data and compute a new path.” See at least [0047]; Examiner Interpretation: The motion planning module 320 illustrated in at least fig. 5 functions as a sequence manager, a planning manager, and a trajectory path manager.)
an intelligence module configured to determine waypoint positions and rotational coordinates including roll, pitch, and yaw; (“FIG. 3 is a block diagram of a system for robotic motion path planning using an external computer in communication with a robot controller, according to an embodiment of the present disclosure. … In FIG. 3, the sensor processing and task management module 210 operates as described above relative to FIG. 2. That is, the module 210 receives camera images or sensor data, and provides the start and goal points for the upcoming robot task, along with obstacle data. The start and goal points and the obstacle data are provided to a motion planning module 320.” See at least [0028-0029] and figs. 1, 3, and 5; “Although the diagrams shown in FIG. 4 are two-dimensional, it is to be understood that all of the calculations performed in the presently disclosed techniques are three-dimensional and include all six degrees of freedom (DOF) of an item where applicable. The start and goal points include position (three coordinates) and pose (three angular orientations). The obstacles are defined as three-dimensional objects, either in terms of surface or solid models from CAD, or by point cloud or surface data from the sensor module 120. The computed tool paths are in three-dimensional space, and include not only the tool center point location but also the orientation (six DOF).” See at least [0035])
a trajectory paths database storing pre-planned trajectory paths between waypoint pairs, wherein: the computing system generates trajectory paths using spline interpolation between neighbouring waypoints; the computing system retrieves stored(“At box 540, the path which was just output on the line 530 is stored in a data repository for later use in initial reference path generation, as discussed below.” See at least [0045], wherein the data repository is the database.; “Because the path currently being computed is very similar to a previously computed path (same obstacle environment, and similar start and goal points), the initial reference generation at the box 550 will be able to quickly provide an initial reference path which is a very good approximation of the path currently being computed. This in turn allows the optimization method box 410 to quickly converge on an optimized path and provide that planned path to the box 520. The process continues in this manner, with each planned path output on the line 530 and executed by the robot, followed by a loop back to store the planned path” See at least [0047]; See at least [0059] for spline interpolation.; “selecting a candidate path from a repository of previously computed paths,” See at least Claim 8)
and the robot controller executes a final trajectory path constructed by (“Motion of the robot 100 is controlled by a controller 110, which typically communicates with the robot 100 via a cable 112. The controller 110 provides joint motion commands to the robot 100 and receives joint position data from encoders in the joints of the robot 100, as known in the art. The controller 110 also provides commands to control operation of the gripper 102.” See at least [0018]; “For each workpiece 130 arriving on the conveyor 140, a new path must be computed by the computer 120 and the controller 110 which causes the robot 100 to move the gripper 102 from a home or approach position along a path segment 180 to pick up the workpiece 130 at the start point 160, and move the workpiece 130 along a path 182 to the goal point 162 while avoiding the obstacle 170” See at least [0022]; “The path function fitting module 440 then provides the sparse sequence of command points P[i] to the tool path motion interface module 450, which transfers the sparse command points to the top-level program execution module 340 on the robot controller 110 as discussed earlier. The robot controller 110 then uses its inherent capabilities including joint kinematics calculations and interpolation point calculation to compute smooth and efficient robot motions to perform the task.” See at least [0061])
Lin does not explicitly teach, but Terada teaches
and the robot controller executes a final trajectory path constructed by concatenating the stored trajectory paths to move the robot arm smoothly from a begin state to an end state. (“the differential motion generation unit 4 extends the branches, one extending from the start point and the other extending from the end point, using the BiRRT method and connects them respectively to the ends of the calculated trajectory part T'longest (FIG. 4A). The differential motion generation unit 4 determines the trajectory, generated by the connection described above, as a temporarily solved trajectory (FIG. 4B). Finally, the differential motion generation unit 4 smoothes the temporarily solved trajectory to make it shorter and determines the smoothed trajectory as the finally solved trajectory (FIG. 4C).” See at least [0053] and figs 4a-4b (provided below))
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It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Lin to further include the teachings of Terada with a reasonable expectation of success to “generate a reliable trajectory quickly.” (See at least [0053])
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 20220063099 A1) in view of Terada (US 20150100194 A1) and Bellicoso (US 20230117928 A1).
Regarding Claim 13,
Lin further teaches
wherein each trajectory path comprises predefined duration, velocity, (“On input lines 602 and 604, candidate planned paths are received from the optimization module 410 and the sampling module 420. The optimization module 410 and the sampling module 420 each independently compute a candidate path based on the data from the camera or sensor module 210 (start and goal points, and obstacle data). … At the box 650, the quality of the candidate paths is evaluated to select one path to use. The quality may be determined as a combination of robot joint path travel distance, path curvature, path execution time, smoothness of the path, and/or other factors.” See at least [0050-0051] and fig. 4)
Lin does not explicitly teach, but Bellicoso teaches
wherein each trajectory path comprises predefined … acceleration … parameters. (“(b) if the candidate trajectory is not feasible, determining, by the computing system, using nonlinear optimization, a different candidate trajectory for the robot to move from the initial state to the goal state, the nonlinear optimization using one or more changed parameters. In some embodiments, steps (a) and/or (b) are performed until (i) a feasible candidate trajectory is determined and provided to the motion control module of the robot … the candidate trajectory includes, for each of one or more joints of the robot, parameters including position, velocity and acceleration.” See at least [0013-0014]; “In some embodiments, the computing device minimizes a cost function of the robot while satisfying a set of one or more constraints. … the candidate trajectory minimizes at least one of the following: (i) one or more task-space accelerations;” See at least [0017])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Lin to further include the teachings of Bellicoso with a reasonable expectation of success to calculate candidate trajectories to minimize acceleration to avoid undesirable consequences, “e.g., the grip between the end effector 912 and any grasped objects to loosen or be fully compromised.” (See at least [0069])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Moriya (US 20090326713 A1) is pertinent because it discusses creating movement routes using reusable partial paths.
Sokabe (US 20190314989 A1) is pertinent because it discusses connecting a set start point and set end point to respective ends of a partial path.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Karston G Evans whose telephone number is (571)272-8480. The examiner can normally be reached Mon-Fri 9:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Lin can be reached at (571)270-3976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KARSTON G. EVANS/Examiner, Art Unit 3657
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