Office Action 2: Final Rejection (2025-01-27)
Rejection types: 103 - Obviousness

CLAIMS:
Claims

The listing of the claims below replaces all previous listings of the claims.

1\. (Currently Amended) A robot motion control method, applied to a robot, the method comprising:
obtaining a center-of-mass reference trajectory for guiding the robot to execute a target motion; the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion;
determining desired center-of-mass motion data of the robot based on the center-of- mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot;
constructing an objective function based on the desired center-of-mass motion data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points, the contact points being contact points between the robot and an environment, a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory;
calculating center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition;
  generating joint control information according to the center-of-mass control information and a structure matrix of the robot; and
controlling the robot to execute the target motion based on the joint control information.

2\. (Canceled).

3\. (Currently Amended) The method according to claim 1, wherein the objective function is:

Jqp = (wdes-Af)TWw(wdes-Af);
where Ww denotes a positive definite weight matrix, wdes denotes the desired center-of- mass motion data, A denotes \[\[a\]\]the matrix data , and f denotes the center-of-mass control information.

4\. (Original) The method according to claim \[\[2\]\]1, wherein the calculating the center- of-mass control information comprises:
utilizing quadratic programming (QP) optimization to calculate the center-of-mass control information.

5\. (Original) The method according to claim \[\[2\]\]1, wherein the calculating the center- of-mass control information comprises:
utilizing interior point optimizer (IPOPT) optimization to calculate the center-of-mass control information.

6\. (Original) The method according to claim \[\[2\]\]1, wherein the obtaining the desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory comprises:
obtaining actual center-of-mass motion data of the robot;
obtaining reference center-of-mass motion data of the robot according to the center-of- mass reference trajectory;
obtaining a center-of-mass motion data adjustment value according to a difference between the actual center-of-mass motion data and the reference center-of-mass motion data;
and obtaining the desired center-of-mass motion data according to the center-of-mass motion data adjustment value and the reference center-of-mass motion data.
CKF\[ACT\[VE-19806712.1

7\. (Original) The method according to claim 6, wherein the obtaining the center-of-mass motion data adjustment value according to the difference between the actual center-of-mass motion data and the reference center-of-mass motion data comprises:
calculating a difference between the actual center-of-mass motion data and the reference center-of-mass motion data; and
obtaining the center-of-mass motion data adjustment value according to a gain matrix and the difference, the gain matrix corresponding to the motion data associated with the center of mass of the robot.

8\. (Original) The method according to claim 6, wherein the center-of-mass motion data adjustment value comprises a center-of-mass acceleration adjustment value and a center-of- mass angular acceleration adjustment value.
9\. (Original) The method according to claim 8, wherein the center-of-mass acceleration adjustment value is represented by , where SVG {EMBED HERE} 0.2030.263 denotes a center-of-mass acceleration adjustment SVG {EMBED HERE} 0.1770.723 denotes a reference center-of-mass velocity, SVG {EMBED HERE} 0.1670.263 denotes an actual center-of-mass velocity, SVG {EMBED HERE} 0.180.263 denotes a reference center-of- mass SVG {EMBED HERE} 0.1630.913 denotes an actual center-of-mass position, K and Kj denote gain matrices associated with center-of-mass acceleration.SVG {EMBED HERE} 0.2172.58
10\. (Original) The method according to claim 8, wherein the center-of-mass angular acceleration adjustment value is represented by SVG {EMBED HERE} 0.180.74(WrefWact)+Kg (RrefRact)v, where cSVG {EMBED HERE} 0.1070.243 denotes a center-of-mass angular acceleration adjustment SVG {EMBED HERE} 0.1670.757 denotes a reference center-of-mass angular velocity, SVG {EMBED HERE} 0.1330.30 denotes an actual center-of-mass angular velocity, Rref denotes a reference rotation matrix of a center-of-mass coordinate system relative to a world coordinate system, Ract denotes an actual rotation matrix of the center-of- mass coordinate system relative to the world coordinate system, (RrefRact)v denotes a rotation vector from Ract to Rref, and Kg and Kd denote gain matrices associated with center- of-mass angular acceleration.
ll. (Original) The method according to claim l, wherein the method further comprises:

performing kinetics analysis on a particle model to obtain a kinetics equation, the particle model being obtained by simplification based on a robot body; and
analyzing the kinetics equation and constructing the objective function based on an objective of making the center-of-mass motion trajectory of the robot consistent with the center-of-mass reference trajectory.

12\. (Original) The method according to claim 1, wherein the generating the joint control information according to the center-of-mass control information and the structure matrix of the robot comprises:
generating intermediate joint control information according to the center-of-mass control information and the structure matrix corresponding to the robot;
obtaining actual joint motion data of the robot;
obtaining reference joint motion data of the robot; and
adjusting the intermediate joint control information according to the actual joint motion data and the reference joint motion data to obtain the joint control information.
13\. (Original) The method according to claim 12, wherein the joint control information comprises joint torque represented by x=(qref\_gact), where - denotes joint torque, J. denotes the structure matrix corresponding to therobot,fopt denotes the center-of-mass control information obtained by SVG {EMBED HERE} 0.181.22 denotes a reference joint angular velocity, SVG {EMBED HERE} 0.160.267 denotes an actual joint angular velocity, gref denotes a reference joint angle, qact denotes an actual joint angle, and K and Kg are gain matrices associated with the joint torque.SVG {EMBED HERE} 0.2071.987

14\. (Original) The method according to claim 13, wherein the robot is a quadruped robot, and the target motion is a motion process from a four-legged lying down state to a two- legged standing state.
15\. (Currently Amended) A robot motion control apparatus, comprising:SVG {EMBED HERE} 0.191.66 to store computer-readable instructions; and Page 6 of 12

a processor circuitry operable to read the computer-readable instructions, the processor circuitry when executing the computer-readable instructions is configured to:obtain a center-of-mass reference trajectory for guiding the robot to execute a target motion; the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion;
determine desired center-of-mass motion data of the robot based on the center-of- mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot;
construct an objective function based on the desired center-of-mass motion data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points, the contact points being contact points between the robot and an environment, a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory;
calculate center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition;
  ~~\-re bo t~z- felow theeentei r-e-ms-referene-traj eetery tesmove;~~
generate joint control information according to the center-of-mass control information and a structure matrix of the robot; and
control the robot to execute the target motion based on the joint control information.

16\. (Canceled).

17\. (Original) The apparatus according to claim \[\[16\]\]15, wherein the processor circuitry is configured to:
obtain actual center-of-mass motion data of the robot;

obtain reference center-of-mass motion data of the robot according to the center-of-mass reference trajectory;
obtain a center-of-mass motion data adjustment value according to a difference between the actual center-of-mass motion data and the reference center-of-mass motion data; and
obtain the desired center-of-mass motion data according to the center-of-mass motion data adjustment value and the reference center-of-mass motion data.

18\. (Original) The apparatus according to claim 15, wherein the processor circuitry is further configured to:
perform kinetics analysis on a particle model to obtain a kinetics equation, the particle model being obtained by simplification based on a robot body; and
analyze the kinetics equation and construct the objective function based on an objective of making the center-of-mass motion trajectory of the robot consistent with the center-of- mass reference trajectory.

19\. (Original) The apparatus according to claim 15, wherein the processor circuitry is configured to:
generate intermediate joint control information according to the center-of-mass control information and the structure matrix corresponding to the robot;
obtain actual joint motion data of the robot;
obtain reference joint motion data of the robot; and
adjust the intermediate joint control information according to the actual joint motion data and the reference joint motion data to obtain the joint control information.

20\. (Currently Amended) A non-transitory machine-readable media, having instructions stored on the machine-readable media, the instructions configured to, when executed, cause a machine to:
obtain a center-of-mass reference trajectory for guiding the robot to execute a target CKF \[ACT\[VE-19806712.1

motion; the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion;determine desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center ofmass of the robot;
construct an objective function based on the desired center-of-mass motion data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points, the contact points being contact points between the robot and an environment, a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory;
calculate center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition;
  generate joint control information according to the center-of-mass control information and a structure matrix of the robot; and
control the robot to execute the target motion based on the joint control information.

OFFICE ACTION:
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 .
Status of Claims
Claims 1, 3, 15 and 20 are amended.
Claims 1, 4 – 8, 11 - 12 and 15 and 17 – 20 are rejected.
Claims 3, 9 - 10 and 13 – 14 are objected
Claims 2 and 16 are cancelled.
Drawings
Applicant’s amendments to the drawings filed on 11/19/2024 have been accepted and overcome the previous objections.
Response to Arguments
Applicant’s amendments, mentioned in page 10 of the applicant’s arguments, have been fully considered and accepted.  They overcome the previous objections.
Applicant’s arguments on pages 10 – 11 have been fully considered but are moot due to the new grounds of rejection, Rinke and Huang.
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.
Claim(s) 1, 4 – 5, 11, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ha et al. (US Pub No: 2018/0107175 A1, hereinafter Ha) in view of Rinke et al. (US Pub No: 2022/0184802 A1, hereinafter Rinke) and Huang (CN 111674486 A, hereinafter Huang).
*Translation and original included in IDS provided by applicant.
Regarding Claim 1:
Ha discloses:
A robot motion control method, applied to a robot, the method comprising.  Paragraph [0037] describes a robot in which the trajectories of the robot are made using a center of mass of the robot and its feet.
obtaining a center-of-mass reference trajectory for guiding the robot to execute a target motion; the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion.  Paragraph [0037] describes a robot in which the trajectories of the robot are made using a center of mass of the robot and its feet.  These trajectories are computing using trajectory optimization.
generating joint control information according to the center-of-mass control information and a structure matrix of the robot; and controlling the robot to execute the target motion based on the joint control information.  Paragraph [0093] describes a Covariance Matrix Adaptation-Evolution Strategy.  Paragraph [0094] describes joint positions being sequentially solved in this matrix to find the joint positions including global position, orientation and joint angles at time t to match the desired trajectory of the motion optimizer.
Ha does not disclose the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot and calculating the center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.
Rinke teaches:
determining desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot.  Paragraph [0089] describes a Center-of-Mass (COM) deviation in graphs 925, 935 and 945 which describes COM motion data.
calculating center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.  Paragraph [0076] describes a differential-algebraic equations (DAE) to retarget motion profiles of the physical robot to minimize the dynamic equilibrium constant to minimize deviations.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha to incorporate the teachings of Rinke to show the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot and calculating the center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.  One would have been motivated to do so in order to yield fast and accurate vibration suppression of robot movement (Abstract of Rinke).
Ha and Rinke do not teach constructing an object function based on center-of-mass rotation data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points and a value of the objective function representing a degree of deviation of the robot from the center-of-mass reference trajectory.
Huang teaches:
constructing an objective function based on the desired center-of-mass motion data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points, the contact points being contact points between the robot and an environment, a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory.  Paragraph [0055] to [0060] and figure 2 describe controlling a stable walking of a biped robot.  In summary, step S104 describes constructing a center of mass state prediction model.  S106 describes simplifying the optimization equations of the center of mass prediction model.  Paragraph [0067] describes these equations having a center of mass, contact points of each supporting leg with the ground and a derivative of the rotation matrix.  Paragraph [0085] describes a weight vector of the center of mass state error at each moment.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date, with a reasonable expectation for success of the claimed invention, to have modified Ha and Rinke to incorporate the teachings of Huang to show constructing an object function based on center-of-mass rotation data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points and a value of the objective function representing a degree of deviation of the robot from the center-of-mass reference trajectory.  One would have been motivated to do so to obtain control parameters for steady walking by solving the system of quadratic programming equations, i.e. the same role that it plays in the present application.
Regarding Claims 15 and 20:
Ha discloses:
A robot motion control apparatus, comprising: a memory operable to store computer-readable instructions; and a processor circuitry operable to read the computer-readable instructions, the processor circuitry when executing the computer-readable instructions is configured to.  Paragraph [0040] describes memory/data storage devices 150 and a processor 11s0.
The rest of claim 15 is substantially similar to claim 1 and is rejected on the same grounds.
Claim 20 is substantially similar to claim 15 and is rejected on the same grounds.
Regarding Claim 4:
Ha discloses:
The method according to claim 1, wherein the calculating the center-of-mass control information comprises: utilizing quadratic programming (QP) optimization to calculate the center-of-mass control information.  Paragraph [0056] describe Sequential Quadratic Programming (SQP) to provide motions of all the key parts of the robot.
Regarding Claim 5:
Ha discloses:
The method according to claim 1, wherein the calculating the center-of-mass control information comprises: utilizing interior point optimizer (IPOPT) optimization to calculate the center-of-mass control information.  Paragraph [0097] describes a interior-point method was used to minimize the iterations to converge and decrease the vibrations from the input sequence.
Regarding Claim 11:
Rinke teaches:
The method according to claim 1, wherein the method further comprises: performing kinetics analysis on a particle model to obtain a kinetics equation, the particle model being obtained by simplification based on a robot body; and analyzing the kinetics equation and constructing the objective function based on an objective of making the center-of-mass motion trajectory of the robot consistent with the center-of-mass reference trajectory.  Paragraph [0147] and figure 17 illustrates data flow during learning of a robot controller.  The neural network performs learning algorithms using unfiltered control parameter 1720.  Kinematics 1730 provides target data over time and outputs a simulation state of the robotic system over time.
Claim 18 is substantially similar to claim 11 and is rejected on the same grounds.
Claim(s) 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke and Huang and further in view of Chen et al. (US Pub No: 2021/0178587 A1, hereinafter Chen).
Regarding Claim 6:
Ha, Rinke and Huang teach the above limitations in claim 1.  Ha, Rinke and Huang do not teach an actual COM motion data of the robot, a reference COM motion data, obtaining a COM motion data adjustment according to a difference between the actual and the reference COM data and obtaining the desired COM data according to the COM adjustment value.
Chen teaches:
The method according to claim 1, wherein the obtaining the desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory comprises: obtaining actual center-of-mass motion data of the robot.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining reference center-of-mass motion data of the robot according to the center-of-mass reference trajectory.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining a center-of-mass motion data adjustment value according to a difference between the actual center-of-mass motion data and the reference center-of-mass motion data.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
and obtaining the desired center-of-mass motion data according to the center-of-mass motion data adjustment value and the reference center-of-mass motion data.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.  Paragraph [0041] describes that the joint angles are adjusted in real time.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha, Rinke and Huang to incorporate the teachings of Chen to show an actual COM motion data of the robot, a reference COM motion data, obtaining a COM motion data adjustment according to a difference between the actual and the reference COM data and obtaining the desired COM data according to the COM adjustment value.  One would have been motivated to do so in order to control the robot according to the desired joint angles (Abstract of Chen).
Claim 17 is substantially similar to claim 6 and is rejected on the same grounds.
Claim(s) 7 – 8 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke, Huang and Chen and further in view of Oh et al. (US Pub No: 2015/0367518 A1, hereinafter Oh).
Regarding Claim 7:
Chen teaches:
The method according to claim 6, wherein the obtaining the center-of-mass motion data adjustment value according to the difference between the actual center-of-mass motion data and the reference center-of-mass motion data comprises: calculating a difference between the actual center-of-mass motion data and the reference center-of-mass motion data.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
Ha, Rinke, Huang and Chen do not teach a gain matrix associated with the center of mass of the robot.
Oh teaches
and obtaining the center-of-mass motion data adjustment value according to a gain matrix and the difference, the gain matrix corresponding to the motion data associated with the center of mass of the robot.  Paragraph [0071] describes a proportional gain of the COM position unit that represents a safety margin from an asymptotic value.  Paragraph [0102] describes that a proportional-differential controller changes the torque by using a COM Jacobian transposed matrix.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha, Rinke, Huang and Chen to incorporate the teachings of Oh to show a gain matrix associated with the center of mass of the robot.  One would have been motivated to do so in order to tune a parameter and to get good approximations of complicated calculations.
Regarding Claim 8:
Rinke teaches:
The method according to claim 6, wherein the center-of-mass motion data adjustment value comprises a center-of-mass acceleration adjustment value and a center-of-mass angular acceleration adjustment value.  Paragraph [0066] describes a general inertial acceleration and angular acceleration.
Claim(s) 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke and Huang and further in view of Chen.
Regarding Claim 12:
Ha, Rinke and Huang teach the above limitations in claim 1.  Ha, Rinke and Huang do not teach intermediate joint control information, obtaining actual joint motion data and adjusting the intermediate joint control information according the actual joint motion data and the reference joint motion data.
Chen teaches:
The method according to claim 1, wherein the generating the joint control information according to the center-of-mass control information and the structure matrix of the robot comprises: generating intermediate joint control information according to the center-of-mass control information and the structure matrix corresponding to the robot.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining actual joint motion data of the robot.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining reference joint motion data of the robot; and adjusting the intermediate joint control information according to the actual joint motion data and the reference joint motion data to obtain the joint control information.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.  Paragraph [0041] describes that the joint angles are adjusted in real time.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha, Rinke and Huang to incorporate the teachings of Chen to show an intermediate joint control information, obtaining actual joint motion data and adjusting the intermediate joint control information according the actual joint motion data and the reference joint motion data.  One would have been motivated to do so in order to control the robot according to the desired joint angles (Abstract of Chen).
Claim 19 is substantially similar to claim 12 and is rejected on the same grounds.
Allowable Subject Matter
Claims 3, 9 - 10 and 13 – 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamane (US Pub No: 2016/0236349 A1): A control method, and a robot controller implementing the method, is provided that adapts human motions to floating-base humanoid robots with time warping techniques. The method of modifying a set of reference motions modifies the timeline of a reference motion so as to speed up or slow down one or more of the motions or motion segments. Through the use of time warping, the velocity and acceleration profiles of the motion are changed to turn an infeasible motion into a feasible one. The optimal time warping is obtained through a generalized motion feasibility index that quantifies the feasibility of a motion considering the friction constraint as well as the center-of-pressure (CoP) constraint. Due to the use of the motion feasibility index, the proposed motion adaptation method taught herein can be applied to motions on arbitrary terrains or with any number of links in contact with the environment.
LaValley (US Pub No: 2020/0369333 A1): A robot actor, or character mobility hardware platform, adapted to unleash or provide a wide variety of characters in the physical world. The robot actor enables the often screen-constrained characters to become life-like, interactive participants with nearby people in ways not presently achievable. The robot actor is an untethered, free-roaming robot that is has two (or more) legs, is adapted for high dexterity, is controlled and designed to be self-balancing, and, due to this combination of characteristics, the robot can provide characters with an illusion of life and, in many cases, in correct proportion and scale. The hardware and software of the robot actor will become a new generation of animatronic figures by providing a hardware platform capable of continuously evolving to become more capable through advances in controls and artificial intelligence (AI).
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 date of this final action.

APPLICANT REBUTTAL: REMARKSI.    Introduction Claims 1, 3-15 and 17-20 are pending. The amendments are made without disclaimer of subject matter, and the Applicant reserves the right to pursue subject matter of any previously pending claim in a continuation or divisional application. No new matter has been introduced. The Applicant requests reconsideration in light of the amendment to the claims and the following remarks.II.   Rejection under 35 U.S.C. § 103 Claim(s) 1, 4 - 5, 11, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke et al. (US Pub No: 2022/0184802 Al, hereinafter "Rinke") and Huang (CN 11167 4486).Applicant amended independent claim 1 to include the allowable subject matter in original claim 3, and respectfully submits that amended claim 1 and its dependent claims are patentable. Independent claim 20 is amended to recite similar features as the amended claim 1. For similar reasons as amended claim 1, amended independent claim 20 is patentable.Withdrawal of the rejections under 35 U.S.C. § 103 is therefore respectfully requested.III. Allowable Subject Matter Applicant notes with appreciation that claims 3, 9-10, and 13-14 have been indicated to contain allowable subject matter.IV.   Conclusion In view of the amendments to the claims and the foregoing remarks, Applicant submits that the pending claims are in condition for allowance. Reconsideration is therefore respectfully requested. The Examiner is invited to contact the undersigned attorney for the Applicant via telephone if such communication would expedite this application.April 7, 2025

NEXT OFFICE ACTION: 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 .
EXAMINER’S AMENDMENT
An examiner’s amendment to the record appears below. Should the changes and/or additions be unacceptable to applicant, an amendment may be filed as provided by 37 CFR 1.312. To ensure consideration of such an amendment, it MUST be submitted no later than the payment of the issue fee.
Authorization for this examiner’s amendment was given during a phone call with Hefeng Su on 04/18/2025.  The amendment completely replaces the previously entered Claim 15 with the following Claim 15:
15. (Currently Amended) A robot motion control apparatus, comprising:
a memory operable to store computer-readable instructions; and
a processor circuitry operable to read the computer-readable instructions, the processor circuitry when executing the computer-readable instructions is configured to:
obtain a center-of-mass reference trajectory for guiding the robot to execute a target motion; the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion;
determine desired center-of-mass motion data of the robot based on the center-of- mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot;
construct an objective function based on the desired center-of-mass motion data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points, the contact points being contact points between the robot and an environment, a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory;
wherein the objective function is:
J
q
p
=
w
d
e
s
-
A
f
T
W
W
w
d
e
s
-
A
f
,
where Ww denotes a positive definite weight matrix, wdes denotes the desired center-of-mass motion data, A denotes the matrix and f denotes the center-of-mass control information.
calculate center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition;
generate joint control information according to the center-of-mass control information and a structure matrix of the robot; and
control the robot to execute the target motion based on the joint control information.
Allowable Subject Matter
The following is an examiner’s statement of reasons for allowance: The prior art of record fails to disclose, teach, suggest, nor sufficiently render obvious a combination of features as claimed and arranged by applicant when read in light of the specification.
Regarding Claim 1:
Ha discloses:
A robot motion control method, applied to a robot, the method comprising.  Paragraph [0037] describes a robot in which the trajectories of the robot are made using a center of mass of the robot and its feet.
obtaining a center-of-mass reference trajectory for guiding the robot to execute a target motion; the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion.  Paragraph [0037] describes a robot in which the trajectories of the robot are made using a center of mass of the robot and its feet.  These trajectories are computing using trajectory optimization.
generating joint control information according to the center-of-mass control information and a structure matrix of the robot; and controlling the robot to execute the target motion based on the joint control information.  Paragraph [0093] describes a Covariance Matrix Adaptation-Evolution Strategy.  Paragraph [0094] describes joint positions being sequentially solved in this matrix to find the joint positions including global position, orientation and joint angles at time t to match the desired trajectory of the motion optimizer.
Ha does not disclose the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot and calculating the center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.
Rinke, in an analogous field of endeavor, teaches:
determining desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot.  Paragraph [0089] describes a Center-of-Mass (COM) deviation in graphs 925, 935 and 945 which describes COM motion data.
calculating center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.  Paragraph [0076] describes a differential-algebraic equations (DAE) to retarget motion profiles of the physical robot to minimize the dynamic equilibrium constant to minimize deviations.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha to incorporate the teachings of Rinke to show the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot and calculating the center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.  One would have been motivated to do so in order to yield fast and accurate vibration suppression of robot movement (Abstract of Rinke).
Ha and Rinke do not disclose or teach constructing an object function based on center-of-mass rotation data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points and a value of the objective function representing a degree of deviation of the robot from the center-of-mass reference trajectory.
Huang, in an analogous field of endeavor, teaches:
constructing an objective function based on the desired center-of-mass motion data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points, the contact points being contact points between the robot and an environment, a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory.  Paragraph [0055] to [0060] and figure 2 describe controlling a stable walking of a biped robot.  In summary, step S104 describes constructing a center of mass state prediction model.  S106 describes simplifying the optimization equations of the center of mass prediction model.  Paragraph [0067] describes these equations having a center of mass, contact points of each supporting leg with the ground and a derivative of the rotation matrix.  Paragraph [0085] describes a weight vector of the center of mass state error at each moment.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date, with a reasonable expectation for success of the claimed invention, to have modified Ha and Rinke to incorporate the teachings of Huang to show constructing an object function based on center-of-mass rotation data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points and a value of the objective function representing a degree of deviation of the robot from the center-of-mass reference trajectory.  One would have been motivated to do so to obtain control parameters for steady walking by solving the system of quadratic programming equations, i.e. the same role that it plays in the present application.
However, the prior art of record does not disclose, teach, suggest, or render obvious:
wherein the objective function is
J
q
p
=
w
d
e
s
-
A
f
T
W
W
w
d
e
s
-
A
f
,
where Ww denotes a positive definite weight matrix, wdes denotes the desired center-of-mass motion data, A denotes the matrix and f denotes the center-of-mass control information.
Regarding Claims 15 and 20:
Ha discloses:
A robot motion control apparatus, comprising: a memory operable to store computer-readable instructions; and a processor circuitry operable to read the computer-readable instructions, the processor circuitry when executing the computer-readable instructions is configured to.  Paragraph [0040] describes memory/data storage devices 150 and a processor 110.
The rest of claim 15 is substantially similar to claim 1 and is allowed for the same reasons.
As such, the independent claims 1, 15, and 20 are allowed for reasons presented above.  Dependent claims 4 – 15 and 17 – 20 are also allowed for requiring at least those same limitations from the respective independent claim in which they each depend.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

CITED REFERENCE SUMMARIES: Ref CN111674486A: Claims:
A method for controlling stable walking of a biped robot, comprising:
constructing a simplified rigid body dynamic model of the biped robot;
constructing a centroid state prediction model by applying a simplified rigid body dynamics model of the biped robot based on a model prediction control method;
simplifying an optimization equation set in the model predictive control method into a quadratic programming equation set according to a centroid state prediction model;
solving a quadratic programming equation set to obtain optimized ground reaction force so as to obtain control parameters of stable walking;
and controlling the biped robot according to the stable walking control parameters.
The method for controlling stable walking of a biped robot according to claim 1, wherein the establishing of the simplified rigid body dynamics model of the biped robot comprises:
based on a linear inverted pendulum model, wherein the mass of the robot is totally concentrated on the mass center, and the legs are taken as connecting rods without mass, a simplified rigid body dynamics model of the biped robot is established.
The method for controlling stable walking of a biped robot according to claim 1, wherein the construction of the centroid state prediction model using the simplified rigid body dynamics model of the biped robot based on the model prediction control method comprises:
the model prediction control method is used for constructing a state equation of a centroid state vector according to a simplified rigid body dynamics model, further converting the state equation into a centroid state prediction model, and predicting the centroid state at the next moment according to the current centroid state at each control moment.
The method for controlling stable walking of a biped robot according to claim 1, wherein the optimization equations in the model predictive control method are simplified into a quadratic programming equation system according to a centroid state prediction model, comprising:
converting a centroid state prediction model in the model prediction control method into a centroid state prediction equation of a future finite time domain based on the current moment centroid state;
and simplifying the optimization equation set of the model predictive control method into a quadratic programming equation set according to a future finite time domain centroid state prediction equation.
The method for controlling stable walking of a biped robot according to claim 1, wherein solving a quadratic programming equation system to obtain optimal ground reaction force to obtain control parameters for stable walking comprises:
solving a quadratic programming equation set to obtain an optimal ground reaction force vector in a future finite time domain, and calculating control parameters for realizing stable walking according to the first ground reaction force value.
The method for controlling the steady walking of the biped robot according to claim 1, wherein the controlling the biped robot according to the steady walking control parameter comprises:
and applying the stable walking control parameters to the control of the supporting legs to realize the stable walking control of the biped robot.
A biped robot stable walking control device, comprising:
the model building module is used for building a simplified rigid body dynamics model of the biped robot;
the prediction model construction module is used for constructing a centroid state prediction model by applying a simplified rigid body dynamics model of the biped robot based on a model prediction control method;
the simplification module is used for simplifying an optimization equation set in the model prediction control method into a quadratic programming equation set according to a centroid state prediction model;
the parameter obtaining module is used for solving a quadratic programming equation set to obtain optimized ground reaction force so as to obtain control parameters for stable walking;
and the control module is used for controlling the biped robot according to the stable walking control parameters.
An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.