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 .
This Office action is responsive to the amendment filed on 3/16/26. As per requested, claims 3, 5-6, 11, 13, and 25 has been cancelled; claims 27-32 has been added. Claims 1-2, 4, 7-10, 12, 14-24, and 26-32 are pending.
Objection
Claims 3, and 5-6 has been cancelled. The dependent of claims 7-9 needed to be corrected because they are depended on the cancelled claims.
Claims 8, 24, 26, 32, objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim should refer to other claims in the alternative only, and/or, cannot depend from any other multiple dependent claim. See MPEP 608.01(n). Accordingly, the claims have not been treated on the merits.
Correction of all the above is required.
Claim Rejections - 35 USC § 112
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 14-15, 17, 27, and 30, 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.
As per claim 14, recites the limitation "the implementation" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 14, lines 4 and 6, recites step (c) and (d). However, claim 14 missing steps (a) and (b).
Claim 14, line 11, recites “step (b) comprising”. However, the claim does not has step (b).
Correction is required.
Claim 15, line 2 recites “step (b)”. Claim 15 depended on claim 14. However, claim 14 does not have step (b).
Claim 17, line 2 recites “step (b)”. Claim 17 depended on claim 14. However, claim 14 does not have step (b).
Claim 27, line 2 recites “step (a) and (b)”. Claim 27 depended on claim 14. However, claim 14 does not have step (a) and (b).
Claim 30, line 1 recites “step c” . Claim 30 depended on claim 14. However, claim 14 does not have “step c”.
Amendment, correction for all of the above is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 14, 20, 27-28, and 30-32, are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Changxin Huang et al. (Hybrid and dynamic policy gradient optimization for bipedal robot locomotion, applicant’s submitted IDS document).
As per claim 14, Changxin Huang et al. disclose a method for training a neural network for stabilizing a bipedal robot (see at least page 5, section 5 “Experiment”, to verify the effectiveness of the proposed method, we conduct our simulator, named AIDA, to support development, training, and validation of biped locomotion; and page 12, section B.1) comprising the implementation by a data processing means of a server of steps of: applying, in a simulation, a sequence of pushes (see at least page 6, section 5.1 and pages 7-8, section 5.2.1, both relating to a sequence of random push disturbances) on a virtual twin of bipedal robot presenting a plurality of degrees of freedom actuated by actuators (see figures 1, 2, 8, and page 12, section B.1, disclose 10DoF, and 10 motors); and performing a reinforcement learning algorithm (“Hybrid and Dynamic Policy Gradient”, see the abstract; and page 3, section 3; and page 12, first paragraph), on said neural network, wherein the neural network provides commands to actuators of the virtual twin of the bipedal robot so as to maximize a reward representative of a recovery of said virtual twin of the bipedal robot from each push (see page 3, section 3.1; and page 9, sections 5.2.4, and 6); wherein the neural network is trained to learn a policy, comprising performing temporal and/or spatial regularization of the policy, so as to improve smoothness of the commands of the neural network (see page 4, section 4 disclose the input of actor to contain temporal information; and page 13, section B.2 disclose the gait occurs when the single support period and double support period arise in time sequence).
As per claim 20, Changxin Huang et al. disclose the regularization is applied to a mean field of the policy (see page 5, section 4.2 disclose the mean and the variance of reward samples are calculated; and page 14, section B.3 disclose table 1, the results show the mean and standard deviation across 10 runs).
As per claim 27, Changxin Huang et al. disclose simulation lasts for a predetermined duration, steps being repeated for a plurality of simulations (see at least pages 6-7, section 5.2, and figure 3, DDPG, MHDDPG, and HDPG training curve), and the pushes are applied periodically over the predetermined duration of the simulation, with forces of constant magnitude applied for a predefined duration (see at least page 6, section 5.1, the disturbance range is from 6N to 14N, and the duration is 0.2s).
As per claim 28, Changxin Huang et al. disclose the pushes are applied on a pelvis of the virtual twin of the bipedal robot with an orientation sampled from a spherical distribution (see figure 4, random push force will be applied to the robot’s pelvis from different directions).
As per claim 30, Changxin Huang et al. disclose storing the trained neural network in a memory of the bipedal robot (see page 12, section B.1, the trained model is evaluated on NUC in real time).
As per claim 31, Changxin Huang et al. disclose a method for stabilizing a bipedal robot comprising providing commands to actuators of a bipedal robot presenting a plurality degrees of freedom actuated by said actuators with a neural network trained using the method according to claim 14 (see figures 1 and 6, and page 9, section 5.3 “Sim-to-real results”).
As per claim 32, Changxin Huang et al. disclose a system comprising a server and a bipedal robot presenting a plurality of degrees of freedom actuated by actuators, each comprising data processing means, wherein said data processing means are respectively configured to implement the method for training a neural network for stabilizing the bipedal robot according to claim 14 and the method for stabilizing the bipedal robot according to claim 31).
Claim Rejections - 35 USC § 103
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 29, is rejected under 35 U.S.C. 103 as being unpatentable over Changxin Huang et al. (Hybrid and dynamic policy gradient optimization for bipedal robot locomotion, applicant’s submitted IDS document) in view of Zhaoming Xie et al. (Feedback control for Cassie with deep reinforcement learning, applicant’s submitted IDS document).
As per claim 29, Changxin Huang et al. do not explicitly disclose a control loop mechanism determines torques to be applied by the actuators as a function of said target positions and/or velocities of the actuators. However, Zhaoming Xie et al. disclose the neural network provides as commands target positions and/or velocities of the actuators at a first frequency, and a control loop mechanism determines torques to be applied by the actuators as a function of said target position and/or velocities of the actuators at a second frequency which is higher that the first frequency (see section IV, and figure 3, “the output of the policy network is added to the current reference angle of the active joints. The result is then used as the target joint angles for PD control”). It 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 to modify the teach of Changxin Huang et al. by combining control loop mechanism determines torques to be applied by the actuators as a function of said target positions and/or velocities of the actuators, for feedback control stabilizing of bipedal robot.
Claims 15-19, 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, after fix the rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph in item 5 above.
Claims 1-2, 4, 10, 12, 21-24, 26, are allowable.
Claims 7-9, need to be corrected the dependency. They will be allowable, after correction, if they are depended on the allowed claims above.
Remarks
Applicant’s response filed 3/16/26 has been fully considered.
The updated rejection as above. The allow of claim 14 is withdrawn, updated of the rejection as above.
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/DALENA TRAN/ Primary Examiner, Art Unit 3657
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