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
This Office Action is in response to the application filed on October 30th, 2024. Claims 1-20 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on December 16th, 2024 and February 4th, 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for priority to provisional application 63/596024 dated November 3rd, 2023.
Claim Rejections - 35 USC § 102
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1, 3-4, 6-7, 9- 12, 14-16, and 18-19 are rejected under 35 U.S.C. 102(a)(1)) as anticipated by “MPC-based Controller with Terrain Insight for Dynamic Legged Locomotion” (hereinafter, “Villarreal”).
Regarding claim 1 Villarreal discloses a method (see at least [abstract]; “We present a novel control strategy for dynamic legged locomotion in complex scenarios that considers information about the morphology of the terrain in contexts when only on-board mapping and computation are available”), comprising:
receiving, by a control system of a legged robot, a target trajectory for the legged robot (see at least [Page 3, right column]; “The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters”);
receiving, by the control system, a state of the legged robot (see at least [Page 3, right column]; “The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters.”);
generating, using a neural network of the control system, a set of gait timing parameters for the legged robot based, at least in part, on the state of the legged robot and the target trajectory (see at least [Page 4, left-right column]; “We use the computational gain obtained by the VFA to evaluate further ahead in the terrain. Knowing that the gait is periodic and defined by the step frequency fs and the duty factor Df, we can estimate the timings for the non-immediate foot contacts. Using these timings, one can compute the predicted foothold locations for each of the legs at every stance change (lift-off or touchdown) replacing them for Dt in (1). We then use our CNN-based foothold adaptation to adjust the predicted foothold location…The CNN continuously provides safe contact sequences at task frequency,” the contact sequence corresponds to the set of gait timing parameters and is based on the robot’s state and target trajectory); and
controlling, by the control system, movement of the legged robot based on the set of gait timing parameters (see at least [Page 3, right column]; “The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters. We use the RCF [14] as controller interface.”).
Regarding claim 3 Villarreal discloses all of the limitations of claim 1. Additionally, Villarreal discloses wherein the gait timing parameters include a contact sequence (see at least [Page 4, left-right column]; “We use the computational gain obtained by the VFA to evaluate further ahead in the terrain. Knowing that the gait is periodic and defined by the step frequency fs and the duty factor Df, we can estimate the timings for the non-immediate foot contacts. Using these timings, one can compute the predicted foothold locations for each of the legs at every stance change (lift-off or touchdown) replacing them for Dt in (1). We then use our CNN-based foothold adaptation to adjust the predicted foothold location…The CNN continuously provides safe contact sequences at task frequency,” the contact sequence corresponds to the set of gait timing parameters and is based on the robot’s state and target trajectory).
Regarding claim 4 Villarreal discloses all of the limitations of claim 3. Additionally, Villarreal discloses wherein the contact sequence includes at least one target stepping time (see at least [Page 4, left column]; “We use the computational gain obtained by the VFA to evaluate further ahead in the terrain. Knowing that the gait is periodic and defined by the step frequency fs and the duty factor Df , we can estimate the timings for the non-immediate foot contacts. Using these timings, one can compute the predicted foothold locations for each of the legs at every stance change (lift-off or touchdown) replacing them for Dt in (1),” the timings for the non-immediate foot contacts corresponds to Applicant’s target stepping time).
Regarding claim 6 Villarreal discloses all of the limitations of claim 1. Additionally, Villarreal discloses further comprising: generating, using a model predictive controller (MPC) of the control system, a set of step parameters based on the gait timing parameters, wherein controlling the movement of the legged robot is further based on the set of step parameters (see at least Fig. 1; model predictive controller, the contact sequence is determined using in part the MPC and includes various tasks which output corresponding step parameters and then used to generate motion of the robot).
Regarding claim 7 Villarreal discloses all of the limitations of claim 6. Additionally, Villarreal discloses wherein the set of step parameters includes at least one of a step placement or a desired center of mass acceleration (see at least [Page 3, right column]; “The contact sequence task provides the future contact locations according to the robot current states and the gait timing parameters,” the contact location corresponds to applicant’s step placement).
Regarding claim 9 Villarreal discloses all of the limitations of claim 1. Additionally, Villarreal discloses further comprising: receiving perception data indicative of an environment of the legged robot (see at least [Page 3, right column]; “terrain information provided by on-board vision sensors,”); and generating a map of the environment based on the perception data (see at least [Page 4, left column]; “After computing the prediction of the next foothold, a 2D representation of the terrain around that foothold is acquired, namely a heightmap”), wherein the neural network further uses the map of the environment as an input (see at least [Page 4, left column]; “The CNN takes on average0.1 ms to evaluate a heightmap and output a safe foothold,” the heightmap is input into the CNN to determine a safe foothold).
Regarding claim 10 Villarreal discloses all of the limitations of claim 1. Additionally, Villarreal discloses wherein the neural network further uses a set of input parameters as inputs, the input parameters comprising one or more of: a terrain height map, a no-step map, a control state, a user-specified desired robot behavior, a body path, or perception data (see at least [Page 4, left column]; “The CNN takes on average0.1 ms to evaluate a heightmap and output a safe foothold,” the heightmap is input into the CNN to determine a safe foothold).
Regarding claim 11 Villarreal discloses all of the limitations of claim 1. Additionally, Villarreal discloses further comprising: receiving obstacle data (see at least [Page 3, right column]; “produce robust and stable locomotion complex scenarios using terrain information provided by on-board vision sensors,” the terrain information can constitute obstacle data as some terrain would be unsurpassable and therefore an obstacle); and generating a body path based on the trajectory and the obstacle data, wherein the neural network further uses the body path as an input (see at least [Page 2, right column]; “The result is a stable locomotion strategy, which is robust to a wide range of disturbances and is able to act preemptively to obstacles based on visual information,” based on the original trajectory and visual information a path avoid obstacles is able to preemptively be determined, the neural network uses the information of the path to further improve motion of the robot).
Regarding claim 12 Villarreal discloses a legged robot (see at least fig. 1) comprising:
a body (see at least fig. 1);
two or more legs coupled to the body (see at least fig. 1);
one or more sensors configured to measure a state of the legged robot (see at least [Page 7, right column]; “We developed a dynamic locomotion strategy to traverse difficult terrain using visual information only coming from on-board sensors”); and
a control system in communication with the body and the two or more legs, the control system comprising data processing hardware and memory hardware in communication with the data processing hardware (see at least [Page 3 right column – Page 4, left column]; “The block diagram shown in Fig. 1 describes our locomotion strategy. It entails three main elements: the contact sequence task, the COM tracking task and the Reactive Controller Framework (RCF) [14]. The contact sequence task provides the future contact locations according to the robot current states and the gait timing parameters. The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters. We use the RCF [14] as controller interface. This modular framework allows us to combine the RCF reactive layer block in Fig. 1 with our vision-based strategy. This layer is comprised by several,” it would be inherent that to perform the methods described in Villarreal that hardware and memory are comprised within the vehicle system), the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to:
receive a target trajectory for the legged robot (see at least [Page 3, right column]; “The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters”);
receive the state of the legged robot from the one or more sensors (see at least [Page 3, right column]; “The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters.”);
generate, using a neural network of the control system, a set of gait timing parameters for the legged robot based, at least in part, on the state of the legged robot and the target trajectory (see at least [Page 4, left-right column]; “We use the computational gain obtained by the VFA to evaluate further ahead in the terrain. Knowing that the gait is periodic and defined by the step frequency fs and the duty factor Df, we can estimate the timings for the non-immediate foot contacts. Using these timings, one can compute the predicted foothold locations for each of the legs at every stance change (lift-off or touchdown) replacing them for Dt in (1). We then use our CNN-based foothold adaptation to adjust the predicted foothold location…The CNN continuously provides safe contact sequences at task frequency,” the contact sequence corresponds to the set of gait timing parameters and is based on the robot’s state and target trajectory); and
control movement of the legged robot based on the set of gait timing parameters (see at least [Page 3, right column]; “The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters. We use the RCF [14] as controller interface.”).
Regarding claim 14 Villarreal discloses all of the limitations of claim 12. Additionally, Villarreal discloses wherein the gait timing parameters include a contact sequence (see at least [Page 4, left-right column]; “We use the computational gain obtained by the VFA to evaluate further ahead in the terrain. Knowing that the gait is periodic and defined by the step frequency fs and the duty factor Df, we can estimate the timings for the non-immediate foot contacts. Using these timings, one can compute the predicted foothold locations for each of the legs at every stance change (lift-off or touchdown) replacing them for Dt in (1). We then use our CNN-based foothold adaptation to adjust the predicted foothold location…The CNN continuously provides safe contact sequences at task frequency,” the contact sequence corresponds to the set of gait timing parameters and is based on the robot’s state and target trajectory).
Regarding claim 15 Villarreal discloses all of the limitations of claim 12. Additionally, Villarreal discloses wherein the instructions, when executed on the data processing hardware, further cause the data processing hardware to: generate, using a model predictive controller (MPC) of the control system, a set of step parameters based on the gait timing parameters, wherein controlling the movement of the legged robot is further based on the set of step parameters (see at least Fig. 1; model predictive controller, the contact sequence is determined using in part the MPC and includes various tasks which output corresponding step parameters and then used to generate motion of the robot).
Regarding claim 16 Villarreal discloses all of the limitations of claim 15. Additionally, Villarreal discloses wherein the set of step parameters includes at least one of a step placement or a desired center of mass acceleration (see at least [Page 3, right column]; “The contact sequence task provides the future contact locations according to the robot current states and the gait timing parameters,” the contact location corresponds to applicant’s step placement).
Regarding claim 18 Villarreal discloses all of the limitations of claim 12. Additionally, Villarreal discloses wherein the instructions, when executed on the data processing hardware, further cause the data processing hardware to: receive perception data indicative of an environment of the legged robot (see at least [Page 3, right column]; “terrain information provided by on-board vision sensors,”); and generate a map of the environment based on the perception data (see at least [Page 4, left column]; “After computing the prediction of the next foothold, a 2D representation of the terrain around that foothold is acquired, namely a heightmap”), wherein the neural network further uses the map of the environment as an input (see at least [Page 4, left column]; “The CNN takes on average0.1 ms to evaluate a heightmap and output a safe foothold,” the heightmap is input into the CNN to determine a safe foothold).
Regarding claim 19 Villarreal discloses a non-transitory computer-readable medium having stored therein instructions that, when executed by data processing hardware of a control system, cause the data processing hardware to (see at least [Page 3 right column – Page 4, left column]; “The block diagram shown in Fig. 1 describes our locomotion strategy. It entails three main elements: the contact sequence task, the COM tracking task and the Reactive Controller Framework (RCF) [14]. The contact sequence task provides the future contact locations according to the robot current states and the gait timing parameters. The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters. We use the RCF [14] as controller interface. This modular framework allows us to combine the RCF reactive layer block in Fig. 1 with our vision-based strategy. This layer is comprised by several,” it would be inherent that to perform the methods described in Villarreal that hardware and memory are comprised within the vehicle system):
receive a target trajectory for a legged robot (see at least [Page 3, right column]; “The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters”);
receive a state of the legged robot (see at least [Page 3, right column]; “The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters.”);
generate, using a neural network of the control system, a set of gait timing parameters for the legged robot based, at least in part, on the state of the legged robot and the target trajectory (see at least [Page 4, left-right column]; “We use the computational gain obtained by the VFA to evaluate further ahead in the terrain. Knowing that the gait is periodic and defined by the step frequency fs and the duty factor Df, we can estimate the timings for the non-immediate foot contacts. Using these timings, one can compute the predicted foothold locations for each of the legs at every stance change (lift-off or touchdown) replacing them for Dt in (1). We then use our CNN-based foothold adaptation to adjust the predicted foothold location…The CNN continuously provides safe contact sequences at task frequency,” the contact sequence corresponds to the set of gait timing parameters and is based on the robot’s state and target trajectory); and
control movement of the legged robot based on the set of gait timing parameters (see at least [Page 3, right column]; “The COM tracking task is in charge of both generating and following a COM trajectory according to the contact sequence task, the current robot states, and the gait parameters. We use the RCF [14] as controller interface.”).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2, 8, 13, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Villarreal, as applied to claims 1, 3, 6, 12, 15, and 19 above, in view of “Learning Impulse-Reduced Gait for Quadruped Robot using CMA-ES” (hereinafter, “Ahn”)
Regarding claim 2 Villarreal discloses all of the limitations of claim 1. Villarreal does not disclose wherein the neural network is trained using reinforcement learning.
Ahn, in the same field of endeavor, teaches wherein the neural network is trained using reinforcement learning (see at least [Page 2, right column]; “In this study, we utilize a neural network to determine these parameters by learning the gait scheduler policy with the RL”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the vehicle control method of Villarreal with the reinforcement learning of Ahn. One of ordinary skill in the art would have been motivated to make this modification for the benefit of taking the advantages of both model-based and RL-based methods to improve walking performance in real robot systems (see at least Ahn [Page 1, right column]).
Regarding claim 8 Villarreal discloses all of the limitations of claim 6. Villarreal does not disclose further comprising: initializing the neural network to reproduce the set of step parameters to within a threshold difference of a previous set of step parameters; and training the initialized neural network using reinforcement learning including simulating the MPC to search a space of possible solutions.
Ahn, in the same field of endeavor, teaches further comprising:
initializing the neural network to reproduce the set of step parameters to within a threshold difference of a previous set of step parameters (see at least [Page 2, right column]; “we utilize a neural network to determine these parameters by learning the gait scheduler policy with the RL…RL agent optimizes the policyπ : S 7→ A that maximizes the accumulated reward during a finite time horizon T of each episode,” and [Page 3, left column-right column]; “The reward function encourages the robot to stably walk following the commanded velocity while minimizing impact force at the landing foot. For this, we present a reward function consisting of the base body velocity and impact… The second term of (3) encourages robot to follow the desired velocity. This penalty term computes the error between the desired and measured velocity of the base in the form of L2 norm with fraction. The denominator serves to ensure that the error value is not too small. By reducing the velocity error for the reward, it is expected the robot can walk successfully. The third term in (3) penalizes for large impact differences. The impact penalty is derived from the following impact equation,” Equation 3 of Ahn shows a formula for calculating a reward for a solution based on its difference to the previous parameter, the formula penalizes for large differences between impact of the current and previous time, which is a step parameter, this penalization trains the neural network to minimize the impact force to be as small as possible, it would be an obvious variant to apply a threshold to the value rather than just simply finding the optimal value; however the objective of both methods would be the same as the neural network would yield an optimal impact parameter); and
training the initialized neural network using reinforcement learning including simulating the MPC to search a space of possible solutions (see at least [Page 4, left column]; “All the training is conducted in physics-based simulations on a computer for fast and safe learning. Then the trained neural network is implemented in the real-robot control system. The hierarchical structure enables a successful sim-to-real without additional effort such as dynamics randomization and simulation tunning process”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the vehicle control method of Villarreal with the reinforcement learning of Ahn. One of ordinary skill in the art would have been motivated to make this modification for the benefit of taking the advantages of both model-based and RL-based methods to improve walking performance in real robot systems (see at least Ahn [Page 1, right column]).
Regarding claim 13 Villarreal discloses all of the limitations of claim 12. Villarreal does not disclose wherein the neural network is trained using reinforcement learning.
Ahn, in the same field of endeavor, teaches wherein the neural network is trained using reinforcement learning (see at least [Page 2, right column]; “In this study, we utilize a neural network to determine these parameters by learning the gait scheduler policy with the RL”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the vehicle control method of Villarreal with the reinforcement learning of Ahn. One of ordinary skill in the art would have been motivated to make this modification for the benefit of taking the advantages of both model-based and RL-based methods to improve walking performance in real robot systems (see at least Ahn [Page 1, right column]).
Regarding claim 17 Villarreal discloses all of the limitations of claim 15. Villarreal does not disclose further comprising: initializing the neural network to reproduce the set of step parameters to within a threshold difference of a previous set of step parameters; and training the initialized neural network using reinforcement learning including simulating the MPC to search a space of possible solutions.
Ahn, in the same field of endeavor, teaches wherein the instructions, when executed on the data processing hardware, further cause the data processing hardware to:
initialize the neural network to reproduce the set of step parameters to within a threshold difference of a previous set of step parameters (see at least [Page 2, right column]; “we utilize a neural network to determine these parameters by learning the gait scheduler policy with the RL…RL agent optimizes the policyπ : S 7→ A that maximizes the accumulated reward during a finite time horizon T of each episode,” and [Page 3, left column-right column]; “The reward function encourages the robot to stably walk following the commanded velocity while minimizing impact force at the landing foot. For this, we present a reward function consisting of the base body velocity and impact… The second term of (3) encourages robot to follow the desired velocity. This penalty term computes the error between the desired and measured velocity of the base in the form of L2 norm with fraction. The denominator serves to ensure that the error value is not too small. By reducing the velocity error for the reward, it is expected the robot can walk successfully. The third term in (3) penalizes for large impact differences. The impact penalty is derived from the following impact equation,” Equation 3 of Ahn shows a formula for calculating a reward for a solution based on its difference to the previous parameter, the formula penalizes for large differences between impact of the current and previous time, which is a step parameter, this penalization trains the neural network to minimize the impact force to be as small as possible, it would be an obvious variant to apply a threshold to the value rather than just simply finding the optimal value; however the objective of both methods would be the same as the neural network would yield an optimal impact parameter); and
train the initialized neural network using reinforcement learning including simulating the MPC to search a space of possible solutions (see at least [Page 4, left column]; “All the training is conducted in physics-based simulations on a computer for fast and safe learning. Then the trained neural network is implemented in the real-robot control system. The hierarchical structure enables a successful sim-to-real without additional effort such as dynamics randomization and simulation tunning process”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the vehicle control method of Villarreal with the reinforcement learning of Ahn. One of ordinary skill in the art would have been motivated to make this modification for the benefit of taking the advantages of both model-based and RL-based methods to improve walking performance in real robot systems (see at least Ahn [Page 1, right column]).
Regarding claim 20 Villarreal discloses all of the limitations of claim 19. Villarreal does not disclose wherein the neural network is trained using reinforcement learning.
Ahn, in the same field of endeavor, teaches wherein the neural network is trained using reinforcement learning (see at least [Page 2, right column]; “In this study, we utilize a neural network to determine these parameters by learning the gait scheduler policy with the RL”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the vehicle control method of Villarreal with the reinforcement learning of Ahn. One of ordinary skill in the art would have been motivated to make this modification for the benefit of taking the advantages of both model-based and RL-based methods to improve walking performance in real robot systems (see at least Ahn [Page 1, right column]).
Claim(s) 25 is rejected under 35 U.S.C. 103 as being unpatentable over Villarreal, as applied to claim 3, in view of US-20210331317 (hereinafter, “Whitman”).
Regarding claim 5 Villarreal discloses all of the limitations of claim 3. Villarreal does not disclose wherein the gait timing parameters further include a speed scaling factor.
Whitman, in the same field of endeavor, teaches wherein the gait timing parameters further include a speed scaling factor (Applicant describes a speed scaling factor as doing the following “The speed rescale controller 312 is configured to receive the body path and the speed limit and rescale the trajectory, if necessary, such that the rescaled trajectory does not exceed the speed limit.”)(see at least [0096]; “the speed of the robot 100 is constrained to be a function of the average slope or actual slope of a detected staircase. In some implementations, an active stair tracker 200 enables the robot 100 to select a speed limit to match the robot's stride length to a step length for a detected staircase (e.g., generating one footstep per stair step). For example, when stair tracker 200 is active, the control system 170 may be configured to select a controller 172 with a cadence to achieve one footstep per stair step. Additionally, or alternatively, when the stair tracker 200 is active, the stair tracker 200 may have an associated specialty stair controller that has been optimized for aspects of speed, cadence, stride length, etc.,” a speed limit is capable of being provided to the robot, causing the robot trajectory to be scaled so as to comply with the speed limit, this would correspond to applicant’s speed scaling factor).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the vehicle control method of Villarreal with the speed limit of Whitman. One of ordinary skill in the art would have been motivated to make this modification for the benefit of allowing the robot to traverse environments with obstacles or features requiring various means of coordinated leg movement (see at least Whitman; [0003]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEIGH NICOLE TURNBAUGH whose telephone number is (703)756-1982. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
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/ASHLEIGH NICOLE TURNBAUGH/Examiner, Art Unit 3666
/HELAL A ALGAHAIM/SPE , Art Unit 3666
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