Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
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)(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.
Claims 1, 2, 3, 5, 9, 10, 11, 13 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wisth et al. (“VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots”, IEEE TRANSACTIONS ON ROBOTICS, VOL. 39, NO. 1, FEBRUARY 2023).
With respect to claim 1, Wisth et al. teach calculating a kinematic factor of the walking robot considering data measured by an inertial measurement unit (IMU) mounted on the walking robot while the walking robot is moving and kinetic dynamics (page 5, D. Measurement Definition the IMU measurements received between two consecutive keyframes. Each measurement includes the proper acceleration and the rotational velocity both expressed in the IMU frame. Similarly, we define the kinematics measurements Kij which include the joint positions and velocities);
obtaining point cloud data with respect to a space where the walking robot is located by using a LiDAR sensor mounted on the walking robot; obtaining image data with respect to the space where the walking robot is located by using an image sensor mounted on the walking robot (The (mono or stereo) camera images and lidar point clouds collected at time ti are expressed with Ci and Li,);
a data fusion operation of fusing the kinematic factor, the point cloud data, and the image data (page 6, left column, The full set of measurements within the smoothing window is defined as:Zk ≜ {Iij ,Kij , Ci, Li}i,j∈Kk; and
estimating a position of the walking robot based on the fused data (page 6, left column, E. Maximum-A-Posteriori Estimation, history of states and landmarks Xk is based on Zk; Xk includes xi (See equation (2) in page 5) and xi include position pi (See equation (1) in page 5)).
With respect to claim 2, Wisth et al. teach the calculating of the kinematic factor includes calculating positions and velocities of a leg and foot of the walking robot based on data of a joint sensor mounted on the walking robot together with the IMU. (page 7, B. Preintegrated Leg Odometry Factors, 1) Stance Estimation: Ground Reaction Force (GRF) based on both ω˙ and v˙ are obtained from the IMU and joint torques)
With respect to claim 3, Wisth et al. teach that the positions and velocities of the leg and foot of the walking robot are calculated through pre-integration from positions of the leg and foot of the walking robot at a previous time to positions of the leg and foot of the walking robot at a current time. (page 8, left column, 4) Preintegrated Velocity Measurements)
With respect to claim 5, Wisth et al. teach that generating feature data by using sliding-window point cloud optimization from the point cloud data of the LiDAR sensor (page 3, A. motivation).
With respect to claim 9, claim 9 is rejected same reason as claim 1 above.
With respect to claim 10, claim 10 is rejected same reason as claim 2 above.
With respect to claim 11, claim 11 is rejected same reason as claim 3 above.
With respect to claim 13, claim 13 is rejected same reason as claim 5 above.
With respect to claim 17, claim 17 is rejected same reason as claim 1 above.
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 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 of this title, 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6 and 14 are rejected under 35 USC 103 as being unpatentable over Wisth et al. (“VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots”, IEEE TRANSACTIONS ON ROBOTICS, VOL. 39, NO. 1, FEBRUARY 2023) in view of Ma et al. (“Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization”, Sensors 2019, 19, 3747; doi:10.3390/s19173747)
Wisth et al. teaches all the limitations of claim 5 as applied above from which claim 6 respectively depend.
Wisth et al. do not teach expressly that extracting features of an image through fast-corner detection and Kanade-Lucas-Tomasi (KLT)-based optical flow estimation based on the image data from the image sensor.
Ma et al. teaches extracting features of an image through fast-corner detection and Kanade-Lucas-Tomasi (KLT)-based optical flow estimation based on the image data from the image sensor. (page 1, abstract, we use the FAST feature detector to track features by the KLT sparse optical flow algorithm).
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to track features by the KLT sparse optical flow algorithm in the method of Wisth et al.
The suggestion/motivation for doing so would have been that to decrease the cost of computation and to improve its efficiency (Ma et al., abstract).
Therefore, it would have been obvious to combine Ma et al. with Wisth et al. to obtain the invention as specified in claim 6.
With respect to claim 14, claim 14 is rejected same reason as claim 6 above.
Allowable Subject Matter
1. Claims 4, 7, 8, 12, 15 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable of rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Randolph Chu whose telephone number is 571-270-1145. The examiner can normally be reached on Monday to Thursday from 7:30 am - 5 pm.
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The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RANDOLPH I CHU/
Primary Examiner, Art Unit 2667
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