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 .
Amendments to the claims have been recorded.
Response to Arguments
Applicant’s arguments have been fully considered but they are not persuasive.
Applicant’s Arguments
Applicant argues are fully addressed with the new rejections made to the newly provided amendments.
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, 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Baron US 10,140,249 in view of Ebrahimi US 20220026920.
1. Baron teaches a computer-implemented method for kernel-based ergodic search using a robot, comprising:
receiving, via a processor, a target distribution indicative of a desired ergodic search coverage; C2L37; optimal Bayesian performance [Also known as Gaussian Kernal distribution or Gaussian distribution or Bell curve with a target center or Gaussian mean i.e. target distribution] for stationary ergodic. Also; C3L40; Fitting Gaussian models; used to fit a given data sequence [target] with a GM model, and the parameters can be learned using a modified expectation-maximization (EM) algorithm.
generating, via a metric generator, a kernel-based ergodic metric based on the target distribution and a candidate trajectory; C3L37 Bayesian [Gaussian kernel; Radial Basis Function, Bell shaped weighted]
generating, via a gradient generator, a kernel-based ergodic gradient having ergodicity based on the kernel-based ergodic metric; C15L10; The performance of the two AMP-UD implementations was compared to (i) the universal CS recovery algorithm SLA-MCMC; and (ii) the empirical Bayesian [Gaussian based ergodic kernal] message passing approaches EM-GM-AMP-MOS for i.i.d. inputs and turboGAMP for non-i.i.d. inputs. The results for other CS algorithms such as compressive sensing matching pursuit (CoSaMP), gradient projection for sparse reconstruction (GPSR) C17L4; AMP-UD is designed to reconstruct stationary ergodic signals. Regarding “kernel-based ergodic gradient having ergodicity” C15L5; distortion ratio (SDR=10 log.sub.10[custom character[X.sup.2]/MSE]), where the MSE is averaged over 50 random draws of x, A, and z.
but does not teach
generating, via a controller, a trajectory based on the kernel-based ergodic gradient; and
implementing, via a trajectory controller, the trajectory for the robot.
However; Ebrahimi teaches
generating, via a controller, a trajectory based on the kernel-based ergodic gradient; and Ebrahimi 67; the motion planning functionality 112 may generate the motion plan 210 based at least in part on the trajectory 242 and provide the motion plan 210 to the motion control functionality 110, Also 86; a kernel function over the trajectories. Thus, SVGD may help ensure a diverse set of samples, while leveraging parallel gradient-based optimization.
implementing, via a trajectory controller, the trajectory for the robot. Ebrahimi 67; the motion planning functionality 112 may generate the motion plan 210 based at least in part on the trajectory 242 and provide the motion plan 210 to the motion control functionality 110,
Therefore, it was well known at the time the invention was filed and would have been obvious to one of ordinary skill in the art to combine the teachings with a reasonable expectation for the purpose of light weight and real time ALAM for robots such that the claimed invention as a whole would have been obvious.
2. The computer-implemented method for kernel-based ergodic search using the robot of claim 1, wherein the kernel-based ergodic metric is based on a delta kernel. C11L15; stationary ergodic signals with bounded components, the optimal estimation error [difference or delta] among all sliding-window denoising schemes despite not knowing the prior for the signal. When the error metric is square error, the optimal error is the MMSE [minimum mean square error expectation of gaussian i.e. Gaussian Kernel].
3. The computer-implemented method for kernel-based ergodic search using the robot of claim 1, wherein the kernel-based ergodic metric is based on an L2 distance between the target distribution and a spatial empirical distribution of the candidate trajectory.C9L28; the initialization to examine the maximal distance between each symbol of the input data sequence and the current initialization of the μ.sub.s's ,distance is greater than 0.1σ.sub.q [L2 distance from Gaussian mean] then a Gaussian component whose mean is initialized as the value of the symbol being examined is added
4. The computer-implemented method for kernel-based ergodic search using the robot of claim 1, wherein the kernel-based ergodic metric includes an information maximization element and a uniform coverage element. Ebrahimi 510; vertices and generates plane equations associated with geometric primitive defined by vertices [areas to be covered i.e. coverage mask]; plane equations are transmitted to a coarse [maximization of element i.e. max coverage low resolution as opposed to fine elements min coverage high res] raster engine to generate coverage information (e.g., an x, y coverage mask for a tile) for primitive;
5. The computer-implemented method for kernel-based ergodic search using the robot of claim 1, wherein the kernel-based ergodic metric is formulated as a Gaussian kernel. C4L50 ergodic Gaussian kernel fomula i.e. E^(-(X-XI)^2)2O^2)
6. The computer-implemented method for kernel-based ergodic search using the robot of claim 1, comprising performing kernel parameter selection for the kernel-based ergodic metric based on a kernel parameter selection objective function by minimizing a derivative of one or more independent and identically distributed (IID) samples from the target distribution with respect to the kernel-based ergodic metric. Fig.1 and C4L10 with the GM-based [ergodic Gaussian kernel model] i.i.d. denoiser η.sub.iid,t(⋅).
7. The computer-implemented method for kernel-based ergodic search using the robot of claim 1, comprising generating the trajectory based on iteratively optimizing a descent direction of a kernel ergodic control objective associated with the kernel-based ergodic metric with a quadratic cost. Ebrahimi 76; The motion planning functionality 112 may modify the candidate distribution iteratively to obtain a set of candidate distributions (represented by a variable Q) and select, as the trajectory distribution, the candidate distribution associated with a minimum divergence [minimum quadratic cost]; 73; corresponds to the cumulative running cost and may be selected based on a domain (e.g., quadratic cost, obstacle cost, etc.)
8. The computer-implemented method for kernel-based ergodic search using the robot of claim 7, wherein the iteratively optimizing the descent direction is based on a linear-quadratic regulator (LQR). Ebrahimi 73; In Eq. 1 above, an expression c.sub.t(x.sub.t, u.sub.t; z) corresponds to the cumulative running cost and may be selected based on a domain (e.g., quadratic cost, obstacle cost, etc.). [Eq.1 is linear quadratic regulator as it is a summation and not exponential] Also 76; The motion planning functionality 112 may modify the candidate distribution iteratively to obtain a set of candidate distributions (represented by a variable Q) and select, as the trajectory distribution, the candidate distribution associated with a minimum divergence using Eq. 3 above
9. The computer-implemented method for kernel-based ergodic search using the robot of claim 1, wherein the kernel-based ergodic metric is generalized to a Lie group. C12L60 Lie group calculation [Lie group parameterized with Lo /l to derived a finite Lh]
10. The computer-implemented method for kernel-based ergodic search using the robot of claim 9, wherein the Lie group is a special orthogonal group SO(3) or a special Euclidean group SE(3). Ebrahimi 108-109; denotes the softmax function computed on Euclidean distances between all points.
Claims 11-15 are rejected using the same rejections as made to claims 1-5 respectively.
Claim 16 is rejected using the same rejections as made to claim 1.
Claims 17-20 are rejected using the same rejections as made to claims 6-9 respectively.
Conclusion
THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIHAR A KARWAN whose telephone number is (571)272-2747. The examiner can normally be reached on M-F 11am.-7pm.
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/SIHAR A KARWAN/Examiner, Art Unit 3664
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