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 . 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.
Joint Inventors
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.
Priority/Benefit
Acknowledgment is made of applicant’s claim for benefit of previously filed provisional application 63/429,936 with an effective filing date of 12/02/2022. Examiner has verified that the subject matter of the instant application is supported by the earlier filed provisional application, and as such, the earlier filed date of 12/02/2022 is granted.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/22/2025 has been entered.
Status of Claims
This action is in response to Applicant’s Request for Continued Examination filed on 12/22/2025. Claims 1-23 are pending and examined below. Examiner notes that as stated in the Advisory Action dated 12/16/2025, the previously stated 35 U.S.C. 101 rejections would be overcome by the entering of a portion of the proposed after-final amendments. As those amendments have now been entered, the 35 U.S.C. 101 rejections have been overcome and the associated sections have been removed from the current action.
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.
Claims 1-23 are rejected under 35 U.S.C. 103 as being obvious over Lambert et al., US 20210334630 A1, herein referred to as Lambert, in view of Guo et al., US 20220105625 A1, herein referred to as Guo, and further in view of Sinyavskiy et al., US 20220026911, herein referred to as Sinyavskiy.
Regarding claim 1, Lambert discloses obtaining a set of terminal states of at least a portion of a device that would result if the portion of the device were to be moved in accordance with a set of trajectories (Fig. 2, Paragraphs 0061, 0066, 0097; a set of trajectories may be established for an autonomous vehicle navigating from an initial state to a final state, each trajectory of the set of trajectories has an associated predicted final state (see Fig. 2 for trajectories that do and do not reach the target region X), each trajectory solution constitutes a posterior distribution q(θ) of particles, where the particles are potential parameter states and actions of the vehicle at a given time), determining costs based at least in part on a measure of by how much the set of terminal states differs from a set of goal states within a goal region (Paragraphs 0083-0087; Stein Variational Inference may be used to optimize the trajectories (distributions), a target distribution p(θ) may be compared to a posterior distribution q(θ) by minimizing a KL divergence, a target distribution may be one in which the obstacles are avoided (see Fig. 2 with at least two different trajectories converging on the destination X that avoid the square obstacles), the trajectories that clear the obstacles and reach destination X may each have associated goal states, goal states could be positions at or around X where the vehicle has successfully avoided the obstacles, the result of the KL divergence may be considered a cost associated with each distribution), selecting a trajectory from the set of trajectories (Paragraphs 0083-0087, 0105; the candidate distribution may be selected based on which distribution results in the smallest KL divergence; the robot may be controlled based on these control inputs), and causing the portion of the device to move along the selected trajectory (Paragraphs 0083-0087, 0105; the candidate distribution may be selected based on which distribution results in the smallest KL divergence; the robot may be controlled based on these control inputs), but fails to disclose determining costs based at least in part on a measure of by how much the set of terminal states differs from a set of goal states within a goal distribution, and selecting a trajectory from a set of trajectories based at least in part on the costs.
However, Guo, in an analogous field of endeavor, teaches determining costs based at least in part on a measure of by how much the set of terminal states differs from a set of goal states within a goal distribution (Paragraph 0060; an end condition model may be used to establish a distribution for probable end states of the robot; multiple trajectories may be determined based on the initial and end distributions). Therefore, from the teaching of Guo, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified, with a reasonable expectation for success, the robotic system of Lambert to include determining costs based at least in part on a measure of by how much the set of terminal states differs from a set of goal states within a goal distribution, as taught/suggested by Guo. The motivation to do so would be to define a desired distribution of end states for the robot to achieve which can give a better cost analysis during trajectory evaluation which can lead to higher accuracy control.
Additionally, Sinyavskiy, in an analogous field of view, teaches selecting a trajectory from a set of trajectories based at least in part on the costs (Paragraph 0037; trajectories may have costs associated with them; trajectories may be chosen based on a minimization of cost). Therefore, from the teaching of Sinyavskiy, it would have been obvious to one of ordinary skill in the art before the effective filing date to have further modified, with a reasonable expectation for success, the robotic system of Lambert and Guo to include selecting a trajectory from a set of trajectories based at least in part on the costs, as taught/suggested by Sinyavskiy. The motivation to do so would be to select the best trajectories with the lowest costs. This can reduce not only the time of travel, but also allow for better obstacle avoidance and less computation performed.
Regarding claim 2, Lambert, in view of Guo, and further in view of Sinyavskiy renders obvious all the limitations of claim 1. Lambert further discloses the trajectory comprises a sequence of states of the device and actions to be performed by the device (Fig. 2, Paragraphs 0061, 0066, 0097; each trajectory (posterior distribution) is comprised of potential states and actions at given times).
Regarding claim 3, Lambert, in view of Guo, and further in view of Sinyavskiy renders obvious all the limitations of claim 1. Lambert further discloses obtaining the set of goal states by simulating moving the portion of the device to simulated states within the goal region and selecting desirable ones of the simulated states as the set of goal states (Fig. 2, Paragraphs 0061, 0066; goal states (states where a trajectory has avoided the obstacles and reached the target region X) may be determined based on a set of trajectories whose states and actions are predicted (see Fig. 2 for simulated trajectories of an autonomous vehicle navigating from an initial state to a goal state), desirable goal states may be those that avoid all obstacles and reach target region X), but fails to disclose obtaining the set of goal states by simulating moving the portion of the device to simulated states within the goal distribution and selecting desirable ones of the simulated states as the set of goal states. However, the obviousness of using a goal distribution is shown in the rationale for claim 1 and would be applicable here as well.
Regarding claim 4, Lambert, in view of Guo, and further in view of Sinyavskiy renders obvious all the limitations of claim 1. Lambert further discloses obtaining the set of goal states from one or more human demonstrations (Paragraph 0195; vehicle may obtain destination changes for the vehicle based on lip reading of the human, different destinations can be considered a set of goal states for the vehicle, a user voicing their desire for a destination change can be considered a human demonstration).
Regarding claim 5, Lambert, in view of Guo, and further in view of Sinyavskiy renders obvious all the limitations of claim 1. Lambert further discloses the set of trajectories is determined using Stein Variational Inference (Paragraph 0066; the set of trajectories may be determined using Stein Variational Inference).
Regarding claim 6, Lambert, in view of Guo, and further in view of Sinyavskiy renders obvious all the limitations of claim 1. Lambert further discloses generating a motion plan to position the portion of the device based at least in part on the trajectory (Paragraph 0066; an autonomous vehicle may navigate to the destination region X using the best trajectory based on the KL divergence minimization).
Regarding claim 7, Lambert, in view of Guo, and further in view of Sinyavskiy renders obvious all the limitations of claim 1. Lambert further discloses calculating the measure using at least one of a Kullback-Leibler (KL) divergence, cross entropy, Kernel Maximum Mean Discrepancy, Smooth K-Nearest Neighbor, Stein Discrepancy, or Energy Statistics (Paragraphs 0083-0087; KL divergence may be used to determine differences between a posterior distribution and a target distribution).
Regarding claim 8, the claim limitations are similar to those in claim 1 and are rejected using the same rationale as seen in claim 1.
Regarding claim 9, the claim limitations are similar to those in claim 7 and are rejected using the same rationale as seen in claim 7.
Regarding claim 10, the claim limitations are similar to a portion of those in claim 1 and are rejected using the same rationale as seen in claim 1.
Regarding claim 11, the claim limitations are similar to those in claim 3 and are rejected using the same rationale as seen in claim 3.
Regarding claim 12, the claim limitations are similar to those in claim 4 and are rejected using the same rationale as seen in claim 4.
Regarding claim 13, the claim limitations are similar to those in claim 5 and are rejected using the same rationale as seen in claim 5.
Regarding claim 14, the claim limitations are similar to those in claim 6 and are rejected using the same rationale as seen in claim 6.
Regarding claim 15, the claim limitations are similar to those in claim 1 and are rejected using the same rationale as seen in claim 1. Additionally, Lambert discloses at least one processor (see at least Fig. 9C, item 910).
Regarding claim 16, the claim limitations are similar to those in claim 7 and are rejected using the same rationale as seen in claim 7.
Regarding claim 17, the claim limitations are similar to a portion of those in claim 1 and are rejected using the same rationale as seen in claim 1.
Regarding claim 18, the claim limitations are similar to those in claim 3 and are rejected using the same rationale as seen in claim 3.
Regarding claim 19, the claim limitations are similar to those in claim 4 and are rejected using the same rationale as seen in claim 4.
Regarding claim 20, the claim limitations are similar to those in claim 5 and are rejected using the same rationale as seen in claim 5.
Regarding claim 21, Lambert, in view of Guo, and further in view of Sinyavskiy renders obvious all the limitations of claim 15. Lambert further discloses the device is an autonomous machine or a semi-autonomous machine (Paragraphs 066, 0144; device may be an autonomous vehicle which is a machine).
Regarding claim 22, Lambert, in view of Guo, and further in view of Sinyavskiy renders obvious all the limitations of claim 15. Lambert further discloses the device is an autonomous vehicle (Paragraphs 066, 0144; device may be an autonomous vehicle).
Regarding claim 23, Lambert, in view of Guo, and further in view of Sinyavskiy renders obvious all the limitations of claim 15. Lambert further discloses the device is an aerial drone, a cleaning device, a robot, a legged robot, or a walking robot (Paragraphs 066, 0144; device may be an autonomous vehicle such as an airplane which is a drone).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 8, and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER ALLEN BUKSA whose telephone number is (571)272-5346. The examiner can normally be reached M-F 7:30 AM-4:30 PM.
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/CHRISTOPHER A BUKSA/Examiner, Art Unit 3658