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 .
Summary
This communication is a First Office Action Non-Final Rejection on the merits.
Claims 1 – 20 are currently pending and considered below.
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.
Claims 1 – 10 and 12 – 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Houston et al. (Hereinafter Houston) (US 2021/0197720 A1).
As per claim 1, Houston teaches the limitations of:
a method comprising:
obtaining, by an autonomous vehicle, scene data characterizing a scene in an environment at a current time point (See at least abstract; a method includes accessing contextual data associated with a vehicle, the contextual data being captured using one or more sensors associated with the vehicle and including perception data, generating, based on at least a portion of the perception data, one or more representations of an environment of the vehicle, determining a predicted risk score by processing the one or more representations of the environment of the vehicle using a machine-learning model, wherein the machine-learning model has been trained using human-driven vehicle risk observations and corresponding representations of environments associated with the observations, determining that one or more vehicle operations are to be performed based on a comparison of the predicted risk score to a threshold risk score, and causing the vehicle to perform the one or more vehicle operations based on the predicted risk score and the threshold risk score.);
receiving route data specifying an intended route through the environment for the autonomous vehicle after the current time point (See at least paragraph 37; a machine-learning model may be trained to recognize unusual conditions in vehicle environments. For example, a machine-learning model may be trained to determine a probability of a collision for a particular environment represented by input from sensors (e.g., cameras, Inertial Measurement Unit (IMU), steering angle, braking force, and the like). The vehicle's trajectory may also be determined based on the sensor input and used as input to the model.);
processing the route data and the scene data using a planner neural network to generate a plurality of candidate control trajectories, wherein each candidate control trajectory comprises respective controls for a controller for the autonomous vehicle at each of a plurality of future time points that are after the current time point (See at least paragraph 88; FIG. 2C illustrates an example vehicle system 200 having an example prediction module 215 that predicts appropriate target speeds 236 based on images 214 of the environment and predicted future locations of the vehicle and/or agents. The image-based prediction module 215 and smoothing filter 234 are described above with reference to FIG. 2B. FIG. 2C shows additional details of the image-based prediction module 215, including a trajectory-predicting neural network 216 that generate predicted trajectories 218 to be provided as input to the image-based speed-predicting neural network 230. The predicted trajectories 218 may be added to (e.g., rendered in) the images 214 to form augmented images 226, which may be provided to the image-based speed-predicting neural network 230. The augmented images 226 may include one or more previous images 214 generated at previous times, e.g., to provide a representation of changes in position of the vehicle and agents over time as input to the speed-predicting neural network 230. The speed-predicting neural network 230 may generate the predicted vehicle speed 232 based on the augmented images 226, so that the predicted trajectories 218 and/or previous images 214 are used as factors in generating the predicted vehicle speed 232. Alternatively or additionally, the speed-predicting neural network 230 may generate the predicted vehicle speed 232 based on the images 214 (e.g., without predicted trajectories 218 and/or without past images). While the trajectory-predicting neural network 216 and the speed-predicting neural network 230 are shown and described as being separate neural networks, one of ordinary skill in the art would appreciate that the functions of the neural networks 216 and 230 described herein may be performed by a single neural network or by any suitable configuration of one or more machine-learning models, which may be neural networks or other types of machine-learning models. Further, although future trajectories of the vehicle are described as being generated based on predictions, future trajectories may be generated using any suitable technique.);
generating, using the candidate control trajectories, a final control trajectory (See at least paragraph 95; one or more of the scoring criteria may involve comparison of an attribute of a candidate trajectory plan, such as a planned speed, to a signal 242. The comparison may be performed by the cost function 246, which may calculate a score for each candidate trajectory plan based on a difference between an attribute of the candidate trajectory plan and a value of a signal 242. The planning module 240 may calculate the score of a candidate trajectory plan as a sum of individual scores, where each individual score is for a particular one of the scoring criteria. Thus, each of the individual scores represents a term in the sum that forms the score for the candidate trajectory plan. The planning module 240 may select the candidate trajectory plan that has the highest total score as the trajectory plan 248 to be used by the vehicle.); and
controlling the autonomous vehicle using the final control trajectory (See at least paragraph 95 and 97; the plan generator 244 may determine one or more points 252 of the trajectory plan 248. The points 252 may form a navigation path for the vehicle. The points 252 may be successive locations on the trajectory. The plan generator 244 may also determine one or more speeds 250, which may include a constant speed 254 for the vehicle to use for the trajectory plan 248, or multiple different speeds 254 for the vehicle to use at the different corresponding points 252. Three points 252A, 252B, and 252N are shown in the trajectory plan 248. One or more speeds 250 may be associated with the trajectory plan 248. If the trajectory plan 248 is associated with a constant speed 253, each of the points 252 may be associated with the same constant speed 253. Alternatively, each of the points 252 may be associated with a corresponding speed 254, in which case each point 252 may be associated with a different speed value (though one or more of the speeds 254 may have the same values). Three speeds 254A, 254B, and 254N are shown, which are associated with the respective points 252A, 252B, 252N. The trajectory plan 248 and/or the speeds 250 may correspond to driving operations, such as operations that specify amounts of acceleration, deceleration, braking, steering angle, and so on, to be performed by the vehicle. The driving operations may be determined by the planning module 240 or the control module 1725 based on the trajectory plan 248 and speeds 250.).
As per claim 2, Houston teaches the limitations of:
wherein the respective controls at each future time point comprise: a first control input that specifies a change in a heading of the autonomous vehicle as of the future time point (See at least paragraph 97 and 165).
As per claim 3, Houston teaches the limitations of:
wherein the respective controls at each future time point comprise: a second control input that specifies a change in a longitudinal displacement of the autonomous vehicle as of the future time point (See at least paragraph 97 and 165).
As per claim 4, Houston teaches the limitations of:
wherein generating, using the candidate control trajectories, a final control trajectory comprises:
selecting one of the plurality of candidate control trajectories (See at least paragraph 95 – 96); and
generating the final control trajectory from the selected candidate control trajectory (See at least paragraph 97).
As per claim 5, Houston teaches the limitations of: wherein the planner neural network generates a respective likelihood score for each of the candidate control trajectories (See at least abstract and paragraph 94 – 96), and wherein selecting one of the plurality of candidate control trajectories comprises:
selecting the candidate control trajectory based on the respective likelihood scores for the candidate control trajectories and on a set of driving criteria (See at least abstract and paragraph 94 – 96).
As per claim 6, Houston teaches the limitations of:
obtaining respective trajectory predictions for each of one or more agents in the environment at the current time point, wherein one or more of the set of driving criteria include one or more criteria that, for each candidate control trajectory, compare a trajectory of the autonomous vehicle defined by the candidate control trajectory to the trajectory predictions for the one or more agents (See at least abstract and paragraph 94 – 96).
As per claim 7, Houston teaches the limitations of:
wherein the planner neural network generates the respective trajectory predictions for each of the one or more agents (See at least paragraph 99 and 111).
As per claim 8, Houston teaches the limitations of:
wherein obtaining respective trajectory predictions for each of the one or more agents comprises obtaining the respective trajectory predictions from a behavior prediction system (See at least paragraph 99 – 101and 108).
As per claim 9, Houston teaches the limitations of:
wherein selecting the candidate control trajectory based on the respective likelihood scores for the candidate control trajectories and on a set of driving criteria comprises:
post-processing each candidate control trajectory by adjusting a geometry of the candidate control trajectory to generate an adjusted control trajectory (See at least paragraph 106 – 108); and
applying the set of driving criteria to the adjusted control trajectories (See at least paragraph 106 – 108).
As per claim 10, Houston teaches the limitations of:
wherein generating the final control trajectory from the selected candidate control trajectory comprises:
post-processing the selected candidate control trajectory by adjusting a geometry of the selected candidate control trajectory to generate an adjusted control trajectory (See at least paragraph 106 – 108); and
selecting, as the final control trajectory, the adjusted control trajectory (See at least paragraph 134).
Regarding claims 12 – 20:
Claims 12 – 20 are rejected using the same rationale, mutatis mutandis, applied to claims 1 – 10 above, respectively.
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 11 is rejected under 35 U.S.C. 103 as being unpatentable over Houston in view of Philion et al. (Hereinafter Phlion) (US 2024/0092390 A1).
As per claim 11, Houston does not teach the limitations of:
determining that none of the candidate controls sequences satisfy a set of driving criteria ; and
in response, generating a default control trajectory; and
selecting the default control trajectory as the final control trajectory.
Philion teaches the limtiations of:
determining that none of the candidate controls sequences satisfy a set of driving criteria ; and in response, generating a default control trajectory; and selecting the default control trajectory as the final control trajectory (See at least paragraph 36 – 38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include determining that none of the candidate controls sequences satisfy a set of driving criteria ; and in response, generating a default control trajectory; and selecting the default control trajectory as the final control trajectory as taught by Philion in the system of Houston, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
Floor et al. (US 2023/0339505 A1) discloses bidirectional path optimization in a grid.
Shoemaker et al. (US 2025/0058803 A1) discloses courtesy lane selection paradigm.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IG T AN whose telephone number is (571)270-5110. The examiner can normally be reached M - F: 10:00AM- 4:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached at (571) 270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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IG T AN
Primary Examiner
Art Unit 3662
/IG T AN/Primary Examiner, Art Unit 3662