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 amendments filed on April 20, 2026. Claims 5-8 are currently pending, and Claims 5, and 7-8 being amended.
Response to Amendments
In response to applicant’s amendments, filed April 20, 2026, the Examiner withdraws the previous 35 U.S.C. 112 rejections, withdraws the previous 35 U.S.C. 101 rejections, and withdraws the previous 35 U.S.C. 102 and 103 rejections.
Response to arguments
Applicant's arguments filed April 20, 2026, with respect to the teachings of “a quality value” (see page 5 of instant arguments), have been fully considered but they are not persuasive. Philips teaches a method for generating trajectories, where the vehicle computing system can predict one or more actions from other drivers or operators and may generate one or more confidence levels about the motion of other vehicles in the environment, and adjust the determined score for a particular trajectory in response (i.e. determines a degree of reliability of a detection value) (see at least Paragraphs [0067], [0128] of Philips). A confidence level is used to indicate a degree of reliability that the prediction is correct. Philips teaches that a confidence value (i.e., quality value) for detection of objects and predictions of movements is determined. As such, the Examiner is unpersuaded and maintains the corresponding rejections relating to Philips.
Applicant's arguments filed April 20, 2026, with respect to the teachings of “a minimally permissible acceleration” (see page 6 of instant arguments), have been fully considered but they are not persuasive. Philips teaches a method for generating trajectories, where the vehicle computing system an calculate for each position along a path, an acceleration value for that moment, such that the vehicle slows to preserve its ability to stop when conflicting predictions are likely (i.e. determines acceptable acceleration values when coming up on a detected object) (see at least Paragraphs [0059]-[0060], [0069] of Philips). Gutjahr, more explicitly teaches that a minimum required acceleration is determined such that vehicle reduces jerk when coming upon an object or changing trajectories for example (see at least Paragraphs [0016], [0019] of Gutjahr). Philips, in view of Gutjahr, teaches determining a reliability level of a detection, and determines a minimal acceleration for the vehicle, and the combination teaches the features of the claims, as they are written. As such, the Examiner is unpersuaded, and maintains the corresponding rejections.
The remaining arguments are essentially the same as those addressed above and/or below and are unpersuasive for essentially the same reasons. Therefore, the corresponding rejections are maintained.
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.
Claims 5-7 are rejected under 35 U.S.C. 103 as being anticipated by U.S. 2021/0114617 A1, to Phillips, et al (hereinafter referred to as Phillips; previously of record), in view of German Patent Publication No. 102016218121 A1, to Gutjahr, et al (hereinafter referred to as Gutjahr; newly of record).
As per Claim 5, Phillips discloses the features of a method for planning a target trajectory for an automatically driving vehicle (e.g. Paragraphs [0037], [0039]; where the system generates one or more trajectories that can be used to direct an autonomous vehicle from a first position to a second position), the method comprising:
predetermining a number of trajectory candidates for a predetermined planning horizon (e.g. Paragraphs [0039]-[0040]; where the vehicle computing system may generate a plurality of candidate trajectories based on the sensor data and the initial path or over a particular time for which the vehicle may travel along the trajectory), wherein
each of the number of trajectory candidates predetermines a path the automatically driving vehicle is to follow upon selecting the trajectory candidate as the target trajectory (e.g. Paragraphs [0035], [0039]; where the vehicle computing system can generate candidate trajectories for the autonomous vehicle to follow as it traverses the route, by defining the spatial path and steering qualities for the vehicle to follow for each trajectory), and
an acceleration with which the vehicle is to follow on the path of the respective one of the number of trajectory candidates (e.g. Paragraphs [0026], [0041]-[0042]; where each trajectory can include a velocity profile and an offset profile, where the velocity profile describes one or more acceleration values and associated timing information);
detecting objects within the predetermined planning horizon (e.g. Paragraph [0026], [0051], [0102]; where the vehicle computing system can utilize sensor data to detect, classify, and track objects and predict future movement of the detected objects);
respectively allocating trajectory costs to each of the number trajectory candidates using a predetermined cost function (e.g. Paragraphs [0027], [0048]; where the vehicle computing system can determine for each respective trajectory, a cost associated with the respective candidate trajectory), wherein
the predetermined cost function comprises object costs depending on the detected objects (e.g. Paragraphs [0048]-[0049], [0051]; where the vehicle computing system can score each trajectory using one or more cost functions, which consider costs for avoiding object collision, actor caution costs, behavioral blocking costs, costs associated with overtaking another actor, and object collision detection costs), wherein
object costs of an object for a trajectory candidate increase with decreasing distance between the object and the trajectory candidate (e.g. Paragraphs [0053], [0058]-[0059]; where the cost generated for each candidate trajectory may be based on determining whether an object is tracking the same path as the autonomous vehicle, determine if the distance between the autonomous vehicle and the object is between a particular distance, and associate a cost to the candidate trajectory based on the distance, where trajectories that pass too close to other vehicles have an increased cost that increases the longer the distance is too close);
selecting the target trajectory based on the trajectory costs of the number of trajectory candidates (e.g. Paragraphs [0027]-[0029]; where the vehicle computing system may select the candidate trajectory with the lowest determined cost, and the selected trajectory is provided to the vehicle controller), and
autonomously controlling the vehicle along the selected trajectory (e.g. Paragraphs [0029], [0036]; where once a candidate trajectory has been selected and optimized, the data associated with the trajectory can be communicated to a vehicle controller, which can cause the autonomous vehicle to move in accordance with the trajectory; and the vehicle can select a trajectory that allows the autonomous vehicle to navigate a specific segment of the route, by translating specific control signals for the autonomous vehicle (e.g., adjust steering, braking, velocity, and so on)); wherein
for each of the detected objects, a quality value is determined, the quality value specifying a measure for a degree of reliability of the detection of the respective one of the detected objects (e.g. Paragraph [0067]; where the vehicle computing system can predict one or more actions from other drivers or operators and may generate one or more confidence levels about the motion of other vehicles in the environment, and adjust the determined score for a particular trajectory in response), and
for each of the detected objects, depending on the quality value for the respective one of the detected objects, a minimally permissible acceleration is determined with which the automatically driving vehicle may be braked if the vehicle follows one of the number of trajectories including the respective one of the detected objects (e.g. Paragraphs [0048], [0054], [0059]-[0060], [0069]; where the behavioral blocking cost function can generate a penalty for trajectories that bring the autonomous vehicle within a certain distance of the object and can limit braking to a certain amount, such that the autonomous vehicle will pass, rather than braking too hard; and where the vehicle computing system an calculate for each position along a path, an acceleration value for that moment, such that the vehicle slows to preserve its ability to stop when conflicting predictions are likely (i.e. determines acceptable acceleration values when coming up on a detected object)), wherein
the trajectory candidates and the object costs determined for the trajectory candidates are evaluated depending on the acceleration predetermined by the respective trajectory candidate and depending on the minimally permissible acceleration that the vehicle may be braked if the vehicle follows one of the number of trajectories including the respective one of the detected objects (e.g. Paragraphs [0027], [0029], [0048]; where the vehicle computing system can determine a cost for each respective candidate trajectory based on collision avoidance, or costs associated with avoiding potential collision, costs for minimizing the speed or energy of impact, or costs for maintaining speed with gentle acceleration, etc.), and wherein
the selection of the target trajectory accounts for the evaluation of the trajectory costs and the object costs determined for the trajectory costs (e.g. Paragraphs [0027]-[0029]; where the vehicle computing system may select the candidate trajectory with the lowest determined cost, and the selected trajectory is provided to the vehicle controller).
Gutjahr, more explicitly teaches the features of for each of the detected objects, depending on the quality value for the respective one of the detected objects, a minimally permissible acceleration is determined with which the automatically driving vehicle may be braked if the vehicle follows one of the number of trajectories including the respective one of the detected objects.
Gutjahr, in a similar field of endeavor, teaches a method for planning vehicle motion based on optimized driving trajectories, where the safety-relevant criterion restriction can be extended to achieve an optimal comprise between a jerk-optimized longitudinal movement and a simultaneously minimum required acceleration; and the algorithm plans a braking maneuver close to an acceleration limit right from the start (e.g. Paragraphs [0016], [0019]).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the method for optimizing vehicle trajectories in the system of Phillips, with the feature of determining minimum acceleration criteria in the system of Gutjahr, in order to provide comfortable deceleration profiles (see at least Paragraphs [0016], [0019] of Gutjahr).
As per Claim 6, Phillips, in view of Gutjahr, teaches the features of Claim 5, and Phillips further discloses the features of wherein a trajectory candidate of the number of trajectory candidates with a lowest trajectory costs is selected as the target trajectory from the number of trajectory candidates (e.g. Paragraphs [0027]-[0029]; where the vehicle computing system may select the candidate trajectory with the lowest determined cost, and the selected trajectory is provided to the vehicle controller).
As per Claim 7, Phillips, in view of Gutjahr, teaches the features of Claim 5, and Phillips further discloses the features of wherein
the evaluation of the trajectory candidates and the object costs determined for the trajectory candidates involves a categorization (e.g. Paragraphs [0057], [0064], [0070]; where the costs for a particular trajectory is generated based on the degree to which the trajectory follows established criteria for evaluating trajectories and for costs associated with a spatial relationship between the particular trajectory and other objects in the environment; and where the cost for each trajectory can be based on vehicle motion policies that represent legal rules that govern the area in which the autonomous vehicle is traveling, and may have one or more motion policies not related to illegal restrictions (i.e. categorizes and prioritizes different vehicle policies)),
the categorization categorizes the number of trajectory candidates into an unfiltered category and a filtered category (e.g. Paragraphs [0057], [0070], [0111], [0138]; where the cost function can be safety-critical or not safety-critical, or may be related to legal rules or illegal restrictions; and where the motion planning system can obtain an initial travel path, which represents an ideal travel path from a first position to a second position without regard to any objects that may be in the current environment but are not included in the existing map data (i.e. unfiltered), and candidate trajectories can be removed which are not feasible, such that they will not be selected unless no other trajectories are possible (i.e. filtered)),
all object costs and the corresponding trajectory candidates are allocated to the unfiltered category (e.g. Paragraphs [0057], [0070], [0111]; where the cost function can be safety-critical or not safety-critical, or may be related to legal rules or illegal restrictions; and where the motion planning system can obtain an initial travel path, which represents an ideal travel path from a first position to a second position without regard to any objects that may be in the current environment but are not included in the existing map data (i.e. unfiltered)), and
only those object costs and the corresponding trajectory candidates are allocated to the filtered category for which a minimum value of the acceleration predetermined by the respective trajectory candidate is greater than the minimally permissible acceleration if the vehicle follows one of the number of trajectories including the respective detected object (e.g. Paragraphs [0051]-[0052], [0138]; where candidate trajectories can be removed such that they will not be selected unless no other trajectories are possible (i.e. filtered); and where the object collision detection cost function can output the relative speed and velocity of the object when predicting a collision to determine the cost of each trajectory).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Phillips, in view of Gutjahr, as applied to Claim 7 above, and further in view of U.S. Patent Publication No. 2022/0219726 A1, to Yadmellat, et al (hereinafter referred to Yadmellat; previously of record).
As per Claim 8, Phillips, in view of Gutjahr, teaches the features of Claim 7, and Phillip further discloses the features of wherein,
responsive to selecting the target trajectory, a trajectory candidate of the number of trajectory candidates with the lowest trajectory costs is respectively selected from the unfiltered category and the filtered category (e.g. Paragraphs [0027]-[0029]; where the vehicle computing system may select the candidate trajectory with the lowest determined cost, and the selected trajectory is provided to the vehicle controller),
the minimum value of the acceleration predetermined by the respective trajectory candidate is respectively determined for the trajectory candidates with the lowest trajectory costs in the unfiltered category and the filtered category (e.g. Paragraphs [0057], [0070], [0111], [0138]; where the cost function can be safety-critical or not safety-critical, or may be related to legal rules or illegal restrictions; and where the motion planning system can obtain an initial travel path, which represents an ideal travel path from a first position to a second position without regard to any objects that may be in the current environment but are not included in the existing map data (i.e. unfiltered), and candidate trajectories can be removed which are not feasible, such that they will not be selected unless no other trajectories are possible (i.e. filtered); and trajectories with the lowest calculated cost are selected).
Phillips, in view of Gutjahr, fails to teach every feature of a trajectory candidate of the trajectory candidates with the lowest trajectory costs in the unfiltered category and the filtered category with a greatest minimum value of the acceleration is selected as the target trajectory.
However, Yadmellat, in a similar field of endeavor, teaches a method for evaluating trajectories for a vehicle, where trajectories that fail to satisfy the physical limit criterion may be rejected or filtered if at least one candidate trajectory does not satisfy the physical limit criterion, and the candidate trajectories are then ranked and prioritized into the a higher evaluation value category; and where the trajectory evaluator may perform a secondary trajectory sorting within each category using an overall cost function so that the entire set of candidate trajectories constitutes a sorted plurality of candidate trajectories, which are ordered by their satisfaction of various criteria, such that the trajectory selector may select the first (i.e. lowest-cost) trajectory of the first value category (i.e. the greatest minimum of the filtered or unfiltered categories is selected) (e.g. Paragraphs [0087]-[0088], [0099], [0102]; Tables 1, 3).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the method for optimizing vehicle trajectories in the system of Phillips, in view of Gutjahr, with the feature of selecting the trajectory with the minimum value in the system of Yadmellat, in order to look for the best candidate trajectory to follow (see at least Paragraph [0075] of Yadmellat).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 MERRITT LEVY whose telephone number is (571)270-5595. The examiner can normally be reached Mon-Fri 0630-1600.
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/MERRITT LEVY/Examiner, Art Unit 3663
/KYLE J KINGSLAND/Primary Examiner, Art Unit 3663
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