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
The Amendment filed on 20 April 2026 has been acknowledged.
Claims 1 and 11 are amended.
Claim 10 is cancelled.
Currently, claims 1 – 9 and 11 are pending and considered as set forth.
Response to Amendment
Applicant’s amendments to the claims are sufficient to overcome the 35 U.S.C. 101 rejections set forth in the previous office action.
Response to Arguments
Regarding 35 U.S.C. 103 rejections,
The Examiner notes, said applicant’s claim amendment, necessitated the new grounds of rejection. Claims 1 and 11 remain rejected under 35 U.S.C. 103(a) as being unpatentable over Das et al. and in further view Shalev-Shwartz et al., however after a reevaluation of the references, in view of said applicant’s claim amendment, a rearrangement of the rejection occurred.
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 1 – 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. (Hereinafter Das) (US 11663726 B2) in view of Shalev-Shwartz et al. (Hereinafter Shalev) (US 2021/0171023 A1)
As per claim 1, Das teaches limitations of: a method for ascertaining a dynamic foreign object-driving corridor association using an image sensor of an ego vehicle, the method comprising the following steps:
generating images of an environment of the ego vehicle using the image sensor (See at least column 1 line 13 – 15; Autonomous vehicle may use sensors to capture data regarding an environment through which the autonomous vehicle traverses);
using the images in a native measuring space of the image sensor (See at least abstract, column 1 line 13 – 15 and column 3 line 10 – 21; Tracking a current and/or previous position, velocity, acceleration, and/or heading of an object using sensor data may comprise determining whether to associate a current object detection generated from recently received (e.g., current) sensor data with a previous object detection generated from formerly received sensor data. In other words, a track may identify that an object detected in former sensor data is the same object detected in current sensor data. However, multiple types of sensor data may be used to detect objects and some objects may not be detected by different sensor types or may be detected differently, which may confound attempts to track an object. An ML model may be trained to receive outputs associated with different sensor types and/or a track associated with an object, and determine a data structure comprising a region of interest, object classification, and/or a pose associated with the object. … multiple object detections may be generated in association with a same object in the environment. These multiple object detections may be generated by different perception pipelines, which may be associated with different sensor types. For example, a lidar perception pipeline may receive lidar data and determine an object detection associated with an object, a hybrid lidar-vision perception pipeline may receive lidar and vision data and generate a different object detection associated with the same object, a vision perception pipeline may receive image(s) from a camera and generate an additional object detection associated the same object, and so on.) performing:
ascertaining a driving corridor of the ego vehicle along which the ego vehicle will be moving from roadway information and/or using odometry (See at least column 2 line 14 – 23; In other words, a track may identify that an object detected in former sensor data is the same object detected in current sensor data. In some examples, the track may additionally or alternatively comprise various current and/or previous data about the object useful for a planning component of an autonomous vehicle to predict motion/behavior of the object and to determine a trajectory and/or path for controlling the autonomous vehicle),
detecting foreign objects (See at least abstract and column 2 line 9 – 30; Techniques for tracking a current and/or previous position, velocity, acceleration, or heading of an object using sensor data may comprise determining whether to associate a current object detection generated from recently received (e.g., current) sensor data with a previous object detection generated from formerly received sensor data. In other words, a track may identify that an object detected in former sensor data is the same object detected in current sensor data. In some examples, the track may additionally or alternatively comprise various current and/or previous data about the object useful for a planning component of an autonomous vehicle to predict motion/behavior of the object and to determine a trajectory and/or path for controlling the autonomous vehicle. For example, the track may additionally or alternatively comprise an indication of region(s) of the environment currently and/or previously occupied by the object, an object classification associated with the object (e.g., a vehicle, an oversized vehicle, a pedestrian, a cyclist), a current/or previous heading associated with the object, a current and/or previous velocity and/or acceleration of the object, and/or a current position and/or velocity of the object, though any other parameter is contemplated.), and
ascertaining at least one kinematic variable is ascertained for at least one of the foreign objects (See at least abstract and column 2 line 9 – 30);
based on the at least one kinematic variable and the driving corridor, ascertaining at least one dynamic foreign object-driving corridor association for a lateral movement of the foreign object (See at least abstract and column 2 line 9 – 30 and column 18 line 49 – 61; The multi-channel data structure 322 may be provided as input to the ML architecture 328, which may be trained to determine a final environment representation comprising one or more estimated object detection(s) 330. For example, the ML architecture 328 may determine a top-down representation of the environment comprising an indication that a portion of the environment is occupied, an ROI and/or object classification associated with the occupied portion (e.g., an object), an orientation of the object (e.g., yaw and/or yaw/heading bin), a velocity associated with the object (e.g., stationary/moving indication, a lateral and/or longitudinal velocity, yaw rate), a height associated with the object, and/or a predicted ROI associated with a future time step); and
making the at least one dynamic foreign object-driving corridor association available to a driver assistance system of the ego vehicle (See at least column 5 line 17 – 48).
However, Das does not teach the amended claim limitation of:
ascertaining a driving corridor of the ego vehicle along which the ego vehicle will be moving from roadway information represented as polygon chains or splines and/or using odometry of the ego vehicle.
Shalev teaches the limitation of:
ascertaining a driving corridor of the ego vehicle along which the ego vehicle will be moving from roadway information represented as polygon chains or splines and/or using odometry of the ego vehicle (See at least paragraph 810; the prediction of the path of the host vehicle at the motion prediction time may be based on at least a determined speed for the host vehicle and a target trajectory for the host vehicle included in a map of a road segment on which the host vehicle travels. For example, the predicted path may be generated according to a semantic high-definition mapping technology, such as REM, discussed above. As an example, the road segment on which the host vehicle is traveling may be associated with a plurality of trajectories which may be used to navigate autonomous vehicles on the road segment and the predicted path may include a position on one of the target trajectories associated with the road segment. As another example, if the host vehicle is determined to be traveling according to a predetermined target trajectory, the predicted path at the motion prediction time may include a position, speed, and/or acceleration along the same target trajectory. In some embodiments, the target trajectory may include a predetermined three-dimensional spline representative of a preferred path along at least one lane of the road segment. For example, the three-dimensional spline may include a plurality of landmarks, road features, and other objects that define the target trajectory on the road segment);
controlling the ego vehicle using the driver assistance system based on the at least one dynamic foreign object- driving corridor association, wherein the controlling of the ego vehicle includes at least one of: performing at least semiautonomous driving of the ego vehicle, performing adaptive cruise control, or performing autonomous emergency braking (See at least paragraph 158 and 324; system 100 may use two image capture devices (e.g., image capture devices 122 and 124) in providing navigation assistance for vehicle 200 and use a third image capture device (e.g., image capture device 126) to provide redundancy and validate the analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices 122 and 124 may provide images for stereo analysis by system 100 for navigating vehicle 200, while image capture device 126 may provide images for monocular analysis by system 100 to provide redundancy and validation of information obtained based on images captured from image capture device 122 and/or image capture device 124. That is, image capture device 126 (and a corresponding processing device) may be considered to provide a redundant sub-system for providing a check on the analysis derived from image capture devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). Furthermore, in some embodiments, redundancy and validation of received data may be supplemented based on information received from one more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers outside of a vehicle, etc.). … RL may be performed in a sequence of consecutive rounds. At round t, the planner (a.k.a. the agent or driving policy module 803) may observe a state, s.sub.t ∈S, which represents the agent as well as the environment. It then should decide on an action a.sub.t ∈A. After performing the action, the agent receives an immediate reward, r.sub.t∈ custom-character, and is moved to a new state, s.sub.t+1). As an example, the host vehicle may include an adaptive cruise control (ACC) system, in which the vehicle should autonomously implement acceleration/braking so as to keep an adequate distance to a preceding vehicle while maintaining smooth driving. The state can be modeled as a pair, s.sub.t=(x.sub.t, v.sub.t)∈custom-character.sup.2, where x.sub.i is the distance to the preceding vehicle and v.sub.t is the velocity of the host vehicle relative to the velocity of the preceding vehicle. The action a.sub.t ∈custom-character will be the acceleration command (where the host vehicle slows down if a.sub.t<0). The reward can be a function that depends on |a.sub.t| (reflecting the smoothness of driving) and on s.sub.t (reflecting that the host vehicle maintains a safe distance from the preceding vehicle). The goal of the planner is to maximize the cumulative reward (may be up to a time horizon or a discounted sum of future rewards). To do so, the planner may rely on a policy, π: S.fwdarw.A, which maps a state into an action.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include ascertaining a driving corridor of the ego vehicle along which the ego vehicle will be moving from roadway information represented as polygon chains or splines and/or using odometry of the ego vehicle as taught by Shalev in the system of Das, 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.
As per claim 2, Das teaches limitation of:
wherein a lateral velocity of the foreign object relative to the driving corridor is ascertained as one of the at least one kinematic variable (See at least abstract and column 2 line 9 – 30 and column 18 line 49 – 61).
As per claim 3, Das teaches limitation of:
wherein a bounding box is assigned to the foreign object in the native measuring space, a movement of the bounding box is tracked over time and the lateral velocity of the foreign object relative to the driving corridor is ascertained from the movement (See at least abstract and column 2 line 9 – 30 and line 51 – 59 and Figure 4).
As per claim 4, Das teaches limitation of:
wherein an ego movement of the ego vehicle is utilized, and based on the velocity, a relative movement of the foreign object with regard to the driving corridor is ascertained as a dynamic foreign object- driving corridor association of the at least one dynamic foreign object-driving corridor association (See at least column 2 line 17 – 23, and column 7 line 5 – 23).
Regarding claim 11:
Claim 11 is rejected using the same rationale, mutatis mutandis, applied to claim 1 above, respectively.
Claims 5 – 9 are rejected under 35 U.S.C. 103 as being unpatentable over Das and Shalev and in further view of Silva et al. (Hereinafter Silva) (US 2021/0370921 A1).
As per claim 5, Das teaches wherein a distance of the foreign object from the host vehicle is ascertained as a kinematic variable of the at least one kinematic variable (See at least column 16 line 6 – 7), but does not expressly teach limitation of:
wherein a distance of the foreign object from the driving corridor is ascertained as a kinematic variable of the at least one kinematic variable.
Silva teaches limitation of:
wherein a distance of the foreign object from the driving corridor is ascertained as a kinematic variable of the at least one kinematic variable (See at least paragraph 30 and 37).
Das and Silva both are in endeavored art of predicting the trajectories of objects and the vehicles and changing the vehicle’s action courses to avoid the potential collision with the object.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include wherein a distance of the foreign object from the driving corridor is ascertained as a kinematic variable of the at least one kinematic variable as taught by Silva in the system of Das and Shalev, 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 with motivation of calculate offset distance for the vehicle to avoid potential collision with the object (paragraph 37).
As per claim 6, the combination of Das, Shalev and Silva teaches limitation of:
foreign objects that are located outside of the driving corridor are detected in the native measuring space (Das, see at least abstract and column 2 line 9 – 30, and Silva, see at least paragraph 1), a time-to-enter corridor at which the foreign object enters the driving corridor is ascertained as a dynamic foreign object-driving corridor association of the at least one dynamic foreign object-driving corridor association (Silva, see at least paragraph 41 – 42).
As per claim 7, the combination of Das, Shalev and Silva teaches limitation of:
foreign objects that are located within the driving corridor are detected in the native measuring space, a time-to-leave corridor at which the foreign object leaves the driving corridor is ascertained as a dynamic foreign object-driving corridor association of the at least one dynamic foreign object-driving corridor association (Silva, see at least paragraph 41 – 42).
As per claim 8, the combination of Das, Shalev and Silva teaches limitation of:
wherein a ratio of s distance of the foreign object from a boundary of the driving corridor and the lateral velocity is ascertained as the time-to- enter corridor (Silva, see at least paragraph 41).
As per claim 9, the combination of Das, Shalev and Silva teaches limitation of:
at least one geometrical variable of the foreign object is used, an exit probability of the foreign object leaving the driving corridor is ascertained using the at least one geometrical variable and the time-to-leave corridor (Das, see at least figure 5 and column 2 line 54 – 57, and Silva, see at least figure 2A – 2B and paragraph 41 – 42).
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 IG T AN whose telephone number is (571)270-5110. The examiner can normally be reached M - F: 10:00AM- 4:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
IG T AN
Primary Examiner
Art Unit 3662
/IG T AN/Primary Examiner, Art Unit 3662
Prosecution Insights