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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/16/2026 has been entered.
Status of Claims
This is a Non-Final Action for Request for Continued Examination (RCE) application Serial No. 18/358,443, filed June 16, 2026. Claims 21-40 have been examined and fully considered.
Claims 21, 23-25, 30, 32-34, and 39 have been amended.
Claims 21-40 are pending in Instant Application.
Response to Arguments/Rejections
Applicant’s arguments, see Remarks, filed 05/07/2025, with respect to the rejection(s) of claim(s) 21-40 under 35 USC § 112 and 103 have been fully considered and are persuasive. However, upon further consideration, a new ground(s) of rejection is made in view of Damerow at el. (Pub. No.: US 2015/0344030).
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(s) 21-22, 30-31, and 39-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang at el. (Pub. No.: US-2019/0317512; previously recorded), hereinafter, referred to as “Zhang” in view of Damerow at el. (Pub. No.: US 2015/0344030), hereinafter, referred to as “Damerow”.
Regarding [claim 21], Zhang teaches a computer-implemented method (see, Abstract), comprising:
obtaining sensor data associated with an environment surrounding an autonomous vehicle (see, Paragraphs and [0030]: “LIDAR unit 215 may sense objects in the environment in which the autonomous vehicle is located using lasers. LIDAR unit 215 could include one or more laser sources, a laser scanner, and one or more detectors, among other system components. Cameras 211 may include one or more devices to capture images of the environment surrounding the autonomous vehicle. Cameras 211 may be still cameras and/or video cameras. A camera may be mechanically movable, for example, by mounting the camera on a rotating and/or tilting a platform.”; and [0031]: “Sensor system 115 may further include other sensors, such as, a sonar sensor, an infrared sensor, a steering sensor, a throttle sensor, a braking sensor, and an audio sensor (e.g., microphone). An audio sensor may be configured to capture sound from the environment surrounding the autonomous vehicle.”; [0041]: “While autonomous vehicle 300 is moving along the route, localization module 301 may also obtain real-time traffic information from a traffic information system or server.”; and [0042]: “Based on the sensor data provided by sensor system 115 and localization information obtained by localization module 301, a perception of the surrounding environment is determined by perception module 302. The perception information may represent what an ordinary driver would perceive surrounding a vehicle in which the driver is driving.”;
generating, by a machine-learned cost model and based at least in part on the sensor data,…cost data descriptive of costs for a plurality of positions in the environment (see, Paragraph [0038]: “Based on driving statistics 123, machine learning engine 122 generates or trains a set of rules, algorithms, and/or models 124 for a variety of purposes, including trained models to determine if an obstacle detected by sensors of the ADV is a stop sign or a traffic light. Rules/algorithms 124 may further include traffic rules for the ADV to follow and algorithms to calculate a quartic and/or a quintic polynomial for a trajectory. Algorithms 124 may further include algorithms to generate trajectory candidates as a final trajectory. Algorithms 124 may further include algorithms to calculate trajectory costs for selecting a target trajectory from the trajectory candidates, including algorithms to calculate objective costs, safety costs, and comfort costs. Algorithms 124 can then be uploaded onto ADVs for real-time trajectory generation for autonomous driving”);
generating a plurality of trajectory proposals for the autonomous vehicle, the plurality of trajectory proposals respectively corresponding to a plurality of potential paths of the autonomous vehicle through the environment (see, Paragraph [0021]: “a system generates a plurality of trajectory candidates for an autonomous driving vehicle (ADV) from a starting point to an end point of a particular driving scenario. The system generates a reference trajectory corresponding to the driving scenario based on a current state of the ADV associated with the starting point and an end state of the ADV associated with the end point, where the reference trajectory is associated with an objective. For each of the trajectory candidates, the system compares the trajectory candidate with the reference trajectory to generate an objective cost representing a similarity between the trajectory candidate and the reference trajectory”);
…
selecting a target trajectory from the one or more trajectory proposals based at least in part on the respective scores for the plurality of trajectory proposals (see, Paragraphs [0076]: “At block 1103, for each of the trajectory candidates, processing logic compares the trajectory candidate with the reference trajectory to generate an objective cost representing a similarity between the trajectory candidate and the reference trajectory. At block 1104, processing logic selects one of the trajectory candidates as a target trajectory for driving the ADV based on objective costs of the trajectory candidates”; and [0078][-[0079]: “for each of the trajectory candidates, processing logic further determines a changing rate of acceleration along the trajectory candidate, calculates a comfort cost for the trajectory candidate based on the changing rate of acceleration along the trajectory candidate, and calculates a total trajectory cost for the trajectory candidate based on the objective cost and the comfort cost.
The target trajectory can then be selected from the trajectory candidates as having a lowest total trajectory cost. In another embodiment, calculating a comfort cost for the trajectory candidate includes segmenting the trajectory candidate into a number of candidate segments, for each of the candidate segments, calculating a segment comfort cost for the candidate segment based on a changing rate of an acceleration associated with the candidate segment, and calculating the
comfort cost based on the segment comfort costs of the candidate segments. In another embodiment, the comfort cost is calculated based on a sum of segment comfort costs
of the candidate segments.”); and
controlling a motion of the autonomous vehicle based at least in part on the selected target trajectory (see, Paragraph [0052]: “trajectory generation module 308 can generate longitudinal trajectory candidates for ADV 101. The trajectory candidates may be generated in view of factors such as safety, comfort, and traffic rules. Trajectory evaluation module 309, also referred to as trajectory selection module, can then select one of the generated trajectory candidates as the trajectory to control the ADV based on evaluation criteria 315 and using cost
functions 316”; and [0063]: “Once trajectory candidates are generated from all possible end conditions, in one embodiment, an evaluation module, such as evaluation module 309 of FIG. 3A, evaluates each of the trajectory candidates for a best trajectory candidate. Trajectory candidate selector 411 can then select this best trajectory candidate to control the ADV.”).
As Zhang discloses
…the generated cost data to obtain, for the plurality of trajectory proposals, respective scores for the plurality of trajectory proposals (see, Paragraph [0076]: “Referring to FIG. 11, at block 1101, generates a number of trajectory candidates for an autonomous driving vehicle (ADV) from a starting point to an end point of a particular driving scenario. At block 1102, processing logic generates a reference trajectory corresponding to the driving scenario based on a current state of the ADV associated with the starting point and an end state of the ADV associated with the end point, where the reference trajectory is associated with an objective. At block 1103, for each of the trajectory candidates, processing logic compares the trajectory candidate with the reference trajectory to generate an objective cost representing a similarity between the trajectory candidate and the reference trajectory”, however, Zhang does not explicitly disclose
…
indexing into, based at least in part on the plurality of potential paths, the generated indexed cost data to obtain, for the plurality of trajectory proposals, respective scores for the plurality of trajectory proposals;
…
Additionally, Damerow teaches
….
indexing into, based at least in part on the plurality of potential paths, the generated indexed cost data to obtain, for the plurality of trajectory proposals, respective scores for the plurality of trajectory proposals (see, Abstract; Figure 6; and Paragraph [0048]: We repeat this for a plurality of potential future ego-car trajectories, which represent the ego-car behavior alternatives. These alternative trajectories may be obtained by varying the ego-trajectory it was started with. Such initial ego-trajectory may be the most like trajectory as an outcome of a prediction of future movement behavior. The other traffic participant is also represented by at least one predicted trajectory (there may also be several predicted trajectories to capture a certain variability in the others' behavior, the one shown in the Figs. may be the one which is most likely again). For each pair of predicted trajectories (one from the ego car and one from the other traffic participant), we get a function of risk over future time or, (which can be calculated equivalently by scaling the time axis), of risk over driven ego-car trajectory length. If the other traffic participant is represented by several trajectories the risk functions of these trajectories are averaged.”; and [0051]: “In addition to the risk calculation for each timestep, in one particular embodiment we discount for the assumption that predictions lying more ahead in the future are less certain. This has the effect that maxima in the risk map lying in the more distant future are lower or broader than maxima in the near future and they will sharpen (they will get narrower) and increase as they get closer in time. In a particular embodiment of the invention, this time effect is incorporated by calculating a “survival probability function”, which indicates how probable it is that a car moving along a predicted trajectory will survive up to a point in the future without getting involved into a risky event like a collision.”; and also see, [0059]: “In an extension, it is sometimes useful to work with several risk maps in parallel and to do a search of risk-aversive paths by combining the findings and constraints from several risk maps. A typical embodiment of such a system is shown in FIG. 6. In this case, the system has to be complemented by a situation classification step, and followed at the end by a path combination step using several risk maps.”; [0060]: “Especially if there are several alternative interpretations of a traffic scene which lead to different involved predicted risks, this becomes necessary. We then at first search which situation classes apply to a scene (in FIG. 6 upper left, one traffic scene is shown which is compatible with several different “prototypical” situation classes, with different risks), and select a subset of the possible candidates according to some criteria (like a mixture between evidence for the class and past experienced risk for the involved class).” *** Examiner interprets index cost data aa a risk function as being calculated at each step a time-horizon during which the operations are considered to be safe, and to update the system as soon as this time-horizon is reached or something anomalous happens***) ;
…
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention by implement each chosen situation candidate, we build a separate risk map. In a Subset of all of these risk maps, we can then search for risk-aversive paths. For a decision, we then have to find a good compromise from the path proposals of the risk maps, or choose the most favorable one. The chosen path will then again be the basis for actuation Support, control, or situation and risk dependent warning. taught by Damerow to yield similar successful results. One would be motivated to make this modification in order to convey systems where several traffic participants are involved and where a dynamical prediction provides benefits in terms of risk avoidance and efficiency maximization. Particular application scenarios would be inner city assistance or autonomous functions at crossings, where purely spatial analysis of the trajectories provides many risky areas and where it is not only important if a driver will cross the other cars trajectories at all, but when and in which sequence (see at least Paragraph [0062]).
As to [claim 22], Zhang in view of Damerow discloses the computer-implemented method of claim 21. Zhang in view of Damerow teaches wherein: the cost data describes costs for possible positions that the autonomous vehicle can take within a planning horizon;
a respective potential path describes a plurality of proposed positions that the autonomous vehicle can take at a plurality of timesteps within the planning horizon; and the method comprises:
obtaining, from the indexed cost data, the cost values associated with the proposed positions at the plurality of timesteps. (see, Paragraph [0067]: “Referring to FIGS. 7 and 8, in one embodiment, a safety cost can be evaluated (via safety cost determiner 703) using a station-time graph, such as station-time graph 800 of FIG. 8. Station-time graph 800 can be station-time graph 610 of FIG. 6B having obstacles 612, 614, 616, and trajectory candidate 615 to be evaluated for safety. In one embodiment, trajectory candidate 615 is segmented, by trajectory segmenter 701, into a number of segments 801 according to a predetermined time resolution. Each of the segments 801 can have an associated end point 803 ( e.g., a timestamp ). Trajectory segmenter 701 then bounds end points 803 (or timestamps) with boxes 805. Boxes 805 can have a predetermined width that can represent a width of the ADV. In one embodiment, safety cost determiner 703 traverses each of the boxes 805 to determine if any edges of boxes 805 would intersect a boundary of any static/dynamic obstacles in station-time graph 800, e.g., obstacles 612, 614, 616. If there is an intersection ( e.g., a possible collision) then the trajectory candidate may be removed from selection. In another embodiment, a closest distance from trajectory candidate 615 to any boundaries of obstacles 612, 614, 615 can be calculated along all timestamps of trajectory candidate 615. The safety cost can be an inverse of the calculated distance, e.g., a closer distance has a higher cost. In this case, a trajectory candidate which is far away from surrounding obstacles, e.g., a lower cost, may be deemed safer than a trajectory candidate which may be closer to the surrounding obstacles.”; and [0078]: “for each of the trajectory candidates, processing logic further identifies an obstacle that is closest to the trajectory candidate, measure a distance between the trajectory candidate and the obstacle, calculates a safety cost associated with the trajectory candidate based on the distance between the trajectory candidate and the obstacle, and calculates a total trajectory cost for the trajectory candidate based on the objective cost and the safety cost. The target trajectory can then be selected from the trajectory candidates as having a lowest total trajectory cost.”; and [0079]: “In one embodiment, for each of the trajectory candidates, processing logic further determines a changing rate of acceleration along the trajectory candidate, calculates a comfort cost for the trajectory candidate based on the changing rate of acceleration along the trajectory candidate, and calculates a total trajectory cost for the trajectory candidate based on the objective cost and the comfort cost. The target trajectory can then be selected from the trajectory candidates as having a lowest total trajectory cost. In another embodiment, calculating a comfort cost for the trajectory candidate includes segmenting the trajectory candidate into a number of candidate segments, for each of the candidate segments, calculating a segment comfort cost for the candidate segment based on a changing rate of an acceleration associated with the candidate segment, and calculating the comfort cost based on the segment comfort costs of the candidate segments. In another embodiment, the comfort cost is calculated based on a sum of segment comfort costs of the candidate segments.”)
Additionally, Damerow teaches wherein:
…
obtaining, from the indexed cost data, the cost values associated with the proposed positions at the plurality of timesteps (see, Paragraph [0057]-[0058]; and [0061]: “Since evidence for situation as well as the assumptions that were valid for the ego-car and the other traffic entities State predictions changes over time, a calculated risk aversive path has only a limited validity time-horizon. When situation evidence, or other scene parameters change, we have to revalidate any of the steps of situation classification, selection, risk, map calculation, planning and behavior decision. One common way is to calculate at each step a time horizon during which the operations are considered to be safe, and to update the system as soon as this time-horizon is reached or something anomalous happens.”):
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention by implement each chosen situation candidate, we build a separate risk map. In a Subset of all of these risk maps, we can then search for risk-aversive paths. For a decision, we then have to find a good compromise from the path proposals of the risk maps, or choose the most favorable one. The chosen path will then again be the basis for actuation Support, control, or situation and risk dependent warning. taught by Damerow to yield similar successful results. One would be motivated to make this modification in order to convey systems where several traffic participants are involved and where a dynamical prediction provides benefits in terms of risk avoidance and efficiency maximization. Particular application scenarios would be inner city assistance or autonomous functions at crossings, where purely spatial analysis of the trajectories provides many risky areas and where it is not only important if a driver will cross the other cars trajectories at all, but when and in which sequence (see at least Paragraph [0062]).
Regarding claim 30 , recites analogous limitations that are present in claim 21, therefore claim 30 would be rejected for the same/similar premise above.
Regarding claim 31, recites analogous limitations that are present in claim 22, therefore claim 31 would be rejected for the same/similar premise above.
Regarding claim 39 recites analogous limitations that are present in claim 21, therefore claim 39 would be rejected for the same/similar premise above.
Regarding claim 40 recites analogous limitations that are present in claim 22, therefore claim 40 would be rejected for the same/similar premise above.
Claim(s) 27 and 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Damerow, and in view of Kobilarov et al. (Pub. No.: US-20200387158; previously recorded), hereinafter, referred to as “Kobilarov”.
As to [claim 27], Zhang in view of Damerow teaches the computer-implemented method of claim 26. Kobilarov teaches further wherein the cost volume comprises a temporal dimension comprising the plurality of timesteps (see at least Paragraph [0020]: “the temporal logic formulas can include statements about the world that reflect proper driving behavior for an autonomous vehicle, for example. As candidate routes and trajectories are generated for the autonomous vehicles, the routes and trajectories can be evaluated using the temporal logic formulas to determine if the trajectories satisfy the temporal logic formulas , in which case , trajectories can be rejected, or evaluated with respect to other costs and constraints to select the highest performing trajectory”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Zhang in view of Damerow r by combining wherein the cost volume comprises a temporal dimension comprising the plurality of timesteps as taught by Kobilarov. One would be motivated to make this modification in order to determine a trajectory based on optimizing safety, costs, and performance can correspond to improved safety outcomes and/or increased comfort for occupants of an autonomous vehicle (see at least Paragraph [0025]).
Regarding claim 36, recites analogous limitations that are present in claim 27, therefore claim 36 would be rejected for the same/similar premise above.
Claim(s) 28-29 and 37-38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Damerow, and in view of Muller et al. (US-20190384303; previously recorded), hereinafter, referred to as “Muller”.
As to [claim 28], Zhang in view of Damerow teaches the computer-implemented method of claim 21. As Zhang wherein generating the one or more trajectory proposals for the autonomous vehicle, however, neither Zhang nor Damerow teaches
However, Muller teaches
….comprises: sampling a set of physically possible trajectories for the autonomous vehicle (see at least Paragraph [0024]: “Path planning may be used to aid in navigation through environments by autonomous machines controlled by autonomous systems (e.g., an autonomous driving software stack). In embodiments of the present disclosure, and to help determine a path for the autonomous vehicles , a deep neural network (DNN) e.g., a convolutional neural network (CNN) may be used to generate one or more trajectory points that correspond to a predicted or recommended trajectory for the autonomous vehicle”).
As to [claim 29], the combination of Zhang, Damerow and Muller teaches the computer-implemented method of claim 28. Muller teaches further wherein the sampling comprises: sampling a shape of a curve (see at least Figure 2B; Paragraph [0026]: “the trajectory generated by the machine learning model may be more accurate and thus more effective in path planning for the vehicle. For example, where only two-dimensional (2D) points are used, the path around a lateral curve or turn that also includes a vertical curve (e.g., a hill) may be different than the same lateral curve or tum on a flat surface. This may result in less accurate trajectory points from the machine learning model (e.g., the path through the curve may be offset to the left or right, closer to the lane markings or boundary than center-of-the-lane driving)”); sampling a motion parameter comprising at least one of: a velocity parameter or an acceleration parameter; and combining the sampled shape and the sampled motion parameter to obtain a respective possible trajectory (see at least Paragraph [0066]: “The trajectory data 112 may be representative of a recommended trajectory 142. However, in some examples, the output 110 may include control data for following the recommended trajectory 142 (e.g., for controlling the vehicle 138 according to the recommended trajectory 142, such as steering angle, acceleration, deceleration, etc.). The trajectory data 112 may include, in some examples, a trajectory point(s) 140 (e.g., as represented by 2D or 3D coordinates in world space, and/or 2D coordinates in image space) along the recommended vehicle trajectory 142. In some examples, only a single trajectory point 140 (e.g., the next trajectory point for the vehicle 138 in the sequence of discretized trajectory steps) may be output by the machine learning model(s) 108A. In other examples, more than one trajectory point 140 may be output. As another example, an entire trajectory may be output, which may be extrapolated from two or more trajectory points 140”).
Regarding claim 37 recites analogous limitations that are present in claim 28, therefore claim 37 would be rejected for the same/similar premise above.
Regarding claim 38 recites analogous limitations that are present in claim 29, therefore claim 38 would be rejected for the same/similar premise above.
Possible Allowable Subject Matter
Claims 23-26 and 32-35 are objected to as being dependent upon a rejected base claims under 35 U.S.C. 103, however, would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
With respect to the independent claim 23, the claim features below are rendered to be novel and non-obvious. In light of the prior art of record, either individually, in combination and/or dependently on other prior art does not teach the claim features
generating a feature map based at least in part on the sensor data; and
generating, by the machine-learned cost model, the indexed cost data based at least in part on the feature map.
With respect to the independent claim 24, the claim features below are rendered to be novel and non-obvious. In light of the prior art of record, either individually, in combination and/or dependently on other prior art does not teach the claim features
generating, by the machine-learned cost model and based on a feature map generated using a backbone machine-learned model, the indexed cost data; and generating, by a machine-learned forecasting model and based on the feature map generated using the backbone machine-learned model, forecasting data indicating one or more forecasted positions of an object in the environment.
With respect to the independent claim 25, the claim features below are rendered to be novel and non-obvious. In light of the prior art of record, either individually, in combination and/or dependently on other prior art does not teach the claim features
generating, by the backbone machine-learned model and based at least in part on map data, the feature map.
With respect to the independent claim 26, the claim features below are rendered to be novel and non-obvious. In light of the prior art of record, either individually, in combination and/or dependently on other prior art does not teach the claim features
wherein the indexed cost data comprises a cost volume having dimensions associated with a region of interest that comprises the plurality of positions in the environment.
With respect to the independent claim 32, the claim features below are rendered to be novel and non-obvious. In light of the prior art of record, either individually, in combination and/or dependently on other prior art does not teach the claim features
the operations comprising: generating a feature map based at least in part on the sensor data; and generating, by the machine-learned cost model, the cost data based at least in part on the feature map.
With respect to the independent claim 33, the claim features below are rendered to be novel and non-obvious. In light of the prior art of record, either individually, in combination and/or dependently on other prior art does not teach the claim features
wherein the operations comprise: generating, by the machine-learned cost model and based on a feature map generated using a backbone machine-learned model, the indexed cost data; and generating, by a machine-learned forecasting model and based on the feature map generated using the backbone machine-learned model, forecasting data indicating one or more forecasted positions of an object in the environment.
With respect to the independent claim 34, the claim features below are rendered to be novel and non-obvious. In light of the prior art of record, either individually, in combination and/or dependently on other prior art does not teach the claim features
the operations comprising: generating, by the backbone machine-learned model and based at least in part on map data, the feature map.
With respect to the independent claim 35, the claim features below are rendered to be novel and non-obvious. In light of the prior art of record, either individually, in combination and/or dependently on other prior art does not teach the claim features
wherein the indexed cost data comprises a cost volume having dimensions associated with a region of interest that comprises the plurality of positions in the environment.
While the aforementioned references disclose each of the elements of the invention, the combination of references does not fully capture the structure and interplay of the elements as recited in the claims. Therefore, upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious all the features of applicant’s invention as the features amount to more than a predictable use of elements in the prior art.
Conclusion
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/B.U./Examiner, Art Unit 3663
/JAMES M MCPHERSON/Examiner, Art Unit 3663
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