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 .
DETAILED ACTION
Status of the Application
The amendment filed 12/15/2025 has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to the drawings and the claims have overcome each and every objection, as well as rejection under 35 U.S.C. 112 previously set forth in the Non-Final Office Action mailed 10/07/2025. This communication is a Non Final Office Action on the on merits. The Information Disclosure Statement (IDS) filed on 12/15/2025 has been acknowledged by the Office.
Response to Arguments
Applicant further clarifies that Pei does not constitute prior art, and the Examiner agrees. The distinction is made moot because the arguments do not apply to the combination of references and/or rationale being used in the current rejection.
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.
Claim(s) 1 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stelzer et al., hereinafter Stelzer (Document ID: WO2022089990A1) in view of Kloppenburg Ernst (Document ID: DE102018222542A1).
Regarding claim 1, Stelzer teaches a system comprising:
one or more processors (control unit 30); and
one or more non-transitory computer-readable media storing computer-executable instructions (“computer” with “computer-executable instructions in the form of program code”, see P [0073]) that, when executed, cause the system to perform operations comprising:
receiving a candidate trajectory associated with an autonomous vehicle (see at least P [0010]: “determining a second trajectory for the motor vehicle by the control unit”; note that “second” here is a label since the other road user has a first trajectory determined for it);
receiving data associated with an object in an environment of the autonomous vehicle (see at least P [0010]: “Determining a first trajectory for the road user by the control unit”;
determining, for the object, a set of parameters associated with a distribution of a predicted state of the object at a future time after a current time (see at least P [0010]: “a first probability distribution of the road user's location around the future position of the road user”, see also P [0026]: “probability distributions of the road user and the motor vehicle are determined in particular also on the basis of the object parameters of the motor vehicle and the road user.”);
determining, based at least in part on the candidate trajectory and a first parameter of the set of parameters, first data representing that a first sampled state is predicted to lead to collision, wherein the first sampled state is determined based at least in part on the first parameter (see at least P [0063]: “the control unit can determine a preliminary probability of collision based on the Mahalanobis distance of the first and second probability distributions…. Small values of the Mahalanobis distance imply large preliminary collision probabilities” See also P [0029]: “the control unit therefore determines, for a starting point corresponding to the time of recording the environmental data, the probability distributions for the motor vehicle and/or the road user”, wherein it is established that the sampled state at the starting time takes into account the object parameters);
determining a second distribution associated with a second predicted state of the object at a second time (see at least P [0111]-[0114] for sampling the future positions of the motor vehicle 12 and the road user 22 as a second sampled state, which involves “the object parameters O of the motor vehicle 12 and the road user 22.” See also FIG. 7 for a second sampling state based on the approach point 58 of the motor vehicle 12, as well as FIG. 8 for future distributions that flatten out with each time iteration);
Though Stelzer teaches the Mahalanobis function check, Stelzer does not explicitly teach
determining a cost associated with the candidate trajectory based at least in part on the first data; and
controlling the autonomous vehicle based at least in part on the cost.
Instead, Kloppenburg Ernst, whose invention pertains to predicting object movement to adapt own vehicle movement, teaches in at least P [0021] the establishment of a cost function for a vehicle, as well as tracking the planned trajectory of the vehicle in P [0041]. Finally, in P [0039] Kloppenburg Ernst teaches controlling a vehicle based on the cost analysis for the vehicle and the object.
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have modified the probabilistic distribution determination tracking for a vehicle and an object of Stelzer with the cost analysis for collision avoidance of Kloppenburg Ernst in order to consider the differences in preference for different types of road users, such as in P [0020] of Kloppenburg Ernst which allows for different preferences to be accounted for as parameters.
Regarding claim 5, modified Stelzer teaches the system of claim 1, and Stelzer further teaches
determining, based at least in part on the distribution, that the predicted state of the autonomous vehicle is inside an ellipse associated with a threshold probability of a location of the object (see at least FIG. 6 and FIG. 7 which establish sample states for the vehicle and the object within an ellipse associated with the position probability).
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stelzer in view of Kloppenburg Ernst, and further in view of Liu.
Regarding claim 2, modified Stelzer teaches the system of claim 1, but Stelzer and Kloppenburg Ernst do not explicitly teach that
the set of parameters comprise a first set of sigma points determined based at least in part on the distribution.
Instead, Liu, whose invention pertains to unscented Kalman filtering, teaches in at least P [0069] the ability to adjust “the number and position of Sigma points” through a process of updating the covariance associated with the distribution.
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have modified the probability sampling method of Stelzer and Kloppenburg Ernst with the sigma point parameterization for vehicle position tracking of Liu in order to apply a known method of state estimation filtering to probabilistic sampling, and improve the accuracy of state variables for vehicle position tracking.
Regarding claim 3, modified Stelzer teaches the system of claim 2, but Stelzer and Kloppenburg Ernst do not explicitly teach that determining the set of parameters comprises:
determining a covariance associated with the distribution; and
determining, based at least in part on the covariance, to determine a numerosity of the first set of sigma points.
Instead, Liu, whose invention pertains to unscented Kalman filtering, teaches in at least P [0069] the ability to adjust “the number and position of Sigma points” through a process of updating the covariance associated with the distribution.
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have modified the probability sampling method of Stelzer and Kloppenburg Ernst with the sigma point parameterization for vehicle position tracking of Liu in order to apply a known method of state estimation filtering to probabilistic sampling, and improve the accuracy of state variables for vehicle position tracking.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stelzer in view of Campos et al., hereinafter Campos (NPL Reference: Collision avoidance at intersections).
Regarding claim 4, modified Stelzer teaches the system of claim 2, and Stelzer teaches in P [0030] the use of symmetric Gaussian distributions, but Stelzer and Kloppenburg Ernst do not explicitly teach that
the first set of sigma points are uniformly distributed, and
determining the first data representing that the first sampled state is predicted to lead to collision is determined based at least in part on a sum of probabilities associated with the distribution within a range of a first sigma point from the first set of sigma points.
Instead, Campos, whose work pertains to collision probabilistic detection, teaches in at least FIG. 7 and FIG. 8 a set of discrete points in the distribution that are evenly space, or uniformly distributed. Additionally, using the set-membership test for checking each discrete point, an area called “PInside” is denoted as the sum of probabilities within a range of a first sigma point from the set.
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have modified the Gaussian probability distribution of Stelzer and Kloppenburg Ernst with the set-membership checking of Campos in order to develop an efficient decision making protocol that associates specific discrete points with probability amounts, and a sum depending on the region of overlap, to assess the risk of a possible collision as in FIG. 7 and Page 653 of Campos.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 6, 11, and 15-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stelzer.
Regarding claims 6 and 16, Stelzer teaches one or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations, and a method, comprising:
receiving a candidate trajectory associated with a vehicle (see at least P [0010]: “determining a second trajectory for the motor vehicle by the control unit”; note that “second” here is a label since the other road user has a first trajectory determined for it);
determining a distribution associated with a predicted state of an object in an environment of the vehicle, the distribution associated with a first set of parameters (see at least P [0010]: “a first probability distribution of the road user's location around the future position of the road user”, see also P [0026]: “probability distributions of the road user and the motor vehicle are determined in particular also on the basis of the object parameters of the motor vehicle and the road user.”);
determining a first sampled state based at least in part on the first set of parameters (see at least P [0029]: “the control unit therefore determines, for a starting point corresponding to the time of recording the environmental data, the probability distributions for the motor vehicle and/or the road user”, wherein it is established that the sampled state at the starting time takes into account the object parameters);
determining, based at least in part on the distribution and the first sampled state, first data representing that the first sampled state is predicted to collide with the candidate trajectory (see at least P [0063]: “the control unit can determine a preliminary probability of collision based on the Mahalanobis distance of the first and second probability distributions…. Small values of the Mahalanobis distance imply large preliminary collision probabilities”);
determining a second sampled state based at least in part on the first set of parameters (see at least P [0111]-[0114] for sampling the future positions of the motor vehicle 12 and the road user 22 as a second sampled state, which involves “the object parameters O of the motor vehicle 12 and the road user 22.” See also FIG. 7 for a second sampling state based on the approach point 58 of the motor vehicle 12);
determining second data representing that the second sampled state is predicted to collide with the candidate trajectory (see at least P [0143]: “If the preliminary collision probability K* is greater than a certain threshold (or if the Mahalanobis distance M is less than a corresponding threshold), then the collision calculation module 44 determines an exact collision probability K.”);
determining a likelihood of collision based at least in part on the first data and the second data (see at least P [0151]: “In this way, the collision calculation module 44 provides the collision probability K, which takes a value in the range of 0 to 1.” Note that the first data is incorporated in the determination since the calculation is only performed if the preliminary collision probability exceeds a certain threshold); and
controlling the vehicle based at least in part on the likelihood of collision (see at least P [0152]: “In a final step, the driving maneuver planning module 46 then calculates a future driving maneuver for the motor vehicle 12 and/or determines a corresponding warning message A (Figure 3) if the collision probability K is greater than a certain threshold.”).
Regarding claim 11, Stelzer teaches the one or more non-transitory computer-readable media of claim 6, and Stelzer further teaches
determining, based at least in part on the distribution, that the first sampled state of the vehicle is inside an ellipse associated with a threshold probability of a location of the object (see at least FIG. 6 and FIG. 7 which establish sample states for the vehicle and the object within an ellipse associated with the position probability).
Regarding claim 15, modified Stelzer teaches the one or more non-transitory computer-readable media of claim 6, and Stelzer further teaches
determining a second distribution associated with a second predicted state of the object at a second time, the second distribution associated with a second set of parameters (See at least FIG. 7 for a second sampling state based on the approach point 58 of the motor vehicle 12, as well as FIG. 8 for future distributions that flatten out with each time iteration); and
determining the second sampled state based at least in part on the second distribution (see at least P [0111]-[0114] for sampling the future positions of the motor vehicle 12 and the road user 22 as a second sampled state, which involves “the object parameters O of the motor vehicle 12 and the road user 22.” See again FIG. 7 for a second sampling state).
Claim Rejections - 35 USC § 103
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stelzer in view of Liu et al., hereinafter Liu (Document ID: CN 111669150 A).
Regarding claims 7 and 17, Stelzer teaches the one or more non-transitory computer-readable media of claim 6, and the method of claim 16, but Stelzer does not explicitly teach that
the first set of parameters comprise a set of sigma points, and a numerosity of the set of sigma points is based at least in part on a covariance associated with the distribution.
Instead, Liu, whose invention pertains to unscented Kalman filtering, teaches in at least P [0069] the ability to adjust “the number and position of Sigma points” through a process of updating the covariance associated with the distribution.
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have modified the probability sampling method of Stelzer with the sigma point parameterization for vehicle position tracking of Liu in order to apply a known method of state estimation filtering to probabilistic sampling, and improve the accuracy of state variables for vehicle position tracking.
Claim(s) 10, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stelzer in view of Campos.
Regarding claims 10 and 20, Stelzer teaches the one or more non-transitory computer-readable media of claim 6, and the method of claim 16, and Stelzer further teaches
the first set of parameters comprises a first set of sigma points,
the first set of sigma points are uniformly distributed about a mean of a Gaussian, and
a probability associated with a first sigma point in the first set of sigma points is determined based at least in part on the distribution within a range of the first sigma point.
Regarding claim 13, Stelzer teaches the one or more non-transitory computer-readable media of claim 6, and Stelzer teaches in P [0030] the use of symmetric Gaussian distributions
but Stelzer does not explicitly teach that
the first set of parameters comprise a set of uniformly spaced sigma points, and wherein determining the likelihood of collision is based at least in part on a sum of probabilities associated with sigma points associated with a collision.
Instead, Campos, whose work pertains to collision probabilistic detection, teaches in at least FIG. 7 and FIG. 8 a set of discrete points in the distribution that are evenly space, or uniformly distributed. Additionally, using the set-membership test for checking each discrete point, an area called “PInside” is denoted as the sum of probabilities within a range of a first sigma point from the set.
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have modified the Gaussian probability distribution of Stelzer with the set-membership checking of Campos in order to develop an efficient decision making protocol that associates specific discrete points with probability amounts, and a sum depending on the region of overlap, to assess the risk of a possible collision as in FIG. 7 and Page 653 of Campos.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stelzer in view of Berntorp et al., hereinafter Berntorp (Document ID: US 20180281785 A1).
Regarding claim 12, modified Stelzer teaches the one or more non-transitory computer-readable media of claim 6, and Stelzer further teaches
determining a second distribution at a second time (see at least P [0111]-[0114] for sampling the future positions of the motor vehicle 12 and the road user 22 as a second sampled state, which involves “the object parameters O of the motor vehicle 12 and the road user 22.” See also FIG. 7 for a second sampling state based on the approach point 58 of the motor vehicle 12, as well as FIG. 8 for future distributions that flatten out with each time iteration);
But Stelzer does not explicitly teach
determining a first set of sampled states based at least in part on the second distribution;
determining a first subset of the first set of sampled states that are predicted to lead to collision; and
determining a collision probability associated with the second time based at least in part on a first probability associated with the first set of sampled states and a second probability associated with the first subset.
Instead, Berntorp, whose invention pertains to implementing a collision free motion sequence, teaches in at least P [0099] the ability to determine “a set of sampled states” which are derived from a probability distribution determining step 810b. In P [0060] Berntorp teaches the ability to detect “a collision of each region with at least one object at each time step of control” to generate collision-free regions and regions where collision is likely. Specifically, in P [0087] the collision detector estimates a “probability of intersection of the future trajectory with each region at each time step of control and detect the region at the time step of control as collision free if the probability of intersection is below a threshold”.
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have modified the probability collision monitoring system of Stelzer with the regional subset of sampled states of Berntorp in order to generate a feasible trajectory for a vehicle traveling in an environment after checking the probability of collision for different sampled regions.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stelzer in view of Moustafa et al., hereinafter Moustafa (Document ID: US 20220126863 A1).
Regarding claim 14, Stelzer teaches the one or more non-transitory computer-readable media of claim 6, and Stelzer further teaches
the candidate trajectory is determined by a first computing device associated with the vehicle (see at least P[0010] wherein the candidate trajectory is determined by the control unit)
But Stelzer does not explicitly teach that
the operations are performed by a second computing device that is configured to validate an output of the first computing device, and
the second computing device is configured to cause the vehicle to perform a safety maneuver based at least in part on the likelihood of collision meeting or exceeding a threshold value.
Instead Moustafa, whose invention pertains to path planning for a vehicle based on another vehicle in its proximity, teaches in at least P [0108] establishing consensus on the road by having a second computing device that validates the model of another computing device. Moustafa additionally teaches in P [0112] the ability to act in a safe method when an action is likely to result in collision.
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have modified the candidate trajectory tracking and probabilistic collision monitoring of Stelzer with the road collaboration and validation of Moustafa in order to rely on knowledge of others in a surrounding area to reduce risk by conforming to commonplace road behavior as in P [0090] of Moustafa.
Allowable Subject Matter
Claims 8-9 and 18-19 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Document ID: US 20210339741 A1
Invention pertains to determining risk probabilities associated with operations of an autonomous vehicle.
Document ID: DE 102008046488 A
Invention pertains to performing a probabilistic situation analysis for a driving situation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Dairon Estevez whose telephone number is (703)756-4552. The examiner can normally be reached M-F 8:00AM - 4:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Khoi Tran can be reached at (571) 272-6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.E./Examiner, Art Unit 3656
/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656